diff --git a/.github/actions/poetry_setup/action.yml b/.github/actions/poetry_setup/action.yml index 851ddda11f..22dcb05e16 100644 --- a/.github/actions/poetry_setup/action.yml +++ b/.github/actions/poetry_setup/action.yml @@ -27,7 +27,7 @@ runs: using: composite steps: - uses: actions/setup-python@v4 - name: Setup python $${ inputs.python-version }} + name: Setup python ${{ inputs.python-version }} with: python-version: ${{ inputs.python-version }} diff --git a/docs/api_reference/guide_imports.json b/docs/api_reference/guide_imports.json index 176c686e52..f35c7805a0 100644 --- a/docs/api_reference/guide_imports.json +++ b/docs/api_reference/guide_imports.json @@ -338,6 +338,7 @@ "Neptune Open Cypher QA Chain": "https://python.langchain.com/docs/use_cases/more/graph/neptune_cypher_qa", "NebulaGraphQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_nebula_qa", "KuzuQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_kuzu_qa", + "FalkorDBQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_falkordb_qa", "HugeGraph QA Chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_hugegraph_qa", "GraphSparqlQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_sparql_qa", "ArangoDB QA chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_arangodb_qa", @@ -3174,6 +3175,12 @@ "KuzuQAChain": { "KuzuQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_kuzu_qa" }, + "FalkorDBGraph": { + "KuzuQAChain": "https://python.langchain.com/docs/use_cases/more/graph/graph_falkordb_qa" + }, + "FalkorDBQAChain": { + "FalkorDB QA Chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_falkordb_qa" + }, "HugeGraphQAChain": { "HugeGraph QA Chain": "https://python.langchain.com/docs/use_cases/more/graph/graph_hugegraph_qa" }, diff --git a/docs/api_reference/templates/redirects.html b/docs/api_reference/templates/redirects.html index 6cca53544d..39c934f357 100644 --- a/docs/api_reference/templates/redirects.html +++ b/docs/api_reference/templates/redirects.html @@ -5,9 +5,10 @@ - + + - scikit-learn: machine learning in Python + LangChain Python API Reference Documentation.

You will be automatically redirected to the new location of this page.

diff --git a/docs/docs_skeleton/docs/expression_language/index.mdx b/docs/docs_skeleton/docs/expression_language/index.mdx new file mode 100644 index 0000000000..e014256145 --- /dev/null +++ b/docs/docs_skeleton/docs/expression_language/index.mdx @@ -0,0 +1,14 @@ +--- +sidebar_class_name: hidden +--- + +# LangChain Expression Language (LCEL) + +LangChain Expression Language or LCEL is a declarative way to easily compose chains together. +Any chain constructed this way will automatically have full sync, async, and streaming support. + +#### [Interface](/docs/expression_language/interface) +The base interface shared by all LCEL objects + +#### [Cookbook](/docs/expression_language/cookbook) +Examples of common LCEL usage patterns diff --git a/docs/docs_skeleton/docs/get_started/introduction.mdx b/docs/docs_skeleton/docs/get_started/introduction.mdx index e1af50a12b..08d156525f 100644 --- a/docs/docs_skeleton/docs/get_started/introduction.mdx +++ b/docs/docs_skeleton/docs/get_started/introduction.mdx @@ -51,7 +51,7 @@ Walkthroughs and best-practices for common end-to-end use cases, like: Learn best practices for developing with LangChain. ### [Ecosystem](/docs/ecosystem/) -LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/integrations/) and [dependent repos](/docs/ecosystem/dependents). +LangChain is part of a rich ecosystem of tools that integrate with our framework and build on top of it. Check out our growing list of [integrations](/docs/integrations/) and [dependent repos](/docs/additional_resources/dependents). ### [Additional resources](/docs/additional_resources/) Our community is full of prolific developers, creative builders, and fantastic teachers. Check out [YouTube tutorials](/docs/additional_resources/youtube.html) for great tutorials from folks in the community, and [Gallery](https://github.com/kyrolabs/awesome-langchain) for a list of awesome LangChain projects, compiled by the folks at [KyroLabs](https://kyrolabs.com). diff --git a/docs/docs_skeleton/docs/get_started/quickstart.mdx b/docs/docs_skeleton/docs/get_started/quickstart.mdx index 0db8c03eea..cfb40166e4 100644 --- a/docs/docs_skeleton/docs/get_started/quickstart.mdx +++ b/docs/docs_skeleton/docs/get_started/quickstart.mdx @@ -59,8 +59,8 @@ LangChain provides several objects to easily distinguish between different roles If none of those roles sound right, there is also a `ChatMessage` class where you can specify the role manually. For more information on how to use these different messages most effectively, see our prompting guide. -LangChain exposes a standard interface for both, but it's useful to understand this difference in order to construct prompts for a given language model. -The standard interface that LangChain exposes has two methods: +LangChain provides a standard interface for both, but it's useful to understand this difference in order to construct prompts for a given language model. +The standard interface that LangChain provides has two methods: - `predict`: Takes in a string, returns a string - `predict_messages`: Takes in a list of messages, returns a message. diff --git a/docs/docs_skeleton/docs/guides/expression_language/index.mdx b/docs/docs_skeleton/docs/guides/expression_language/index.mdx deleted file mode 100644 index 338a37a060..0000000000 --- a/docs/docs_skeleton/docs/guides/expression_language/index.mdx +++ /dev/null @@ -1,9 +0,0 @@ -# LangChain Expression Language - -import DocCardList from "@theme/DocCardList"; - -LangChain Expression Language is a declarative way to easily compose chains together. -Any chain constructed this way will automatically have full sync, async, and streaming support. -See guides below for how to interact with chains constructed this way as well as cookbook examples. - - \ No newline at end of file diff --git a/docs/docs_skeleton/docs/guides/langsmith/index.md b/docs/docs_skeleton/docs/guides/langsmith/index.md index 9915e70c5f..c4cfee065a 100644 --- a/docs/docs_skeleton/docs/guides/langsmith/index.md +++ b/docs/docs_skeleton/docs/guides/langsmith/index.md @@ -2,11 +2,21 @@ import DocCardList from "@theme/DocCardList"; -LangSmith helps you trace and evaluate your language model applications and intelligent agents to help you +[LangSmith](https://smith.langchain.com) helps you trace and evaluate your language model applications and intelligent agents to help you move from prototype to production. Check out the [interactive walkthrough](/docs/guides/langsmith/walkthrough) below to get started. -For more information, please refer to the [LangSmith documentation](https://docs.smith.langchain.com/) +For more information, please refer to the [LangSmith documentation](https://docs.smith.langchain.com/). + +For tutorials and other end-to-end examples demonstrating ways to integrate LangSmith in your workflow, +check out the [LangSmith Cookbook](https://github.com/langchain-ai/langsmith-cookbook). Some of the guides therein include: + +- Leveraging user feedback in your JS application ([link](https://github.com/langchain-ai/langsmith-cookbook/blob/main/feedback-examples/nextjs/README.md)). +- Building an automated feedback pipeline ([link](https://github.com/langchain-ai/langsmith-cookbook/blob/main/feedback-examples/algorithmic-feedback/algorithmic_feedback.ipynb)). +- How to evaluate and audit your RAG workflows ([link](https://github.com/langchain-ai/langsmith-cookbook/tree/main/testing-examples/qa-correctness)). +- How to fine-tune a LLM on real usage data ([link](https://github.com/langchain-ai/langsmith-cookbook/blob/main/fine-tuning-examples/export-to-openai/fine-tuning-on-chat-runs.ipynb)). +- How to use the [LangChain Hub](https://smith.langchain.com/hub) to version your prompts ([link](https://github.com/langchain-ai/langsmith-cookbook/blob/main/hub-examples/retrieval-qa-chain/retrieval-qa.ipynb)) + diff --git a/docs/docs_skeleton/docs/guides/safety/amazon_comprehend_chain.ipynb b/docs/docs_skeleton/docs/guides/safety/amazon_comprehend_chain.ipynb new file mode 100644 index 0000000000..614db1b885 --- /dev/null +++ b/docs/docs_skeleton/docs/guides/safety/amazon_comprehend_chain.ipynb @@ -0,0 +1,1422 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "25a3f834-60b7-4c21-bfb4-ad16d30fd3f7", + "metadata": {}, + "source": [ + "# Amazon Comprehend Moderation Chain\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c4236d8-4054-473d-84a4-87a4db278a62", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install boto3 nltk" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b39ac41a", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install -U langchain" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3f8518ad-c762-413c-b8c9-f1c211fc311d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import boto3\n", + "\n", + "comprehend_client = boto3.client('comprehend', region_name='us-east-1')" + ] + }, + { + "cell_type": "markdown", + "id": "d1f0ba28", + "metadata": {}, + "source": [ + "Import `AmazonComprehendModerationChain`" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "74550d74-3c01-4ba7-ad32-ca66d955d001", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain_experimental.comprehend_moderation import AmazonComprehendModerationChain" + ] + }, + { + "cell_type": "markdown", + "id": "f00c338b-de9f-40e5-9295-93c9e26058e3", + "metadata": {}, + "source": [ + "Initialize an instance of the Amazon Comprehend Moderation Chain to be used with your LLM chain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cde58cc6-ff83-493a-9aed-93d755f984a7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "comprehend_moderation = AmazonComprehendModerationChain(\n", + " client=comprehend_client, #optional\n", + " verbose=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "ad646d01-82d2-435a-939b-c450693857ab", + "metadata": {}, + "source": [ + "Using it with your LLM chain. \n", + "\n", + "**Note**: The example below uses the _Fake LLM_ from LangChain, but same concept could be applied to other LLMs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0efa1946-d4a9-467a-920a-a8fb78720fc2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain import PromptTemplate, LLMChain\n", + "from langchain.llms.fake import FakeListLLM\n", + "from langchain_experimental.comprehend_moderation.base_moderation_exceptions import ModerationPiiError\n", + "\n", + "template = \"\"\"Question: {question}\n", + "\n", + "Answer:\"\"\"\n", + "\n", + "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "\n", + "responses = [\n", + " \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\", \n", + " # replace with your own expletive\n", + " \"Final Answer: This is a really way of constructing a birdhouse. This is insane to think that any birds would actually create their nests here.\"\n", + "]\n", + "llm = FakeListLLM(responses=responses)\n", + "\n", + "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", + "\n", + "chain = (\n", + " prompt \n", + " | comprehend_moderation \n", + " | {llm_chain.input_keys[0]: lambda x: x['output'] } \n", + " | llm_chain \n", + " | { \"input\": lambda x: x['text'] } \n", + " | comprehend_moderation \n", + ")\n", + "\n", + "try:\n", + " response = chain.invoke({\"question\": \"A sample SSN number looks like this . Can you give me some more samples?\"})\n", + "except ModerationPiiError as e:\n", + " print(str(e))\n", + "else:\n", + " print(response['output'])\n" + ] + }, + { + "cell_type": "markdown", + "id": "6da25d96-0d96-4c01-94ae-a2ead17f10aa", + "metadata": {}, + "source": [ + "## Using `moderation_config` to customize your moderation\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "bfd550e7-5012-41fa-9546-8b78ddf1c673", + "metadata": {}, + "source": [ + "Use Amazon Comprehend Moderation with a configuration to control what moderations you wish to perform and what actions should be taken for each of them. There are three different moderations that happen when no configuration is passed as demonstrated above. These moderations are:\n", + "\n", + "- PII (Personally Identifiable Information) checks \n", + "- Toxicity content detection\n", + "- Intention detection\n", + "\n", + "Here is an example of a moderation config." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d6e8900a-44ef-4967-bde8-b88af282139d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain_experimental.comprehend_moderation import (BaseModerationConfig, \n", + " ModerationIntentConfig, \n", + " ModerationPiiConfig, \n", + " ModerationToxicityConfig\n", + ")\n", + "\n", + "pii_config = ModerationPiiConfig(\n", + " labels=[\"SSN\"],\n", + " redact=True,\n", + " mask_character=\"X\"\n", + ")\n", + "\n", + "toxicity_config = ModerationToxicityConfig(\n", + " threshold=0.5\n", + ")\n", + "\n", + "intent_config = ModerationIntentConfig(\n", + " threshold=0.5\n", + ")\n", + "\n", + "moderation_config = BaseModerationConfig(\n", + " filters=[pii_config, toxicity_config, intent_config]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "3634376b-5938-43df-9ed6-70ca7e99290f", + "metadata": {}, + "source": [ + "At the core of the the configuration there are three configuration models to be used\n", + "\n", + "- `ModerationPiiConfig` used for configuring the behavior of the PII validations. Following are the parameters it can be initialized with\n", + " - `labels` the PII entity labels. Defaults to an empty list which means that the PII validation will consider all PII entities.\n", + " - `threshold` the confidence threshold for the detected entities, defaults to 0.5 or 50%\n", + " - `redact` a boolean flag to enforce whether redaction should be performed on the text, defaults to `False`. When `False`, the PII validation will error out when it detects any PII entity, when set to `True` it simply redacts the PII values in the text.\n", + " - `mask_character` the character used for masking, defaults to asterisk (*)\n", + "- `ModerationToxicityConfig` used for configuring the behavior of the toxicity validations. Following are the parameters it can be initialized with\n", + " - `labels` the Toxic entity labels. Defaults to an empty list which means that the toxicity validation will consider all toxic entities. all\n", + " - `threshold` the confidence threshold for the detected entities, defaults to 0.5 or 50% \n", + "- `ModerationIntentConfig` used for configuring the behavior of the intent validation\n", + " - `threshold` the confidence threshold for the the intent classification, defaults to 0.5 or 50% \n", + "\n", + "Finally, you use the `BaseModerationConfig` to define the order in which each of these checks are to be performed. The `BaseModerationConfig` takes an optional `filters` parameter which can be a list of one or more than one of the above validation checks, as seen in the previous code block. The `BaseModerationConfig` can also be initialized with any `filters` in which case it will use all the checks with default configuration (more on this explained later).\n", + "\n", + "Using the configuration in the previous cell will perform PII checks and will allow the prompt to pass through however it will mask any SSN numbers present in either the prompt or the LLM output.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a4f7e65-f733-4863-ae6d-34c9faffd849", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "comp_moderation_with_config = AmazonComprehendModerationChain(\n", + " moderation_config=moderation_config, #specify the configuration\n", + " client=comprehend_client, #optionally pass the Boto3 Client\n", + " verbose=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a25e6f93-765b-4f99-8c1c-929157dbd4aa", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "template = \"\"\"Question: {question}\n", + "\n", + "Answer:\"\"\"\n", + "\n", + "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "\n", + "responses = [\n", + " \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\", \n", + " # replace with your own expletive\n", + " \"Final Answer: This is a really way of constructing a birdhouse. This is insane to think that any birds would actually create their nests here.\"\n", + "]\n", + "llm = FakeListLLM(responses=responses)\n", + "\n", + "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", + "\n", + "chain = ( \n", + " prompt \n", + " | comp_moderation_with_config \n", + " | {llm_chain.input_keys[0]: lambda x: x['output'] } \n", + " | llm_chain \n", + " | { \"input\": lambda x: x['text'] } \n", + " | comp_moderation_with_config \n", + ")\n", + "\n", + "try:\n", + " response = chain.invoke({\"question\": \"A sample SSN number looks like this 123-456-7890. Can you give me some more samples?\"})\n", + "except Exception as e:\n", + " print(str(e))\n", + "else:\n", + " print(response['output'])" + ] + }, + { + "cell_type": "markdown", + "id": "ba890681-feeb-43ca-a0d5-9c11d2d9de3e", + "metadata": { + "tags": [] + }, + "source": [ + "## Unique ID, and Moderation Callbacks\n", + "---\n", + "\n", + "When Amazon Comprehend moderation action is specified as `STOP`, the chain will raise one of the following exceptions-\n", + " - `ModerationPiiError`, for PII checks\n", + " - `ModerationToxicityError`, for Toxicity checks \n", + " - `ModerationIntentionError` for Intent checks\n", + "\n", + "In addition to the moderation configuration, the `AmazonComprehendModerationChain` can also be initialized with the following parameters\n", + "\n", + "- `unique_id` [Optional] a string parameter. This parameter can be used to pass any string value or ID. For example, in a chat application you may want to keep track of abusive users, in this case you can pass the user's username/email id etc. This defaults to `None`.\n", + "\n", + "- `moderation_callback` [Optional] the `BaseModerationCallbackHandler` that will be called asynchronously (non-blocking to the chain). Callback functions are useful when you want to perform additional actions when the moderation functions are executed, for example logging into a database, or writing a log file. You can override three functions by subclassing `BaseModerationCallbackHandler` - `on_after_pii()`, `on_after_toxicity()`, and `on_after_intent()`. Note that all three functions must be `async` functions. These callback functions receive two arguments:\n", + " - `moderation_beacon` a dictionary that will contain information about the moderation function, the full response from Amazon Comprehend model, a unique chain id, the moderation status, and the input string which was validated. The dictionary is of the following schema-\n", + " \n", + " ```\n", + " { \n", + " 'moderation_chain_id': 'xxx-xxx-xxx', # Unique chain ID\n", + " 'moderation_type': 'Toxicity' | 'PII' | 'Intent', \n", + " 'moderation_status': 'LABELS_FOUND' | 'LABELS_NOT_FOUND',\n", + " 'moderation_input': 'A sample SSN number looks like this 123-456-7890. Can you give me some more samples?',\n", + " 'moderation_output': {...} #Full Amazon Comprehend PII, Toxicity, or Intent Model Output\n", + " }\n", + " ```\n", + " \n", + " - `unique_id` if passed to the `AmazonComprehendModerationChain`" + ] + }, + { + "cell_type": "markdown", + "id": "3c178835-0264-4ac6-aef4-091d2993d06c", + "metadata": {}, + "source": [ + "
NOTE: moderation_callback is different from LangChain Chain Callbacks. You can still use LangChain Chain callbacks with AmazonComprehendModerationChain via the callbacks parameter. Example:
\n", + "
\n",
+    "from langchain.callbacks.stdout import StdOutCallbackHandler\n",
+    "comp_moderation_with_config = AmazonComprehendModerationChain(verbose=True, callbacks=[StdOutCallbackHandler()])\n",
+    "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0ec38536-8cc9-408e-860b-e4a439283643", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain_experimental.comprehend_moderation import BaseModerationCallbackHandler" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1be744c7-3f99-4165-bf7f-9c5c249bbb53", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Define callback handlers by subclassing BaseModerationCallbackHandler\n", + "\n", + "class MyModCallback(BaseModerationCallbackHandler):\n", + " \n", + " async def on_after_pii(self, output_beacon, unique_id):\n", + " import json\n", + " moderation_type = output_beacon['moderation_type']\n", + " chain_id = output_beacon['moderation_chain_id']\n", + " with open(f'output-{moderation_type}-{chain_id}.json', 'w') as file:\n", + " data = { 'beacon_data': output_beacon, 'unique_id': unique_id }\n", + " json.dump(data, file)\n", + " \n", + " '''\n", + " async def on_after_toxicity(self, output_beacon, unique_id):\n", + " pass\n", + " \n", + " async def on_after_intent(self, output_beacon, unique_id):\n", + " pass\n", + " '''\n", + " \n", + "\n", + "my_callback = MyModCallback()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "362a3fe0-f09f-411e-9df1-d79b3e87510c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "pii_config = ModerationPiiConfig(\n", + " labels=[\"SSN\"],\n", + " redact=True,\n", + " mask_character=\"X\"\n", + ")\n", + "\n", + "toxicity_config = ModerationToxicityConfig(\n", + " threshold=0.5\n", + ")\n", + "\n", + "moderation_config = BaseModerationConfig(\n", + " filters=[pii_config, toxicity_config]\n", + ")\n", + "\n", + "comp_moderation_with_config = AmazonComprehendModerationChain(\n", + " moderation_config=moderation_config, # specify the configuration\n", + " client=comprehend_client, # optionally pass the Boto3 Client\n", + " unique_id='john.doe@email.com', # A unique ID\n", + " moderation_callback=my_callback, # BaseModerationCallbackHandler\n", + " verbose=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2af07937-67ea-4738-8343-c73d4d28c2cc", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain import PromptTemplate, LLMChain\n", + "from langchain.llms.fake import FakeListLLM\n", + "\n", + "template = \"\"\"Question: {question}\n", + "\n", + "Answer:\"\"\"\n", + "\n", + "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "\n", + "responses = [\n", + " \"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.\", \n", + " # replace with your own expletive\n", + " \"Final Answer: This is a really way of constructing a birdhouse. This is insane to think that any birds would actually create their nests here.\"\n", + "]\n", + "\n", + "llm = FakeListLLM(responses=responses)\n", + "\n", + "llm_chain = LLMChain(prompt=prompt, llm=llm)\n", + "\n", + "chain = (\n", + " prompt \n", + " | comp_moderation_with_config \n", + " | {llm_chain.input_keys[0]: lambda x: x['output'] } \n", + " | llm_chain \n", + " | { \"input\": lambda x: x['text'] } \n", + " | comp_moderation_with_config \n", + ") \n", + "\n", + "try:\n", + " response = chain.invoke({\"question\": \"A sample SSN number looks like this 123-456-7890. Can you give me some more samples?\"})\n", + "except Exception as e:\n", + " print(str(e))\n", + "else:\n", + " print(response['output'])" + ] + }, + { + "cell_type": "markdown", + "id": "706454b2-2efa-4d41-abc8-ccf2b4e87822", + "metadata": { + "tags": [] + }, + "source": [ + "## `moderation_config` and moderation execution order\n", + "---\n", + "\n", + "If `AmazonComprehendModerationChain` is not initialized with any `moderation_config` then it is initialized with the default values of `BaseModerationConfig`. If no `filters` are used then the sequence of moderation check is as follows.\n", + "\n", + "```\n", + "AmazonComprehendModerationChain\n", + "│\n", + "└──Check PII with Stop Action\n", + " ├── Callback (if available)\n", + " ├── Label Found ⟶ [Error Stop]\n", + " └── No Label Found \n", + " └──Check Toxicity with Stop Action\n", + " ├── Callback (if available)\n", + " ├── Label Found ⟶ [Error Stop]\n", + " └── No Label Found\n", + " └──Check Intent with Stop Action\n", + " ├── Callback (if available)\n", + " ├── Label Found ⟶ [Error Stop]\n", + " └── No Label Found\n", + " └── Return Prompt\n", + "```\n", + "\n", + "If any of the check raises a validation exception then the subsequent checks will not be performed. If a `callback` is provided in this case, then it will be called for each of the checks that have been performed. For example, in the case above, if the Chain fails due to presence of PII then the Toxicity and Intent checks will not be performed.\n", + "\n", + "You can override the execution order by passing `moderation_config` and simply specifying the desired order in the `filters` parameter of the `BaseModerationConfig`. In case you specify the filters, then the order of the checks as specified in the `filters` parameter will be maintained. For example, in the configuration below, first Toxicity check will be performed, then PII, and finally Intent validation will be performed. In this case, `AmazonComprehendModerationChain` will perform the desired checks in the specified order with default values of each model `kwargs`.\n", + "\n", + "```python\n", + "pii_check = ModerationPiiConfig()\n", + "toxicity_check = ModerationToxicityConfig()\n", + "intent_check = ModerationIntentConfig()\n", + "\n", + "moderation_config = BaseModerationConfig(filters=[toxicity_check, pii_check, intent_check])\n", + "```\n", + "\n", + "You can have also use more than one configuration for a specific moderation check, for example in the sample below, two consecutive PII checks are performed. First the configuration checks for any SSN, if found it would raise an error. If any SSN isn't found then it will next check if any NAME and CREDIT_DEBIT_NUMBER is present in the prompt and will mask it.\n", + "\n", + "```python\n", + "pii_check_1 = ModerationPiiConfig(labels=[\"SSN\"])\n", + "pii_check_2 = ModerationPiiConfig(labels=[\"NAME\", \"CREDIT_DEBIT_NUMBER\"], redact=True)\n", + "\n", + "moderation_config = BaseModerationConfig(filters=[pii_check_1, pii_check_2])\n", + "```\n", + "\n", + "1. For a list of PII labels see Amazon Comprehend Universal PII entity types - https://docs.aws.amazon.com/comprehend/latest/dg/how-pii.html#how-pii-types\n", + "2. Following are the list of available Toxicity labels-\n", + " - `HATE_SPEECH`: Speech that criticizes, insults, denounces or dehumanizes a person or a group on the basis of an identity, be it race, ethnicity, gender identity, religion, sexual orientation, ability, national origin, or another identity-group.\n", + " - `GRAPHIC`: Speech that uses visually descriptive, detailed and unpleasantly vivid imagery is considered as graphic. Such language is often made verbose so as to amplify an insult, discomfort or harm to the recipient.\n", + " - `HARASSMENT_OR_ABUSE`: Speech that imposes disruptive power dynamics between the speaker and hearer, regardless of intent, seeks to affect the psychological well-being of the recipient, or objectifies a person should be classified as Harassment.\n", + " - `SEXUAL`: Speech that indicates sexual interest, activity or arousal by using direct or indirect references to body parts or physical traits or sex is considered as toxic with toxicityType \"sexual\". \n", + " - `VIOLENCE_OR_THREAT`: Speech that includes threats which seek to inflict pain, injury or hostility towards a person or group.\n", + " - `INSULT`: Speech that includes demeaning, humiliating, mocking, insulting, or belittling language.\n", + " - `PROFANITY`: Speech that contains words, phrases or acronyms that are impolite, vulgar, or offensive is considered as profane.\n", + "3. For a list of Intent labels refer to documentation [link here]" + ] + }, + { + "cell_type": "markdown", + "id": "78905aec-55ae-4fc3-a23b-8a69bd1e33f2", + "metadata": {}, + "source": [ + "# Examples\n", + "---\n", + "\n", + "## With HuggingFace Hub Models\n", + "\n", + "Get your API Key from Huggingface hub - https://huggingface.co/docs/api-inference/quicktour#get-your-api-token" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "359b9627-769b-46ce-8be2-c8a5cf7728ba", + "metadata": { + "scrolled": true, + "tags": [] + }, + "outputs": [], + "source": [ + "%pip install huggingface_hub" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "41b7ea98-ad16-4454-8f12-c03c17113a86", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = \"\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b235427-cc06-4c07-874b-1f67c2d1f924", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options\n", + "repo_id = \"google/flan-t5-xxl\" " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d86e256-34fb-4c8e-8092-1a4f863a5c96", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain import HuggingFaceHub\n", + "from langchain import PromptTemplate, LLMChain\n", + "\n", + "template = \"\"\"Question: {question}\"\"\"\n", + "\n", + "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", + "llm = HuggingFaceHub(\n", + " repo_id=repo_id, model_kwargs={\"temperature\": 0.5, \"max_length\": 256}\n", + ")\n", + "llm_chain = LLMChain(prompt=prompt, llm=llm)" + ] + }, + { + "cell_type": "markdown", + "id": "ad603796-ad8b-4599-9022-a486f1c1b89a", + "metadata": {}, + "source": [ + "Create a configuration and initialize an Amazon Comprehend Moderation chain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "decc3409-5be5-433d-b6da-38b9e5c5ee3f", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "pii_config = ModerationPiiConfig(\n", + " labels=[\"SSN\", \"CREDIT_DEBIT_NUMBER\"],\n", + " redact=True,\n", + " mask_character=\"X\"\n", + ")\n", + "\n", + "toxicity_config = ModerationToxicityConfig(\n", + " threshold=0.5\n", + ")\n", + "\n", + "intent_config = ModerationIntentConfig(\n", + " threshold=0.8\n", + ")\n", + "\n", + "moderation_config = BaseModerationConfig(\n", + " filters=[pii_config, toxicity_config, intent_config]\n", + ")\n", + "# with callback\n", + "amazon_comp_moderation = AmazonComprehendModerationChain(moderation_config=moderation_config, \n", + " client=comprehend_client,\n", + " moderation_callback=my_callback,\n", + " verbose=True)\n", + "\n", + "# without callback\n", + "amazon_comp_moderation_out = AmazonComprehendModerationChain(moderation_config=moderation_config, \n", + " client=comprehend_client,\n", + " verbose=True)" + ] + }, + { + "cell_type": "markdown", + "id": "b1256bc8-1321-4624-9e8a-a2d4a8df59bf", + "metadata": {}, + "source": [ + "The `moderation_config` will now prevent any inputs and model outputs containing obscene words or sentences, bad intent, or PII with entities other than SSN with score above threshold or 0.5 or 50%. If it finds Pii entities - SSN - it will redact them before allowing the call to proceed. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0337becc-7c3c-483e-a55c-a225226cb9ee", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "chain = (\n", + " prompt \n", + " | amazon_comp_moderation \n", + " | {llm_chain.input_keys[0]: lambda x: x['output'] } \n", + " | llm_chain \n", + " | { \"input\": lambda x: x['text'] } \n", + " | amazon_comp_moderation_out\n", + ")\n", + "\n", + "try:\n", + " response = chain.invoke({\"question\": \"\"\"What is John Doe's address, phone number and SSN from the following text?\n", + "\n", + "John Doe, a resident of 1234 Elm Street in Springfield, recently celebrated his birthday on January 1st. Turning 43 this year, John reflected on the years gone by. He often shares memories of his younger days with his close friends through calls on his phone, (555) 123-4567. Meanwhile, during a casual evening, he received an email at johndoe@example.com reminding him of an old acquaintance's reunion. As he navigated through some old documents, he stumbled upon a paper that listed his SSN as 123-45-6789, reminding him to store it in a safer place.\n", + "\"\"\"})\n", + "except Exception as e:\n", + " print(str(e))\n", + "else:\n", + " print(response['output'])" + ] + }, + { + "cell_type": "markdown", + "id": "ee52c7b8-6526-4f68-a2b3-b5ad3cf82489", + "metadata": { + "tags": [] + }, + "source": [ + "---\n", + "## With Amazon SageMaker Jumpstart\n", + "\n", + "The exmaple below shows how to use Amazon Comprehend Moderation chain with an Amazon SageMaker Jumpstart hosted LLM. You should have an Amazon SageMaker Jumpstart hosted LLM endpoint within your AWS Account. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd49d075-bc23-4ab8-a92c-0ddbbc436c30", + "metadata": {}, + "outputs": [], + "source": [ + "endpoint_name = \"\" # replace with your SageMaker Endpoint name" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5978a5e6-667d-4926-842c-d965f88e5640", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain import SagemakerEndpoint\n", + "from langchain.llms.sagemaker_endpoint import LLMContentHandler\n", + "from langchain.chains import LLMChain\n", + "from langchain.prompts import load_prompt, PromptTemplate\n", + "import json\n", + "\n", + "class ContentHandler(LLMContentHandler):\n", + " content_type = \"application/json\"\n", + " accepts = \"application/json\"\n", + "\n", + " def transform_input(self, prompt: str, model_kwargs: dict) -> bytes:\n", + " input_str = json.dumps({\"text_inputs\": prompt, **model_kwargs})\n", + " return input_str.encode('utf-8')\n", + " \n", + " def transform_output(self, output: bytes) -> str:\n", + " response_json = json.loads(output.read().decode(\"utf-8\"))\n", + " return response_json['generated_texts'][0]\n", + "\n", + "content_handler = ContentHandler()\n", + "\n", + "#prompt template for input text\n", + "llm_prompt = PromptTemplate(input_variables=[\"input_text\"], template=\"{input_text}\")\n", + "\n", + "llm_chain = LLMChain(\n", + " llm=SagemakerEndpoint(\n", + " endpoint_name=endpoint_name, \n", + " region_name='us-east-1',\n", + " model_kwargs={\"temperature\":0.97,\n", + " \"max_length\": 200,\n", + " \"num_return_sequences\": 3,\n", + " \"top_k\": 50,\n", + " \"top_p\": 0.95,\n", + " \"do_sample\": True},\n", + " content_handler=content_handler\n", + " ),\n", + " prompt=llm_prompt\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "d577b036-99a4-47fe-9a8e-4a34aa4cd88d", + "metadata": {}, + "source": [ + "Create a configuration and initialize an Amazon Comprehend Moderation chain" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "859da135-94d3-4a9c-970e-a873913592e2", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "pii_config = ModerationPiiConfig(\n", + " labels=[\"SSN\"],\n", + " redact=True,\n", + " mask_character=\"X\"\n", + ")\n", + "\n", + "toxicity_config = ModerationToxicityConfig(\n", + " threshold=0.5\n", + ")\n", + "\n", + "intent_config = ModerationIntentConfig(\n", + " threshold=0.8\n", + ")\n", + "\n", + "moderation_config = BaseModerationConfig(\n", + " filters=[pii_config, toxicity_config, intent_config]\n", + ")\n", + "\n", + "amazon_comp_moderation = AmazonComprehendModerationChain(moderation_config=moderation_config, \n", + " client=comprehend_client,\n", + " verbose=True)" + ] + }, + { + "cell_type": "markdown", + "id": "9abb191f-7a96-4077-8c30-b9ddc225bd6b", + "metadata": {}, + "source": [ + "The `moderation_config` will now prevent any inputs and model outputs containing obscene words or sentences, bad intent, or Pii with entities other than SSN with score above threshold or 0.5 or 50%. If it finds Pii entities - SSN - it will redact them before allowing the call to proceed. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6db5aa2a-9c00-42a0-8e24-c5ba39994f7d", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "chain = (\n", + " prompt \n", + " | amazon_comp_moderation \n", + " | {llm_chain.input_keys[0]: lambda x: x['output'] } \n", + " | llm_chain \n", + " | { \"input\": lambda x: x['text'] } \n", + " | amazon_comp_moderation \n", + ")\n", + "\n", + "try:\n", + " response = chain.invoke({\"question\": \"\"\"What is John Doe's address, phone number and SSN from the following text?\n", + "\n", + "John Doe, a resident of 1234 Elm Street in Springfield, recently celebrated his birthday on January 1st. Turning 43 this year, John reflected on the years gone by. He often shares memories of his younger days with his close friends through calls on his phone, (555) 123-4567. Meanwhile, during a casual evening, he received an email at johndoe@example.com reminding him of an old acquaintance's reunion. As he navigated through some old documents, he stumbled upon a paper that listed his SSN as 123-45-6789, reminding him to store it in a safer place.\n", + "\"\"\"})\n", + "except Exception as e:\n", + " print(str(e))\n", + "else:\n", + " print(response['output'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7fdfedf9-1a0a-4a9f-a6b0-d9ed2dbaa5ad", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "availableInstances": [ + { + "_defaultOrder": 0, + "_isFastLaunch": true, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 4, + "name": "ml.t3.medium", + "vcpuNum": 2 + }, + { + "_defaultOrder": 1, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.t3.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 2, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.t3.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 3, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.t3.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 4, + "_isFastLaunch": true, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.m5.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 5, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.m5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 6, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.m5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 7, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.m5.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 8, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.m5.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 9, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.m5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 10, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.m5.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 11, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.m5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 12, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": 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"ml.m5d.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 18, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.m5d.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 19, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.m5d.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 20, + "_isFastLaunch": false, + "category": "General purpose", + "gpuNum": 0, + "hideHardwareSpecs": true, + "memoryGiB": 0, + "name": "ml.geospatial.interactive", + "supportedImageNames": [ + "sagemaker-geospatial-v1-0" + ], + "vcpuNum": 0 + }, + { + "_defaultOrder": 21, + "_isFastLaunch": true, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 4, + "name": "ml.c5.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 22, + "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 8, + "name": "ml.c5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 23, + "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.c5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 24, + "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.c5.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 25, + "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 72, + "name": "ml.c5.9xlarge", + "vcpuNum": 36 + }, + { + "_defaultOrder": 26, + "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 96, + "name": "ml.c5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 27, + "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 144, + "name": "ml.c5.18xlarge", + "vcpuNum": 72 + }, + { + "_defaultOrder": 28, + "_isFastLaunch": false, + "category": "Compute optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.c5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 29, + "_isFastLaunch": true, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.g4dn.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 30, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.g4dn.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 31, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.g4dn.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 32, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.g4dn.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 33, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.g4dn.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 34, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.g4dn.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 35, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 61, + "name": "ml.p3.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 36, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 244, + "name": "ml.p3.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 37, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 488, + "name": "ml.p3.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 38, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 768, + "name": "ml.p3dn.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 39, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.r5.large", + "vcpuNum": 2 + }, + { + "_defaultOrder": 40, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.r5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 41, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.r5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 42, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.r5.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 43, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.r5.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 44, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.r5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 45, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 512, + "name": "ml.r5.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 46, + "_isFastLaunch": false, + "category": "Memory Optimized", + "gpuNum": 0, + "hideHardwareSpecs": false, + "memoryGiB": 768, + "name": "ml.r5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 47, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 16, + "name": "ml.g5.xlarge", + "vcpuNum": 4 + }, + { + "_defaultOrder": 48, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 32, + "name": "ml.g5.2xlarge", + "vcpuNum": 8 + }, + { + "_defaultOrder": 49, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 64, + "name": "ml.g5.4xlarge", + "vcpuNum": 16 + }, + { + "_defaultOrder": 50, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 128, + "name": "ml.g5.8xlarge", + "vcpuNum": 32 + }, + { + "_defaultOrder": 51, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 1, + "hideHardwareSpecs": false, + "memoryGiB": 256, + "name": "ml.g5.16xlarge", + "vcpuNum": 64 + }, + { + "_defaultOrder": 52, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 192, + "name": "ml.g5.12xlarge", + "vcpuNum": 48 + }, + { + "_defaultOrder": 53, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 4, + "hideHardwareSpecs": false, + "memoryGiB": 384, + "name": "ml.g5.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 54, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 768, + "name": "ml.g5.48xlarge", + "vcpuNum": 192 + }, + { + "_defaultOrder": 55, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 1152, + "name": "ml.p4d.24xlarge", + "vcpuNum": 96 + }, + { + "_defaultOrder": 56, + "_isFastLaunch": false, + "category": "Accelerated computing", + "gpuNum": 8, + "hideHardwareSpecs": false, + "memoryGiB": 1152, + "name": "ml.p4de.24xlarge", + "vcpuNum": 96 + } + ], + "instance_type": "ml.t3.medium", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/docs_skeleton/docs/guides/safety/index.mdx b/docs/docs_skeleton/docs/guides/safety/index.mdx index 029eea7d44..966bcd54ab 100644 --- a/docs/docs_skeleton/docs/guides/safety/index.mdx +++ b/docs/docs_skeleton/docs/guides/safety/index.mdx @@ -4,4 +4,5 @@ One of the key concerns with using LLMs is that they may generate harmful or une - [Moderation chain](/docs/guides/safety/moderation): Explicitly check if any output text is harmful and flag it. - [Constitutional chain](/docs/guides/safety/constitutional_chain): Prompt the model with a set of principles which should guide it's behavior. +- [Logical Fallacy chain](/docs/guides/safety/logical_fallacy_chain): Checks the model output against logical fallacies to correct any deviation. - [Amazon Comprehend moderation chain](/docs/guides/safety/amazon_comprehend_chain): Use [Amazon Comprehend](https://aws.amazon.com/comprehend/) to detect and handle PII and toxicity. diff --git a/docs/docs_skeleton/docs/guides/safety/logical_fallacy_chain.mdx b/docs/docs_skeleton/docs/guides/safety/logical_fallacy_chain.mdx new file mode 100644 index 0000000000..f355c0727a --- /dev/null +++ b/docs/docs_skeleton/docs/guides/safety/logical_fallacy_chain.mdx @@ -0,0 +1,85 @@ +# Removing logical fallacies from model output +Logical fallacies are flawed reasoning or false arguments that can undermine the validity of a model's outputs. Examples include circular reasoning, false +dichotomies, ad hominem attacks, etc. Machine learning models are optimized to perform well on specific metrics like accuracy, perplexity, or loss. However, +optimizing for metrics alone does not guarantee logically sound reasoning. + +Language models can learn to exploit flaws in reasoning to generate plausible-sounding but logically invalid arguments. When models rely on fallacies, their outputs become unreliable and untrustworthy, even if they achieve high scores on metrics. Users cannot depend on such outputs. Propagating logical fallacies can spread misinformation, confuse users, and lead to harmful real-world consequences when models are deployed in products or services. + +Monitoring and testing specifically for logical flaws is challenging unlike other quality issues. It requires reasoning about arguments rather than pattern matching. + +Therefore, it is crucial that model developers proactively address logical fallacies after optimizing metrics. Specialized techniques like causal modeling, robustness testing, and bias mitigation can help avoid flawed reasoning. Overall, allowing logical flaws to persist makes models less safe and ethical. Eliminating fallacies ensures model outputs remain logically valid and aligned with human reasoning. This maintains user trust and mitigates risks. + + + +```python +# Imports +from langchain.llms import OpenAI +from langchain.prompts import PromptTemplate +from langchain.chains.llm import LLMChain +from langchain_experimental.fallacy_removal.base import FallacyChain +``` + +```python +# Example of a model output being returned with a logical fallacy +misleading_prompt = PromptTemplate( + template="""You have to respond by using only logical fallacies inherent in your answer explanations. + +Question: {question} + +Bad answer:""", + input_variables=["question"], +) + +llm = OpenAI(temperature=0) + +misleading_chain = LLMChain(llm=llm, prompt=misleading_prompt) + +misleading_chain.run(question="How do I know the earth is round?") +``` + + + +``` + 'The earth is round because my professor said it is, and everyone believes my professor' +``` + + + + +```python +fallacies = FallacyChain.get_fallacies(["correction"]) +fallacy_chain = FallacyChain.from_llm( + chain=misleading_chain, + logical_fallacies=fallacies, + llm=llm, + verbose=True, +) + +fallacy_chain.run(question="How do I know the earth is round?") +``` + + + +``` + + + > Entering new FallacyChain chain... + Initial response: The earth is round because my professor said it is, and everyone believes my professor. + + Applying correction... + + Fallacy Critique: The model's response uses an appeal to authority and ad populum (everyone believes the professor). Fallacy Critique Needed. + + Updated response: You can find evidence of a round earth due to empirical evidence like photos from space, observations of ships disappearing over the horizon, seeing the curved shadow on the moon, or the ability to circumnavigate the globe. + + + > Finished chain. + + + + + + 'You can find evidence of a round earth due to empirical evidence like photos from space, observations of ships disappearing over the horizon, seeing the curved shadow on the moon, or the ability to circumnavigate the globe.' +``` + + diff --git a/docs/docs_skeleton/docs/modules/agents/agent_types/index.mdx b/docs/docs_skeleton/docs/modules/agents/agent_types/index.mdx index ddb38c4d9c..17b93e1274 100644 --- a/docs/docs_skeleton/docs/modules/agents/agent_types/index.mdx +++ b/docs/docs_skeleton/docs/modules/agents/agent_types/index.mdx @@ -37,11 +37,11 @@ This agent is designed to be used in conversational settings. The prompt is designed to make the agent helpful and conversational. It uses the ReAct framework to decide which tool to use, and uses memory to remember the previous conversation interactions. -### [Self ask with search](/docs/modules/agents/agent_types/self_ask_with_search.html) +### [Self-ask with search](/docs/modules/agents/agent_types/self_ask_with_search.html) This agent utilizes a single tool that should be named `Intermediate Answer`. This tool should be able to lookup factual answers to questions. This agent -is equivalent to the original [self ask with search paper](https://ofir.io/self-ask.pdf), +is equivalent to the original [self-ask with search paper](https://ofir.io/self-ask.pdf), where a Google search API was provided as the tool. ### [ReAct document store](/docs/modules/agents/agent_types/react_docstore.html) @@ -54,4 +54,4 @@ This agent is equivalent to the original [ReAct paper](https://arxiv.org/pdf/2210.03629.pdf), specifically the Wikipedia example. ## [Plan-and-execute agents](/docs/modules/agents/agent_types/plan_and_execute.html) -Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091). +Plan-and-execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091). diff --git a/docs/docs_skeleton/docs/modules/agents/agent_types/plan_and_execute.mdx b/docs/docs_skeleton/docs/modules/agents/agent_types/plan_and_execute.mdx index 14b36a8b48..ce3c1775b4 100644 --- a/docs/docs_skeleton/docs/modules/agents/agent_types/plan_and_execute.mdx +++ b/docs/docs_skeleton/docs/modules/agents/agent_types/plan_and_execute.mdx @@ -1,6 +1,6 @@ -# Plan and execute +# Plan-and-execute -Plan and execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091). +Plan-and-execute agents accomplish an objective by first planning what to do, then executing the sub tasks. This idea is largely inspired by [BabyAGI](https://github.com/yoheinakajima/babyagi) and then the ["Plan-and-Solve" paper](https://arxiv.org/abs/2305.04091). The planning is almost always done by an LLM. diff --git a/docs/docs_skeleton/docs/modules/agents/how_to/custom_llm_agent.mdx b/docs/docs_skeleton/docs/modules/agents/how_to/custom_llm_agent.mdx index 5ed60d6ade..5efebc342c 100644 --- a/docs/docs_skeleton/docs/modules/agents/how_to/custom_llm_agent.mdx +++ b/docs/docs_skeleton/docs/modules/agents/how_to/custom_llm_agent.mdx @@ -1,13 +1,13 @@ -# Custom LLM Agent +# Custom LLM agent This notebook goes through how to create your own custom LLM agent. An LLM agent consists of three parts: -- PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do +- `PromptTemplate`: This is the prompt template that can be used to instruct the language model on what to do - LLM: This is the language model that powers the agent - `stop` sequence: Instructs the LLM to stop generating as soon as this string is found -- OutputParser: This determines how to parse the LLMOutput into an AgentAction or AgentFinish object +- `OutputParser`: This determines how to parse the LLM output into an `AgentAction` or `AgentFinish` object import Example from "@snippets/modules/agents/how_to/custom_llm_agent.mdx" diff --git a/docs/docs_skeleton/docs/modules/agents/how_to/custom_llm_chat_agent.mdx b/docs/docs_skeleton/docs/modules/agents/how_to/custom_llm_chat_agent.mdx index d075b8f46e..7f79f2dca3 100644 --- a/docs/docs_skeleton/docs/modules/agents/how_to/custom_llm_chat_agent.mdx +++ b/docs/docs_skeleton/docs/modules/agents/how_to/custom_llm_chat_agent.mdx @@ -4,10 +4,10 @@ This notebook goes through how to create your own custom agent based on a chat m An LLM chat agent consists of three parts: -- PromptTemplate: This is the prompt template that can be used to instruct the language model on what to do -- ChatModel: This is the language model that powers the agent +- `PromptTemplate`: This is the prompt template that can be used to instruct the language model on what to do +- `ChatModel`: This is the language model that powers the agent - `stop` sequence: Instructs the LLM to stop generating as soon as this string is found -- OutputParser: This determines how to parse the LLMOutput into an AgentAction or AgentFinish object +- `OutputParser`: This determines how to parse the LLM output into an `AgentAction` or `AgentFinish` object import Example from "@snippets/modules/agents/how_to/custom_llm_chat_agent.mdx" diff --git a/docs/docs_skeleton/docs/modules/chains/document/index.mdx b/docs/docs_skeleton/docs/modules/chains/document/index.mdx index 37dd602f56..2cdd550581 100644 --- a/docs/docs_skeleton/docs/modules/chains/document/index.mdx +++ b/docs/docs_skeleton/docs/modules/chains/document/index.mdx @@ -3,7 +3,7 @@ sidebar_position: 2 --- # Documents -These are the core chains for working with Documents. They are useful for summarizing documents, answering questions over documents, extracting information from documents, and more. +These are the core chains for working with documents. They are useful for summarizing documents, answering questions over documents, extracting information from documents, and more. These chains all implement a common interface: diff --git a/docs/docs_skeleton/docs/modules/chains/document/refine.mdx b/docs/docs_skeleton/docs/modules/chains/document/refine.mdx index e4ea5a65e3..902a652b1b 100644 --- a/docs/docs_skeleton/docs/modules/chains/document/refine.mdx +++ b/docs/docs_skeleton/docs/modules/chains/document/refine.mdx @@ -3,10 +3,10 @@ sidebar_position: 1 --- # Refine -The refine documents chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer. +The Refine documents chain constructs a response by looping over the input documents and iteratively updating its answer. For each document, it passes all non-document inputs, the current document, and the latest intermediate answer to an LLM chain to get a new answer. Since the Refine chain only passes a single document to the LLM at a time, it is well-suited for tasks that require analyzing more documents than can fit in the model's context. The obvious tradeoff is that this chain will make far more LLM calls than, for example, the Stuff documents chain. There are also certain tasks which are difficult to accomplish iteratively. For example, the Refine chain can perform poorly when documents frequently cross-reference one another or when a task requires detailed information from many documents. -![refine_diagram](/img/refine.jpg) \ No newline at end of file +![refine_diagram](/img/refine.jpg) diff --git a/docs/docs_skeleton/docs/modules/chains/foundational/llm_chain.mdx b/docs/docs_skeleton/docs/modules/chains/foundational/llm_chain.mdx index d8e9211174..793f765d43 100644 --- a/docs/docs_skeleton/docs/modules/chains/foundational/llm_chain.mdx +++ b/docs/docs_skeleton/docs/modules/chains/foundational/llm_chain.mdx @@ -1,11 +1,11 @@ # LLM -An LLMChain is a simple chain that adds some functionality around language models. It is used widely throughout LangChain, including in other chains and agents. +An `LLMChain` is a simple chain that adds some functionality around language models. It is used widely throughout LangChain, including in other chains and agents. -An LLMChain consists of a PromptTemplate and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output. +An `LLMChain` consists of a `PromptTemplate` and a language model (either an LLM or chat model). It formats the prompt template using the input key values provided (and also memory key values, if available), passes the formatted string to LLM and returns the LLM output. ## Get started import Example from "@snippets/modules/chains/foundational/llm_chain.mdx" - \ No newline at end of file + diff --git a/docs/docs_skeleton/docs/modules/chains/foundational/sequential_chains.mdx b/docs/docs_skeleton/docs/modules/chains/foundational/sequential_chains.mdx index 2e89506fda..5e5c699978 100644 --- a/docs/docs_skeleton/docs/modules/chains/foundational/sequential_chains.mdx +++ b/docs/docs_skeleton/docs/modules/chains/foundational/sequential_chains.mdx @@ -4,7 +4,7 @@ The next step after calling a language model is make a series of calls to a language model. This is particularly useful when you want to take the output from one call and use it as the input to another. -In this notebook we will walk through some examples for how to do this, using sequential chains. Sequential chains allow you to connect multiple chains and compose them into pipelines that execute some specific scenario.. There are two types of sequential chains: +In this notebook we will walk through some examples for how to do this, using sequential chains. Sequential chains allow you to connect multiple chains and compose them into pipelines that execute some specific scenario. There are two types of sequential chains: - `SimpleSequentialChain`: The simplest form of sequential chains, where each step has a singular input/output, and the output of one step is the input to the next. - `SequentialChain`: A more general form of sequential chains, allowing for multiple inputs/outputs. diff --git a/docs/docs_skeleton/docs/modules/chains/index.mdx b/docs/docs_skeleton/docs/modules/chains/index.mdx index a4c3d6ae3a..b1e0bee5bc 100644 --- a/docs/docs_skeleton/docs/modules/chains/index.mdx +++ b/docs/docs_skeleton/docs/modules/chains/index.mdx @@ -30,4 +30,4 @@ Chains allow us to combine multiple components together to create a single, cohe import GetStarted from "@snippets/modules/chains/get_started.mdx" - \ No newline at end of file + diff --git a/docs/docs_skeleton/docs/modules/data_connection/document_loaders/index.mdx b/docs/docs_skeleton/docs/modules/data_connection/document_loaders/index.mdx index 735f294d8d..79c93c1b42 100644 --- a/docs/docs_skeleton/docs/modules/data_connection/document_loaders/index.mdx +++ b/docs/docs_skeleton/docs/modules/data_connection/document_loaders/index.mdx @@ -11,7 +11,7 @@ Use document loaders to load data from a source as `Document`'s. A `Document` is and associated metadata. For example, there are document loaders for loading a simple `.txt` file, for loading the text contents of any web page, or even for loading a transcript of a YouTube video. -Document loaders expose a "load" method for loading data as documents from a configured source. They optionally +Document loaders provide a "load" method for loading data as documents from a configured source. They optionally implement a "lazy load" as well for lazily loading data into memory. ## Get started diff --git a/docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter.mdx b/docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter.mdx index 2055d1f766..727399587b 100644 --- a/docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter.mdx +++ b/docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/character_text_splitter.mdx @@ -2,8 +2,8 @@ This is the simplest method. This splits based on characters (by default "\n\n") and measure chunk length by number of characters. -1. How the text is split: by single character -2. How the chunk size is measured: by number of characters +1. How the text is split: by single character. +2. How the chunk size is measured: by number of characters. import Example from "@snippets/modules/data_connection/document_transformers/text_splitters/character_text_splitter.mdx" diff --git a/docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx b/docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx index da1928a7d9..a21ba3753f 100644 --- a/docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx +++ b/docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx @@ -1,6 +1,6 @@ # Split code -CodeTextSplitter allows you to split your code with multiple language support. Import enum `Language` and specify the language. +CodeTextSplitter allows you to split your code with multiple languages supported. Import enum `Language` and specify the language. import Example from "@snippets/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx" diff --git a/docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter.mdx b/docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter.mdx index 3ac2eaa273..38d147b2dd 100644 --- a/docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter.mdx +++ b/docs/docs_skeleton/docs/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter.mdx @@ -2,8 +2,8 @@ This text splitter is the recommended one for generic text. It is parameterized by a list of characters. It tries to split on them in order until the chunks are small enough. The default list is `["\n\n", "\n", " ", ""]`. This has the effect of trying to keep all paragraphs (and then sentences, and then words) together as long as possible, as those would generically seem to be the strongest semantically related pieces of text. -1. How the text is split: by list of characters -2. How the chunk size is measured: by number of characters +1. How the text is split: by list of characters. +2. How the chunk size is measured: by number of characters. import Example from "@snippets/modules/data_connection/document_transformers/text_splitters/recursive_text_splitter.mdx" diff --git a/docs/docs_skeleton/docs/modules/data_connection/index.mdx b/docs/docs_skeleton/docs/modules/data_connection/index.mdx index b7b1bf87c9..0409f42637 100644 --- a/docs/docs_skeleton/docs/modules/data_connection/index.mdx +++ b/docs/docs_skeleton/docs/modules/data_connection/index.mdx @@ -18,9 +18,9 @@ This encompasses several key modules. **[Document loaders](/docs/modules/data_connection/document_loaders/)** Load documents from many different sources. -LangChain provides over a 100 different document loaders as well as integrations with other major providers in the space, +LangChain provides over 100 different document loaders as well as integrations with other major providers in the space, like AirByte and Unstructured. -We provide integrations to load all types of documents (html, PDF, code) from all types of locations (private s3 buckets, public websites). +We provide integrations to load all types of documents (HTML, PDF, code) from all types of locations (private s3 buckets, public websites). **[Document transformers](/docs/modules/data_connection/document_transformers/)** @@ -32,18 +32,18 @@ LangChain provides several different algorithms for doing this, as well as logic **[Text embedding models](/docs/modules/data_connection/text_embedding/)** Another key part of retrieval has become creating embeddings for documents. -Embeddings capture the semantic meaning of text, allowing you to quickly and +Embeddings capture the semantic meaning of the text, allowing you to quickly and efficiently find other pieces of text that are similar. LangChain provides integrations with over 25 different embedding providers and methods, from open-source to proprietary API, allowing you to choose the one best suited for your needs. -LangChain exposes a standard interface, allowing you to easily swap between models. +LangChain provides a standard interface, allowing you to easily swap between models. **[Vector stores](/docs/modules/data_connection/vectorstores/)** With the rise of embeddings, there has emerged a need for databases to support efficient storage and searching of these embeddings. LangChain provides integrations with over 50 different vectorstores, from open-source local ones to cloud-hosted proprietary ones, -allowing you choose the one best suited for your needs. +allowing you to choose the one best suited for your needs. LangChain exposes a standard interface, allowing you to easily swap between vector stores. **[Retrievers](/docs/modules/data_connection/retrievers/)** @@ -55,7 +55,7 @@ However, we have also added a collection of algorithms on top of this to increas These include: - [Parent Document Retriever](/docs/modules/data_connection/retrievers/parent_document_retriever): This allows you to create multiple embeddings per parent document, allowing you to look up smaller chunks but return larger context. -- [Self Query Retriever](/docs/modules/data_connection/retrievers/self_query): User questions often contain reference to something that isn't just semantic, but rather expresses some logic that can best be represented as a metadata filter. Self-query allows you to parse out the *semantic* part of a query from other *metadata filters* present in the query +- [Self Query Retriever](/docs/modules/data_connection/retrievers/self_query): User questions often contain a reference to something that isn't just semantic but rather expresses some logic that can best be represented as a metadata filter. Self-query allows you to parse out the *semantic* part of a query from other *metadata filters* present in the query. - [Ensemble Retriever](/docs/modules/data_connection/retrievers/ensemble): Sometimes you may want to retrieve documents from multiple different sources, or using multiple different algorithms. The ensemble retriever allows you to easily do this. - And more! diff --git a/docs/docs_skeleton/docs/modules/data_connection/retrievers/contextual_compression/index.mdx b/docs/docs_skeleton/docs/modules/data_connection/retrievers/contextual_compression/index.mdx index af21a1b9a7..38584b964f 100644 --- a/docs/docs_skeleton/docs/modules/data_connection/retrievers/contextual_compression/index.mdx +++ b/docs/docs_skeleton/docs/modules/data_connection/retrievers/contextual_compression/index.mdx @@ -5,10 +5,10 @@ One challenge with retrieval is that usually you don't know the specific queries Contextual compression is meant to fix this. The idea is simple: instead of immediately returning retrieved documents as-is, you can compress them using the context of the given query, so that only the relevant information is returned. “Compressing” here refers to both compressing the contents of an individual document and filtering out documents wholesale. To use the Contextual Compression Retriever, you'll need: -- a base Retriever +- a base retriever - a Document Compressor -The Contextual Compression Retriever passes queries to the base Retriever, takes the initial documents and passes them through the Document Compressor. The Document Compressor takes a list of Documents and shortens it by reducing the contents of Documents or dropping Documents altogether. +The Contextual Compression Retriever passes queries to the base retriever, takes the initial documents and passes them through the Document Compressor. The Document Compressor takes a list of documents and shortens it by reducing the contents of documents or dropping documents altogether. ![](https://drive.google.com/uc?id=1CtNgWODXZudxAWSRiWgSGEoTNrUFT98v) diff --git a/docs/docs_skeleton/docs/modules/data_connection/retrievers/index.mdx b/docs/docs_skeleton/docs/modules/data_connection/retrievers/index.mdx index 8ba4cedc3a..d25e6e422b 100644 --- a/docs/docs_skeleton/docs/modules/data_connection/retrievers/index.mdx +++ b/docs/docs_skeleton/docs/modules/data_connection/retrievers/index.mdx @@ -8,7 +8,7 @@ Head to [Integrations](/docs/integrations/retrievers/) for documentation on buil ::: A retriever is an interface that returns documents given an unstructured query. It is more general than a vector store. -A retriever does not need to be able to store documents, only to return (or retrieve) it. Vector stores can be used +A retriever does not need to be able to store documents, only to return (or retrieve) them. Vector stores can be used as the backbone of a retriever, but there are other types of retrievers as well. ## Get started diff --git a/docs/docs_skeleton/docs/modules/data_connection/retrievers/self_query/index.mdx b/docs/docs_skeleton/docs/modules/data_connection/retrievers/self_query/index.mdx index 22209cc3b7..ee9c2b4eca 100644 --- a/docs/docs_skeleton/docs/modules/data_connection/retrievers/self_query/index.mdx +++ b/docs/docs_skeleton/docs/modules/data_connection/retrievers/self_query/index.mdx @@ -1,6 +1,6 @@ # Self-querying -A self-querying retriever is one that, as the name suggests, has the ability to query itself. Specifically, given any natural language query, the retriever uses a query-constructing LLM chain to write a structured query and then applies that structured query to it's underlying VectorStore. This allows the retriever to not only use the user-input query for semantic similarity comparison with the contents of stored documented, but to also extract filters from the user query on the metadata of stored documents and to execute those filters. +A self-querying retriever is one that, as the name suggests, has the ability to query itself. Specifically, given any natural language query, the retriever uses a query-constructing LLM chain to write a structured query and then applies that structured query to its underlying VectorStore. This allows the retriever to not only use the user-input query for semantic similarity comparison with the contents of stored documents but to also extract filters from the user query on the metadata of stored documents and to execute those filters. ![](https://drive.google.com/uc?id=1OQUN-0MJcDUxmPXofgS7MqReEs720pqS) diff --git a/docs/docs_skeleton/docs/modules/data_connection/retrievers/time_weighted_vectorstore.mdx b/docs/docs_skeleton/docs/modules/data_connection/retrievers/time_weighted_vectorstore.mdx index 0b62952247..5160879ae7 100644 --- a/docs/docs_skeleton/docs/modules/data_connection/retrievers/time_weighted_vectorstore.mdx +++ b/docs/docs_skeleton/docs/modules/data_connection/retrievers/time_weighted_vectorstore.mdx @@ -8,7 +8,7 @@ The algorithm for scoring them is: semantic_similarity + (1.0 - decay_rate) ^ hours_passed ``` -Notably, `hours_passed` refers to the hours passed since the object in the retriever **was last accessed**, not since it was created. This means that frequently accessed objects remain "fresh." +Notably, `hours_passed` refers to the hours passed since the object in the retriever **was last accessed**, not since it was created. This means that frequently accessed objects remain "fresh". import Example from "@snippets/modules/data_connection/retrievers/how_to/time_weighted_vectorstore.mdx" diff --git a/docs/docs_skeleton/docs/modules/data_connection/retrievers/vectorstore.mdx b/docs/docs_skeleton/docs/modules/data_connection/retrievers/vectorstore.mdx index a14e5022c0..87d2ee7455 100644 --- a/docs/docs_skeleton/docs/modules/data_connection/retrievers/vectorstore.mdx +++ b/docs/docs_skeleton/docs/modules/data_connection/retrievers/vectorstore.mdx @@ -1,9 +1,9 @@ # Vector store-backed retriever -A vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the Vector Store class to make it conform to the Retriever interface. +A vector store retriever is a retriever that uses a vector store to retrieve documents. It is a lightweight wrapper around the vector store class to make it conform to the retriever interface. It uses the search methods implemented by a vector store, like similarity search and MMR, to query the texts in the vector store. -Once you construct a Vector store, it's very easy to construct a retriever. Let's walk through an example. +Once you construct a vector store, it's very easy to construct a retriever. Let's walk through an example. import Example from "@snippets/modules/data_connection/retrievers/how_to/vectorstore.mdx" diff --git a/docs/docs_skeleton/docs/modules/data_connection/text_embedding/index.mdx b/docs/docs_skeleton/docs/modules/data_connection/text_embedding/index.mdx index 5c710f4322..9e13a5314b 100644 --- a/docs/docs_skeleton/docs/modules/data_connection/text_embedding/index.mdx +++ b/docs/docs_skeleton/docs/modules/data_connection/text_embedding/index.mdx @@ -11,7 +11,7 @@ The Embeddings class is a class designed for interfacing with text embedding mod Embeddings create a vector representation of a piece of text. This is useful because it means we can think about text in the vector space, and do things like semantic search where we look for pieces of text that are most similar in the vector space. -The base Embeddings class in LangChain exposes two methods: one for embedding documents and one for embedding a query. The former takes as input multiple texts, while the latter takes a single text. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself). +The base Embeddings class in LangChain provides two methods: one for embedding documents and one for embedding a query. The former takes as input multiple texts, while the latter takes a single text. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be searched over) vs queries (the search query itself). ## Get started diff --git a/docs/docs_skeleton/docs/modules/data_connection/vectorstores/index.mdx b/docs/docs_skeleton/docs/modules/data_connection/vectorstores/index.mdx index 390b642d17..a8a35e0845 100644 --- a/docs/docs_skeleton/docs/modules/data_connection/vectorstores/index.mdx +++ b/docs/docs_skeleton/docs/modules/data_connection/vectorstores/index.mdx @@ -16,7 +16,7 @@ for you. ## Get started -This walkthrough showcases basic functionality related to VectorStores. A key part of working with vector stores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the [text embedding model](/docs/modules/data_connection/text_embedding/) interfaces before diving into this. +This walkthrough showcases basic functionality related to vector stores. A key part of working with vector stores is creating the vector to put in them, which is usually created via embeddings. Therefore, it is recommended that you familiarize yourself with the [text embedding model](/docs/modules/data_connection/text_embedding/) interfaces before diving into this. import GetStarted from "@snippets/modules/data_connection/vectorstores/get_started.mdx" diff --git a/docs/docs_skeleton/docs/modules/memory/chat_messages/index.mdx b/docs/docs_skeleton/docs/modules/memory/chat_messages/index.mdx index de4a335a7a..c44f1ac15b 100644 --- a/docs/docs_skeleton/docs/modules/memory/chat_messages/index.mdx +++ b/docs/docs_skeleton/docs/modules/memory/chat_messages/index.mdx @@ -8,10 +8,10 @@ Head to [Integrations](/docs/integrations/memory/) for documentation on built-in ::: One of the core utility classes underpinning most (if not all) memory modules is the `ChatMessageHistory` class. -This is a super lightweight wrapper which exposes convenience methods for saving Human messages, AI messages, and then fetching them all. +This is a super lightweight wrapper which provides convenience methods for saving HumanMessages, AIMessages, and then fetching them all. You may want to use this class directly if you are managing memory outside of a chain. import GetStarted from "@snippets/modules/memory/chat_messages/get_started.mdx" - \ No newline at end of file + diff --git a/docs/docs_skeleton/docs/modules/memory/index.mdx b/docs/docs_skeleton/docs/modules/memory/index.mdx index d8f9d25590..598d36a5e1 100644 --- a/docs/docs_skeleton/docs/modules/memory/index.mdx +++ b/docs/docs_skeleton/docs/modules/memory/index.mdx @@ -32,7 +32,7 @@ Even if these are not all used directly, they need to be stored in some form. One of the key parts of the LangChain memory module is a series of integrations for storing these chat messages, from in-memory lists to persistent databases. -- [Chat message storage](/docs/modules/memory/chat_messages/): How to work with Chat Messages, and the various integrations offered +- [Chat message storage](/docs/modules/memory/chat_messages/): How to work with Chat Messages, and the various integrations offered. ### Querying: Data structures and algorithms on top of chat messages Keeping a list of chat messages is fairly straight-forward. diff --git a/docs/docs_skeleton/docs/modules/memory/types/buffer.mdx b/docs/docs_skeleton/docs/modules/memory/types/buffer.mdx index d417b63174..0ffd152f73 100644 --- a/docs/docs_skeleton/docs/modules/memory/types/buffer.mdx +++ b/docs/docs_skeleton/docs/modules/memory/types/buffer.mdx @@ -1,6 +1,6 @@ # Conversation Buffer -This notebook shows how to use `ConversationBufferMemory`. This memory allows for storing of messages and then extracts the messages in a variable. +This notebook shows how to use `ConversationBufferMemory`. This memory allows for storing messages and then extracts the messages in a variable. We can first extract it as a string. diff --git a/docs/docs_skeleton/docs/modules/memory/types/buffer_window.mdx b/docs/docs_skeleton/docs/modules/memory/types/buffer_window.mdx index 465e918b0a..71918079a7 100644 --- a/docs/docs_skeleton/docs/modules/memory/types/buffer_window.mdx +++ b/docs/docs_skeleton/docs/modules/memory/types/buffer_window.mdx @@ -1,6 +1,6 @@ # Conversation Buffer Window -`ConversationBufferWindowMemory` keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so the buffer does not get too large +`ConversationBufferWindowMemory` keeps a list of the interactions of the conversation over time. It only uses the last K interactions. This can be useful for keeping a sliding window of the most recent interactions, so the buffer does not get too large. Let's first explore the basic functionality of this type of memory. diff --git a/docs/docs_skeleton/docs/modules/memory/types/entity_summary_memory.mdx b/docs/docs_skeleton/docs/modules/memory/types/entity_summary_memory.mdx index e3dc63b6dd..cc6441a5c4 100644 --- a/docs/docs_skeleton/docs/modules/memory/types/entity_summary_memory.mdx +++ b/docs/docs_skeleton/docs/modules/memory/types/entity_summary_memory.mdx @@ -1,6 +1,6 @@ # Entity -Entity Memory remembers given facts about specific entities in a conversation. It extracts information on entities (using an LLM) and builds up its knowledge about that entity over time (also using an LLM). +Entity memory remembers given facts about specific entities in a conversation. It extracts information on entities (using an LLM) and builds up its knowledge about that entity over time (also using an LLM). Let's first walk through using this functionality. diff --git a/docs/docs_skeleton/docs/modules/memory/types/index.mdx b/docs/docs_skeleton/docs/modules/memory/types/index.mdx index 5caaa4e3f7..16e9a34cde 100644 --- a/docs/docs_skeleton/docs/modules/memory/types/index.mdx +++ b/docs/docs_skeleton/docs/modules/memory/types/index.mdx @@ -1,7 +1,7 @@ --- sidebar_position: 2 --- -# Memory Types +# Memory types There are many different types of memory. Each has their own parameters, their own return types, and is useful in different scenarios. diff --git a/docs/docs_skeleton/docs/modules/memory/types/vectorstore_retriever_memory.mdx b/docs/docs_skeleton/docs/modules/memory/types/vectorstore_retriever_memory.mdx index 76c05cd95d..6e576798f7 100644 --- a/docs/docs_skeleton/docs/modules/memory/types/vectorstore_retriever_memory.mdx +++ b/docs/docs_skeleton/docs/modules/memory/types/vectorstore_retriever_memory.mdx @@ -1,6 +1,6 @@ # Backed by a Vector Store -`VectorStoreRetrieverMemory` stores memories in a VectorDB and queries the top-K most "salient" docs every time it is called. +`VectorStoreRetrieverMemory` stores memories in a vector store and queries the top-K most "salient" docs every time it is called. This differs from most of the other Memory classes in that it doesn't explicitly track the order of interactions. diff --git a/docs/docs_skeleton/docs/modules/model_io/models/chat/chat_model_caching.mdx b/docs/docs_skeleton/docs/modules/model_io/models/chat/chat_model_caching.mdx index c34cb22326..f38a1d8bab 100644 --- a/docs/docs_skeleton/docs/modules/model_io/models/chat/chat_model_caching.mdx +++ b/docs/docs_skeleton/docs/modules/model_io/models/chat/chat_model_caching.mdx @@ -1,5 +1,5 @@ # Caching -LangChain provides an optional caching layer for Chat Models. This is useful for two reasons: +LangChain provides an optional caching layer for chat models. This is useful for two reasons: It can save you money by reducing the number of API calls you make to the LLM provider, if you're often requesting the same completion multiple times. It can speed up your application by reducing the number of API calls you make to the LLM provider. diff --git a/docs/docs_skeleton/docs/modules/model_io/models/chat/index.mdx b/docs/docs_skeleton/docs/modules/model_io/models/chat/index.mdx index 742b06a532..ddef889977 100644 --- a/docs/docs_skeleton/docs/modules/model_io/models/chat/index.mdx +++ b/docs/docs_skeleton/docs/modules/model_io/models/chat/index.mdx @@ -8,8 +8,8 @@ Head to [Integrations](/docs/integrations/chat/) for documentation on built-in i ::: Chat models are a variation on language models. -While chat models use language models under the hood, the interface they expose is a bit different. -Rather than expose a "text in, text out" API, they expose an interface where "chat messages" are the inputs and outputs. +While chat models use language models under the hood, the interface they use is a bit different. +Rather than using a "text in, text out" API, they use an interface where "chat messages" are the inputs and outputs. Chat model APIs are fairly new, so we are still figuring out the correct abstractions. diff --git a/docs/docs_skeleton/docs/modules/model_io/models/chat/prompts.mdx b/docs/docs_skeleton/docs/modules/model_io/models/chat/prompts.mdx index b85eb8a8ce..4d5c46d3d5 100644 --- a/docs/docs_skeleton/docs/modules/model_io/models/chat/prompts.mdx +++ b/docs/docs_skeleton/docs/modules/model_io/models/chat/prompts.mdx @@ -1,6 +1,6 @@ # Prompts -Prompts for Chat models are built around messages, instead of just plain text. +Prompts for chat models are built around messages, instead of just plain text. import Prompts from "@snippets/modules/model_io/models/chat/how_to/prompts.mdx" diff --git a/docs/docs_skeleton/docs/modules/model_io/models/chat/streaming.mdx b/docs/docs_skeleton/docs/modules/model_io/models/chat/streaming.mdx index b4d74b8038..96d4e7c2d8 100644 --- a/docs/docs_skeleton/docs/modules/model_io/models/chat/streaming.mdx +++ b/docs/docs_skeleton/docs/modules/model_io/models/chat/streaming.mdx @@ -1,6 +1,6 @@ # Streaming -Some Chat models provide a streaming response. This means that instead of waiting for the entire response to be returned, you can start processing it as soon as it's available. This is useful if you want to display the response to the user as it's being generated, or if you want to process the response as it's being generated. +Some chat models provide a streaming response. This means that instead of waiting for the entire response to be returned, you can start processing it as soon as it's available. This is useful if you want to display the response to the user as it's being generated, or if you want to process the response as it's being generated. import StreamingChatModel from "@snippets/modules/model_io/models/chat/how_to/streaming.mdx" diff --git a/docs/docs_skeleton/docs/modules/model_io/models/index.mdx b/docs/docs_skeleton/docs/modules/model_io/models/index.mdx index 0a97352ac5..287f4f552b 100644 --- a/docs/docs_skeleton/docs/modules/model_io/models/index.mdx +++ b/docs/docs_skeleton/docs/modules/model_io/models/index.mdx @@ -8,16 +8,16 @@ LangChain provides interfaces and integrations for two types of models: - [LLMs](/docs/modules/model_io/models/llms/): Models that take a text string as input and return a text string - [Chat models](/docs/modules/model_io/models/chat/): Models that are backed by a language model but take a list of Chat Messages as input and return a Chat Message -## LLMs vs Chat Models +## LLMs vs chat models -LLMs and Chat Models are subtly but importantly different. LLMs in LangChain refer to pure text completion models. +LLMs and chat models are subtly but importantly different. LLMs in LangChain refer to pure text completion models. The APIs they wrap take a string prompt as input and output a string completion. OpenAI's GPT-3 is implemented as an LLM. Chat models are often backed by LLMs but tuned specifically for having conversations. -And, crucially, their provider APIs expose a different interface than pure text completion models. Instead of a single string, +And, crucially, their provider APIs use a different interface than pure text completion models. Instead of a single string, they take a list of chat messages as input. Usually these messages are labeled with the speaker (usually one of "System", -"AI", and "Human"). And they return a ("AI") chat message as output. GPT-4 and Anthropic's Claude are both implemented as Chat Models. +"AI", and "Human"). And they return an AI chat message as output. GPT-4 and Anthropic's Claude are both implemented as chat models. -To make it possible to swap LLMs and Chat Models, both implement the Base Language Model interface. This exposes common +To make it possible to swap LLMs and chat models, both implement the Base Language Model interface. This includes common methods "predict", which takes a string and returns a string, and "predict messages", which takes messages and returns a message. -If you are using a specific model it's recommended you use the methods specific to that model class (i.e., "predict" for LLMs and "predict messages" for Chat Models), +If you are using a specific model it's recommended you use the methods specific to that model class (i.e., "predict" for LLMs and "predict messages" for chat models), but if you're creating an application that should work with different types of models the shared interface can be helpful. diff --git a/docs/docs_skeleton/docs/use_cases/question_answering/how_to/chat_vector_db.mdx b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/chat_vector_db.mdx index 906d576c51..56f9dda39c 100644 --- a/docs/docs_skeleton/docs/use_cases/question_answering/how_to/chat_vector_db.mdx +++ b/docs/docs_skeleton/docs/use_cases/question_answering/how_to/chat_vector_db.mdx @@ -5,7 +5,7 @@ sidebar_position: 2 # Store and reference chat history The ConversationalRetrievalQA chain builds on RetrievalQAChain to provide a chat history component. -It first combines the chat history (either explicitly passed in or retrieved from the provided memory) and the question into a standalone question, then looks up relevant documents from the retriever, and finally passes those documents and the question to a question answering chain to return a response. +It first combines the chat history (either explicitly passed in or retrieved from the provided memory) and the question into a standalone question, then looks up relevant documents from the retriever, and finally passes those documents and the question to a question-answering chain to return a response. To create one, you will need a retriever. In the below example, we will create one from a vector store, which can be created from embeddings. diff --git a/docs/docs_skeleton/docs/use_cases/web_scraping/index.mdx b/docs/docs_skeleton/docs/use_cases/web_scraping/index.mdx index c62c8e6821..ce28ca3839 100644 --- a/docs/docs_skeleton/docs/use_cases/web_scraping/index.mdx +++ b/docs/docs_skeleton/docs/use_cases/web_scraping/index.mdx @@ -6,4 +6,4 @@ sidebar_position: 3 Web scraping has historically been a challenging endeavor due to the ever-changing nature of website structures, making it tedious for developers to maintain their scraping scripts. Traditional methods often rely on specific HTML tags and patterns which, when altered, can disrupt data extraction processes. -Enter the LLM-based method for parsing HTML: By leveraging the capabilities of LLMs, and especially OpenAI Functions in LangChain's extraction chain, developers can instruct the model to extract only the desired data in a specified format. This method not only streamlines the extraction process but also significantly reduces the time spent on manual debugging and script modifications. Its adaptability means that even if websites undergo significant design changes, the extraction remains consistent and robust. This level of resilience translates to reduced maintenance efforts, cost savings, and ensures a higher quality of extracted data. Compared to its predecessors, LLM-based approach wins out the web scraping domain by transforming a historically cumbersome task into a more automated and efficient process. +Enter the LLM-based method for parsing HTML: By leveraging the capabilities of LLMs, and especially OpenAI Functions in LangChain's extraction chain, developers can instruct the model to extract only the desired data in a specified format. This method not only streamlines the extraction process but also significantly reduces the time spent on manual debugging and script modifications. Its adaptability means that even if websites undergo significant design changes, the extraction remains consistent and robust. This level of resilience translates to reduced maintenance efforts, cost savings, and ensures a higher quality of extracted data. Compared to its predecessors, the LLM-based approach wins out in the web scraping domain by transforming a historically cumbersome task into a more automated and efficient process. diff --git a/docs/docs_skeleton/sidebars.js b/docs/docs_skeleton/sidebars.js index 90950592be..fea37592d9 100644 --- a/docs/docs_skeleton/sidebars.js +++ b/docs/docs_skeleton/sidebars.js @@ -44,6 +44,16 @@ module.exports = { id: "modules/index" }, }, + { + type: "category", + label: "LangChain Expression Language", + collapsed: true, + items: [{ type: "autogenerated", dirName: "expression_language" } ], + link: { + type: 'doc', + id: "expression_language/index" + }, + }, { type: "category", label: "Guides", @@ -52,17 +62,7 @@ module.exports = { link: { type: 'generated-index', description: 'Design guides for key parts of the development process', - slug: "guides", - }, - }, - { - type: "category", - label: "Ecosystem", - collapsed: true, - items: [{ type: "autogenerated", dirName: "ecosystem" }], - link: { - type: 'generated-index', - slug: "ecosystem", + slug: "guides", }, }, { @@ -72,7 +72,7 @@ module.exports = { items: [{ type: "autogenerated", dirName: "additional_resources" }, { type: "link", label: "Gallery", href: "https://github.com/kyrolabs/awesome-langchain" }], link: { type: 'generated-index', - slug: "additional_resources", + slug: "additional_resources", }, }, 'community' diff --git a/docs/docs_skeleton/vercel.json b/docs/docs_skeleton/vercel.json index b88a1f8e27..dfa378952a 100644 --- a/docs/docs_skeleton/vercel.json +++ b/docs/docs_skeleton/vercel.json @@ -2952,6 +2952,46 @@ "source": "/docs/modules/model_io/models/llms/integrations/writer", "destination": "/docs/integrations/llms/writer" }, + { + "source": "/docs/integrations/llms/amazon_api_gateway_example", + "destination": "/docs/integrations/llms/amazon_api_gateway" + }, + { + "source": "/docs/integrations/llms/azureml_endpoint_example", + "destination": "/docs/integrations/llms/azure_ml" + }, + { + "source": "/docs/integrations/llms/azure_openai_example", + "destination": "/docs/integrations/llms/azure_openai" + }, + { + "source": "/docs/integrations/llms/cerebriumai_example", + "destination": "/docs/integrations/llms/cerebriumai" + }, + { + "source": "/docs/integrations/llms/deepinfra_example", + "destination": "/docs/integrations/llms/deepinfra" + }, + { + "source": "/docs/integrations/llms/Fireworks", + "destination": "/docs/integrations/llms/fireworks" + }, + { + "source": "/docs/integrations/llms/forefrontai_example", + "destination": "/docs/integrations/llms/forefrontai" + }, + { + "source": "/docs/integrations/llms/gooseai_example", + "destination": "/docs/integrations/llms/gooseai" + }, + { + "source": "/docs/integrations/llms/petals_example", + "destination": "/docs/integrations/llms/petals" + }, + { + "source": "/docs/integrations/llms/pipelineai_example", + "destination": "/docs/integrations/llms/pipelineai" + }, { "source": "/en/latest/modules/prompts.html", "destination": "/docs/modules/model_io/prompts" @@ -3436,6 +3476,14 @@ "source": "/docs/modules/chains/additional/graph_kuzu_qa", "destination": "/docs/use_cases/more/graph/graph_kuzu_qa" }, + { + "source": "/docs/use_cases/graph/graph_falkordb_qa", + "destination": "/docs/use_cases/more/graph/graph_falkordb_qa" + }, + { + "source": "/docs/modules/chains/additional/graph_falkordb_qa", + "destination": "/docs/use_cases/more/graph/graph_falkordb_qa" + }, { "source": "/docs/use_cases/graph/graph_nebula_qa", "destination": "/docs/use_cases/more/graph/graph_nebula_qa" @@ -3547,6 +3595,18 @@ { "source": "/en/latest/integrations/:path*", "destination": "/docs/integrations/providers/:path*" + }, + { + "source": "/docs/guides/expression_language(/?)", + "destination": "/docs/expression_language/" + }, + { + "source": "/docs/guides/expression_language/:path*", + "destination": "/docs/expression_language/:path*" + }, + { + "source": "/docs/ecosystem/dependents", + "destination": "/docs/additional_resources/dependents" } ] } \ No newline at end of file diff --git a/docs/extras/_templates/integration.mdx b/docs/extras/_templates/integration.mdx index 0263992039..5e4b994ebf 100644 --- a/docs/extras/_templates/integration.mdx +++ b/docs/extras/_templates/integration.mdx @@ -47,7 +47,7 @@ from langchain.embeddings import integration_class_REPLACE_ME ``` -## Chat Models +## Chat models See a [usage example](/docs/integrations/chat/INCLUDE_REAL_NAME) diff --git a/docs/extras/ecosystem/dependents.mdx b/docs/extras/additional_resources/dependents.mdx similarity index 100% rename from docs/extras/ecosystem/dependents.mdx rename to docs/extras/additional_resources/dependents.mdx diff --git a/docs/extras/guides/expression_language/cookbook.ipynb b/docs/extras/expression_language/cookbook.ipynb similarity index 100% rename from docs/extras/guides/expression_language/cookbook.ipynb rename to docs/extras/expression_language/cookbook.ipynb diff --git a/docs/extras/guides/expression_language/interface.ipynb b/docs/extras/expression_language/interface.ipynb similarity index 100% rename from docs/extras/guides/expression_language/interface.ipynb rename to docs/extras/expression_language/interface.ipynb diff --git a/docs/extras/guides/debugging.md b/docs/extras/guides/debugging.md index 2527034dcb..9b23137250 100644 --- a/docs/extras/guides/debugging.md +++ b/docs/extras/guides/debugging.md @@ -2,7 +2,7 @@ If you're building with LLMs, at some point something will break, and you'll need to debug. A model call will fail, or the model output will be misformatted, or there will be some nested model calls and it won't be clear where along the way an incorrect output was created. -Here's a few different tools and functionalities to aid in debugging. +Here are a few different tools and functionalities to aid in debugging. @@ -18,9 +18,9 @@ For anyone building production-grade LLM applications, we highly recommend using If you're prototyping in Jupyter Notebooks or running Python scripts, it can be helpful to print out the intermediate steps of a Chain run. -There's a number of ways to enable printing at varying degrees of verbosity. +There are a number of ways to enable printing at varying degrees of verbosity. -Let's suppose we have a simple agent and want to visualize the actions it takes and tool outputs it receives. Without any debugging, here's what we see: +Let's suppose we have a simple agent, and want to visualize the actions it takes and tool outputs it receives. Without any debugging, here's what we see: ```python diff --git a/docs/extras/guides/deployments/template_repos.mdx b/docs/extras/guides/deployments/template_repos.mdx index 45c4a43dec..723718d1de 100644 --- a/docs/extras/guides/deployments/template_repos.mdx +++ b/docs/extras/guides/deployments/template_repos.mdx @@ -14,7 +14,7 @@ It also contains instructions for how to deploy this app on the Streamlit platfo ## [Gradio (on Hugging Face)](https://github.com/hwchase17/langchain-gradio-template) -This repo serves as a template for how deploy a LangChain with Gradio. +This repo serves as a template for how to deploy a LangChain with Gradio. It implements a chatbot interface, with a "Bring-Your-Own-Token" approach (nice for not wracking up big bills). It also contains instructions for how to deploy this app on the Hugging Face platform. This is heavily influenced by James Weaver's [excellent examples](https://huggingface.co/JavaFXpert). @@ -27,7 +27,7 @@ Chainlit [doc](https://docs.chainlit.io/langchain) on the integration with LangC ## [Beam](https://github.com/slai-labs/get-beam/tree/main/examples/langchain-question-answering) -This repo serves as a template for how deploy a LangChain with [Beam](https://beam.cloud). +This repo serves as a template for how to deploy a LangChain with [Beam](https://beam.cloud). It implements a Question Answering app and contains instructions for deploying the app as a serverless REST API. @@ -49,7 +49,7 @@ A minimal example of how to deploy LangChain to [Fly.io](https://fly.io/) using ## [Digitalocean App Platform](https://github.com/homanp/digitalocean-langchain) -A minimal example on how to deploy LangChain to DigitalOcean App Platform. +A minimal example of how to deploy LangChain to DigitalOcean App Platform. ## [CI/CD Google Cloud Build + Dockerfile + Serverless Google Cloud Run](https://github.com/g-emarco/github-assistant) @@ -57,7 +57,7 @@ Boilerplate LangChain project on how to deploy to Google Cloud Run using Docker ## [Google Cloud Run](https://github.com/homanp/gcp-langchain) -A minimal example on how to deploy LangChain to Google Cloud Run. +A minimal example of how to deploy LangChain to Google Cloud Run. ## [SteamShip](https://github.com/steamship-core/steamship-langchain/) @@ -82,4 +82,4 @@ These templates serve as examples of how to build, deploy, and share LangChain a ## [AzureML Online Endpoint](https://github.com/Azure/azureml-examples/blob/main/sdk/python/endpoints/online/llm/langchain/1_langchain_basic_deploy.ipynb) -A minimal example of how to deploy LangChain to an Azure Machine Learning Online Endpoint. \ No newline at end of file +A minimal example of how to deploy LangChain to an Azure Machine Learning Online Endpoint. diff --git a/docs/extras/guides/local_llms.ipynb b/docs/extras/guides/local_llms.ipynb index b9f3843296..90c3232a2b 100644 --- a/docs/extras/guides/local_llms.ipynb +++ b/docs/extras/guides/local_llms.ipynb @@ -146,7 +146,7 @@ "source": [ "## Environment\n", "\n", - "Inference speed is a chllenge when running models locally (see above).\n", + "Inference speed is a challenge when running models locally (see above).\n", "\n", "To minimize latency, it is desiable to run models locally on GPU, which ships with many consumer laptops [e.g., Apple devices](https://www.apple.com/newsroom/2022/06/apple-unveils-m2-with-breakthrough-performance-and-capabilities/).\n", "\n", @@ -264,88 +264,19 @@ "metadata": {}, "outputs": [], "source": [ - "pip install llama-cpp-python" + "CMAKE_ARGS=\"-DLLAMA_METAL=on\" FORCE_CMAKE=1 pip install -U llama-cpp-python --no-cache-dirclear" ] }, { "cell_type": "code", - "execution_count": 43, - "id": "9d5f94b5", + "execution_count": null, + "id": "a88bf0c8-e989-4bcd-bcb7-4d7757e684f2", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "objc[10142]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x2a0c4c208) and /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/libllama.dylib (0x2c28bc208). One of the two will be used. Which one is undefined.\n", - "llama.cpp: loading model from /Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\n", - "llama_model_load_internal: format = ggjt v3 (latest)\n", - "llama_model_load_internal: n_vocab = 32000\n", - "llama_model_load_internal: n_ctx = 2048\n", - "llama_model_load_internal: n_embd = 5120\n", - "llama_model_load_internal: n_mult = 256\n", - "llama_model_load_internal: n_head = 40\n", - "llama_model_load_internal: n_layer = 40\n", - "llama_model_load_internal: n_rot = 128\n", - "llama_model_load_internal: freq_base = 10000.0\n", - "llama_model_load_internal: freq_scale = 1\n", - "llama_model_load_internal: ftype = 2 (mostly Q4_0)\n", - "llama_model_load_internal: n_ff = 13824\n", - "llama_model_load_internal: model size = 13B\n", - "llama_model_load_internal: ggml ctx size = 0.09 MB\n", - "llama_model_load_internal: mem required = 8953.71 MB (+ 1608.00 MB per state)\n", - "llama_new_context_with_model: kv self size = 1600.00 MB\n", - "ggml_metal_init: allocating\n", - "ggml_metal_init: using MPS\n", - "ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'\n", - "ggml_metal_init: loaded kernel_add 0x47774af60\n", - "ggml_metal_init: loaded kernel_mul 0x47774bc00\n", - "ggml_metal_init: loaded kernel_mul_row 0x47774c230\n", - "ggml_metal_init: loaded kernel_scale 0x47774c890\n", - "ggml_metal_init: loaded kernel_silu 0x47774cef0\n", - "ggml_metal_init: loaded kernel_relu 0x10e33e500\n", - "ggml_metal_init: loaded kernel_gelu 0x47774b2f0\n", - "ggml_metal_init: loaded kernel_soft_max 0x47771a580\n", - "ggml_metal_init: loaded kernel_diag_mask_inf 0x47774dab0\n", - "ggml_metal_init: loaded kernel_get_rows_f16 0x47774e110\n", - "ggml_metal_init: loaded kernel_get_rows_q4_0 0x47774e7d0\n", - "ggml_metal_init: loaded kernel_get_rows_q4_1 0x13efd7170\n", - "ggml_metal_init: loaded kernel_get_rows_q2_K 0x13efd73d0\n", - "ggml_metal_init: loaded kernel_get_rows_q3_K 0x13efd7630\n", - "ggml_metal_init: loaded kernel_get_rows_q4_K 0x13efd7890\n", - "ggml_metal_init: loaded kernel_get_rows_q5_K 0x4744c9740\n", - "ggml_metal_init: loaded kernel_get_rows_q6_K 0x4744ca6b0\n", - "ggml_metal_init: loaded kernel_rms_norm 0x4744cb250\n", - "ggml_metal_init: loaded kernel_norm 0x4744cb970\n", - "ggml_metal_init: loaded kernel_mul_mat_f16_f32 0x10e33f700\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32 0x10e33fcd0\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32 0x4744cc2d0\n", - "ggml_metal_init: loaded kernel_mul_mat_q2_K_f32 0x4744cc6f0\n", - "ggml_metal_init: loaded kernel_mul_mat_q3_K_f32 0x4744cd6b0\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_K_f32 0x4744cde20\n", - "ggml_metal_init: loaded kernel_mul_mat_q5_K_f32 0x10e33ff30\n", - "ggml_metal_init: loaded kernel_mul_mat_q6_K_f32 0x10e340190\n", - "ggml_metal_init: loaded kernel_rope 0x10e3403f0\n", - "ggml_metal_init: loaded kernel_alibi_f32 0x10e340de0\n", - "ggml_metal_init: loaded kernel_cpy_f32_f16 0x10e3416d0\n", - "ggml_metal_init: loaded kernel_cpy_f32_f32 0x10e342080\n", - "ggml_metal_init: loaded kernel_cpy_f16_f16 0x10e342ca0\n", - "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n", - "ggml_metal_init: hasUnifiedMemory = true\n", - "ggml_metal_init: maxTransferRate = built-in GPU\n", - "ggml_metal_add_buffer: allocated 'data ' buffer, size = 6984.06 MB, ( 6986.19 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'eval ' buffer, size = 1032.00 MB, ( 8018.19 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'kv ' buffer, size = 1602.00 MB, ( 9620.19 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'scr0 ' buffer, size = 426.00 MB, (10046.19 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'scr1 ' buffer, size = 512.00 MB, (10558.19 / 21845.34)\n", - "AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | \n" - ] - } - ], + "outputs": [], "source": [ "from langchain.llms import LlamaCpp\n", "llm = LlamaCpp(\n", - " model_path=\"/Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\",\n", + " model_path=\"/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin\",\n", " n_gpu_layers=1,\n", " n_batch=512,\n", " n_ctx=2048,\n", @@ -448,87 +379,10 @@ }, { "cell_type": "code", - "execution_count": 46, - "id": "b55a2147", + "execution_count": null, + "id": "915ecd4c-8f6b-4de3-a787-b64cb7c682b4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found model file at /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n", - "llama_new_context_with_model: max tensor size = 87.89 MB\n", - "llama_new_context_with_model: max tensor size = 87.89 MB\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "llama.cpp: using Metal\n", - "llama.cpp: loading model from /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n", - "llama_model_load_internal: format = ggjt v3 (latest)\n", - "llama_model_load_internal: n_vocab = 32001\n", - "llama_model_load_internal: n_ctx = 2048\n", - "llama_model_load_internal: n_embd = 5120\n", - "llama_model_load_internal: n_mult = 256\n", - "llama_model_load_internal: n_head = 40\n", - "llama_model_load_internal: n_layer = 40\n", - "llama_model_load_internal: n_rot = 128\n", - "llama_model_load_internal: ftype = 2 (mostly Q4_0)\n", - "llama_model_load_internal: n_ff = 13824\n", - "llama_model_load_internal: n_parts = 1\n", - "llama_model_load_internal: model size = 13B\n", - "llama_model_load_internal: ggml ctx size = 0.09 MB\n", - "llama_model_load_internal: mem required = 9031.71 MB (+ 1608.00 MB per state)\n", - "llama_new_context_with_model: kv self size = 1600.00 MB\n", - "ggml_metal_init: allocating\n", - "ggml_metal_init: using MPS\n", - "ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/ggml-metal.metal'\n", - "ggml_metal_init: loaded kernel_add 0x37944d850\n", - "ggml_metal_init: loaded kernel_mul 0x37944f350\n", - "ggml_metal_init: loaded kernel_mul_row 0x37944fdd0\n", - "ggml_metal_init: loaded kernel_scale 0x3794505a0\n", - "ggml_metal_init: loaded kernel_silu 0x379450800\n", - "ggml_metal_init: loaded kernel_relu 0x379450a60\n", - "ggml_metal_init: loaded kernel_gelu 0x379450cc0\n", - "ggml_metal_init: loaded kernel_soft_max 0x379450ff0\n", - "ggml_metal_init: loaded kernel_diag_mask_inf 0x379451250\n", - "ggml_metal_init: loaded kernel_get_rows_f16 0x3794514b0\n", - "ggml_metal_init: loaded kernel_get_rows_q4_0 0x379451710\n", - "ggml_metal_init: loaded kernel_get_rows_q4_1 0x379451970\n", - "ggml_metal_init: loaded kernel_get_rows_q2_k 0x379451bd0\n", - "ggml_metal_init: loaded kernel_get_rows_q3_k 0x379451e30\n", - "ggml_metal_init: loaded kernel_get_rows_q4_k 0x379452090\n", - "ggml_metal_init: loaded kernel_get_rows_q5_k 0x3794522f0\n", - "ggml_metal_init: loaded kernel_get_rows_q6_k 0x379452550\n", - "ggml_metal_init: loaded kernel_rms_norm 0x3794527b0\n", - "ggml_metal_init: loaded kernel_norm 0x379452a10\n", - "ggml_metal_init: loaded kernel_mul_mat_f16_f32 0x379452c70\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32 0x379452ed0\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32 0x379453130\n", - "ggml_metal_init: loaded kernel_mul_mat_q2_k_f32 0x379453390\n", - "ggml_metal_init: loaded kernel_mul_mat_q3_k_f32 0x3794535f0\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_k_f32 0x379453850\n", - "ggml_metal_init: loaded kernel_mul_mat_q5_k_f32 0x379453ab0\n", - "ggml_metal_init: loaded kernel_mul_mat_q6_k_f32 0x379453d10\n", - "ggml_metal_init: loaded kernel_rope 0x379453f70\n", - "ggml_metal_init: loaded kernel_alibi_f32 0x3794541d0\n", - "ggml_metal_init: loaded kernel_cpy_f32_f16 0x379454430\n", - "ggml_metal_init: loaded kernel_cpy_f32_f32 0x379454690\n", - "ggml_metal_init: loaded kernel_cpy_f16_f16 0x3794548f0\n", - "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n", - "ggml_metal_init: hasUnifiedMemory = true\n", - "ggml_metal_init: maxTransferRate = built-in GPU\n", - "ggml_metal_add_buffer: allocated 'data ' buffer, size = 6984.06 MB, (17542.94 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'eval ' buffer, size = 1024.00 MB, (18566.94 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'kv ' buffer, size = 1602.00 MB, (20168.94 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'scr0 ' buffer, size = 512.00 MB, (20680.94 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'scr1 ' buffer, size = 512.00 MB, (21192.94 / 21845.34)\n", - "ggml_metal_free: deallocating\n" - ] - } - ], + "outputs": [], "source": [ "from langchain.llms import GPT4All\n", "llm = GPT4All(model=\"/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\")" @@ -564,89 +418,21 @@ "\n", "Some LLMs will benefit from specific prompts.\n", "\n", - "For example, llama2 can use [special tokens](https://twitter.com/RLanceMartin/status/1681879318493003776?s=20).\n", + "For example, LLaMA will use [special tokens](https://twitter.com/RLanceMartin/status/1681879318493003776?s=20).\n", "\n", "We can use `ConditionalPromptSelector` to set prompt based on the model type." ] }, { "cell_type": "code", - "execution_count": 57, - "id": "d082b10a", + "execution_count": null, + "id": "16759b7c-7903-4269-b7b4-f83b313d8091", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "llama.cpp: loading model from /Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\n", - "llama_model_load_internal: format = ggjt v3 (latest)\n", - "llama_model_load_internal: n_vocab = 32000\n", - "llama_model_load_internal: n_ctx = 2048\n", - "llama_model_load_internal: n_embd = 5120\n", - "llama_model_load_internal: n_mult = 256\n", - "llama_model_load_internal: n_head = 40\n", - "llama_model_load_internal: n_layer = 40\n", - "llama_model_load_internal: n_rot = 128\n", - "llama_model_load_internal: freq_base = 10000.0\n", - "llama_model_load_internal: freq_scale = 1\n", - "llama_model_load_internal: ftype = 2 (mostly Q4_0)\n", - "llama_model_load_internal: n_ff = 13824\n", - "llama_model_load_internal: model size = 13B\n", - "llama_model_load_internal: ggml ctx size = 0.09 MB\n", - "llama_model_load_internal: mem required = 8953.71 MB (+ 1608.00 MB per state)\n", - "llama_new_context_with_model: kv self size = 1600.00 MB\n", - "ggml_metal_init: allocating\n", - "ggml_metal_init: using MPS\n", - "ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'\n", - "ggml_metal_init: loaded kernel_add 0x4744d09d0\n", - "ggml_metal_init: loaded kernel_mul 0x3781cb3d0\n", - "ggml_metal_init: loaded kernel_mul_row 0x37813bb60\n", - "ggml_metal_init: loaded kernel_scale 0x474481080\n", - "ggml_metal_init: loaded kernel_silu 0x4744d29f0\n", - "ggml_metal_init: loaded kernel_relu 0x3781254c0\n", - "ggml_metal_init: loaded kernel_gelu 0x47447f280\n", - "ggml_metal_init: loaded kernel_soft_max 0x4744cf470\n", - "ggml_metal_init: loaded kernel_diag_mask_inf 0x4744cf6d0\n", - "ggml_metal_init: loaded kernel_get_rows_f16 0x4744cf930\n", - "ggml_metal_init: loaded kernel_get_rows_q4_0 0x4744cfb90\n", - "ggml_metal_init: loaded kernel_get_rows_q4_1 0x4744cfdf0\n", - "ggml_metal_init: loaded kernel_get_rows_q2_K 0x4744d0050\n", - "ggml_metal_init: loaded kernel_get_rows_q3_K 0x4744ce980\n", - "ggml_metal_init: loaded kernel_get_rows_q4_K 0x4744cebe0\n", - "ggml_metal_init: loaded kernel_get_rows_q5_K 0x4744cee40\n", - "ggml_metal_init: loaded kernel_get_rows_q6_K 0x4744cf0a0\n", - "ggml_metal_init: loaded kernel_rms_norm 0x474482450\n", - "ggml_metal_init: loaded kernel_norm 0x4744826b0\n", - "ggml_metal_init: loaded kernel_mul_mat_f16_f32 0x474482910\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32 0x474482b70\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32 0x474482dd0\n", - "ggml_metal_init: loaded kernel_mul_mat_q2_K_f32 0x474483030\n", - "ggml_metal_init: loaded kernel_mul_mat_q3_K_f32 0x474483290\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_K_f32 0x4744834f0\n", - "ggml_metal_init: loaded kernel_mul_mat_q5_K_f32 0x474483750\n", - "ggml_metal_init: loaded kernel_mul_mat_q6_K_f32 0x4744839b0\n", - "ggml_metal_init: loaded kernel_rope 0x474483c10\n", - "ggml_metal_init: loaded kernel_alibi_f32 0x474483e70\n", - "ggml_metal_init: loaded kernel_cpy_f32_f16 0x4744840d0\n", - "ggml_metal_init: loaded kernel_cpy_f32_f32 0x474484330\n", - "ggml_metal_init: loaded kernel_cpy_f16_f16 0x474484590\n", - "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n", - "ggml_metal_init: hasUnifiedMemory = true\n", - "ggml_metal_init: maxTransferRate = built-in GPU\n", - "ggml_metal_add_buffer: allocated 'data ' buffer, size = 6984.06 MB, ( 6986.94 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'eval ' buffer, size = 1032.00 MB, ( 8018.94 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'kv ' buffer, size = 1602.00 MB, ( 9620.94 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'scr0 ' buffer, size = 426.00 MB, (10046.94 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'scr1 ' buffer, size = 512.00 MB, (10558.94 / 21845.34)\n", - "AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | \n" - ] - } - ], + "outputs": [], "source": [ "# Set our LLM\n", "llm = LlamaCpp(\n", - " model_path=\"/Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\",\n", + " model_path=\"/Users/rlm/Desktop/Code/llama.cpp/models/openorca-platypus2-13b.gguf.q4_0.bin\",\n", " n_gpu_layers=1,\n", " n_batch=512,\n", " n_ctx=2048,\n", @@ -661,7 +447,7 @@ "id": "66656084", "metadata": {}, "source": [ - "Set the associated prompt." + "Set the associated prompt based upon the model version." ] }, { @@ -759,6 +545,18 @@ "llm_chain.run({\"question\":question})" ] }, + { + "cell_type": "markdown", + "id": "6e0d37e7-f1d9-4848-bf2c-c22392ee141f", + "metadata": {}, + "source": [ + "We also can use the LangChain Prompt Hub to fetch and / or store prompts that are model specific.\n", + "\n", + "This will work with your [LangSmith API key](https://docs.smith.langchain.com/).\n", + "\n", + "For example, [here](https://smith.langchain.com/hub/rlm/rag-prompt-llama) is a prompt for RAG with LLaMA-specific tokens." + ] + }, { "cell_type": "markdown", "id": "6ba66260", @@ -770,16 +568,12 @@ "\n", "For example, here is a guide to [RAG](docs/use_cases/question_answering/how_to/local_retrieval_qa) with local LLMs.\n", "\n", - "In general, use cases for local model can be driven by at least two factors:\n", + "In general, use cases for local LLMs can be driven by at least two factors:\n", "\n", "* `Privacy`: private data (e.g., journals, etc) that a user does not want to share \n", "* `Cost`: text preprocessing (extraction/tagging), summarization, and agent simulations are token-use-intensive tasks\n", "\n", - "There are a few approach to support specific use-cases: \n", - "\n", - "* Fine-tuning (e.g., [gpt-llm-trainer](https://github.com/mshumer/gpt-llm-trainer), [Anyscale](https://www.anyscale.com/blog/fine-tuning-llama-2-a-comprehensive-case-study-for-tailoring-models-to-unique-applications)) \n", - "* [Function-calling](https://github.com/MeetKai/functionary/tree/main) for use-cases like extraction or tagging\n", - "\n" + "In addition, [here](https://blog.langchain.dev/using-langsmith-to-support-fine-tuning-of-open-source-llms/) is an overview on fine-tuning, which can utilize open source LLMs." ] } ], diff --git a/docs/extras/integrations/callbacks/context.ipynb b/docs/extras/integrations/callbacks/context.ipynb index 50e422562e..bf05268f6b 100644 --- a/docs/extras/integrations/callbacks/context.ipynb +++ b/docs/extras/integrations/callbacks/context.ipynb @@ -93,7 +93,7 @@ "metadata": {}, "source": [ "## Usage\n", - "### Using the Context callback within a Chat Model\n", + "### Using the Context callback within a chat model\n", "\n", "The Context callback handler can be used to directly record transcripts between users and AI assistants.\n", "\n", diff --git a/docs/extras/integrations/callbacks/infino.ipynb b/docs/extras/integrations/callbacks/infino.ipynb index 2b038932ca..0aa362f74b 100644 --- a/docs/extras/integrations/callbacks/infino.ipynb +++ b/docs/extras/integrations/callbacks/infino.ipynb @@ -210,61 +210,10 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "id": "f078c612-89e0-4a1d-b1a8-bf36b664a10e", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Helper function to create a graph using matplotlib.\n", "def plot(data, title):\n", @@ -397,7 +346,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.3" } }, "nbformat": 4, diff --git a/docs/extras/integrations/chat/bedrock.ipynb b/docs/extras/integrations/chat/bedrock.ipynb new file mode 100644 index 0000000000..7669fd915e --- /dev/null +++ b/docs/extras/integrations/chat/bedrock.ipynb @@ -0,0 +1,106 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "bf733a38-db84-4363-89e2-de6735c37230", + "metadata": {}, + "source": [ + "# Bedrock Chat\n", + "\n", + "[Amazon Bedrock](https://aws.amazon.com/bedrock/) is a fully managed service that makes FMs from leading AI startups and Amazon available via an API, so you can choose from a wide range of FMs to find the model that is best suited for your use case" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d51edc81", + "metadata": {}, + "outputs": [], + "source": [ + "%pip install boto3" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d4a7c55d-b235-4ca4-a579-c90cc9570da9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain.chat_models import BedrockChat\n", + "from langchain.schema import HumanMessage" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "70cf04e8-423a-4ff6-8b09-f11fb711c817", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "chat = BedrockChat(model_id=\"anthropic.claude-v2\", model_kwargs={\"temperature\":0.1})" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8199ef8f-eb8b-4253-9ea0-6c24a013ca4c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "AIMessage(content=\" Voici la traduction en français : J'adore programmer.\", additional_kwargs={}, example=False)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "messages = [\n", + " HumanMessage(\n", + " content=\"Translate this sentence from English to French. I love programming.\"\n", + " )\n", + "]\n", + "chat(messages)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c253883f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/extras/integrations/document_loaders/aws_s3_directory.ipynb b/docs/extras/integrations/document_loaders/aws_s3_directory.ipynb index 683408995b..4d474ca64a 100644 --- a/docs/extras/integrations/document_loaders/aws_s3_directory.ipynb +++ b/docs/extras/integrations/document_loaders/aws_s3_directory.ipynb @@ -102,13 +102,34 @@ "loader.load()" ] }, + { + "cell_type": "markdown", + "source": [ + "## Configuring the AWS Boto3 client\n", + "You can configure the AWS [Boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html) client by passing\n", + "named arguments when creating the S3DirectoryLoader.\n", + "This is useful for instance when AWS credentials can't be set as environment variables.\n", + "See the [list of parameters](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session) that can be configured." + ], + "metadata": {} + }, { "cell_type": "code", "execution_count": null, - "id": "885dc280", - "metadata": {}, "outputs": [], - "source": [] + "source": [ + "loader = S3DirectoryLoader(\"testing-hwc\", aws_access_key_id=\"xxxx\", aws_secret_access_key=\"yyyy\")" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "loader.load()" + ], + "metadata": {} } ], "metadata": { diff --git a/docs/extras/integrations/document_loaders/aws_s3_file.ipynb b/docs/extras/integrations/document_loaders/aws_s3_file.ipynb index 4646e4101e..91d7751eb6 100644 --- a/docs/extras/integrations/document_loaders/aws_s3_file.ipynb +++ b/docs/extras/integrations/document_loaders/aws_s3_file.ipynb @@ -66,12 +66,34 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "id": "93689594", "metadata": {}, + "source": [ + "## Configuring the AWS Boto3 client\n", + "You can configure the AWS [Boto3](https://boto3.amazonaws.com/v1/documentation/api/latest/index.html) client by passing\n", + "named arguments when creating the S3DirectoryLoader.\n", + "This is useful for instance when AWS credentials can't be set as environment variables.\n", + "See the [list of parameters](https://boto3.amazonaws.com/v1/documentation/api/latest/reference/core/session.html#boto3.session.Session) that can be configured." + ] + }, + { + "cell_type": "code", + "execution_count": null, "outputs": [], - "source": [] + "source": [ + "loader = S3FileLoader(\"testing-hwc\", \"fake.docx\", aws_access_key_id=\"xxxx\", aws_secret_access_key=\"yyyy\")" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "loader.load()" + ], + "metadata": {} } ], "metadata": { diff --git a/docs/extras/integrations/document_loaders/azure_document_intelligence.ipynb b/docs/extras/integrations/document_loaders/azure_document_intelligence.ipynb new file mode 100644 index 0000000000..b28acaebe0 --- /dev/null +++ b/docs/extras/integrations/document_loaders/azure_document_intelligence.ipynb @@ -0,0 +1,138 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Azure Document Intelligence" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Azure Document Intelligence (formerly known as Azure Forms Recognizer) is machine-learning \n", + "based service that extracts text (including handwriting), tables or key-value-pairs from\n", + "scanned documents or images.\n", + "\n", + "This current implementation of a loader using Document Intelligence is able to incorporate content page-wise and turn it into LangChain documents.\n", + "\n", + "Document Intelligence supports PDF, JPEG, PNG, BMP, or TIFF.\n", + "\n", + "Further documentation is available at https://learn.microsoft.com/en-us/azure/ai-services/document-intelligence/?view=doc-intel-3.1.0.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install langchain azure-ai-formrecognizer -q" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example 1" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The first example uses a local file which will be sent to Azure Document Intelligence.\n", + "\n", + "First, an instance of a DocumentAnalysisClient is created with endpoint and key for the Azure service. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from azure.ai.formrecognizer import DocumentAnalysisClient\n", + "from azure.core.credentials import AzureKeyCredential\n", + "\n", + "document_analysis_client = DocumentAnalysisClient(\n", + " endpoint=\"\", credential=AzureKeyCredential(\"\")\n", + " )" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the initialized document analysis client, we can proceed to create an instance of the DocumentIntelligenceLoader:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.document_loaders.pdf import DocumentIntelligenceLoader\n", + "loader = DocumentIntelligenceLoader(\n", + " \"\",\n", + " client=document_analysis_client,\n", + " model=\"\") # e.g. prebuilt-document\n", + "\n", + "documents = loader.load()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output contains each page of the source document as a LangChain document: " + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Document(page_content='...', metadata={'source': '...', 'page': 1})]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "documents" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.9.5" + }, + "vscode": { + "interpreter": { + "hash": "f9f85f796d01129d0dd105a088854619f454435301f6ffec2fea96ecbd9be4ac" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/extras/integrations/document_loaders/geopandas.ipynb b/docs/extras/integrations/document_loaders/geopandas.ipynb index 3d6764fdb8..c330aef6a3 100644 --- a/docs/extras/integrations/document_loaders/geopandas.ipynb +++ b/docs/extras/integrations/document_loaders/geopandas.ipynb @@ -100,21 +100,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "id": "8148a63e", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -191,7 +180,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.11.3" } }, "nbformat": 4, diff --git a/docs/extras/integrations/document_loaders/google_drive.ipynb b/docs/extras/integrations/document_loaders/google_drive.ipynb index 9d17e5df97..35b856153e 100644 --- a/docs/extras/integrations/document_loaders/google_drive.ipynb +++ b/docs/extras/integrations/document_loaders/google_drive.ipynb @@ -210,7 +210,7 @@ "id": "83ac576b-48c9-4aad-a35e-e978ea32f746", "metadata": {}, "source": [ - "# Extended usage\n", + "## Extended usage\n", "An external component can manage the complexity of Google Drive : `langchain-googledrive`\n", "It's compatible with the ̀`langchain.document_loaders.GoogleDriveLoader` and can be used\n", "in its place.\n", @@ -319,7 +319,7 @@ "id": "cd13d7d1-db7a-498d-ac98-76ccd9ad9019", "metadata": {}, "source": [ - "## Customize the search pattern\n", + "### Customize the search pattern\n", "\n", "All parameter compatible with Google [`list()`](https://developers.google.com/drive/api/v3/reference/files/list)\n", "API can be set.\n", @@ -398,7 +398,7 @@ "id": "375bb465-8f69-407b-94bd-ffa3718ef500", "metadata": {}, "source": [ - "### Modes for GSlide and GSheet\n", + "#### Modes for GSlide and GSheet\n", "The parameter mode accepts different values:\n", "\n", "- \"document\": return the body of each document\n", @@ -469,7 +469,7 @@ "id": "09acb864-e919-4add-9e06-deba6f7f0cd8", "metadata": {}, "source": [ - "## Advanced usage\n", + "### Advanced usage\n", "All Google File have a 'description' in the metadata. This field can be used to memorize a summary of the document or others indexed tags (See method `lazy_update_description_with_summary()`).\n", "\n", "If you use the `mode=\"snippet\"`, only the description will be used for the body. Else, the `metadata['summary']` has the field.\n", @@ -525,7 +525,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.10.12" } }, "nbformat": 4, diff --git a/docs/extras/integrations/document_loaders/image_captions.ipynb b/docs/extras/integrations/document_loaders/image_captions.ipynb index d8974c89f7..b9d113c768 100644 --- a/docs/extras/integrations/document_loaders/image_captions.ipynb +++ b/docs/extras/integrations/document_loaders/image_captions.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ac0a2a76-c36a-4952-b511-7906ca840e08", "metadata": { "pycharm": { @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "9a400568-5fea-47e6-8703-d9c1a1cc00ea", "metadata": { "tags": [] @@ -76,37 +76,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "fb392517-72d8-416e-852c-da90b77267ed", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/saitosean/dev/langchain/.venv/lib/python3.10/site-packages/transformers/generation/utils.py:1313: UserWarning: Using `max_length`'s default (20) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n", - " warnings.warn(\n" - ] - }, - { - "data": { - "text/plain": [ - "[Document(page_content='an image of a frog on a flower [SEP]', metadata={'image_path': 'https://upload.wikimedia.org/wikipedia/commons/thumb/5/5a/Hyla_japonica_sep01.jpg/260px-Hyla_japonica_sep01.jpg'}),\n", - " Document(page_content='an image of a shark swimming in the ocean [SEP]', metadata={'image_path': 'https://upload.wikimedia.org/wikipedia/commons/thumb/7/71/Tibur%C3%B3n_azul_%28Prionace_glauca%29%2C_canal_Fayal-Pico%2C_islas_Azores%2C_Portugal%2C_2020-07-27%2C_DD_14.jpg/270px-Tibur%C3%B3n_azul_%28Prionace_glauca%29%2C_canal_Fayal-Pico%2C_islas_Azores%2C_Portugal%2C_2020-07-27%2C_DD_14.jpg'}),\n", - " Document(page_content='an image of a painting of a battle scene [SEP]', metadata={'image_path': 'https://upload.wikimedia.org/wikipedia/commons/thumb/2/21/Thure_de_Thulstrup_-_Battle_of_Shiloh.jpg/251px-Thure_de_Thulstrup_-_Battle_of_Shiloh.jpg'}),\n", - " Document(page_content='an image of a passion fruit and a half cut passion [SEP]', metadata={'image_path': 'https://upload.wikimedia.org/wikipedia/commons/thumb/2/21/Passion_fruits_-_whole_and_halved.jpg/270px-Passion_fruits_-_whole_and_halved.jpg'}),\n", - " Document(page_content='an image of the spiral galaxy [SEP]', metadata={'image_path': 'https://upload.wikimedia.org/wikipedia/commons/thumb/5/5e/Messier83_-_Heic1403a.jpg/277px-Messier83_-_Heic1403a.jpg'}),\n", - " Document(page_content='an image of a man on skis in the snow [SEP]', metadata={'image_path': 'https://upload.wikimedia.org/wikipedia/commons/thumb/b/b6/2022-01-22_Men%27s_World_Cup_at_2021-22_St._Moritz%E2%80%93Celerina_Luge_World_Cup_and_European_Championships_by_Sandro_Halank%E2%80%93257.jpg/288px-2022-01-22_Men%27s_World_Cup_at_2021-22_St._Moritz%E2%80%93Celerina_Luge_World_Cup_and_European_Championships_by_Sandro_Halank%E2%80%93257.jpg'}),\n", - " Document(page_content='an image of a flower in the dark [SEP]', metadata={'image_path': 'https://upload.wikimedia.org/wikipedia/commons/thumb/9/99/Wiesen_Pippau_%28Crepis_biennis%29-20220624-RM-123950.jpg/224px-Wiesen_Pippau_%28Crepis_biennis%29-20220624-RM-123950.jpg'})]" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "loader = ImageCaptionLoader(path_images=list_image_urls)\n", "list_docs = loader.load()\n", @@ -115,24 +90,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "0f56db67-99bb-4543-ba40-1871a58b2da5", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from PIL import Image\n", "import requests\n", @@ -150,24 +113,12 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "7b7a15ac-d2c7-4359-9c5c-a543c8eebf80", "metadata": { "tags": [] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/saitosean/dev/langchain/.venv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n", - "/Users/saitosean/dev/langchain/.venv/lib/python3.10/site-packages/transformers/generation/utils.py:1313: UserWarning: Using `max_length`'s default (20) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n", - " warnings.warn(\n", - "Using embedded DuckDB without persistence: data will be transient\n" - ] - } - ], + "outputs": [], "source": [ "from langchain.indexes import VectorstoreIndexCreator\n", "\n", @@ -184,23 +135,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "e03e31c6-3018-434d-bcad-5c25144509e1", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "' The painting is about a battle scene.'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "query = \"What's the painting about?\"\n", "index.query(query)" @@ -208,23 +148,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "c3ec2b5a-9c03-4e32-b571-be5af9a22223", "metadata": { "tags": [] }, - "outputs": [ - { - "data": { - "text/plain": [ - "' There are images of a spiral galaxy, a painting of a battle scene, a flower in the dark, and a frog on a flower.'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "query = \"What kind of images are there?\"\n", "index.query(query)" @@ -247,7 +176,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.11.3" } }, "nbformat": 4, diff --git a/docs/extras/integrations/llms/amazon_api_gateway_example.ipynb b/docs/extras/integrations/llms/amazon_api_gateway.ipynb similarity index 99% rename from docs/extras/integrations/llms/amazon_api_gateway_example.ipynb rename to docs/extras/integrations/llms/amazon_api_gateway.ipynb index d0eca47577..4eae26fa17 100644 --- a/docs/extras/integrations/llms/amazon_api_gateway_example.ipynb +++ b/docs/extras/integrations/llms/amazon_api_gateway.ipynb @@ -221,9 +221,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.10.12" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/docs/extras/integrations/llms/azureml_endpoint_example.ipynb b/docs/extras/integrations/llms/azure_ml.ipynb similarity index 95% rename from docs/extras/integrations/llms/azureml_endpoint_example.ipynb rename to docs/extras/integrations/llms/azure_ml.ipynb index 634c08dc0c..96fdf7494d 100644 --- a/docs/extras/integrations/llms/azureml_endpoint_example.ipynb +++ b/docs/extras/integrations/llms/azure_ml.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# AzureML Online Endpoint\n", + "# Azure ML\n", "\n", - "[AzureML](https://azure.microsoft.com/en-us/products/machine-learning/) is a platform used to build, train, and deploy machine learning models. Users can explore the types of models to deploy in the Model Catalog, which provides Azure Foundation Models and OpenAI Models. Azure Foundation Models include various open-source models and popular Hugging Face models. Users can also import models of their liking into AzureML.\n", + "[Azure ML](https://azure.microsoft.com/en-us/products/machine-learning/) is a platform used to build, train, and deploy machine learning models. Users can explore the types of models to deploy in the Model Catalog, which provides Azure Foundation Models and OpenAI Models. Azure Foundation Models include various open-source models and popular Hugging Face models. Users can also import models of their liking into AzureML.\n", "\n", "This notebook goes over how to use an LLM hosted on an `AzureML online endpoint`" ] @@ -236,9 +236,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.10.12" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/extras/integrations/llms/azure_openai_example.ipynb b/docs/extras/integrations/llms/azure_openai.ipynb similarity index 99% rename from docs/extras/integrations/llms/azure_openai_example.ipynb rename to docs/extras/integrations/llms/azure_openai.ipynb index 0c7262197b..128cd2665e 100644 --- a/docs/extras/integrations/llms/azure_openai_example.ipynb +++ b/docs/extras/integrations/llms/azure_openai.ipynb @@ -216,7 +216,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.3" + "version": "3.10.12" }, "vscode": { "interpreter": { diff --git a/docs/extras/integrations/llms/bittensor.ipynb b/docs/extras/integrations/llms/bittensor.ipynb index e6fd5a6947..d6f97dc6cb 100644 --- a/docs/extras/integrations/llms/bittensor.ipynb +++ b/docs/extras/integrations/llms/bittensor.ipynb @@ -4,11 +4,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# NIBittensorLLM\n", + "# Bittensor\n", "\n", - "NIBittensorLLM is developed by [Neural Internet](https://neuralinternet.ai/), powered by [Bittensor](https://bittensor.com/).\n", + ">[Bittensor](https://bittensor.com/) is a mining network, similar to Bitcoin, that includes built-in incentives designed to encourage miners to contribute compute + knowledge.\n", + ">\n", + ">`NIBittensorLLM` is developed by [Neural Internet](https://neuralinternet.ai/), powered by `Bittensor`.\n", "\n", - "This LLM showcases true potential of decentralized AI by giving you the best response(s) from the Bittensor protocol, which consist of various AI models such as OpenAI, LLaMA2 etc.\n", + ">This LLM showcases true potential of decentralized AI by giving you the best response(s) from the `Bittensor protocol`, which consist of various AI models such as `OpenAI`, `LLaMA2` etc.\n", "\n", "Users can view their logs, requests, and API keys on the [Validator Endpoint Frontend](https://api.neuralinternet.ai/). However, changes to the configuration are currently prohibited; otherwise, the user's queries will be blocked.\n", "\n", @@ -157,11 +159,24 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "orig_nbformat": 4 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/extras/integrations/llms/cerebriumai_example.ipynb b/docs/extras/integrations/llms/cerebriumai.ipynb similarity index 100% rename from docs/extras/integrations/llms/cerebriumai_example.ipynb rename to docs/extras/integrations/llms/cerebriumai.ipynb diff --git a/docs/extras/integrations/llms/deepinfra_example.ipynb b/docs/extras/integrations/llms/deepinfra.ipynb similarity index 100% rename from docs/extras/integrations/llms/deepinfra_example.ipynb rename to docs/extras/integrations/llms/deepinfra.ipynb diff --git a/docs/extras/integrations/llms/edenai.ipynb b/docs/extras/integrations/llms/edenai.ipynb index e5095738e3..4b5ab69bdd 100644 --- a/docs/extras/integrations/llms/edenai.ipynb +++ b/docs/extras/integrations/llms/edenai.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Eden AI is an AI consulting company that was founded to use its resources to empower people and create impactful products that use AI to improve the quality of life for individuals, businesses and societies at large." + "Eden AI is revolutionizing the AI landscape by uniting the best AI providers, empowering users to unlock limitless possibilities and tap into the true potential of artificial intelligence. With an all-in-one comprehensive and hassle-free platform, it allows users to deploy AI features to production lightning fast, enabling effortless access to the full breadth of AI capabilities via a single API. (website: https://edenai.co/)" ] }, { @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -65,11 +65,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "llm = EdenAI(edenai_api_key=\"...\",provider=\"openai\", params={\"temperature\" : 0.2,\"max_tokens\" : 250})" + "llm = EdenAI(edenai_api_key=\"...\",provider=\"openai\", temperature=0.2, max_tokens=250)" ] }, { @@ -85,7 +85,7 @@ "source": [ "The EdenAI API brings together various providers, each offering multiple models.\n", "\n", - "To access a specific model, you can simply use the \"settings\" when calling.\n", + "To access a specific model, you can simply add 'model' during instantiation.\n", "\n", "For instance, let's explore the models provided by OpenAI, such as GPT3.5 " ] @@ -99,30 +99,19 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "\" No, a dog cannot drive a car.\\n\\nReasoning: \\n\\n1. A dog does not have the physical capability to operate a car. \\n2. A dog does not have the cognitive ability to understand the rules of the road and the mechanics of driving. \\n3. A dog does not have a driver's license, which is a legal requirement to operate a motor vehicle. \\n\\nTherefore, a dog cannot drive a car.\"" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from langchain import PromptTemplate, LLMChain\n", - "llm=EdenAI(feature=\"text\",provider=\"openai\", params={\"temperature\" : 0.2,\"max_tokens\" : 250})\n", + "llm=EdenAI(feature=\"text\",provider=\"openai\",model=\"text-davinci-003\",temperature=0.2, max_tokens=250)\n", "\n", "prompt = \"\"\"\n", "User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?\n", "Assistant:\n", "\"\"\"\n", "\n", - "llm(prompt,settings={'openai' : 'text-davinci-003'})" + "llm(prompt)" ] }, { @@ -134,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -158,22 +147,20 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "text2image = EdenAI(\n", " feature=\"image\" ,\n", " provider= \"openai\",\n", - " params={\n", - " \"resolution\" : \"512x512\"\n", - " }\n", + " resolution=\"512x512\"\n", ")" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -182,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -198,31 +185,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " No, a dog cannot drive a car.\n", - "\n", - "Reasoning:\n", - "\n", - "1. Driving a car requires a driver's license, which only humans can obtain. \n", - "2. Driving a car requires the ability to understand and follow traffic laws, which only humans can do. \n", - "3. Driving a car requires the ability to operate the car's controls, which only humans can do. \n", - "4. Therefore, a dog cannot drive a car.\n" - ] - } - ], + "outputs": [], "source": [ "from langchain.llms import EdenAI\n", "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n", "\n", "llm = EdenAI(\n", " callbacks=[StreamingStdOutCallbackHandler()],\n", - " feature=\"text\",provider=\"openai\", params={\"temperature\" : 0.2,\"max_tokens\" : 250}\n", + " feature=\"text\",provider=\"openai\", temperature=0.2,max_tokens=250\n", ")\n", "prompt = \"\"\"\n", "User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?\n", @@ -240,7 +212,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -251,21 +223,21 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "llm = EdenAI(\n", - "feature=\"text\" ,provider=\"openai\" , params={\"temperature\" : 0.2,\"max_tokens\" : 250}\n", + "feature=\"text\", provider=\"openai\", temperature=0.2, max_tokens=250\n", ")\n", "text2image = EdenAI(\n", - "feature=\"image\" ,provider=\"openai\", params={\"resolution\" : \"512x512\"}\n", + "feature=\"image\", provider=\"openai\", resolution=\"512x512\"\n", ")" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -279,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -292,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -305,32 +277,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[36;1m\u001b[1;3m\n", - "\n", - "Headwear Haven\u001b[0m\n", - "\u001b[33;1m\u001b[1;3m. It should be a simple and modern design that conveys the idea of a safe and comfortable place to purchase headwear. The logo should feature a stylized image of a hat, with a warm and inviting color palette. The design should be eye-catching and memorable, and should evoke a sense of trust and reliability.\u001b[0m\n", - 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- "\n", - "\u001b[1m> Finished chain.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "# Run the chain specifying only the input variable for the first chain.\n", "overall_chain = SimpleSequentialChain(\n", @@ -341,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -352,7 +301,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -366,9 +315,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.11" - }, - "orig_nbformat": 4 + "version": "3.11.3" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/docs/extras/integrations/llms/Fireworks.ipynb b/docs/extras/integrations/llms/fireworks.ipynb similarity index 100% rename from docs/extras/integrations/llms/Fireworks.ipynb rename to docs/extras/integrations/llms/fireworks.ipynb diff --git a/docs/extras/integrations/llms/forefrontai_example.ipynb b/docs/extras/integrations/llms/forefrontai.ipynb similarity index 100% rename from docs/extras/integrations/llms/forefrontai_example.ipynb rename to docs/extras/integrations/llms/forefrontai.ipynb diff --git a/docs/extras/integrations/llms/google_vertex_ai_palm.ipynb b/docs/extras/integrations/llms/google_vertex_ai_palm.ipynb index 968325c9ef..cff7d9bdd0 100644 --- a/docs/extras/integrations/llms/google_vertex_ai_palm.ipynb +++ b/docs/extras/integrations/llms/google_vertex_ai_palm.ipynb @@ -206,13 +206,86 @@ "\n", "llm_chain.run(question)" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using models deployed on Vertex Model Garden" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Vertex Model Garden [exposes](https://cloud.google.com/vertex-ai/docs/start/explore-models) open-sourced models that can be deployed and served on Vertex AI. If you have successfully deployed a model from Vertex Model Garden, you can find a corresponding Vertex AI [endpoint](https://cloud.google.com/vertex-ai/docs/general/deployment#what_happens_when_you_deploy_a_model) in the console or via API." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.llms import VertexAIModelGarden" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "llm = VertexAIModelGarden(\n", + " project=\"YOUR PROJECT\",\n", + " endpoint_id=\"YOUR ENDPOINT_ID\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "llm(\"What is the meaning of life?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Like all LLMs, we can then compose it with other components:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.prompts import PromptTemplate\n", + "\n", + "prompt = PromptTemplate.from_template(\"What is the meaning of {thing}?\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "llm_oss_chain = prompt | llm\n", + "\n", + "llm_oss_chain.invoke({\"thing\": \"life\"})" + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "poetry-venv", "language": "python", - "name": "python3" + "name": "poetry-venv" }, "language_info": { "codemirror_mode": { @@ -224,7 +297,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.9.1" }, "vscode": { "interpreter": { diff --git a/docs/extras/integrations/llms/gooseai_example.ipynb b/docs/extras/integrations/llms/gooseai.ipynb similarity index 100% rename from docs/extras/integrations/llms/gooseai_example.ipynb rename to docs/extras/integrations/llms/gooseai.ipynb diff --git a/docs/extras/integrations/llms/octoai.ipynb b/docs/extras/integrations/llms/octoai.ipynb index e3fda0c405..d783c1e511 100644 --- a/docs/extras/integrations/llms/octoai.ipynb +++ b/docs/extras/integrations/llms/octoai.ipynb @@ -1,13 +1,16 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "# OctoAI Compute Service\n", + "# OctoAI\n", + "\n", + ">[OctoML](https://docs.octoai.cloud/docs) is a service with efficient compute. It enables users to integrate their choice of AI models into applications. The `OctoAI` compute service helps you run, tune, and scale AI applications.\n", + "\n", "This example goes over how to use LangChain to interact with `OctoAI` [LLM endpoints](https://octoai.cloud/templates)\n", - "## Environment setup\n", + "\n", + "## Setup\n", "\n", "To run our example app, there are four simple steps to take:\n", "\n", @@ -43,6 +46,13 @@ "from langchain import PromptTemplate, LLMChain" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example" + ] + }, { "cell_type": "code", "execution_count": 15, @@ -98,7 +108,7 @@ ], "metadata": { "kernelspec": { - "display_name": "langchain", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -112,9 +122,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.10.12" }, - "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "97697b63fdcee0a640856f91cb41326ad601964008c341809e43189d1cab1047" @@ -122,5 +131,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/extras/integrations/llms/petals_example.ipynb b/docs/extras/integrations/llms/petals.ipynb similarity index 99% rename from docs/extras/integrations/llms/petals_example.ipynb rename to docs/extras/integrations/llms/petals.ipynb index 8232ecd6c6..d2d386af7f 100644 --- a/docs/extras/integrations/llms/petals_example.ipynb +++ b/docs/extras/integrations/llms/petals.ipynb @@ -186,7 +186,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.10.12" }, "vscode": { "interpreter": { diff --git a/docs/extras/integrations/llms/pipelineai_example.ipynb b/docs/extras/integrations/llms/pipelineai.ipynb similarity index 84% rename from docs/extras/integrations/llms/pipelineai_example.ipynb rename to docs/extras/integrations/llms/pipelineai.ipynb index 92f735c263..509a61f03a 100644 --- a/docs/extras/integrations/llms/pipelineai_example.ipynb +++ b/docs/extras/integrations/llms/pipelineai.ipynb @@ -1,23 +1,25 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# PipelineAI\n", "\n", - "PipelineAI allows you to run your ML models at scale in the cloud. It also provides API access to [several LLM models](https://pipeline.ai).\n", + ">[PipelineAI](https://pipeline.ai) allows you to run your ML models at scale in the cloud. It also provides API access to [several LLM models](https://pipeline.ai).\n", "\n", - "This notebook goes over how to use Langchain with [PipelineAI](https://docs.pipeline.ai/docs)." + "This notebook goes over how to use Langchain with [PipelineAI](https://docs.pipeline.ai/docs).\n", + "\n", + "## PipelineAI example\n", + "\n", + "[This example shows how PipelineAI integrated with LangChain](https://docs.pipeline.ai/docs/langchain) and it is created by PipelineAI." ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Install pipeline-ai\n", + "## Setup\n", "The `pipeline-ai` library is required to use the `PipelineAI` API, AKA `Pipeline Cloud`. Install `pipeline-ai` using `pip install pipeline-ai`." ] }, @@ -35,7 +37,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Imports" + "## Example\n", + "\n", + "### Imports" ] }, { @@ -50,11 +54,10 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "## Set the Environment API Key\n", + "### Set the Environment API Key\n", "Make sure to get your API key from PipelineAI. Check out the [cloud quickstart guide](https://docs.pipeline.ai/docs/cloud-quickstart). You'll be given a 30 day free trial with 10 hours of serverless GPU compute to test different models." ] }, @@ -68,7 +71,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -89,7 +91,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Create a Prompt Template\n", + "### Create a Prompt Template\n", "We will create a prompt template for Question and Answer." ] }, @@ -110,7 +112,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Initiate the LLMChain" + "### Initiate the LLMChain" ] }, { @@ -126,7 +128,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run the LLMChain\n", + "### Run the LLMChain\n", "Provide a question and run the LLMChain." ] }, @@ -158,7 +160,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.10.12" }, "vscode": { "interpreter": { diff --git a/docs/extras/integrations/llms/replicate.ipynb b/docs/extras/integrations/llms/replicate.ipynb index ad37f49a22..4e31e3c5b2 100644 --- a/docs/extras/integrations/llms/replicate.ipynb +++ b/docs/extras/integrations/llms/replicate.ipynb @@ -268,22 +268,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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", 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", - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from PIL import Image\n", "import requests\n", @@ -543,22 +530,9 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/jpeg": 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", - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "response = requests.get(\n", " \"https://replicate.delivery/pbxt/682XgeUlFela7kmZgPOf39dDdGDDkwjsCIJ0aQ0AO5bTbbkiA/out-0.png\"\n", @@ -584,7 +558,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.11.3" }, "vscode": { "interpreter": { diff --git a/docs/extras/integrations/llms/titan_takeoff.ipynb b/docs/extras/integrations/llms/titan_takeoff.ipynb index b7150168d8..5b8fddcb1e 100644 --- a/docs/extras/integrations/llms/titan_takeoff.ipynb +++ b/docs/extras/integrations/llms/titan_takeoff.ipynb @@ -1,19 +1,17 @@ { "cells": [ { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Titan Takeoff\n", "\n", - "TitanML helps businesses build and deploy better, smaller, cheaper, and faster NLP models through our training, compression, and inference optimization platform. \n", + ">`TitanML` helps businesses build and deploy better, smaller, cheaper, and faster NLP models through our training, compression, and inference optimization platform. \n", "\n", - "Our inference server, [Titan Takeoff](https://docs.titanml.co/docs/titan-takeoff/getting-started) enables deployment of LLMs locally on your hardware in a single command. Most generative model architectures are supported, such as Falcon, Llama 2, GPT2, T5 and many more." + ">Our inference server, [Titan Takeoff](https://docs.titanml.co/docs/titan-takeoff/getting-started) enables deployment of LLMs locally on your hardware in a single command. Most generative model architectures are supported, such as Falcon, Llama 2, GPT2, T5 and many more." ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -40,12 +38,11 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Choose a Model\n", - "Iris Takeoff supports many of the most powerful generative text models, such as Falcon, MPT, and Llama. See the [supported models](https://docs.titanml.co/docs/titan-takeoff/supported-models) for more information. For information about using your own models, see the [custom models](https://docs.titanml.co/docs/titan-takeoff/Advanced/custom-models).\n", + "Takeoff supports many of the most powerful generative text models, such as Falcon, MPT, and Llama. See the [supported models](https://docs.titanml.co/docs/titan-takeoff/supported-models) for more information. For information about using your own models, see the [custom models](https://docs.titanml.co/docs/titan-takeoff/Advanced/custom-models).\n", "\n", "Going forward in this demo we will be using the falcon 7B instruct model. This is a good open source model that is trained to follow instructions, and is small enough to easily inference even on CPUs.\n", "\n", @@ -67,8 +64,30 @@ "source": [ "iris takeoff --model tiiuae/falcon-7b-instruct --device cpu\n", "iris takeoff --model tiiuae/falcon-7b-instruct --device cuda # Nvidia GPU required\n", - "iris takeoff --model tiiuae/falcon-7b-instruct --device cpu --port 5000 # run on port 5000 (default: 8000)\n", - "```" + "iris takeoff --model tiiuae/falcon-7b-instruct --device cpu --port 5000 # run on port 5000 (default: 8000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You will then be directed to a login page, where you will need to create an account to proceed.\n", + "After logging in, run the command onscreen to check whether the server is ready. When it is ready, you can start using the Takeoff integration.\n", + "\n", + "To shutdown the server, run the following command. You will be presented with options on which Takeoff server to shut down, in case you have multiple running servers.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "vscode": { + "languageId": "shellscript" + } + }, + "outputs": [], + "source": [ + "iris takeoff --shutdown # shutdown the server" ] }, { @@ -76,9 +95,6 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "You will then be directed to a login page, where you will need to create an account to proceed.\n", - "After logging in, run the command onscreen to check whether the server is ready. When it is ready, you can start using the Takeoff integration\n", - "\n", "## Inferencing your model\n", "To access your LLM, use the TitanTakeoff LLM wrapper:" ] @@ -92,7 +108,7 @@ "from langchain.llms import TitanTakeoff\n", "\n", "llm = TitanTakeoff(\n", - " port=8000,\n", + " baseURL=\"http://localhost:8000\",\n", " generate_max_length=128,\n", " temperature=1.0\n", ")\n", @@ -103,11 +119,10 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ - "No parameters are needed by default, but a port can be specified and [generation parameters](https://docs.titanml.co/docs/titan-takeoff/Advanced/generation-parameters) can be supplied.\n", + "No parameters are needed by default, but a baseURL that points to your desired URL where Takeoff is running can be specified and [generation parameters](https://docs.titanml.co/docs/titan-takeoff/Advanced/generation-parameters) can be supplied.\n", "\n", "### Streaming\n", "Streaming is also supported via the streaming flag:" @@ -122,7 +137,7 @@ "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n", "from langchain.callbacks.manager import CallbackManager\n", "\n", - "llm = TitanTakeoff(port=8000, callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), streaming=True)\n", + "llm = TitanTakeoff(callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]), streaming=True)\n", "\n", "prompt = \"What is the capital of France?\"\n", "\n", @@ -130,7 +145,6 @@ ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -159,11 +173,24 @@ } ], "metadata": { - "language_info": { - "name": "python" + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "orig_nbformat": 4 + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/extras/integrations/memory/dynamodb_chat_message_history.ipynb b/docs/extras/integrations/memory/dynamodb_chat_message_history.ipynb index a5c4dd0981..53e7230e2b 100644 --- a/docs/extras/integrations/memory/dynamodb_chat_message_history.ipynb +++ b/docs/extras/integrations/memory/dynamodb_chat_message_history.ipynb @@ -28,7 +28,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 10, "id": "93ce1811", "metadata": {}, "outputs": [ @@ -71,7 +71,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 11, "id": "d15e3302", "metadata": {}, "outputs": [], @@ -87,18 +87,15 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "id": "64fc465e", "metadata": {}, "outputs": [ { "data": { - "text/plain": [ - "[HumanMessage(content='hi!', additional_kwargs={}, example=False),\n", - " AIMessage(content='whats up?', additional_kwargs={}, example=False)]" - ] + "text/plain": "[HumanMessage(content='hi!', additional_kwargs={}, example=False),\n AIMessage(content='whats up?', additional_kwargs={}, example=False),\n HumanMessage(content='hi!', additional_kwargs={}, example=False),\n AIMessage(content='whats up?', additional_kwargs={}, example=False)]" }, - "execution_count": 3, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -119,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "225713c8", "metadata": {}, "outputs": [], @@ -133,6 +130,81 @@ ")" ] }, + { + "cell_type": "markdown", + "source": [ + "## DynamoDBChatMessageHistory With Different Keys Composite Keys\n", + "The default key for DynamoDBChatMessageHistory is ```{\"SessionId\": self.session_id}```, but you can modify this to match your table design.\n", + "\n", + "### Primary Key Name\n", + "You may modify the primary key by passing in a primary_key_name value in the constructor, resulting in the following:\n", + "```{self.primary_key_name: self.session_id}```\n", + "\n", + "### Composite Keys\n", + "When using an existing DynamoDB table, you may need to modify the key structure from the default of to something including a Sort Key. To do this you may use the ```key``` parameter.\n", + "\n", + "Passing a value for key will override the primary_key parameter, and the resulting key structure will be the passed value.\n" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 14, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + }, + { + "data": { + "text/plain": "[HumanMessage(content='hello, composite dynamodb table!', additional_kwargs={}, example=False)]" + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from langchain.memory.chat_message_histories import DynamoDBChatMessageHistory\n", + "\n", + "composite_table = dynamodb.create_table(\n", + " TableName=\"CompositeTable\",\n", + " KeySchema=[{\"AttributeName\": \"PK\", \"KeyType\": \"HASH\"}, {\"AttributeName\": \"SK\", \"KeyType\": \"RANGE\"}],\n", + " AttributeDefinitions=[{\"AttributeName\": \"PK\", \"AttributeType\": \"S\"}, {\"AttributeName\": \"SK\", \"AttributeType\": \"S\"}],\n", + " BillingMode=\"PAY_PER_REQUEST\",\n", + ")\n", + "\n", + "# Wait until the table exists.\n", + "composite_table.meta.client.get_waiter(\"table_exists\").wait(TableName=\"CompositeTable\")\n", + "\n", + "# Print out some data about the table.\n", + "print(composite_table.item_count)\n", + "\n", + "my_key = {\n", + " \"PK\": \"session_id::0\",\n", + " \"SK\": \"langchain_history\",\n", + "}\n", + "\n", + "composite_key_history = DynamoDBChatMessageHistory(\n", + " table_name=\"CompositeTable\",\n", + " session_id=\"0\",\n", + " endpoint_url=\"http://localhost.localstack.cloud:4566\",\n", + " key=my_key,\n", + ")\n", + "\n", + "composite_key_history.add_user_message(\"hello, composite dynamodb table!\")\n", + "\n", + "composite_key_history.messages" + ], + "metadata": { + "collapsed": false + } + }, { "attachments": {}, "cell_type": "markdown", @@ -144,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 15, "id": "f92d9499", "metadata": {}, "outputs": [], @@ -165,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 16, "id": "1167eeba", "metadata": {}, "outputs": [], @@ -184,10 +256,24 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 17, "id": "fce085c5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "ename": "ValidationError", + "evalue": "1 validation error for ChatOpenAI\n__root__\n Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. (type=value_error)", + "output_type": "error", + "traceback": [ + "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m", + "\u001B[0;31mValidationError\u001B[0m Traceback (most recent call last)", + "Cell \u001B[0;32mIn[17], line 1\u001B[0m\n\u001B[0;32m----> 1\u001B[0m llm \u001B[38;5;241m=\u001B[39m \u001B[43mChatOpenAI\u001B[49m\u001B[43m(\u001B[49m\u001B[43mtemperature\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;241;43m0\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[1;32m 2\u001B[0m agent_chain \u001B[38;5;241m=\u001B[39m initialize_agent(\n\u001B[1;32m 3\u001B[0m tools,\n\u001B[1;32m 4\u001B[0m llm,\n\u001B[0;32m (...)\u001B[0m\n\u001B[1;32m 7\u001B[0m memory\u001B[38;5;241m=\u001B[39mmemory,\n\u001B[1;32m 8\u001B[0m )\n", + "File \u001B[0;32m~/Documents/projects/langchain/libs/langchain/langchain/load/serializable.py:74\u001B[0m, in \u001B[0;36mSerializable.__init__\u001B[0;34m(self, **kwargs)\u001B[0m\n\u001B[1;32m 73\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m__init__\u001B[39m(\u001B[38;5;28mself\u001B[39m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs: Any) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m---> 74\u001B[0m \u001B[38;5;28;43msuper\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43m)\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[38;5;21;43m__init__\u001B[39;49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 75\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_lc_kwargs \u001B[38;5;241m=\u001B[39m kwargs\n", + "File \u001B[0;32m~/Documents/projects/langchain/.venv/lib/python3.9/site-packages/pydantic/main.py:341\u001B[0m, in \u001B[0;36mpydantic.main.BaseModel.__init__\u001B[0;34m()\u001B[0m\n", + "\u001B[0;31mValidationError\u001B[0m: 1 validation error for ChatOpenAI\n__root__\n Did not find openai_api_key, please add an environment variable `OPENAI_API_KEY` which contains it, or pass `openai_api_key` as a named parameter. (type=value_error)" + ] + } + ], "source": [ "llm = ChatOpenAI(temperature=0)\n", "agent_chain = initialize_agent(\n", @@ -201,152 +287,42 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "952a3103", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3m{\n", - " \"action\": \"Final Answer\",\n", - " \"action_input\": \"Hello! How can I assist you today?\"\n", - "}\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "'Hello! How can I assist you today?'" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "agent_chain.run(input=\"Hello!\")" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "54c4aaf4", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3m{\n", - " \"action\": \"python_repl\",\n", - " \"action_input\": \"import requests\\nfrom bs4 import BeautifulSoup\\n\\nurl = 'https://en.wikipedia.org/wiki/Twitter'\\nresponse = requests.get(url)\\nsoup = BeautifulSoup(response.content, 'html.parser')\\nowner = soup.find('th', text='Owner').find_next_sibling('td').text.strip()\\nprint(owner)\"\n", - "}\u001b[0m\n", - "Observation: \u001b[36;1m\u001b[1;3mX Corp. (2023–present)Twitter, Inc. (2006–2023)\n", - "\u001b[0m\n", - "Thought:\u001b[32;1m\u001b[1;3m{\n", - " \"action\": \"Final Answer\",\n", - " \"action_input\": \"X Corp. (2023–present)Twitter, Inc. (2006–2023)\"\n", - "}\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "'X Corp. (2023–present)Twitter, Inc. (2006–2023)'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "agent_chain.run(input=\"Who owns Twitter?\")" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "f9013118", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3m{\n", - " \"action\": \"Final Answer\",\n", - " \"action_input\": \"Hello Bob! How can I assist you today?\"\n", - "}\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "'Hello Bob! How can I assist you today?'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "agent_chain.run(input=\"My name is Bob.\")" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "405e5315", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3m{\n", - " \"action\": \"Final Answer\",\n", - " \"action_input\": \"Your name is Bob.\"\n", - "}\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n" - ] - }, - { - "data": { - "text/plain": [ - "'Your name is Bob.'" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "agent_chain.run(input=\"Who am I?\")" + "agent_chain.run(input=\"Who am I?\")\n" ] } ], diff --git a/docs/extras/integrations/memory/sql_chat_message_history.ipynb b/docs/extras/integrations/memory/sql_chat_message_history.ipynb new file mode 100644 index 0000000000..54a8d85b3e --- /dev/null +++ b/docs/extras/integrations/memory/sql_chat_message_history.ipynb @@ -0,0 +1,235 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# SQL Chat Message History\n", + "\n", + "This notebook goes over a **SQLChatMessageHistory** class that allows to store chat history in any database supported by SQLAlchemy.\n", + "\n", + "Please note that to use it with databases other than SQLite, you will need to install the corresponding database driver." + ], + "metadata": { + "collapsed": false + }, + "id": "f22eab3f84cbeb37" + }, + { + "cell_type": "markdown", + "source": [ + "### Basic Usage\n", + "\n", + "To use the storage you need to provide only 2 things:\n", + "\n", + "1. Session Id - a unique identifier of the session, like user name, email, chat id etc.\n", + "2. Connection string - a string that specifies the database connection. It will be passed to SQLAlchemy create_engine function." + ], + "metadata": { + "collapsed": false + }, + "id": "f8f2830ee9ca1e01" + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "from langchain.memory.chat_message_histories import SQLChatMessageHistory\n", + "\n", + "chat_message_history = SQLChatMessageHistory(\n", + "\tsession_id='test_session',\n", + "\tconnection_string='sqlite:///sqlite.db'\n", + ")\n", + "\n", + "chat_message_history.add_user_message('Hello')\n", + "chat_message_history.add_ai_message('Hi')" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-28T10:04:38.077748Z", + "start_time": "2023-08-28T10:04:36.105894Z" + } + }, + "id": "4576e914a866fb40" + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "data": { + "text/plain": "[HumanMessage(content='Hello', additional_kwargs={}, example=False),\n AIMessage(content='Hi', additional_kwargs={}, example=False)]" + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chat_message_history.messages" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-28T10:04:38.929396Z", + "start_time": "2023-08-28T10:04:38.915727Z" + } + }, + "id": "b476688cbb32ba90" + }, + { + "cell_type": "markdown", + "source": [ + "### Custom Storage Format\n", + "\n", + "By default, only the session id and message dictionary are stored in the table.\n", + "\n", + "However, sometimes you might want to store some additional information, like message date, author, language etc.\n", + "\n", + "To do that, you can create a custom message converter, by implementing **BaseMessageConverter** interface." + ], + "metadata": { + "collapsed": false + }, + "id": "2e5337719d5614fd" + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "from datetime import datetime\n", + "from langchain.schema import BaseMessage, HumanMessage, AIMessage, SystemMessage\n", + "from typing import Any\n", + "from sqlalchemy import Column, Integer, Text, DateTime\n", + "from sqlalchemy.orm import declarative_base\n", + "from langchain.memory.chat_message_histories.sql import BaseMessageConverter\n", + "\n", + "\n", + "Base = declarative_base()\n", + "\n", + "\n", + "class CustomMessage(Base):\n", + "\t__tablename__ = 'custom_message_store'\n", + "\n", + "\tid = Column(Integer, primary_key=True)\n", + "\tsession_id = Column(Text)\n", + "\ttype = Column(Text)\n", + "\tcontent = Column(Text)\n", + "\tcreated_at = Column(DateTime)\n", + "\tauthor_email = Column(Text)\n", + "\n", + "\n", + "class CustomMessageConverter(BaseMessageConverter):\n", + "\tdef __init__(self, author_email: str):\n", + "\t\tself.author_email = author_email\n", + "\t\n", + "\tdef from_sql_model(self, sql_message: Any) -> BaseMessage:\n", + "\t\tif sql_message.type == 'human':\n", + "\t\t\treturn HumanMessage(\n", + "\t\t\t\tcontent=sql_message.content,\n", + "\t\t\t)\n", + "\t\telif sql_message.type == 'ai':\n", + "\t\t\treturn AIMessage(\n", + "\t\t\t\tcontent=sql_message.content,\n", + "\t\t\t)\n", + "\t\telif sql_message.type == 'system':\n", + "\t\t\treturn SystemMessage(\n", + "\t\t\t\tcontent=sql_message.content,\n", + "\t\t\t)\n", + "\t\telse:\n", + "\t\t\traise ValueError(f'Unknown message type: {sql_message.type}')\n", + "\t\n", + "\tdef to_sql_model(self, message: BaseMessage, session_id: str) -> Any:\n", + "\t\tnow = datetime.now()\n", + "\t\treturn CustomMessage(\n", + "\t\t\tsession_id=session_id,\n", + "\t\t\ttype=message.type,\n", + "\t\t\tcontent=message.content,\n", + "\t\t\tcreated_at=now,\n", + "\t\t\tauthor_email=self.author_email\n", + "\t\t)\n", + "\t\n", + "\tdef get_sql_model_class(self) -> Any:\n", + "\t\treturn CustomMessage\n", + "\n", + "\n", + "chat_message_history = SQLChatMessageHistory(\n", + "\tsession_id='test_session',\n", + "\tconnection_string='sqlite:///sqlite.db',\n", + "\tcustom_message_converter=CustomMessageConverter(\n", + "\t\tauthor_email='test@example.com'\n", + " )\n", + ")\n", + "\n", + "chat_message_history.add_user_message('Hello')\n", + "chat_message_history.add_ai_message('Hi')" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-28T10:04:41.510498Z", + "start_time": "2023-08-28T10:04:41.494912Z" + } + }, + "id": "fdfde84c07d071bb" + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "data": { + "text/plain": "[HumanMessage(content='Hello', additional_kwargs={}, example=False),\n AIMessage(content='Hi', additional_kwargs={}, example=False)]" + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chat_message_history.messages" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-28T10:04:43.497990Z", + "start_time": "2023-08-28T10:04:43.492517Z" + } + }, + "id": "4a6a54d8a9e2856f" + }, + { + "cell_type": "markdown", + "source": [ + "You also might want to change the name of session_id column. In this case you'll need to specify `session_id_field_name` parameter." + ], + "metadata": { + "collapsed": false + }, + "id": "622aded629a1adeb" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/extras/integrations/providers/arangodb.mdx b/docs/extras/integrations/providers/arangodb.mdx index dcf0378a12..624ae82b2a 100644 --- a/docs/extras/integrations/providers/arangodb.mdx +++ b/docs/extras/integrations/providers/arangodb.mdx @@ -11,7 +11,7 @@ pip install python-arango ## Graph QA Chain -Connect your ArangoDB Database with a Chat Model to get insights on your data. +Connect your ArangoDB Database with a chat model to get insights on your data. See the notebook example [here](/docs/use_cases/more/graph/graph_arangodb_qa.html). diff --git a/docs/extras/integrations/providers/bittensor.mdx b/docs/extras/integrations/providers/bittensor.mdx index 531a184418..501a1b52f0 100644 --- a/docs/extras/integrations/providers/bittensor.mdx +++ b/docs/extras/integrations/providers/bittensor.mdx @@ -20,7 +20,7 @@ from langchain.llms import NIBittensorLLM It provides a unified interface for all models: ```python -llm = NIBittensorLLM(system_prompt="Your task is to provide consice and accurate response based on user prompt") +llm = NIBittensorLLM(system_prompt="Your task is to provide concise and accurate response based on user prompt") print(llm('Write a fibonacci function in python with golder ratio')) ``` diff --git a/docs/extras/integrations/providers/datadog.mdx b/docs/extras/integrations/providers/datadog.mdx index aee4d5e24b..fd25e3d47c 100644 --- a/docs/extras/integrations/providers/datadog.mdx +++ b/docs/extras/integrations/providers/datadog.mdx @@ -4,12 +4,12 @@ Key features of the ddtrace integration for LangChain: - Traces: Capture LangChain requests, parameters, prompt-completions, and help visualize LangChain operations. -- Metrics: Capture LangChain request latency, errors, and token/cost usage (for OpenAI LLMs and Chat Models). +- Metrics: Capture LangChain request latency, errors, and token/cost usage (for OpenAI LLMs and chat models). - Logs: Store prompt completion data for each LangChain operation. - Dashboard: Combine metrics, logs, and trace data into a single plane to monitor LangChain requests. - Monitors: Provide alerts in response to spikes in LangChain request latency or error rate. -Note: The ddtrace LangChain integration currently provides tracing for LLMs, Chat Models, Text Embedding Models, Chains, and Vectorstores. +Note: The ddtrace LangChain integration currently provides tracing for LLMs, chat models, Text Embedding Models, Chains, and Vectorstores. ## Installation and Setup diff --git a/docs/extras/integrations/providers/mlflow_ai_gateway.mdx b/docs/extras/integrations/providers/mlflow_ai_gateway.mdx index 0b8591e6c8..aaeb84f2f5 100644 --- a/docs/extras/integrations/providers/mlflow_ai_gateway.mdx +++ b/docs/extras/integrations/providers/mlflow_ai_gateway.mdx @@ -1,6 +1,9 @@ # MLflow AI Gateway ->`The MLflow AI Gateway` service is a powerful tool designed to streamline the usage and management of various large language model (LLM) providers, such as OpenAI and Anthropic, within an organization. It offers a high-level interface that simplifies the interaction with these services by providing a unified endpoint to handle specific LLM related requests. See [the MLflow AI Gateway documentation](https://mlflow.org/docs/latest/gateway/index.html) for more details. +>[The MLflow AI Gateway](https://www.mlflow.org/docs/latest/gateway/index.html) service is a powerful tool designed to streamline the usage and management of various large +> language model (LLM) providers, such as OpenAI and Anthropic, within an organization. It offers a high-level interface +> that simplifies the interaction with these services by providing a unified endpoint to handle specific LLM related requests. +> See [the MLflow AI Gateway documentation](https://mlflow.org/docs/latest/gateway/index.html) for more details. ## Installation and Setup @@ -43,6 +46,16 @@ Start the Gateway server: mlflow gateway start --config-path /path/to/config.yaml ``` +## Example provided by `MLflow` + +>The `mlflow.langchain` module provides an API for logging and loading `LangChain` models. +> This module exports multivariate LangChain models in the langchain flavor and univariate LangChain +> models in the pyfunc flavor. + +See the [API documentation and examples](https://www.mlflow.org/docs/latest/python_api/mlflow.langchain.html). + + + ## Completions Example ```python diff --git a/docs/extras/integrations/providers/wandb_tracing.ipynb b/docs/extras/integrations/providers/wandb_tracing.ipynb index 30697f0604..153cac6f9f 100644 --- a/docs/extras/integrations/providers/wandb_tracing.ipynb +++ b/docs/extras/integrations/providers/wandb_tracing.ipynb @@ -147,18 +147,6 @@ "\n", "agent.run(\"What is 2 raised to .123243 power?\") # this should not be traced" ] - }, - { - "cell_type": "markdown", - "id": "438fd64d", - "metadata": {}, - "source": [ - "**Here's a view of wandb dashboard for the above tracing session:**\n", - "\n", - "\n", - 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)\n", - "\n" - ] } ], "metadata": { @@ -177,7 +165,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.11.3" } }, "nbformat": 4, diff --git a/docs/extras/integrations/retrievers/google_drive.ipynb b/docs/extras/integrations/retrievers/google_drive.ipynb index 3acb14cbc1..caf0bf3092 100644 --- a/docs/extras/integrations/retrievers/google_drive.ipynb +++ b/docs/extras/integrations/retrievers/google_drive.ipynb @@ -59,7 +59,7 @@ }, "outputs": [], "source": [ - "from langchain.retrievers import GoogleDriveRetriever" + "from langchain_googledrive.retrievers import GoogleDriveRetriever" ] }, { diff --git a/docs/extras/integrations/text_embedding/edenai.ipynb b/docs/extras/integrations/text_embedding/edenai.ipynb index aef5056d4b..e99833782a 100644 --- a/docs/extras/integrations/text_embedding/edenai.ipynb +++ b/docs/extras/integrations/text_embedding/edenai.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Eden AI is an AI consulting company that was founded to use its resources to empower people and create impactful products that use AI to improve the quality of life for individuals, businesses and societies at large." + "Eden AI is revolutionizing the AI landscape by uniting the best AI providers, empowering users to unlock limitless possibilities and tap into the true potential of artificial intelligence. With an all-in-one comprehensive and hassle-free platform, it allows users to deploy AI features to production lightning fast, enabling effortless access to the full breadth of AI capabilities via a single API. (website: https://edenai.co/)" ] }, { diff --git a/docs/extras/integrations/toolkits/airbyte_structured_qa.ipynb b/docs/extras/integrations/toolkits/airbyte_structured_qa.ipynb new file mode 100644 index 0000000000..e9139ee16c --- /dev/null +++ b/docs/extras/integrations/toolkits/airbyte_structured_qa.ipynb @@ -0,0 +1,118 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Airbyte Question Answering\n", + "This notebook shows how to do question answering over structured data, in this case using the `AirbyteStripeLoader`.\n", + "\n", + "Vectorstores often have a hard time answering questions that requires computing, grouping and filtering structured data so the high level idea is to use a `pandas` dataframe to help with these types of questions. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1. Load data from Stripe using Airbyte. user the `record_handler` paramater to return a JSON from the data loader." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "\n", + "from langchain.document_loaders.airbyte import AirbyteStripeLoader\n", + "from langchain.chat_models.openai import ChatOpenAI\n", + "from langchain.agents import AgentType, create_pandas_dataframe_agent\n", + "\n", + "stream_name = \"customers\"\n", + "config = {\n", + " \"client_secret\": os.getenv(\"STRIPE_CLIENT_SECRET\"),\n", + " \"account_id\": os.getenv(\"STRIPE_ACCOUNT_D\"),\n", + " \"start_date\": \"2023-01-20T00:00:00Z\",\n", + "}\n", + "\n", + "def handle_record(record: dict, _id: str):\n", + " return record.data\n", + "\n", + "loader = AirbyteStripeLoader(\n", + " config=config,\n", + " record_handler=handle_record,\n", + " stream_name=stream_name,\n", + ")\n", + "data = loader.load()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2. Pass the data to `pandas` dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df = pd.DataFrame(data)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3. Pass the dataframe `df` to the `create_pandas_dataframe_agent` and invoke\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "agent = create_pandas_dataframe_agent(\n", + " ChatOpenAI(temperature=0, model=\"gpt-4\"),\n", + " df,\n", + " verbose=True,\n", + " agent_type=AgentType.OPENAI_FUNCTIONS,\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "4. Run the agent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "output = agent.run(\"How many rows are there?\")" + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/extras/integrations/toolkits/google_drive.ipynb b/docs/extras/integrations/toolkits/google_drive.ipynb index 6cf1cb56d4..38ee843d43 100644 --- a/docs/extras/integrations/toolkits/google_drive.ipynb +++ b/docs/extras/integrations/toolkits/google_drive.ipynb @@ -98,8 +98,8 @@ }, "outputs": [], "source": [ - "from langchain.utilities.google_drive import GoogleDriveAPIWrapper\n", - "from langchain.tools.google_drive.tool import GoogleDriveSearchTool\n", + "from langchain_googledrive.utilities.google_drive import GoogleDriveAPIWrapper\n", + "from langchain_googledrive.tools.google_drive.tool import GoogleDriveSearchTool\n", "\n", "# By default, search only in the filename.\n", "tool = GoogleDriveSearchTool(\n", diff --git a/docs/extras/integrations/tools/edenai_tools.ipynb b/docs/extras/integrations/tools/edenai_tools.ipynb new file mode 100644 index 0000000000..2abd509a64 --- /dev/null +++ b/docs/extras/integrations/tools/edenai_tools.ipynb @@ -0,0 +1,513 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Eden AI" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This Jupyter Notebook demonstrates how to use Eden AI tools with an Agent.\n", + "\n", + "Eden AI is revolutionizing the AI landscape by uniting the best AI providers, empowering users to unlock limitless possibilities and tap into the true potential of artificial intelligence. With an all-in-one comprehensive and hassle-free platform, it allows users to deploy AI features to production lightning fast, enabling effortless access to the full breadth of AI capabilities via a single API. (website: https://edenai.co/ )\n", + "\n", + "\n", + "By including an Edenai tool in the list of tools provided to an Agent, you can grant your Agent the ability to do multiple tasks, such as:\n", + "\n", + "- speech to text\n", + "- text to speech\n", + "- text explicit content detection \n", + "- image explicit content detection\n", + "- object detection\n", + "- OCR invoice parsing\n", + "- OCR ID parsing\n", + "\n", + "\n", + "In this example, we will go through the process of utilizing the Edenai tools to create an Agent that can perform some of the tasks listed above." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---------------------------------------------------------------------------\n", + "Accessing the EDENAI's API requires an API key, \n", + "\n", + "which you can get by creating an account https://app.edenai.run/user/register and heading here https://app.edenai.run/admin/account/settings\n", + "\n", + "Once we have a key we'll want to set it as the environment variable ``EDENAI_API_KEY`` or you can pass the key in directly via the edenai_api_key named parameter when initiating the EdenAI tools, e.g. ``EdenAiTextModerationTool(edenai_api_key=\"...\")``" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.tools.edenai import (\n", + " EdenAiSpeechToTextTool,\n", + " EdenAiTextToSpeechTool,\n", + " EdenAiExplicitImageTool,\n", + " EdenAiObjectDetectionTool,\n", + " EdenAiParsingIDTool,\n", + " EdenAiParsingInvoiceTool,\n", + " EdenAiTextModerationTool,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.llms import EdenAI\n", + "from langchain.agents import initialize_agent, AgentType\n", + "\n", + "llm=EdenAI(feature=\"text\",provider=\"openai\", params={\"temperature\" : 0.2,\"max_tokens\" : 250})\n", + "\n", + "tools = [\n", + " EdenAiTextModerationTool(providers=[\"openai\"],language=\"en\"),\n", + " EdenAiObjectDetectionTool(providers=[\"google\",\"api4ai\"]),\n", + " EdenAiTextToSpeechTool(providers=[\"amazon\"],language=\"en\",voice=\"MALE\"),\n", + " EdenAiExplicitImageTool(providers=[\"amazon\",\"google\"]),\n", + " EdenAiSpeechToTextTool(providers=[\"amazon\"]),\n", + " EdenAiParsingIDTool(providers=[\"amazon\",\"klippa\"],language=\"en\"),\n", + " EdenAiParsingInvoiceTool(providers=[\"amazon\",\"google\"],language=\"en\"),\n", + "]\n", + "agent_chain = initialize_agent(\n", + " tools,\n", + " llm,\n", + " agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n", + " verbose=True,\n", + " return_intermediate_steps=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example with text" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3m I need to scan the text for explicit content and then convert it to speech\n", + "Action: edenai_explicit_content_detection_text\n", + "Action Input: 'i want to slap you'\u001b[0m\n", + "Observation: \u001b[36;1m\u001b[1;3mnsfw_likelihood: 3\n", + "\"sexual\": 1\n", + "\"hate\": 1\n", + "\"harassment\": 1\n", + "\"self-harm\": 1\n", + "\"sexual/minors\": 1\n", + "\"hate/threatening\": 1\n", + "\"violence/graphic\": 1\n", + "\"self-harm/intent\": 1\n", + "\"self-harm/instructions\": 1\n", + "\"harassment/threatening\": 1\n", + "\"violence\": 3\u001b[0m\n", + "Thought:\u001b[32;1m\u001b[1;3m I now need to convert the text to speech\n", + "Action: edenai_text_to_speech\n", + "Action Input: 'i want to slap you'\u001b[0m\n", + "Observation: \u001b[38;5;200m\u001b[1;3mhttps://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tnXX1lV2DGc5PNB66Lqrr0Fpe2trVJj2k8cLduIb8dbtqLPNIDCsV0N4QT10utZmhZcPpcSIBsdomw1Os1IjdG4nA8ZTIddAcLMCWJznttzl66vHPk26rjDpG5doMTTsPEz8ZKILQ__&Key-Pair-Id=K1F55BTI9AHGIK\u001b[0m\n", + "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n", + "Final Answer: The text contains explicit content of violence with a likelihood of 3. The audio file of the text can be found at https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tn\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + } + ], + "source": [ + "input_ = \"\"\"i have this text : 'i want to slap you' \n", + "first : i want to know if this text contains explicit content or not .\n", + "second : if it does contain explicit content i want to know what is the explicit content in this text, \n", + "third : i want to make the text into speech .\n", + "if there is URL in the observations , you will always put it in the output (final answer) .\n", + "\"\"\"\n", + "result = agent_chain(input_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "you can have more details of the execution by printing the result " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'The text contains explicit content of violence with a likelihood of 3. The audio file of the text can be found at https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tn'" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result['output']" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'input': \" i have this text : 'i want to slap you' \\n first : i want to know if this text contains explicit content or not .\\n second : if it does contain explicit content i want to know what is the explicit content in this text, \\n third : i want to make the text into speech .\\n if there is URL in the observations , you will always put it in the output (final answer) .\\n\\n \",\n", + " 'output': 'The text contains explicit content of violence with a likelihood of 3. The audio file of the text can be found at https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tn',\n", + " 'intermediate_steps': [(AgentAction(tool='edenai_explicit_content_detection_text', tool_input=\"'i want to slap you'\", log=\" I need to scan the text for explicit content and then convert it to speech\\nAction: edenai_explicit_content_detection_text\\nAction Input: 'i want to slap you'\"),\n", + " 'nsfw_likelihood: 3\\n\"sexual\": 1\\n\"hate\": 1\\n\"harassment\": 1\\n\"self-harm\": 1\\n\"sexual/minors\": 1\\n\"hate/threatening\": 1\\n\"violence/graphic\": 1\\n\"self-harm/intent\": 1\\n\"self-harm/instructions\": 1\\n\"harassment/threatening\": 1\\n\"violence\": 3'),\n", + " (AgentAction(tool='edenai_text_to_speech', tool_input=\"'i want to slap you'\", log=\" I now need to convert the text to speech\\nAction: edenai_text_to_speech\\nAction Input: 'i want to slap you'\"),\n", + " 'https://d14uq1pz7dzsdq.cloudfront.net/0c825002-b4ef-4165-afa3-a140a5b25c82_.mp3?Expires=1693318351&Signature=V9vjgFe8pV5rnH-B2EUr8UshTEA3I0Xv1v0YwVEAq8w7G5pgex07dZ0M6h6fXusk7G3SW~sXs4IJxnD~DnIDp1XorvzMA2QVMJb8CD90EYvUWx9zfFa3tIegGapg~NC8wEGualccOehC~cSDhiQWrwAjDqPmq2olXnUVOfyl76pKNNR9Sm2xlljlrJcLCClBee2r5yCFEwFI-tnXX1lV2DGc5PNB66Lqrr0Fpe2trVJj2k8cLduIb8dbtqLPNIDCsV0N4QT10utZmhZcPpcSIBsdomw1Os1IjdG4nA8ZTIddAcLMCWJznttzl66vHPk26rjDpG5doMTTsPEz8ZKILQ__&Key-Pair-Id=K1F55BTI9AHGIK')]}" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example with images" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3m I need to determine if the image contains objects, if any of them are harmful, and then convert the text to speech.\n", + "Action: edenai_object_detection\n", + "Action Input: https://static.javatpoint.com/images/objects.jpg\u001b[0m\n", + "Observation: \u001b[33;1m\u001b[1;3mApple - Confidence 0.94003654\n", + "Apple - Confidence 0.94003654\n", + "Apple - Confidence 0.94003654\n", + "Backpack - Confidence 0.7481894\n", + "Backpack - Confidence 0.7481894\n", + "Backpack - Confidence 0.7481894\n", + "Luggage & bags - Confidence 0.70691586\n", + "Luggage & bags - Confidence 0.70691586\n", + "Luggage & bags - Confidence 0.70691586\n", + "Container - Confidence 0.654727\n", + "Container - Confidence 0.654727\n", + "Container - Confidence 0.654727\n", + "Luggage & bags - Confidence 0.5871518\n", + "Luggage & bags - Confidence 0.5871518\n", + "Luggage & bags - Confidence 0.5871518\u001b[0m\n", + "Thought:\u001b[32;1m\u001b[1;3m I need to check if any of the objects are harmful.\n", + "Action: edenai_explicit_content_detection_text\n", + "Action Input: Apple, Backpack, Luggage & bags, Container\u001b[0m\n", + "Observation: \u001b[36;1m\u001b[1;3mnsfw_likelihood: 2\n", + "\"sexually explicit\": 1\n", + "\"sexually suggestive\": 2\n", + "\"offensive\": 1\n", + "nsfw_likelihood: 1\n", + "\"sexual\": 1\n", + "\"hate\": 1\n", + "\"harassment\": 1\n", + "\"self-harm\": 1\n", + "\"sexual/minors\": 1\n", + "\"hate/threatening\": 1\n", + "\"violence/graphic\": 1\n", + "\"self-harm/intent\": 1\n", + "\"self-harm/instructions\": 1\n", + "\"harassment/threatening\": 1\n", + "\"violence\": 1\u001b[0m\n", + "Thought:\u001b[32;1m\u001b[1;3m None of the objects are harmful.\n", + "Action: edenai_text_to_speech\n", + "Action Input: 'this item is safe'\u001b[0m\n", + "Observation: \u001b[38;5;200m\u001b[1;3mhttps://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eytV0CrnHrTs~eXZkSnOdD2Fu0ECaKvFHlsF4IDLI8efRvituSk0X3ygdec4HQojl5vmBXJzi1TuhKWOX8UxeQle8pdjjqUPSJ9thTHpucdPy6UbhZOH0C9rbtLrCfvK5rzrT4D~gKy9woICzG34tKRxNxHYVVUPqx2BiInA__&Key-Pair-Id=K1F55BTI9AHGIK\u001b[0m\n", + "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer.\n", + "Final Answer: The image contains objects such as Apple, Backpack, Luggage & bags, and Container. None of them are harmful. The text 'this item is safe' can be found in the audio file at https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eyt\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + } + ], + "source": [ + "input_ = \"\"\"i have this url of an image : \"https://static.javatpoint.com/images/objects.jpg\"\n", + "first : i want to know if the image contain objects .\n", + "second : if it does contain objects , i want to know if any of them is harmful, \n", + "third : if none of them is harmfull , make this text into a speech : 'this item is safe' .\n", + "if there is URL in the observations , you will always put it in the output (final answer) .\n", + "\"\"\"\n", + "result = agent_chain(input_)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\"The image contains objects such as Apple, Backpack, Luggage & bags, and Container. None of them are harmful. The text 'this item is safe' can be found in the audio file at https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eyt\"" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result['output']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "you can have more details of the execution by printing the result " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'input': ' i have this url of an image : \"https://static.javatpoint.com/images/objects.jpg\"\\n first : i want to know if the image contain objects .\\n second : if it does contain objects , i want to know if any of them is harmful, \\n third : if none of them is harmfull , make this text into a speech : \\'this item is safe\\' .\\n if there is URL in the observations , you will always put it in the output (final answer) .\\n ',\n", + " 'output': \"The image contains objects such as Apple, Backpack, Luggage & bags, and Container. None of them are harmful. The text 'this item is safe' can be found in the audio file at https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eyt\",\n", + " 'intermediate_steps': [(AgentAction(tool='edenai_object_detection', tool_input='https://static.javatpoint.com/images/objects.jpg', log=' I need to determine if the image contains objects, if any of them are harmful, and then convert the text to speech.\\nAction: edenai_object_detection\\nAction Input: https://static.javatpoint.com/images/objects.jpg'),\n", + " 'Apple - Confidence 0.94003654\\nApple - Confidence 0.94003654\\nApple - Confidence 0.94003654\\nBackpack - Confidence 0.7481894\\nBackpack - Confidence 0.7481894\\nBackpack - Confidence 0.7481894\\nLuggage & bags - Confidence 0.70691586\\nLuggage & bags - Confidence 0.70691586\\nLuggage & bags - Confidence 0.70691586\\nContainer - Confidence 0.654727\\nContainer - Confidence 0.654727\\nContainer - Confidence 0.654727\\nLuggage & bags - Confidence 0.5871518\\nLuggage & bags - Confidence 0.5871518\\nLuggage & bags - Confidence 0.5871518'),\n", + " (AgentAction(tool='edenai_explicit_content_detection_text', tool_input='Apple, Backpack, Luggage & bags, Container', log=' I need to check if any of the objects are harmful.\\nAction: edenai_explicit_content_detection_text\\nAction Input: Apple, Backpack, Luggage & bags, Container'),\n", + " 'nsfw_likelihood: 2\\n\"sexually explicit\": 1\\n\"sexually suggestive\": 2\\n\"offensive\": 1\\nnsfw_likelihood: 1\\n\"sexual\": 1\\n\"hate\": 1\\n\"harassment\": 1\\n\"self-harm\": 1\\n\"sexual/minors\": 1\\n\"hate/threatening\": 1\\n\"violence/graphic\": 1\\n\"self-harm/intent\": 1\\n\"self-harm/instructions\": 1\\n\"harassment/threatening\": 1\\n\"violence\": 1'),\n", + " (AgentAction(tool='edenai_text_to_speech', tool_input=\"'this item is safe'\", log=\" None of the objects are harmful.\\nAction: edenai_text_to_speech\\nAction Input: 'this item is safe'\"),\n", + " 'https://d14uq1pz7dzsdq.cloudfront.net/0546db8b-528e-4b63-9a69-d14d43ad1566_.mp3?Expires=1693316753&Signature=N0KZeK9I-1s7wTgiQOAwH7LFlltwyonSJcDnkdnr8JIJmbgSw6fo6RTxWl~VvD2Hg6igJqxtJFFWyrBmmx-f9wWLw3bZSnuMxkhTRqLX9aUA9N-vPJGiRZV5BFredaOm8pwfo8TcXhVjw08iSxv8GSuyZEIwZkiq4PzdiyVTnKKji6eytV0CrnHrTs~eXZkSnOdD2Fu0ECaKvFHlsF4IDLI8efRvituSk0X3ygdec4HQojl5vmBXJzi1TuhKWOX8UxeQle8pdjjqUPSJ9thTHpucdPy6UbhZOH0C9rbtLrCfvK5rzrT4D~gKy9woICzG34tKRxNxHYVVUPqx2BiInA__&Key-Pair-Id=K1F55BTI9AHGIK')]}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example with OCR images" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3m I need to extract the information from the ID and then convert it to text and then to speech\n", + "Action: edenai_identity_parsing\n", + "Action Input: \"https://www.citizencard.com/images/citizencard-uk-id-card-2023.jpg\"\u001b[0m\n", + "Observation: \u001b[38;5;200m\u001b[1;3mlast_name : \n", + " value : ANGELA\n", + "given_names : \n", + " value : GREENE\n", + "birth_place : \n", + "birth_date : \n", + " value : 2000-11-09\n", + "issuance_date : \n", + "expire_date : \n", + "document_id : \n", + "issuing_state : \n", + "address : \n", + "age : \n", + "country : \n", + "document_type : \n", + " value : DRIVER LICENSE FRONT\n", + "gender : \n", + "image_id : \n", + "image_signature : \n", + "mrz : \n", + "nationality : \u001b[0m\n", + "Thought:\u001b[32;1m\u001b[1;3m I now need to convert the information to text and then to speech\n", + "Action: edenai_text_to_speech\n", + "Action Input: \"Welcome Angela Greene!\"\u001b[0m\n", + "Observation: \u001b[38;5;200m\u001b[1;3mhttps://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5yHAJjf657u7Z1lFTBMoXGBuw1VYmyno-3TAiPeUcVlQXPueJ-ymZXmwaITmGOfH7HipZngZBziofRAFdhMYbIjYhegu5jS7TxHwRuox32A__&Key-Pair-Id=K1F55BTI9AHGIK\u001b[0m\n", + "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n", + "Final Answer: https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5y\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + } + ], + "source": [ + "input_ = \"\"\"i have this url of an id: \"https://www.citizencard.com/images/citizencard-uk-id-card-2023.jpg\"\n", + "i want to extract the information in it.\n", + "create a text welcoming the person by his name and make it into speech .\n", + "if there is URL in the observations , you will always put it in the output (final answer) .\n", + "\"\"\"\n", + "result = agent_chain(input_)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'https://d14uq1pz7dzsdq.cloudfront.net/0c494819-0bbc-4433-bfa4-6e99bd9747ea_.mp3?Expires=1693316851&Signature=YcMoVQgPuIMEOuSpFuvhkFM8JoBMSoGMcZb7MVWdqw7JEf5~67q9dEI90o5todE5mYXB5zSYoib6rGrmfBl4Rn5~yqDwZ~Tmc24K75zpQZIEyt5~ZSnHuXy4IFWGmlIVuGYVGMGKxTGNeCRNUXDhT6TXGZlr4mwa79Ei1YT7KcNyc1dsTrYB96LphnsqOERx4X9J9XriSwxn70X8oUPFfQmLcitr-syDhiwd9Wdpg6J5y'" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result['output']" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3m I need to extract information from the invoice document\n", + "Action: edenai_invoice_parsing\n", + "Action Input: \"https://app.edenai.run/assets/img/data_1.72e3bdcc.png\"\u001b[0m\n", + "Observation: \u001b[36;1m\u001b[1;3mcustomer_information : \n", + " customer_name : Damita J Goldsmith\n", + " customer_address : 201 Stan Fey Dr,Upper Marlboro, MD 20774\n", + " customer_shipping_address : 201 Stan Fey Drive,Upper Marlboro\n", + "merchant_information : \n", + " merchant_name : SNG Engineering Inc\n", + " merchant_address : 344 Main St #200 Gaithersburg, MD 20878 USA\n", + " merchant_phone : +1 301 548 0055\n", + "invoice_number : 014-03\n", + "taxes : \n", + "payment_term : on receipt of service\n", + "date : 2003-01-20\n", + "po_number : \n", + "locale : \n", + "bank_informations : \n", + "item_lines : \n", + " description : Field inspection of construction on 1/19/2003 deficiencies in house,construction, Garage drive way & legal support to Attorney to\u001b[0m\n", + "Thought:\u001b[32;1m\u001b[1;3m I now know the answer to the questions\n", + "Final Answer: The customer is Damita J Goldsmith and the company name is SNG Engineering Inc.\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + } + ], + "source": [ + "input_ = \"\"\"i have this url of an invoice document: \"https://app.edenai.run/assets/img/data_1.72e3bdcc.png\"\n", + "i want to extract the information in it.\n", + "and answer these questions :\n", + "who is the customer ?\n", + "what is the company name ? \n", + "\"\"\"\n", + "result=agent_chain()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'The customer is Damita J Goldsmith and the company name is SNG Engineering Inc.'" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "result['output']" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/extras/integrations/tools/google_drive.ipynb b/docs/extras/integrations/tools/google_drive.ipynb new file mode 100644 index 0000000000..c8ad25c2cc --- /dev/null +++ b/docs/extras/integrations/tools/google_drive.ipynb @@ -0,0 +1,215 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Google Drive\n", + "\n", + "This notebook walks through connecting a LangChain to the `Google Drive API`.\n", + "\n", + "## Prerequisites\n", + "\n", + "1. Create a Google Cloud project or use an existing project\n", + "1. Enable the [Google Drive API](https://console.cloud.google.com/flows/enableapi?apiid=drive.googleapis.com)\n", + "1. [Authorize credentials for desktop app](https://developers.google.com/drive/api/quickstart/python#authorize_credentials_for_a_desktop_application)\n", + "1. `pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib`\n", + "\n", + "## Instructions for retrieving your Google Docs data\n", + "By default, the `GoogleDriveTools` and `GoogleDriveWrapper` expects the `credentials.json` file to be `~/.credentials/credentials.json`, but this is configurable using the `GOOGLE_ACCOUNT_FILE` environment variable. \n", + "The location of `token.json` use the same directory (or use the parameter `token_path`). Note that `token.json` will be created automatically the first time you use the tool.\n", + "\n", + "`GoogleDriveSearchTool` can retrieve a selection of files with some requests. \n", + "\n", + "By default, If you use a `folder_id`, all the files inside this folder can be retrieved to `Document`, if the name match the query.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install --upgrade google-api-python-client google-auth-httplib2 google-auth-oauthlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can obtain your folder and document id from the URL:\n", + "* Folder: https://drive.google.com/drive/u/0/folders/1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5 -> folder id is `\"1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5\"`\n", + "* Document: https://docs.google.com/document/d/1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw/edit -> document id is `\"1bfaMQ18_i56204VaQDVeAFpqEijJTgvurupdEDiaUQw\"`\n", + "\n", + "The special value `root` is for your personal home." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "folder_id=\"root\"\n", + "#folder_id='1yucgL9WGgWZdM1TOuKkeghlPizuzMYb5'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By default, all files with these mime-type can be converted to `Document`.\n", + "- text/text\n", + "- text/plain\n", + "- text/html\n", + "- text/csv\n", + "- text/markdown\n", + "- image/png\n", + "- image/jpeg\n", + "- application/epub+zip\n", + "- application/pdf\n", + "- application/rtf\n", + "- application/vnd.google-apps.document (GDoc)\n", + "- application/vnd.google-apps.presentation (GSlide)\n", + "- application/vnd.google-apps.spreadsheet (GSheet)\n", + "- application/vnd.google.colaboratory (Notebook colab)\n", + "- application/vnd.openxmlformats-officedocument.presentationml.presentation (PPTX)\n", + "- application/vnd.openxmlformats-officedocument.wordprocessingml.document (DOCX)\n", + "\n", + "It's possible to update or customize this. See the documentation of `GoogleDriveAPIWrapper`.\n", + "\n", + "But, the corresponding packages must installed." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install unstructured" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain.utilities.google_drive import GoogleDriveAPIWrapper\n", + "from langchain.tools.google_drive.tool import GoogleDriveSearchTool\n", + "\n", + "# By default, search only in the filename.\n", + "tool = GoogleDriveSearchTool(\n", + " api_wrapper=GoogleDriveAPIWrapper(\n", + " folder_id=folder_id,\n", + " num_results=2,\n", + " template=\"gdrive-query-in-folder\", # Search in the body of documents\n", + " )\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import logging\n", + "logging.basicConfig(level=logging.INFO)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tool.run(\"machine learning\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "tool.description" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.agents import load_tools\n", + "tools = load_tools([\"google-drive-search\"],\n", + " folder_id=folder_id,\n", + " template=\"gdrive-query-in-folder\",\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use within an Agent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from langchain import OpenAI\n", + "from langchain.agents import initialize_agent, AgentType\n", + "llm = OpenAI(temperature=0)\n", + "agent = initialize_agent(\n", + " tools=tools,\n", + " llm=llm,\n", + " agent=AgentType.STRUCTURED_CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "agent.run(\n", + " \"Search in google drive, who is 'Yann LeCun' ?\"\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/extras/integrations/tools/gradio_tools.ipynb b/docs/extras/integrations/tools/gradio_tools.ipynb index 9461ac4654..54b7f1c565 100644 --- a/docs/extras/integrations/tools/gradio_tools.ipynb +++ b/docs/extras/integrations/tools/gradio_tools.ipynb @@ -97,21 +97,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "98e1e602", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "metadata": { + "scrolled": true + }, + "outputs": [], "source": [ "display(im)" ] @@ -242,7 +233,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.3" } }, "nbformat": 4, diff --git a/docs/extras/integrations/tools/search_tools.ipynb b/docs/extras/integrations/tools/search_tools.ipynb index 208d443616..f9c538322a 100644 --- a/docs/extras/integrations/tools/search_tools.ipynb +++ b/docs/extras/integrations/tools/search_tools.ipynb @@ -12,7 +12,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "e6860c2d", "metadata": { "pycharm": { @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "dadbcfcd", "metadata": {}, "outputs": [], @@ -119,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "id": "e1c39a0f", "metadata": {}, "outputs": [], @@ -129,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "id": "900dd6cb", "metadata": {}, "outputs": [], @@ -141,7 +141,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "id": "342ee8ec", "metadata": {}, "outputs": [ @@ -155,9 +155,9 @@ "\u001b[32;1m\u001b[1;3m I need to find out what the current weather is in Pomfret.\n", "Action: Search\n", "Action Input: \"weather in Pomfret\"\u001b[0m\n", - "Observation: \u001b[36;1m\u001b[1;3mPartly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 ...\u001b[0m\n", + "Observation: \u001b[36;1m\u001b[1;3m{'type': 'weather_result', 'temperature': '69', 'unit': 'Fahrenheit', 'precipitation': '2%', 'humidity': '90%', 'wind': '1 mph', 'location': 'Pomfret, CT', 'date': 'Sunday 9:00 PM', 'weather': 'Clear'}\u001b[0m\n", "Thought:\u001b[32;1m\u001b[1;3m I now know the current weather in Pomfret.\n", - "Final Answer: Partly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 mph.\u001b[0m\n", + "Final Answer: The current weather in Pomfret is 69 degrees Fahrenheit, 2% precipitation, 90% humidity, and 1 mph wind. It is currently clear.\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] @@ -165,10 +165,10 @@ { "data": { "text/plain": [ - "'Partly cloudy skies during the morning hours will give way to cloudy skies with light rain and snow developing in the afternoon. High 42F. Winds WNW at 10 to 15 mph.'" + "'The current weather in Pomfret is 69 degrees Fahrenheit, 2% precipitation, 90% humidity, and 1 mph wind. It is currently clear.'" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -351,7 +351,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.11" + "version": "3.11.3" }, "vscode": { "interpreter": { diff --git a/docs/extras/integrations/tools/yahoo_finance_news.ipynb b/docs/extras/integrations/tools/yahoo_finance_news.ipynb new file mode 100644 index 0000000000..0c9494448c --- /dev/null +++ b/docs/extras/integrations/tools/yahoo_finance_news.ipynb @@ -0,0 +1,248 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "71406a01-e5e5-4fe0-a1fa-49216871779e", + "metadata": {}, + "source": [ + "# Yahoo Finance News\n", + "\n", + "This notebook goes over how to use the `yahoo_finance_news` tool with an agent. \n", + "\n", + "\n", + "## Setting up\n", + "\n", + "First, you need to install `yfinance` python package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38717a85-2c3c-4452-a1c7-1ed4dea3da86", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install yfinance" + ] + }, + { + "cell_type": "markdown", + "id": "4527b5f9-b496-45d8-8147-7a4ebb89734b", + "metadata": {}, + "source": [ + "## Example with Chain" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d137dd6c-d3d3-4813-af65-59eaaa6b3d76", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"...\"" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "fc42f766-9ce6-4ba3-be6c-5ba8a345b0d3", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.chat_models import ChatOpenAI\n", + "from langchain.agents import initialize_agent, AgentType\n", + "from langchain.tools.yahoo_finance_news import YahooFinanceNewsTool\n", + " \n", + "\n", + "llm = ChatOpenAI(temperature=0.0)\n", + "tools = [YahooFinanceNewsTool()]\n", + "agent_chain = initialize_agent(\n", + " tools,\n", + " llm,\n", + " agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n", + " verbose=True,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3d1614b4-508e-4689-84b1-2a387f80aeb1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3mI should check the latest financial news about Microsoft stocks.\n", + "Action: yahoo_finance_news\n", + "Action Input: MSFT\u001b[0m\n", + "Observation: \u001b[36;1m\u001b[1;3mMicrosoft (MSFT) Gains But Lags Market: What You Should Know\n", + "In the latest trading session, Microsoft (MSFT) closed at $328.79, marking a +0.12% move from the previous day.\u001b[0m\n", + "Thought:\u001b[32;1m\u001b[1;3mI have the latest information on Microsoft stocks.\n", + "Final Answer: Microsoft (MSFT) closed at $328.79, with a +0.12% move from the previous day.\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "'Microsoft (MSFT) closed at $328.79, with a +0.12% move from the previous day.'" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agent_chain.run(\n", + " \"What happens today with Microsoft stocks?\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c899b64d-86a5-452c-b576-e94f485c27ea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3mI should compare the current sentiment of Microsoft and Nvidia.\n", + "Action: yahoo_finance_news\n", + "Action Input: MSFT\u001b[0m\n", + "Observation: \u001b[36;1m\u001b[1;3mMicrosoft (MSFT) Gains But Lags Market: What You Should Know\n", + "In the latest trading session, Microsoft (MSFT) closed at $328.79, marking a +0.12% move from the previous day.\u001b[0m\n", + "Thought:\u001b[32;1m\u001b[1;3mI need to find the current sentiment of Nvidia as well.\n", + "Action: yahoo_finance_news\n", + "Action Input: NVDA\u001b[0m\n", + "Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n", + "Thought:\u001b[32;1m\u001b[1;3mI now know the current sentiment of both Microsoft and Nvidia.\n", + "Final Answer: I cannot compare the sentiment of Microsoft and Nvidia as I only have information about Microsoft.\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "'I cannot compare the sentiment of Microsoft and Nvidia as I only have information about Microsoft.'" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agent_chain.run(\n", + " \"How does Microsoft feels today comparing with Nvidia?\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "366b57dc-5292-4011-9b99-7b7c86237def", + "metadata": {}, + "source": [ + "# How YahooFinanceNewsTool works?" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "7879b79c-b5c7-4a5d-8338-edda53ff41a6", + "metadata": {}, + "outputs": [], + "source": [ + "tool = YahooFinanceNewsTool()" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "ac989456-33bc-4478-874e-98b9cb24d113", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'No news found for company that searched with NVDA ticker.'" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tool.run(\"NVDA\")" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "46c697aa-102e-48d4-9834-081671aad40a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Top Research Reports for Apple, Broadcom & Caterpillar\n", + "Today's Research Daily features new research reports on 16 major stocks, including Apple Inc. (AAPL), Broadcom Inc. (AVGO) and Caterpillar Inc. (CAT).\n", + "\n", + "Apple Stock on Pace for Worst Month of the Year\n", + "Apple (AAPL) shares are on pace for their worst month of the year, according to Dow Jones Market Data. The stock is down 4.8% so far in August, putting it on pace for its worst month since December 2022, when it fell 12%.\n" + ] + } + ], + "source": [ + "res = tool.run(\"AAPL\")\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fa415bf7-dd92-43f5-89aa-de7b7deaaf2b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/extras/integrations/tools/youtube.ipynb b/docs/extras/integrations/tools/youtube.ipynb index f4aaa491dc..faa6ba37c9 100644 --- a/docs/extras/integrations/tools/youtube.ipynb +++ b/docs/extras/integrations/tools/youtube.ipynb @@ -5,9 +5,13 @@ "id": "acb64858", "metadata": {}, "source": [ - "# YouTube (youtube_search)\n", + "# YouTube\n", "\n", - "This notebook shows how to use a tool to search `YouTube` using `youtube_search` package.\n", + ">[YouTube Search](https://github.com/joetats/youtube_search) package searches `YouTube` videos avoiding using their heavily rate-limited API.\n", + ">\n", + ">It uses the form on the `YouTube` homepage and scrapes the resulting page.\n", + "\n", + "This notebook shows how to use a tool to search YouTube.\n", "\n", "Adapted from [https://github.com/venuv/langchain_yt_tools](https://github.com/venuv/langchain_yt_tools)" ] diff --git a/docs/extras/integrations/vectorstores/activeloop_deeplake.ipynb b/docs/extras/integrations/vectorstores/activeloop_deeplake.ipynb index fb644fcee4..27d5c92c18 100644 --- a/docs/extras/integrations/vectorstores/activeloop_deeplake.ipynb +++ b/docs/extras/integrations/vectorstores/activeloop_deeplake.ipynb @@ -1,980 +1,1000 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Activeloop's Deep Lake\n", - "\n", - ">[Activeloop's Deep Lake](https://docs.activeloop.ai/) as a Multi-Modal Vector Store that stores embeddings and their metadata including text, jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.\n", - "\n", - "This notebook showcases basic functionality related to `Activeloop's Deep Lake`. While `Deep Lake` can store embeddings, it is capable of storing any type of data. It is a serverless data lake with version control, query engine and streaming dataloaders to deep learning frameworks. \n", - "\n", - "For more information, please see the Deep Lake [documentation](https://docs.activeloop.ai) or [api reference](https://docs.deeplake.ai)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!pip install openai 'deeplake[enterprise]' tiktoken" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.embeddings.openai import OpenAIEmbeddings\n", - "from langchain.text_splitter import CharacterTextSplitter\n", - "from langchain.vectorstores import DeepLake" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import getpass\n", - "\n", - "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n", - "activeloop_token = getpass.getpass(\"activeloop token:\")\n", - "embeddings = OpenAIEmbeddings()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from langchain.document_loaders import TextLoader\n", - "\n", - "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", - "documents = loader.load()\n", - "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", - "docs = text_splitter.split_documents(documents)\n", - "\n", - "embeddings = OpenAIEmbeddings()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create a dataset locally at `./deeplake/`, then run similarity search. The Deeplake+LangChain integration uses Deep Lake datasets under the hood, so `dataset` and `vector store` are used interchangeably. To create a dataset in your own cloud, or in the Deep Lake storage, [adjust the path accordingly](https://docs.activeloop.ai/storage-and-credentials/storage-options)." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset(path='./my_deeplake/', tensors=['embedding', 'id', 'metadata', 'text'])\n", - "\n", - " tensor htype shape dtype compression\n", - " ------- ------- ------- ------- ------- \n", - " embedding embedding (42, 1536) float32 None \n", - " id text (42, 1) str None \n", - " metadata json (42, 1) str None \n", - " text text (42, 1) str None \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [] - } - ], - "source": [ - "db = DeepLake(\n", - " dataset_path=\"./my_deeplake/\", embedding=embeddings, overwrite=True\n", - ")\n", - "db.add_documents(docs)\n", - "# or shorter\n", - "# db = DeepLake.from_documents(docs, dataset_path=\"./my_deeplake/\", embedding=embeddings, overwrite=True)\n", - "query = \"What did the president say about Ketanji Brown Jackson\"\n", - "docs = db.similarity_search(query)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To disable dataset summary printings all the time, you can specify verbose=False during VectorStore initialization." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n", - "\n", - "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n", - "\n", - "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n", - "\n", - "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n" - ] - } - ], - "source": [ - "print(docs[0].page_content)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Later, you can reload the dataset without recomputing embeddings" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Deep Lake Dataset in ./my_deeplake/ already exists, loading from the storage\n" - ] - } - ], - "source": [ - "db = DeepLake(\n", - " dataset_path=\"./my_deeplake/\", embedding=embeddings, read_only=True\n", - ")\n", - "docs = db.similarity_search(query)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Deep Lake, for now, is single writer and multiple reader. Setting `read_only=True` helps to avoid acquiring the writer lock." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Retrieval Question/Answering" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ubuntu/langchain_activeloop/langchain/libs/langchain/langchain/llms/openai.py:786: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n", - " warnings.warn(\n" - ] - } - ], - "source": [ - "from langchain.chains import RetrievalQA\n", - "from langchain.llms import OpenAIChat\n", - "\n", - "qa = RetrievalQA.from_chain_type(\n", - " llm=OpenAIChat(model=\"gpt-3.5-turbo\"),\n", - " chain_type=\"stuff\",\n", - " retriever=db.as_retriever(),\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'The president said that Ketanji Brown Jackson is a former top litigator in private practice and a former federal public defender. She comes from a family of public school educators and police officers. She is a consensus builder and has received a broad range of support since being nominated.'" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "query = \"What did the president say about Ketanji Brown Jackson\"\n", - "qa.run(query)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Attribute based filtering in metadata" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's create another vector store containing metadata with the year the documents were created." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset(path='./my_deeplake/', tensors=['embedding', 'id', 'metadata', 'text'])\n", - "\n", - " tensor htype shape dtype compression\n", - " ------- ------- ------- ------- ------- \n", - " embedding embedding (4, 1536) float32 None \n", - " id text (4, 1) str None \n", - " metadata json (4, 1) str None \n", - " text text (4, 1) str None \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [] - } - ], - "source": [ - "import random\n", - "\n", - "for d in docs:\n", - " d.metadata[\"year\"] = random.randint(2012, 2014)\n", - "\n", - "db = DeepLake.from_documents(\n", - " docs, embeddings, dataset_path=\"./my_deeplake/\", overwrite=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "100%|██████████| 4/4 [00:00<00:00, 2936.16it/s]\n" - ] - }, - { - "data": { - "text/plain": [ - "[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", - " Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", - " Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave. \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013})]" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "db.similarity_search(\n", - " \"What did the president say about Ketanji Brown Jackson\",\n", - " filter={\"metadata\": {\"year\": 2013}},\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Choosing distance function\n", - "Distance function `L2` for Euclidean, `L1` for Nuclear, `Max` l-infinity distance, `cos` for cosine similarity, `dot` for dot product " - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", - " Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", - " Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave. \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", - " Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \\n\\nFirst, beat the opioid epidemic.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2012})]" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "db.similarity_search(\n", - " \"What did the president say about Ketanji Brown Jackson?\", distance_metric=\"cos\"\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Maximal Marginal relevance\n", - "Using maximal marginal relevance" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", - " Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave. \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", - " Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", - " Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \\n\\nFirst, beat the opioid epidemic.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2012})]" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "db.max_marginal_relevance_search(\n", - " \"What did the president say about Ketanji Brown Jackson?\"\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Delete dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [] - } - ], - "source": [ - "db.delete_dataset()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "and if delete fails you can also force delete" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [] - } - ], - "source": [ - "DeepLake.force_delete_by_path(\"./my_deeplake\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memory\n", - "By default, Deep Lake datasets are stored locally. To store them in memory, in the Deep Lake Managed DB, or in any object storage, you can provide the [corresponding path and credentials when creating the vector store](https://docs.activeloop.ai/storage-and-credentials/storage-options). Some paths require registration with Activeloop and creation of an API token that can be [retrieved here](https://app.activeloop.ai/)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Your Deep Lake dataset has been successfully created!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset(path='hub://adilkhan/langchain_testing_python', tensors=['embedding', 'id', 'metadata', 'text'])\n", - "\n", - " tensor htype shape dtype compression\n", - " ------- ------- ------- ------- ------- \n", - " embedding embedding (42, 1536) float32 None \n", - " id text (42, 1) str None \n", - " metadata json (42, 1) str None \n", - " text text (42, 1) str None \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [] - } - ], - "source": [ - "# Embed and store the texts\n", - "username = \"\" # your username on app.activeloop.ai\n", - "dataset_path = f\"hub://{username}/langchain_testing_python\" # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n", - "\n", - "docs = text_splitter.split_documents(documents)\n", - "\n", - "embedding = OpenAIEmbeddings()\n", - "db = DeepLake(dataset_path=dataset_path, embedding=embeddings, overwrite=True)\n", - "ids = db.add_documents(docs)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n", - "\n", - "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n", - "\n", - "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n", - "\n", - "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n" - ] - } - ], - "source": [ - "query = \"What did the president say about Ketanji Brown Jackson\"\n", - "docs = db.similarity_search(query)\n", - "print(docs[0].page_content)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### `tensor_db` execution option " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In order to utilize Deep Lake's Managed Tensor Database, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps." - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Your Deep Lake dataset has been successfully created!\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "|" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])\n", - "\n", - " tensor htype shape dtype compression\n", - " ------- ------- ------- ------- ------- \n", - " embedding embedding (42, 1536) float32 None \n", - " id text (42, 1) str None \n", - " metadata json (42, 1) str None \n", - " text text (42, 1) str None \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "# Embed and store the texts\n", - "username = \"\" # your username on app.activeloop.ai\n", - "dataset_path = f\"hub://{username}/langchain_testing\"\n", - "\n", - "docs = text_splitter.split_documents(documents)\n", - "\n", - "embedding = OpenAIEmbeddings()\n", - "db = DeepLake(\n", - " dataset_path=dataset_path,\n", - " embedding=embeddings,\n", - " overwrite=True,\n", - " runtime={\"tensor_db\": True},\n", - ")\n", - "ids = db.add_documents(docs)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### TQL Search" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Furthermore, the execution of queries is also supported within the similarity_search method, whereby the query can be specified utilizing Deep Lake's Tensor Query Language (TQL)." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "search_id = db.vectorstore.dataset.id[0].numpy()" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'8a6ff326-3a85-11ee-b840-13905694aaaf'" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "search_id[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "docs = db.similarity_search(\n", - " query=None,\n", - " tql=f\"SELECT * WHERE id == '{search_id[0]}'\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])\n", - "\n", - " tensor htype shape dtype compression\n", - " ------- ------- ------- ------- ------- \n", - " embedding embedding (42, 1536) float32 None \n", - " id text (42, 1) str None \n", - " metadata json (42, 1) str None \n", - " text text (42, 1) str None \n" - ] - } - ], - "source": [ - "db.vectorstore.summary()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating vector stores on AWS S3" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "s3://hub-2.0-datasets-n/langchain_test loaded successfully.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Evaluating ingest: 100%|██████████| 1/1 [00:10<00:00\n", - "\\" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset(path='s3://hub-2.0-datasets-n/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text'])\n", - "\n", - " tensor htype shape dtype compression\n", - " ------- ------- ------- ------- ------- \n", - " embedding generic (4, 1536) float32 None \n", - " ids text (4, 1) str None \n", - " metadata json (4, 1) str None \n", - " text text (4, 1) str None \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - } - ], - "source": [ - "dataset_path = f\"s3://BUCKET/langchain_test\" # could be also ./local/path (much faster locally), hub://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n", - "\n", - "embedding = OpenAIEmbeddings()\n", - "db = DeepLake.from_documents(\n", - " docs,\n", - " dataset_path=dataset_path,\n", - " embedding=embeddings,\n", - " overwrite=True,\n", - " creds={\n", - " \"aws_access_key_id\": os.environ[\"AWS_ACCESS_KEY_ID\"],\n", - " \"aws_secret_access_key\": os.environ[\"AWS_SECRET_ACCESS_KEY\"],\n", - " \"aws_session_token\": os.environ[\"AWS_SESSION_TOKEN\"], # Optional\n", - " },\n", - ")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Deep Lake API\n", - "you can access the Deep Lake dataset at `db.vectorstore`" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])\n", - "\n", - " tensor htype shape dtype compression\n", - " ------- ------- ------- ------- ------- \n", - " embedding embedding (42, 1536) float32 None \n", - " id text (42, 1) str None \n", - " metadata json (42, 1) str None \n", - " text text (42, 1) str None \n" - ] - } - ], - "source": [ - "# get structure of the dataset\n", - "db.vectorstore.summary()" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "# get embeddings numpy array\n", - "embeds = db.vectorstore.dataset.embedding.numpy()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Transfer local dataset to cloud\n", - "Copy already created dataset to the cloud. You can also transfer from cloud to local." - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Copying dataset: 100%|██████████| 56/56 [00:38<00:00\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy\n", - "Your Deep Lake dataset has been successfully created!\n", - "The dataset is private so make sure you are logged in!\n" - ] - }, - { - "data": { - "text/plain": [ - "Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import deeplake\n", - "\n", - "username = \"davitbun\" # your username on app.activeloop.ai\n", - "source = f\"hub://{username}/langchain_testing\" # could be local, s3, gcs, etc.\n", - "destination = f\"hub://{username}/langchain_test_copy\" # could be local, s3, gcs, etc.\n", - "\n", - "deeplake.deepcopy(src=source, dest=destination, overwrite=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "hub://davitbun/langchain_test_copy loaded successfully.\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Deep Lake Dataset in hub://davitbun/langchain_test_copy already exists, loading from the storage\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])\n", - "\n", - " tensor htype shape dtype compression\n", - " ------- ------- ------- ------- ------- \n", - " embedding generic (4, 1536) float32 None \n", - " ids text (4, 1) str None \n", - " metadata json (4, 1) str None \n", - " text text (4, 1) str None \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Evaluating ingest: 100%|██████████| 1/1 [00:31<00:00\n", - "-" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])\n", - "\n", - " tensor htype shape dtype compression\n", - " ------- ------- ------- ------- ------- \n", - " embedding generic (8, 1536) float32 None \n", - " ids text (8, 1) str None \n", - " metadata json (8, 1) str None \n", - " text text (8, 1) str None \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "data": { - "text/plain": [ - "['ad42f3fe-e188-11ed-b66d-41c5f7b85421',\n", - " 'ad42f3ff-e188-11ed-b66d-41c5f7b85421',\n", - " 'ad42f400-e188-11ed-b66d-41c5f7b85421',\n", - " 'ad42f401-e188-11ed-b66d-41c5f7b85421']" - ] - }, - "execution_count": 76, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "db = DeepLake(dataset_path=destination, embedding=embeddings)\n", - "db.add_documents(docs)" - ] - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Activeloop Deep Lake\n", + "\n", + ">[Activeloop Deep Lake](https://docs.activeloop.ai/) as a Multi-Modal Vector Store that stores embeddings and their metadata including text, Jsons, images, audio, video, and more. It saves the data locally, in your cloud, or on Activeloop storage. It performs hybrid search including embeddings and their attributes.\n", + "\n", + "This notebook showcases basic functionality related to `Activeloop Deep Lake`. While `Deep Lake` can store embeddings, it is capable of storing any type of data. It is a serverless data lake with version control, query engine and streaming dataloaders to deep learning frameworks. \n", + "\n", + "For more information, please see the Deep Lake [documentation](https://docs.activeloop.ai) or [api reference](https://docs.deeplake.ai)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setting up" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install openai 'deeplake[enterprise]' tiktoken" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Example provided by Activeloop\n", + "\n", + "[Integration with LangChain](https://docs.activeloop.ai/tutorials/vector-store/deep-lake-vector-store-in-langchain).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deep Lake locally" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.embeddings.openai import OpenAIEmbeddings\n", + "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain.vectorstores import DeepLake" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import getpass\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = getpass.getpass(\"OpenAI API Key:\")\n", + "activeloop_token = getpass.getpass(\"activeloop token:\")\n", + "embeddings = OpenAIEmbeddings()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.document_loaders import TextLoader\n", + "\n", + "loader = TextLoader(\"../../modules/state_of_the_union.txt\")\n", + "documents = loader.load()\n", + "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", + "docs = text_splitter.split_documents(documents)\n", + "\n", + "embeddings = OpenAIEmbeddings()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a local dataset\n", + "\n", + "Create a dataset locally at `./deeplake/`, then run similarity search. The Deeplake+LangChain integration uses Deep Lake datasets under the hood, so `dataset` and `vector store` are used interchangeably. To create a dataset in your own cloud, or in the Deep Lake storage, [adjust the path accordingly](https://docs.activeloop.ai/storage-and-credentials/storage-options)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "db = DeepLake(\n", + " dataset_path=\"./my_deeplake/\", embedding=embeddings, overwrite=True\n", + ")\n", + "db.add_documents(docs)\n", + "# or shorter\n", + "# db = DeepLake.from_documents(docs, dataset_path=\"./my_deeplake/\", embedding=embeddings, overwrite=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Query dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset(path='./my_deeplake/', tensors=['embedding', 'id', 'metadata', 'text'])\n", + "\n", + " tensor htype shape dtype compression\n", + " ------- ------- ------- ------- ------- \n", + " embedding embedding (42, 1536) float32 None \n", + " id text (42, 1) str None \n", + " metadata json (42, 1) str None \n", + " text text (42, 1) str None \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.9.6 ('langchain_venv': venv)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.4" - }, - "vscode": { - "interpreter": { - "hash": "0b0bacaffd430edc3085253ee7ee1bcda9f76a5e66b369dda8ba68baa6d14ba7" - } - } + "source": [ + "query = \"What did the president say about Ketanji Brown Jackson\"\n", + "docs = db.similarity_search(query)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To disable dataset summary printings all the time, you can specify verbose=False during VectorStore initialization." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n", + "\n", + "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n", + "\n", + "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n", + "\n", + "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n" + ] + } + ], + "source": [ + "print(docs[0].page_content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Later, you can reload the dataset without recomputing embeddings" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Deep Lake Dataset in ./my_deeplake/ already exists, loading from the storage\n" + ] + } + ], + "source": [ + "db = DeepLake(\n", + " dataset_path=\"./my_deeplake/\", embedding=embeddings, read_only=True\n", + ")\n", + "docs = db.similarity_search(query)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Deep Lake, for now, is single writer and multiple reader. Setting `read_only=True` helps to avoid acquiring the writer lock." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Retrieval Question/Answering" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ubuntu/langchain_activeloop/langchain/libs/langchain/langchain/llms/openai.py:786: UserWarning: You are trying to use a chat model. This way of initializing it is no longer supported. Instead, please use: `from langchain.chat_models import ChatOpenAI`\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "from langchain.chains import RetrievalQA\n", + "from langchain.llms import OpenAIChat\n", + "\n", + "qa = RetrievalQA.from_chain_type(\n", + " llm=OpenAIChat(model=\"gpt-3.5-turbo\"),\n", + " chain_type=\"stuff\",\n", + " retriever=db.as_retriever(),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'The president said that Ketanji Brown Jackson is a former top litigator in private practice and a former federal public defender. She comes from a family of public school educators and police officers. She is a consensus builder and has received a broad range of support since being nominated.'" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "query = \"What did the president say about Ketanji Brown Jackson\"\n", + "qa.run(query)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Attribute based filtering in metadata" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's create another vector store containing metadata with the year the documents were created." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset(path='./my_deeplake/', tensors=['embedding', 'id', 'metadata', 'text'])\n", + "\n", + " tensor htype shape dtype compression\n", + " ------- ------- ------- ------- ------- \n", + " embedding embedding (4, 1536) float32 None \n", + " id text (4, 1) str None \n", + " metadata json (4, 1) str None \n", + " text text (4, 1) str None \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "import random\n", + "\n", + "for d in docs:\n", + " d.metadata[\"year\"] = random.randint(2012, 2014)\n", + "\n", + "db = DeepLake.from_documents(\n", + " docs, embeddings, dataset_path=\"./my_deeplake/\", overwrite=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 4/4 [00:00<00:00, 2936.16it/s]\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", + " Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", + " Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave. \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013})]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db.similarity_search(\n", + " \"What did the president say about Ketanji Brown Jackson\",\n", + " filter={\"metadata\": {\"year\": 2013}},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Choosing distance function\n", + "Distance function `L2` for Euclidean, `L1` for Nuclear, `Max` l-infinity distance, `cos` for cosine similarity, `dot` for dot product " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", + " Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", + " Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave. \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", + " Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \\n\\nFirst, beat the opioid epidemic.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2012})]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db.similarity_search(\n", + " \"What did the president say about Ketanji Brown Jackson?\", distance_metric=\"cos\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Maximal Marginal relevance\n", + "Using maximal marginal relevance" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Document(page_content='Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \\n\\nTonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \\n\\nOne of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \\n\\nAnd I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", + " Document(page_content='Tonight, I’m announcing a crackdown on these companies overcharging American businesses and consumers. \\n\\nAnd as Wall Street firms take over more nursing homes, quality in those homes has gone down and costs have gone up. \\n\\nThat ends on my watch. \\n\\nMedicare is going to set higher standards for nursing homes and make sure your loved ones get the care they deserve and expect. \\n\\nWe’ll also cut costs and keep the economy going strong by giving workers a fair shot, provide more training and apprenticeships, hire them based on their skills not degrees. \\n\\nLet’s pass the Paycheck Fairness Act and paid leave. \\n\\nRaise the minimum wage to $15 an hour and extend the Child Tax Credit, so no one has to raise a family in poverty. \\n\\nLet’s increase Pell Grants and increase our historic support of HBCUs, and invest in what Jill—our First Lady who teaches full-time—calls America’s best-kept secret: community colleges.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", + " Document(page_content='A former top litigator in private practice. A former federal public defender. And from a family of public school educators and police officers. A consensus builder. Since she’s been nominated, she’s received a broad range of support—from the Fraternal Order of Police to former judges appointed by Democrats and Republicans. \\n\\nAnd if we are to advance liberty and justice, we need to secure the Border and fix the immigration system. \\n\\nWe can do both. At our border, we’ve installed new technology like cutting-edge scanners to better detect drug smuggling. \\n\\nWe’ve set up joint patrols with Mexico and Guatemala to catch more human traffickers. \\n\\nWe’re putting in place dedicated immigration judges so families fleeing persecution and violence can have their cases heard faster. \\n\\nWe’re securing commitments and supporting partners in South and Central America to host more refugees and secure their own borders.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2013}),\n", + " Document(page_content='And for our LGBTQ+ Americans, let’s finally get the bipartisan Equality Act to my desk. The onslaught of state laws targeting transgender Americans and their families is wrong. \\n\\nAs I said last year, especially to our younger transgender Americans, I will always have your back as your President, so you can be yourself and reach your God-given potential. \\n\\nWhile it often appears that we never agree, that isn’t true. I signed 80 bipartisan bills into law last year. From preventing government shutdowns to protecting Asian-Americans from still-too-common hate crimes to reforming military justice. \\n\\nAnd soon, we’ll strengthen the Violence Against Women Act that I first wrote three decades ago. It is important for us to show the nation that we can come together and do big things. \\n\\nSo tonight I’m offering a Unity Agenda for the Nation. Four big things we can do together. \\n\\nFirst, beat the opioid epidemic.', metadata={'source': '../../modules/state_of_the_union.txt', 'year': 2012})]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db.max_marginal_relevance_search(\n", + " \"What did the president say about Ketanji Brown Jackson?\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Delete dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "db.delete_dataset()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and if delete fails you can also force delete" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "DeepLake.force_delete_by_path(\"./my_deeplake\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deep Lake datasets on cloud (Activeloop, AWS, GCS, etc.) or in memory\n", + "By default, Deep Lake datasets are stored locally. To store them in memory, in the Deep Lake Managed DB, or in any object storage, you can provide the [corresponding path and credentials when creating the vector store](https://docs.activeloop.ai/storage-and-credentials/storage-options). Some paths require registration with Activeloop and creation of an API token that can be [retrieved here](https://app.activeloop.ai/)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "os.environ[\"ACTIVELOOP_TOKEN\"] = activeloop_token" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Your Deep Lake dataset has been successfully created!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset(path='hub://adilkhan/langchain_testing_python', tensors=['embedding', 'id', 'metadata', 'text'])\n", + "\n", + " tensor htype shape dtype compression\n", + " ------- ------- ------- ------- ------- \n", + " embedding embedding (42, 1536) float32 None \n", + " id text (42, 1) str None \n", + " metadata json (42, 1) str None \n", + " text text (42, 1) str None \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [] + } + ], + "source": [ + "# Embed and store the texts\n", + "username = \"\" # your username on app.activeloop.ai\n", + "dataset_path = f\"hub://{username}/langchain_testing_python\" # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n", + "\n", + "docs = text_splitter.split_documents(documents)\n", + "\n", + "embedding = OpenAIEmbeddings()\n", + "db = DeepLake(dataset_path=dataset_path, embedding=embeddings, overwrite=True)\n", + "ids = db.add_documents(docs)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tonight. I call on the Senate to: Pass the Freedom to Vote Act. Pass the John Lewis Voting Rights Act. And while you’re at it, pass the Disclose Act so Americans can know who is funding our elections. \n", + "\n", + "Tonight, I’d like to honor someone who has dedicated his life to serve this country: Justice Stephen Breyer—an Army veteran, Constitutional scholar, and retiring Justice of the United States Supreme Court. Justice Breyer, thank you for your service. \n", + "\n", + "One of the most serious constitutional responsibilities a President has is nominating someone to serve on the United States Supreme Court. \n", + "\n", + "And I did that 4 days ago, when I nominated Circuit Court of Appeals Judge Ketanji Brown Jackson. One of our nation’s top legal minds, who will continue Justice Breyer’s legacy of excellence.\n" + ] + } + ], + "source": [ + "query = \"What did the president say about Ketanji Brown Jackson\"\n", + "docs = db.similarity_search(query)\n", + "print(docs[0].page_content)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### `tensor_db` execution option " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to utilize Deep Lake's Managed Tensor Database, it is necessary to specify the runtime parameter as {'tensor_db': True} during the creation of the vector store. This configuration enables the execution of queries on the Managed Tensor Database, rather than on the client side. It should be noted that this functionality is not applicable to datasets stored locally or in-memory. In the event that a vector store has already been created outside of the Managed Tensor Database, it is possible to transfer it to the Managed Tensor Database by following the prescribed steps." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Your Deep Lake dataset has been successfully created!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "|" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])\n", + "\n", + " tensor htype shape dtype compression\n", + " ------- ------- ------- ------- ------- \n", + " embedding embedding (42, 1536) float32 None \n", + " id text (42, 1) str None \n", + " metadata json (42, 1) str None \n", + " text text (42, 1) str None \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "# Embed and store the texts\n", + "username = \"\" # your username on app.activeloop.ai\n", + "dataset_path = f\"hub://{username}/langchain_testing\"\n", + "\n", + "docs = text_splitter.split_documents(documents)\n", + "\n", + "embedding = OpenAIEmbeddings()\n", + "db = DeepLake(\n", + " dataset_path=dataset_path,\n", + " embedding=embeddings,\n", + " overwrite=True,\n", + " runtime={\"tensor_db\": True},\n", + ")\n", + "ids = db.add_documents(docs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### TQL Search" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Furthermore, the execution of queries is also supported within the similarity_search method, whereby the query can be specified utilizing Deep Lake's Tensor Query Language (TQL)." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "search_id = db.vectorstore.dataset.id[0].numpy()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'8a6ff326-3a85-11ee-b840-13905694aaaf'" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "search_id[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "docs = db.similarity_search(\n", + " query=None,\n", + " tql=f\"SELECT * WHERE id == '{search_id[0]}'\",\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])\n", + "\n", + " tensor htype shape dtype compression\n", + " ------- ------- ------- ------- ------- \n", + " embedding embedding (42, 1536) float32 None \n", + " id text (42, 1) str None \n", + " metadata json (42, 1) str None \n", + " text text (42, 1) str None \n" + ] + } + ], + "source": [ + "db.vectorstore.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Creating vector stores on AWS S3" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "s3://hub-2.0-datasets-n/langchain_test loaded successfully.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Evaluating ingest: 100%|██████████| 1/1 [00:10<00:00\n", + "\\" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset(path='s3://hub-2.0-datasets-n/langchain_test', tensors=['embedding', 'ids', 'metadata', 'text'])\n", + "\n", + " tensor htype shape dtype compression\n", + " ------- ------- ------- ------- ------- \n", + " embedding generic (4, 1536) float32 None \n", + " ids text (4, 1) str None \n", + " metadata json (4, 1) str None \n", + " text text (4, 1) str None \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + } + ], + "source": [ + "dataset_path = f\"s3://BUCKET/langchain_test\" # could be also ./local/path (much faster locally), hub://bucket/path/to/dataset, gcs://path/to/dataset, etc.\n", + "\n", + "embedding = OpenAIEmbeddings()\n", + "db = DeepLake.from_documents(\n", + " docs,\n", + " dataset_path=dataset_path,\n", + " embedding=embeddings,\n", + " overwrite=True,\n", + " creds={\n", + " \"aws_access_key_id\": os.environ[\"AWS_ACCESS_KEY_ID\"],\n", + " \"aws_secret_access_key\": os.environ[\"AWS_SECRET_ACCESS_KEY\"],\n", + " \"aws_session_token\": os.environ[\"AWS_SESSION_TOKEN\"], # Optional\n", + " },\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Deep Lake API\n", + "you can access the Deep Lake dataset at `db.vectorstore`" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset(path='hub://adilkhan/langchain_testing', tensors=['embedding', 'id', 'metadata', 'text'])\n", + "\n", + " tensor htype shape dtype compression\n", + " ------- ------- ------- ------- ------- \n", + " embedding embedding (42, 1536) float32 None \n", + " id text (42, 1) str None \n", + " metadata json (42, 1) str None \n", + " text text (42, 1) str None \n" + ] + } + ], + "source": [ + "# get structure of the dataset\n", + "db.vectorstore.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "# get embeddings numpy array\n", + "embeds = db.vectorstore.dataset.embedding.numpy()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Transfer local dataset to cloud\n", + "Copy already created dataset to the cloud. You can also transfer from cloud to local." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Copying dataset: 100%|██████████| 56/56 [00:38<00:00\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy\n", + "Your Deep Lake dataset has been successfully created!\n", + "The dataset is private so make sure you are logged in!\n" + ] + }, + { + "data": { + "text/plain": [ + "Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import deeplake\n", + "\n", + "username = \"davitbun\" # your username on app.activeloop.ai\n", + "source = f\"hub://{username}/langchain_testing\" # could be local, s3, gcs, etc.\n", + "destination = f\"hub://{username}/langchain_test_copy\" # could be local, s3, gcs, etc.\n", + "\n", + "deeplake.deepcopy(src=source, dest=destination, overwrite=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This dataset can be visualized in Jupyter Notebook by ds.visualize() or at https://app.activeloop.ai/davitbun/langchain_test_copy\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "hub://davitbun/langchain_test_copy loaded successfully.\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Deep Lake Dataset in hub://davitbun/langchain_test_copy already exists, loading from the storage\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])\n", + "\n", + " tensor htype shape dtype compression\n", + " ------- ------- ------- ------- ------- \n", + " embedding generic (4, 1536) float32 None \n", + " ids text (4, 1) str None \n", + " metadata json (4, 1) str None \n", + " text text (4, 1) str None \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Evaluating ingest: 100%|██████████| 1/1 [00:31<00:00\n", + "-" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset(path='hub://davitbun/langchain_test_copy', tensors=['embedding', 'ids', 'metadata', 'text'])\n", + "\n", + " tensor htype shape dtype compression\n", + " ------- ------- ------- ------- ------- \n", + " embedding generic (8, 1536) float32 None \n", + " ids text (8, 1) str None \n", + " metadata json (8, 1) str None \n", + " text text (8, 1) str None \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + " \r" + ] + }, + { + "data": { + "text/plain": [ + "['ad42f3fe-e188-11ed-b66d-41c5f7b85421',\n", + " 'ad42f3ff-e188-11ed-b66d-41c5f7b85421',\n", + " 'ad42f400-e188-11ed-b66d-41c5f7b85421',\n", + " 'ad42f401-e188-11ed-b66d-41c5f7b85421']" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "db = DeepLake(dataset_path=destination, embedding=embeddings)\n", + "db.add_documents(docs)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 }, - "nbformat": 4, - "nbformat_minor": 4 + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "vscode": { + "interpreter": { + "hash": "0b0bacaffd430edc3085253ee7ee1bcda9f76a5e66b369dda8ba68baa6d14ba7" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 } diff --git a/docs/extras/integrations/vectorstores/zep.ipynb b/docs/extras/integrations/vectorstores/zep.ipynb index 0354da3210..4456aece0b 100644 --- a/docs/extras/integrations/vectorstores/zep.ipynb +++ b/docs/extras/integrations/vectorstores/zep.ipynb @@ -167,7 +167,7 @@ "Tables necessary to determine the places of the planets are not less\r\n", "necessary than those for the sun, moon, and stars. Some notion of the\r\n", "number and complexity of these tables may be formed, when we state that\r\n", - "the positions of the two principal planets, (and these the most\r\n", + "the positions of the two principal planets, (and these are the most\r\n", "necessary for the navigator,) Jupiter and Saturn, require each not less\r\n", "than one hundred and sixteen tables. Yet it is not only necessary to\r\n", "predict the position of these bodies, but it is likewise expedient to -> 0.8998482592744614 \n", diff --git a/docs/extras/modules/agents/agent_types/openai_multi_functions_agent.ipynb b/docs/extras/modules/agents/agent_types/openai_multi_functions_agent.ipynb index d1dc5cdb94..b2b1885388 100644 --- a/docs/extras/modules/agents/agent_types/openai_multi_functions_agent.ipynb +++ b/docs/extras/modules/agents/agent_types/openai_multi_functions_agent.ipynb @@ -7,11 +7,13 @@ "source": [ "# OpenAI Multi Functions Agent\n", "\n", - "This notebook showcases using an agent that uses the OpenAI functions ability to respond to the prompts of the user using a Large Language Model\n", + "This notebook showcases using an agent that uses the OpenAI functions ability to respond to the prompts of the user using a Large Language Model.\n", "\n", - "Install openai,google-search-results packages which are required as the langchain packages call them internally\n", + "Install `openai`, `google-search-results` packages which are required as the LangChain packages call them internally.\n", "\n", - ">pip install openai google-search-results\n" + "```bash\n", + "pip install openai google-search-results\n", + "```\n" ] }, { @@ -32,10 +34,10 @@ "id": "86198d9c", "metadata": {}, "source": [ - "The agent is given ability to perform search functionalities with the respective tool\n", + "The agent is given the ability to perform search functionalities with the respective tool\n", "\n", - "SerpAPIWrapper:\n", - ">This initializes the SerpAPIWrapper for search functionality (search).\n" + "`SerpAPIWrapper`:\n", + ">This initializes the `SerpAPIWrapper` for search functionality (search).\n" ] }, { @@ -228,7 +230,7 @@ "source": [ "## Configuring max iteration behavior\n", "\n", - "To make sure that our agent doesn't get stuck in excessively long loops, we can set max_iterations. We can also set an early stopping method, which will determine our agent's behavior once the number of max iterations is hit. By default, the early stopping uses method `force` which just returns that constant string. Alternatively, you could specify method `generate` which then does one FINAL pass through the LLM to generate an output." + "To make sure that our agent doesn't get stuck in excessively long loops, we can set `max_iterations`. We can also set an early stopping method, which will determine our agent's behavior once the number of max iterations is hit. By default, the early stopping uses method `force` which just returns that constant string. Alternatively, you could specify method `generate` which then does one FINAL pass through the LLM to generate an output." ] }, { @@ -428,7 +430,7 @@ "id": "067a8d3e", "metadata": {}, "source": [ - "Notice that we never get around to looking up the weather the day before yesterday, due to hitting our max_iterations limit." + "Notice that we never get around to looking up the weather the day before yesterday, due to hitting our `max_iterations` limit." ] }, { diff --git a/docs/extras/modules/agents/agent_types/self_ask_with_search.ipynb b/docs/extras/modules/agents/agent_types/self_ask_with_search.ipynb index cdf17e547b..673a13eddb 100644 --- a/docs/extras/modules/agents/agent_types/self_ask_with_search.ipynb +++ b/docs/extras/modules/agents/agent_types/self_ask_with_search.ipynb @@ -5,9 +5,9 @@ "id": "0c3f1df8", "metadata": {}, "source": [ - "# Self ask with search\n", + "# Self-ask with search\n", "\n", - "This walkthrough showcases the Self Ask With Search chain." + "This walkthrough showcases the self-ask with search chain." ] }, { diff --git a/docs/extras/modules/agents/how_to/add_memory_openai_functions.ipynb b/docs/extras/modules/agents/how_to/add_memory_openai_functions.ipynb index 51815a069d..c69ce64976 100644 --- a/docs/extras/modules/agents/how_to/add_memory_openai_functions.ipynb +++ b/docs/extras/modules/agents/how_to/add_memory_openai_functions.ipynb @@ -7,24 +7,15 @@ "source": [ "# Add Memory to OpenAI Functions Agent\n", "\n", - "This notebook goes over how to add memory to OpenAI Functions agent." + "This notebook goes over how to add memory to an OpenAI Functions agent." ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "ac594f26", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/harrisonchase/.pyenv/versions/3.9.1/envs/langchain/lib/python3.9/site-packages/deeplake/util/check_latest_version.py:32: UserWarning: A newer version of deeplake (3.6.4) is available. It's recommended that you update to the latest version using `pip install -U deeplake`.\n", - " warnings.warn(\n" - ] - } - ], + "outputs": [], "source": [ "from langchain import (\n", " LLMMathChain,\n", diff --git a/docs/extras/modules/agents/how_to/agent_iter.ipynb b/docs/extras/modules/agents/how_to/agent_iter.ipynb index a7baf8a115..35a32f83b5 100644 --- a/docs/extras/modules/agents/how_to/agent_iter.ipynb +++ b/docs/extras/modules/agents/how_to/agent_iter.ipynb @@ -56,7 +56,7 @@ "source": [ "Define tools which provide:\n", "- The `n`th prime number (using a small subset for this example) \n", - "- The LLMMathChain to act as a calculator" + "- The `LLMMathChain` to act as a calculator" ] }, { diff --git a/docs/extras/modules/agents/how_to/agent_vectorstore.ipynb b/docs/extras/modules/agents/how_to/agent_vectorstore.ipynb index e849a7d9a6..7259982e94 100644 --- a/docs/extras/modules/agents/how_to/agent_vectorstore.ipynb +++ b/docs/extras/modules/agents/how_to/agent_vectorstore.ipynb @@ -7,9 +7,9 @@ "source": [ "# Combine agents and vector stores\n", "\n", - "This notebook covers how to combine agents and vectorstores. The use case for this is that you've ingested your data into a vectorstore and want to interact with it in an agentic manner.\n", + "This notebook covers how to combine agents and vector stores. The use case for this is that you've ingested your data into a vector store and want to interact with it in an agentic manner.\n", "\n", - "The recommended method for doing so is to create a RetrievalQA and then use that as a tool in the overall agent. Let's take a look at doing this below. You can do this with multiple different vectordbs, and use the agent as a way to route between them. There are two different ways of doing this - you can either let the agent use the vectorstores as normal tools, or you can set `return_direct=True` to really just use the agent as a router." + "The recommended method for doing so is to create a `RetrievalQA` and then use that as a tool in the overall agent. Let's take a look at doing this below. You can do this with multiple different vector DBs, and use the agent as a way to route between them. There are two different ways of doing this - you can either let the agent use the vector stores as normal tools, or you can set `return_direct=True` to really just use the agent as a router." ] }, { @@ -18,7 +18,7 @@ "id": "9b22020a", "metadata": {}, "source": [ - "## Create the Vectorstore" + "## Create the vector store" ] }, { @@ -419,9 +419,9 @@ "id": "49a0cbbe", "metadata": {}, "source": [ - "## Multi-Hop vectorstore reasoning\n", + "## Multi-Hop vector store reasoning\n", "\n", - "Because vectorstores are easily usable as tools in agents, it is easy to use answer multi-hop questions that depend on vectorstores using the existing agent framework" + "Because vector stores are easily usable as tools in agents, it is easy to use answer multi-hop questions that depend on vector stores using the existing agent framework." ] }, { diff --git a/docs/extras/modules/agents/how_to/async_agent.ipynb b/docs/extras/modules/agents/how_to/async_agent.ipynb index aa8096f134..ac9d0695fc 100644 --- a/docs/extras/modules/agents/how_to/async_agent.ipynb +++ b/docs/extras/modules/agents/how_to/async_agent.ipynb @@ -9,7 +9,7 @@ "\n", "LangChain provides async support for Agents by leveraging the [asyncio](https://docs.python.org/3/library/asyncio.html) library.\n", "\n", - "Async methods are currently supported for the following `Tools`: [`GoogleSerperAPIWrapper`](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/utilities/google_serper.py), [`SerpAPIWrapper`](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/utilities/serpapi.py), [`LLMMathChain`](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/llm_math/base.py) and [`Qdrant`](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/vectorstores/qdrant.py). Async support for other agent tools are on the roadmap.\n", + "Async methods are currently supported for the following `Tool`s: [`GoogleSerperAPIWrapper`](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/utilities/google_serper.py), [`SerpAPIWrapper`](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/utilities/serpapi.py), [`LLMMathChain`](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/chains/llm_math/base.py) and [`Qdrant`](https://github.com/langchain-ai/langchain/blob/master/libs/langchain/langchain/vectorstores/qdrant.py). Async support for other agent tools are on the roadmap.\n", "\n", "For `Tool`s that have a `coroutine` implemented (the four mentioned above), the `AgentExecutor` will `await` them directly. Otherwise, the `AgentExecutor` will call the `Tool`'s `func` via `asyncio.get_event_loop().run_in_executor` to avoid blocking the main runloop.\n", "\n", @@ -21,7 +21,7 @@ "id": "97800378-cc34-4283-9bd0-43f336bc914c", "metadata": {}, "source": [ - "## Serial vs. Concurrent Execution\n", + "## Serial vs. concurrent execution\n", "\n", "In this example, we kick off agents to answer some questions serially vs. concurrently. You can see that concurrent execution significantly speeds this up." ] diff --git a/docs/extras/modules/agents/how_to/custom-functions-with-openai-functions-agent.ipynb b/docs/extras/modules/agents/how_to/custom-functions-with-openai-functions-agent.ipynb index bca6afa97b..5505bd4eef 100644 --- a/docs/extras/modules/agents/how_to/custom-functions-with-openai-functions-agent.ipynb +++ b/docs/extras/modules/agents/how_to/custom-functions-with-openai-functions-agent.ipynb @@ -17,9 +17,11 @@ "id": "LFKylC3CPtTl" }, "source": [ - "Install libraries which are required to run this example notebook\n", + "Install libraries which are required to run this example notebook:\n", "\n", - "`pip install -q openai langchain yfinance`" + "```bash\n", + "pip install -q openai langchain yfinance\n", + "```\n" ] }, { diff --git a/docs/extras/modules/agents/how_to/custom_agent_with_tool_retrieval.ipynb b/docs/extras/modules/agents/how_to/custom_agent_with_tool_retrieval.ipynb index 89a1fba88d..5d8bbd372d 100644 --- a/docs/extras/modules/agents/how_to/custom_agent_with_tool_retrieval.ipynb +++ b/docs/extras/modules/agents/how_to/custom_agent_with_tool_retrieval.ipynb @@ -11,7 +11,7 @@ "\n", "The novel idea introduced in this notebook is the idea of using retrieval to select the set of tools to use to answer an agent query. This is useful when you have many many tools to select from. You cannot put the description of all the tools in the prompt (because of context length issues) so instead you dynamically select the N tools you do want to consider using at run time.\n", "\n", - "In this notebook we will create a somewhat contrieved example. We will have one legitimate tool (search) and then 99 fake tools which are just nonsense. We will then add a step in the prompt template that takes the user input and retrieves tool relevant to the query." + "In this notebook we will create a somewhat contrived example. We will have one legitimate tool (search) and then 99 fake tools which are just nonsense. We will then add a step in the prompt template that takes the user input and retrieves tool relevant to the query." ] }, { @@ -51,7 +51,7 @@ "source": [ "## Set up tools\n", "\n", - "We will create one legitimate tool (search) and then 99 fake tools" + "We will create one legitimate tool (search) and then 99 fake tools." ] }, { @@ -92,7 +92,7 @@ "source": [ "## Tool Retriever\n", "\n", - "We will use a vectorstore to create embeddings for each tool description. Then, for an incoming query we can create embeddings for that query and do a similarity search for relevant tools." + "We will use a vector store to create embeddings for each tool description. Then, for an incoming query we can create embeddings for that query and do a similarity search for relevant tools." ] }, { @@ -206,7 +206,7 @@ "id": "2e7a075c", "metadata": {}, "source": [ - "## Prompt Template\n", + "## Prompt template\n", "\n", "The prompt template is pretty standard, because we're not actually changing that much logic in the actual prompt template, but rather we are just changing how retrieval is done." ] @@ -245,7 +245,7 @@ "id": "1583acdc", "metadata": {}, "source": [ - "The custom prompt template now has the concept of a tools_getter, which we call on the input to select the tools to use" + "The custom prompt template now has the concept of a `tools_getter`, which we call on the input to select the tools to use." ] }, { @@ -308,7 +308,7 @@ "id": "ef3a1af3", "metadata": {}, "source": [ - "## Output Parser\n", + "## Output parser\n", "\n", "The output parser is unchanged from the previous notebook, since we are not changing anything about the output format." ] @@ -360,7 +360,7 @@ "source": [ "## Set up LLM, stop sequence, and the agent\n", "\n", - "Also the same as the previous notebook" + "Also the same as the previous notebook." ] }, { diff --git a/docs/extras/modules/agents/how_to/custom_mrkl_agent.ipynb b/docs/extras/modules/agents/how_to/custom_mrkl_agent.ipynb index ee7eb33a01..9892f2450c 100644 --- a/docs/extras/modules/agents/how_to/custom_mrkl_agent.ipynb +++ b/docs/extras/modules/agents/how_to/custom_mrkl_agent.ipynb @@ -10,13 +10,13 @@ "This notebook goes through how to create your own custom MRKL agent.\n", "\n", "A MRKL agent consists of three parts:\n", - " \n", - " - Tools: The tools the agent has available to use.\n", - " - LLMChain: The LLMChain that produces the text that is parsed in a certain way to determine which action to take.\n", - " - The agent class itself: this parses the output of the LLMChain to determine which action to take.\n", + "\n", + "- Tools: The tools the agent has available to use.\n", + "- `LLMChain`: The `LLMChain` that produces the text that is parsed in a certain way to determine which action to take.\n", + "- The agent class itself: this parses the output of the `LLMChain` to determine which action to take.\n", " \n", " \n", - "In this notebook we walk through how to create a custom MRKL agent by creating a custom LLMChain." + "In this notebook we walk through how to create a custom MRKL agent by creating a custom `LLMChain`." ] }, { @@ -26,16 +26,16 @@ "source": [ "### Custom LLMChain\n", "\n", - "The first way to create a custom agent is to use an existing Agent class, but use a custom LLMChain. This is the simplest way to create a custom Agent. It is highly recommended that you work with the `ZeroShotAgent`, as at the moment that is by far the most generalizable one. \n", + "The first way to create a custom agent is to use an existing Agent class, but use a custom `LLMChain`. This is the simplest way to create a custom Agent. It is highly recommended that you work with the `ZeroShotAgent`, as at the moment that is by far the most generalizable one. \n", "\n", - "Most of the work in creating the custom LLMChain comes down to the prompt. Because we are using an existing agent class to parse the output, it is very important that the prompt say to produce text in that format. Additionally, we currently require an `agent_scratchpad` input variable to put notes on previous actions and observations. This should almost always be the final part of the prompt. However, besides those instructions, you can customize the prompt as you wish.\n", + "Most of the work in creating the custom `LLMChain` comes down to the prompt. Because we are using an existing agent class to parse the output, it is very important that the prompt say to produce text in that format. Additionally, we currently require an `agent_scratchpad` input variable to put notes on previous actions and observations. This should almost always be the final part of the prompt. However, besides those instructions, you can customize the prompt as you wish.\n", "\n", "To ensure that the prompt contains the appropriate instructions, we will utilize a helper method on that class. The helper method for the `ZeroShotAgent` takes the following arguments:\n", "\n", - "- tools: List of tools the agent will have access to, used to format the prompt.\n", - "- prefix: String to put before the list of tools.\n", - "- suffix: String to put after the list of tools.\n", - "- input_variables: List of input variables the final prompt will expect.\n", + "- `tools`: List of tools the agent will have access to, used to format the prompt.\n", + "- `prefix`: String to put before the list of tools.\n", + "- `suffix`: String to put after the list of tools.\n", + "- `input_variables`: List of input variables the final prompt will expect.\n", "\n", "For this exercise, we will give our agent access to Google Search, and we will customize it in that we will have it answer as a pirate." ] diff --git a/docs/extras/modules/agents/how_to/custom_multi_action_agent.ipynb b/docs/extras/modules/agents/how_to/custom_multi_action_agent.ipynb index dd5615c0ca..1dedebc194 100644 --- a/docs/extras/modules/agents/how_to/custom_multi_action_agent.ipynb +++ b/docs/extras/modules/agents/how_to/custom_multi_action_agent.ipynb @@ -10,9 +10,9 @@ "This notebook goes through how to create your own custom agent.\n", "\n", "An agent consists of two parts:\n", - " \n", - " - Tools: The tools the agent has available to use.\n", - " - The agent class itself: this decides which action to take.\n", + "\n", + "- Tools: The tools the agent has available to use.\n", + "- The agent class itself: this decides which action to take.\n", " \n", " \n", "In this notebook we walk through how to create a custom agent that predicts/takes multiple steps at a time." diff --git a/docs/extras/modules/agents/how_to/handle_parsing_errors.ipynb b/docs/extras/modules/agents/how_to/handle_parsing_errors.ipynb index f95a771dd3..c56c1b36ae 100644 --- a/docs/extras/modules/agents/how_to/handle_parsing_errors.ipynb +++ b/docs/extras/modules/agents/how_to/handle_parsing_errors.ipynb @@ -7,7 +7,7 @@ "source": [ "# Handle parsing errors\n", "\n", - "Occasionally the LLM cannot determine what step to take because it outputs format in incorrect form to be handled by the output parser. In this case, by default the agent errors. But you can easily control this functionality with `handle_parsing_errors`! Let's explore how." + "Occasionally the LLM cannot determine what step to take because its outputs are not correctly formatted to be handled by the output parser. In this case, by default the agent errors. But you can easily control this functionality with `handle_parsing_errors`! Let's explore how." ] }, { @@ -130,7 +130,7 @@ "source": [ "## Default error handling\n", "\n", - "Handle errors with `Invalid or incomplete response`" + "Handle errors with `Invalid or incomplete response`:" ] }, { @@ -202,9 +202,9 @@ "id": "6613cc9c", "metadata": {}, "source": [ - "## Custom Error Message\n", + "## Custom error message\n", "\n", - "You can easily customize the message to use when there are parsing errors" + "You can easily customize the message to use when there are parsing errors." ] }, { diff --git a/docs/extras/modules/agents/how_to/intermediate_steps.ipynb b/docs/extras/modules/agents/how_to/intermediate_steps.ipynb index 6bc5c73cfa..b26ba42750 100644 --- a/docs/extras/modules/agents/how_to/intermediate_steps.ipynb +++ b/docs/extras/modules/agents/how_to/intermediate_steps.ipynb @@ -47,7 +47,7 @@ "id": "1d329c3d", "metadata": {}, "source": [ - "Initialize the agent with `return_intermediate_steps=True`" + "Initialize the agent with `return_intermediate_steps=True`:" ] }, { diff --git a/docs/extras/modules/agents/how_to/max_iterations.ipynb b/docs/extras/modules/agents/how_to/max_iterations.ipynb index 23ed4266a4..1fbae75b4b 100644 --- a/docs/extras/modules/agents/how_to/max_iterations.ipynb +++ b/docs/extras/modules/agents/how_to/max_iterations.ipynb @@ -54,7 +54,7 @@ "id": "5e9d92c2", "metadata": {}, "source": [ - "First, let's do a run with a normal agent to show what would happen without this parameter. For this example, we will use a specifically crafter adversarial example that tries to trick it into continuing forever.\n", + "First, let's do a run with a normal agent to show what would happen without this parameter. For this example, we will use a specifically crafted adversarial example that tries to trick it into continuing forever.\n", "\n", "Try running the cell below and see what happens!" ] @@ -203,7 +203,7 @@ "id": "0f7a80fb", "metadata": {}, "source": [ - "By default, the early stopping uses method `force` which just returns that constant string. Alternatively, you could specify method `generate` which then does one FINAL pass through the LLM to generate an output." + "By default, the early stopping uses the `force` method which just returns that constant string. Alternatively, you could specify the `generate` method which then does one FINAL pass through the LLM to generate an output." ] }, { diff --git a/docs/extras/modules/agents/how_to/max_time_limit.ipynb b/docs/extras/modules/agents/how_to/max_time_limit.ipynb index 03201ee486..8453465946 100644 --- a/docs/extras/modules/agents/how_to/max_time_limit.ipynb +++ b/docs/extras/modules/agents/how_to/max_time_limit.ipynb @@ -54,7 +54,7 @@ "id": "5e9d92c2", "metadata": {}, "source": [ - "First, let's do a run with a normal agent to show what would happen without this parameter. For this example, we will use a specifically crafter adversarial example that tries to trick it into continuing forever.\n", + "First, let's do a run with a normal agent to show what would happen without this parameter. For this example, we will use a specifically crafted adversarial example that tries to trick it into continuing forever.\n", "\n", "Try running the cell below and see what happens!" ] @@ -199,7 +199,7 @@ "id": "0f7a80fb", "metadata": {}, "source": [ - "By default, the early stopping uses method `force` which just returns that constant string. Alternatively, you could specify method `generate` which then does one FINAL pass through the LLM to generate an output." + "By default, the early stopping uses the `force` method which just returns that constant string. Alternatively, you could specify the `generate` method which then does one FINAL pass through the LLM to generate an output." ] }, { diff --git a/docs/extras/modules/agents/how_to/sharedmemory_for_tools.ipynb b/docs/extras/modules/agents/how_to/sharedmemory_for_tools.ipynb index 5e7cd9dfdd..66bd13f7cf 100644 --- a/docs/extras/modules/agents/how_to/sharedmemory_for_tools.ipynb +++ b/docs/extras/modules/agents/how_to/sharedmemory_for_tools.ipynb @@ -7,12 +7,12 @@ "source": [ "# Shared memory across agents and tools\n", "\n", - "This notebook goes over adding memory to **both** of an Agent and its tools. Before going through this notebook, please walk through the following notebooks, as this will build on top of both of them:\n", + "This notebook goes over adding memory to **both** an Agent and its tools. Before going through this notebook, please walk through the following notebooks, as this will build on top of both of them:\n", "\n", "- [Adding memory to an LLM Chain](/docs/modules/memory/integrations/adding_memory.html)\n", "- [Custom Agents](/docs/modules/agents/how_to/custom_agent.html)\n", "\n", - "We are going to create a custom Agent. The agent has access to a conversation memory, search tool, and a summarization tool. And, the summarization tool also needs access to the conversation memory." + "We are going to create a custom Agent. The agent has access to a conversation memory, search tool, and a summarization tool. The summarization tool also needs access to the conversation memory." ] }, { @@ -102,7 +102,7 @@ "id": "0021675b", "metadata": {}, "source": [ - "We can now construct the LLMChain, with the Memory object, and then create the agent." + "We can now construct the `LLMChain`, with the Memory object, and then create the agent." ] }, { diff --git a/docs/extras/modules/agents/how_to/streaming_stdout_final_only.ipynb b/docs/extras/modules/agents/how_to/streaming_stdout_final_only.ipynb index 4ec4983537..1d9796ebe0 100644 --- a/docs/extras/modules/agents/how_to/streaming_stdout_final_only.ipynb +++ b/docs/extras/modules/agents/how_to/streaming_stdout_final_only.ipynb @@ -187,7 +187,7 @@ "}\n", "`\n", "\n", - "and you don't only want the action_input to be streamed, but the entire JSON." + "and you don't only want the `action_input` to be streamed, but the entire JSON." ] } ], diff --git a/docs/extras/modules/agents/how_to/use_toolkits_with_openai_functions.ipynb b/docs/extras/modules/agents/how_to/use_toolkits_with_openai_functions.ipynb index 78a70c47d1..8fba48771f 100644 --- a/docs/extras/modules/agents/how_to/use_toolkits_with_openai_functions.ipynb +++ b/docs/extras/modules/agents/how_to/use_toolkits_with_openai_functions.ipynb @@ -36,7 +36,7 @@ "id": "1b7ee35f", "metadata": {}, "source": [ - "Load the toolkit" + "Load the toolkit:" ] }, { @@ -55,7 +55,7 @@ "id": "203fa80a", "metadata": {}, "source": [ - "Set a system message specific to that toolkit" + "Set a system message specific to that toolkit:" ] }, { diff --git a/docs/extras/modules/agents/tools/custom_tools.ipynb b/docs/extras/modules/agents/tools/custom_tools.ipynb index ecd1245046..11e629ffe5 100644 --- a/docs/extras/modules/agents/tools/custom_tools.ipynb +++ b/docs/extras/modules/agents/tools/custom_tools.ipynb @@ -10,10 +10,10 @@ "\n", "When constructing your own agent, you will need to provide it with a list of Tools that it can use. Besides the actual function that is called, the Tool consists of several components:\n", "\n", - "- name (str), is required and must be unique within a set of tools provided to an agent\n", - "- description (str), is optional but recommended, as it is used by an agent to determine tool use\n", - "- return_direct (bool), defaults to False\n", - "- args_schema (Pydantic BaseModel), is optional but recommended, can be used to provide more information (e.g., few-shot examples) or validation for expected parameters.\n", + "- `name` (str), is required and must be unique within a set of tools provided to an agent\n", + "- `description` (str), is optional but recommended, as it is used by an agent to determine tool use\n", + "- `return_direct` (bool), defaults to False\n", + "- `args_schema` (Pydantic BaseModel), is optional but recommended, can be used to provide more information (e.g., few-shot examples) or validation for expected parameters.\n", "\n", "\n", "There are two main ways to define a tool, we will cover both in the example below." @@ -116,7 +116,7 @@ "id": "e9b560f7", "metadata": {}, "source": [ - "You can also define a custom `args_schema`` to provide more information about inputs." + "You can also define a custom `args_schema` to provide more information about inputs." ] }, { @@ -442,7 +442,7 @@ "id": "de34a6a3", "metadata": {}, "source": [ - "You can also provide `args_schema` to provide more information about the argument" + "You can also provide `args_schema` to provide more information about the argument." ] }, { @@ -533,7 +533,7 @@ "source": [ "## Subclassing the BaseTool\n", "\n", - "The BaseTool automatically infers the schema from the _run method's signature." + "The BaseTool automatically infers the schema from the `_run` method's signature." ] }, { @@ -840,7 +840,7 @@ "metadata": {}, "source": [ "## Using tools to return directly\n", - "Often, it can be desirable to have a tool output returned directly to the user, if it’s called. You can do this easily with LangChain by setting the return_direct flag for a tool to be True." + "Often, it can be desirable to have a tool output returned directly to the user, if it’s called. You can do this easily with LangChain by setting the `return_direct` flag for a tool to be True." ] }, { diff --git a/docs/extras/modules/agents/tools/human_approval.ipynb b/docs/extras/modules/agents/tools/human_approval.ipynb index 2a11d7f749..b0764feb0f 100644 --- a/docs/extras/modules/agents/tools/human_approval.ipynb +++ b/docs/extras/modules/agents/tools/human_approval.ipynb @@ -7,11 +7,11 @@ "source": [ "# Human-in-the-loop Tool Validation\n", "\n", - "This walkthrough demonstrates how to add Human validation to any Tool. We'll do this using the `HumanApprovalCallbackhandler`.\n", + "This walkthrough demonstrates how to add human validation to any Tool. We'll do this using the `HumanApprovalCallbackhandler`.\n", "\n", - "Let's suppose we need to make use of the ShellTool. Adding this tool to an automated flow poses obvious risks. Let's see how we could enforce manual human approval of inputs going into this tool.\n", + "Let's suppose we need to make use of the `ShellTool`. Adding this tool to an automated flow poses obvious risks. Let's see how we could enforce manual human approval of inputs going into this tool.\n", "\n", - "**Note**: We generally recommend against using the ShellTool. There's a lot of ways to misuse it, and it's not required for most use cases. We employ it here only for demonstration purposes." + "**Note**: We generally recommend against using the `ShellTool`. There's a lot of ways to misuse it, and it's not required for most use cases. We employ it here only for demonstration purposes." ] }, { @@ -60,7 +60,7 @@ "metadata": {}, "source": [ "## Adding Human Approval\n", - "Adding the default HumanApprovalCallbackHandler to the tool will make it so that a user has to manually approve every input to the tool before the command is actually executed." + "Adding the default `HumanApprovalCallbackHandler` to the tool will make it so that a user has to manually approve every input to the tool before the command is actually executed." ] }, { diff --git a/docs/extras/modules/agents/tools/multi_input_tool.ipynb b/docs/extras/modules/agents/tools/multi_input_tool.ipynb index ff93597529..dad96af6cb 100644 --- a/docs/extras/modules/agents/tools/multi_input_tool.ipynb +++ b/docs/extras/modules/agents/tools/multi_input_tool.ipynb @@ -139,7 +139,7 @@ "source": [ "## Multi-Input Tools with a string format\n", "\n", - "An alternative to the structured tool would be to use the regular `Tool` class and accept a single string. The tool would then have to handle the parsing logic to extract the relavent values from the text, which tightly couples the tool representation to the agent prompt. This is still useful if the underlying language model can't reliabl generate structured schema. \n", + "An alternative to the structured tool would be to use the regular `Tool` class and accept a single string. The tool would then have to handle the parsing logic to extract the relavent values from the text, which tightly couples the tool representation to the agent prompt. This is still useful if the underlying language model can't reliably generate structured schema. \n", "\n", "Let's take the multiplication function as an example. In order to use this, we will tell the agent to generate the \"Action Input\" as a comma-separated list of length two. We will then write a thin wrapper that takes a string, splits it into two around a comma, and passes both parsed sides as integers to the multiplication function." ] diff --git a/docs/extras/modules/callbacks/async_callbacks.ipynb b/docs/extras/modules/callbacks/async_callbacks.ipynb index 66a33ce223..d58783a755 100644 --- a/docs/extras/modules/callbacks/async_callbacks.ipynb +++ b/docs/extras/modules/callbacks/async_callbacks.ipynb @@ -9,7 +9,7 @@ "\n", "If you are planning to use the async API, it is recommended to use `AsyncCallbackHandler` to avoid blocking the runloop. \n", "\n", - "**Advanced** if you use a sync `CallbackHandler` while using an async method to run your llm/chain/tool/agent, it will still work. However, under the hood, it will be called with [`run_in_executor`](https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor) which can cause issues if your `CallbackHandler` is not thread-safe." + "**Advanced** if you use a sync `CallbackHandler` while using an async method to run your LLM / Chain / Tool / Agent, it will still work. However, under the hood, it will be called with [`run_in_executor`](https://docs.python.org/3/library/asyncio-eventloop.html#asyncio.loop.run_in_executor) which can cause issues if your `CallbackHandler` is not thread-safe." ] }, { diff --git a/docs/extras/modules/callbacks/tags.mdx b/docs/extras/modules/callbacks/tags.mdx index 87d9b7beb0..84eb1a9bab 100644 --- a/docs/extras/modules/callbacks/tags.mdx +++ b/docs/extras/modules/callbacks/tags.mdx @@ -1,3 +1,3 @@ # Tags -You can add tags to your callbacks by passing a `tags` argument to the `call()`/`run()`/`apply()` methods. This is useful for filtering your logs, e.g. if you want to log all requests made to a specific LLMChain, you can add a tag, and then filter your logs by that tag. You can pass tags to both constructor and request callbacks, see the examples above for details. These tags are then passed to the `tags` argument of the "start" callback methods, ie. `on_llm_start`, `on_chat_model_start`, `on_chain_start`, `on_tool_start`. +You can add tags to your callbacks by passing a `tags` argument to the `call()`/`run()`/`apply()` methods. This is useful for filtering your logs, e.g. if you want to log all requests made to a specific `LLMChain`, you can add a tag, and then filter your logs by that tag. You can pass tags to both constructor and request callbacks, see the examples above for details. These tags are then passed to the `tags` argument of the "start" callback methods, ie. `on_llm_start`, `on_chat_model_start`, `on_chain_start`, `on_tool_start`. diff --git a/docs/extras/modules/chains/foundational/router.ipynb b/docs/extras/modules/chains/foundational/router.ipynb index 22141ac69e..36609bea14 100644 --- a/docs/extras/modules/chains/foundational/router.ipynb +++ b/docs/extras/modules/chains/foundational/router.ipynb @@ -11,8 +11,8 @@ "\n", "Router chains are made up of two components:\n", "\n", - "- The RouterChain itself (responsible for selecting the next chain to call)\n", - "- destination_chains: chains that the router chain can route to\n", + "- The `RouterChain` itself (responsible for selecting the next chain to call)\n", + "- `destination_chains`: chains that the router chain can route to\n", "\n", "\n", "In this notebook, we will focus on the different types of routing chains. We will show these routing chains used in a `MultiPromptChain` to create a question-answering chain that selects the prompt which is most relevant for a given question, and then answers the question using that prompt." @@ -241,7 +241,7 @@ "source": [ "## EmbeddingRouterChain\n", "\n", - "The EmbeddingRouterChain uses embeddings and similarity to route between destination chains." + "The `EmbeddingRouterChain` uses embeddings and similarity to route between destination chains." ] }, { diff --git a/docs/extras/modules/chains/foundational/transformation.ipynb b/docs/extras/modules/chains/foundational/transformation.ipynb index eb896ab7aa..9114ab7359 100644 --- a/docs/extras/modules/chains/foundational/transformation.ipynb +++ b/docs/extras/modules/chains/foundational/transformation.ipynb @@ -9,7 +9,7 @@ "\n", "This notebook showcases using a generic transformation chain.\n", "\n", - "As an example, we will create a dummy transformation that takes in a super long text, filters the text to only the first 3 paragraphs, and then passes that into an LLMChain to summarize those." + "As an example, we will create a dummy transformation that takes in a super long text, filters the text to only the first 3 paragraphs, and then passes that into an `LLMChain` to summarize those." ] }, { diff --git a/docs/extras/modules/chains/how_to/openai_functions.ipynb b/docs/extras/modules/chains/how_to/openai_functions.ipynb index 16b2df1291..dbb018a5df 100644 --- a/docs/extras/modules/chains/how_to/openai_functions.ipynb +++ b/docs/extras/modules/chains/how_to/openai_functions.ipynb @@ -36,7 +36,7 @@ "metadata": {}, "source": [ "## Getting structured outputs\n", - "We can take advantage of OpenAI functions to try and force the model to return a particular kind of structured output. We'll use the `create_structured_output_chain` to create our chain, which takes the desired structured output either as a Pydantic class or as JsonSchema.\n", + "We can take advantage of OpenAI functions to try and force the model to return a particular kind of structured output. We'll use `create_structured_output_chain` to create our chain, which takes the desired structured output either as a Pydantic class or as JsonSchema.\n", "\n", "See here for relevant [reference docs](https://api.python.langchain.com/en/latest/chains/langchain.chains.openai_functions.base.create_structured_output_chain.html)." ] @@ -178,7 +178,7 @@ "source": [ "### Using JsonSchema\n", "\n", - "We can also pass in JsonSchema instead of Pydantic classes to specify the desired structure. When we do this, our chain will output json corresponding to the properties described in the JsonSchema, instead of a Pydantic class." + "We can also pass in JsonSchema instead of Pydantic classes to specify the desired structure. When we do this, our chain will output JSON corresponding to the properties described in the JsonSchema, instead of a Pydantic class." ] }, { @@ -409,7 +409,7 @@ "id": "403ea5dd", "metadata": {}, "source": [ - "If we pass in multiple Python functions or OpenAI functions, then the returned output will be of the form\n", + "If we pass in multiple Python functions or OpenAI functions, then the returned output will be of the form:\n", "```python\n", "{\"name\": \"<>\", \"arguments\": {<>}}\n", "```" @@ -471,7 +471,7 @@ "id": "5f93686b", "metadata": {}, "source": [ - "## Other Chains using OpenAI functions\n", + "## Other Chains using OpenAI functions\n", "\n", "There are a number of more specific chains that use OpenAI functions.\n", "- [Extraction](/docs/modules/chains/additional/extraction): very similar to structured output chain, intended for information/entity extraction specifically.\n", diff --git a/docs/extras/modules/chains/how_to/serialization.ipynb b/docs/extras/modules/chains/how_to/serialization.ipynb index 1906b506d4..409d495fa0 100644 --- a/docs/extras/modules/chains/how_to/serialization.ipynb +++ b/docs/extras/modules/chains/how_to/serialization.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Serialization\n", - "This notebook covers how to serialize chains to and from disk. The serialization format we use is json or yaml. Currently, only some chains support this type of serialization. We will grow the number of supported chains over time.\n" + "This notebook covers how to serialize chains to and from disk. The serialization format we use is JSON or YAML. Currently, only some chains support this type of serialization. We will grow the number of supported chains over time.\n" ] }, { @@ -15,7 +15,7 @@ "metadata": {}, "source": [ "## Saving a chain to disk\n", - "First, let's go over how to save a chain to disk. This can be done with the `.save` method, and specifying a file path with a json or yaml extension." + "First, let's go over how to save a chain to disk. This can be done with the `.save` method, and specifying a file path with a `.json` or `.yaml` extension." ] }, { @@ -49,7 +49,7 @@ "id": "ea82665d", "metadata": {}, "source": [ - "Let's now take a look at what's inside this saved file" + "Let's now take a look at what's inside this saved file:" ] }, { @@ -167,7 +167,7 @@ "metadata": {}, "source": [ "## Saving components separately\n", - "In the above example, we can see that the prompt and llm configuration information is saved in the same json as the overall chain. Alternatively, we can split them up and save them separately. This is often useful to make the saved components more modular. In order to do this, we just need to specify `llm_path` instead of the `llm` component, and `prompt_path` instead of the `prompt` component." + "In the above example, we can see that the prompt and LLM configuration information is saved in the same JSON as the overall chain. Alternatively, we can split them up and save them separately. This is often useful to make the saved components more modular. In order to do this, we just need to specify `llm_path` instead of the `llm` component, and `prompt_path` instead of the `prompt` component." ] }, { @@ -296,7 +296,7 @@ "id": "662731c0", "metadata": {}, "source": [ - "We can then load it in the same way" + "We can then load it in the same way:" ] }, { diff --git a/docs/extras/modules/data_connection/document_transformers/post_retrieval/long_context_reorder.ipynb b/docs/extras/modules/data_connection/document_transformers/post_retrieval/long_context_reorder.ipynb index ac6cc006c4..7a67dd3c5f 100644 --- a/docs/extras/modules/data_connection/document_transformers/post_retrieval/long_context_reorder.ipynb +++ b/docs/extras/modules/data_connection/document_transformers/post_retrieval/long_context_reorder.ipynb @@ -9,7 +9,7 @@ "# Lost in the middle: The problem with long contexts\n", "\n", "No matter the architecture of your model, there is a substantial performance degradation when you include 10+ retrieved documents.\n", - "In brief: When models must access relevant information in the middle of long contexts, then tend to ignore the provided documents.\n", + "In brief: When models must access relevant information in the middle of long contexts, they tend to ignore the provided documents.\n", "See: https://arxiv.org/abs/2307.03172\n", "\n", "To avoid this issue you can re-order documents after retrieval to avoid performance degradation." diff --git a/docs/extras/modules/data_connection/document_transformers/text_splitters/markdown_header_metadata.ipynb b/docs/extras/modules/data_connection/document_transformers/text_splitters/markdown_header_metadata.ipynb index 88c89cd121..616fd82ad0 100644 --- a/docs/extras/modules/data_connection/document_transformers/text_splitters/markdown_header_metadata.ipynb +++ b/docs/extras/modules/data_connection/document_transformers/text_splitters/markdown_header_metadata.ipynb @@ -17,17 +17,7 @@ "When a full paragraph or document is embedded, the embedding process considers both the overall context and the relationships between the sentences and phrases within the text. This can result in a more comprehensive vector representation that captures the broader meaning and themes of the text.\n", "```\n", " \n", - "As mentioned, chunking often aims to keep text with common context together.\n", - "\n", - "With this in mind, we might want to specifically honor the structure of the document itself.\n", - "\n", - "For example, a markdown file is organized by headers.\n", - "\n", - "Creating chunks within specific header groups is an intuitive idea.\n", - "\n", - "To address this challenge, we can use `MarkdownHeaderTextSplitter`.\n", - "\n", - "This will split a markdown file by a specified set of headers. \n", + "As mentioned, chunking often aims to keep text with common context together. With this in mind, we might want to specifically honor the structure of the document itself. For example, a markdown file is organized by headers. Creating chunks within specific header groups is an intuitive idea. To address this challenge, we can use `MarkdownHeaderTextSplitter`. This will split a markdown file by a specified set of headers. \n", "\n", "For example, if we want to split this markdown:\n", "```\n", diff --git a/docs/extras/modules/data_connection/document_transformers/text_splitters/split_by_token.ipynb b/docs/extras/modules/data_connection/document_transformers/text_splitters/split_by_token.ipynb index 1a99e3c417..aa02061b5a 100644 --- a/docs/extras/modules/data_connection/document_transformers/text_splitters/split_by_token.ipynb +++ b/docs/extras/modules/data_connection/document_transformers/text_splitters/split_by_token.ipynb @@ -22,8 +22,8 @@ "\n", "We can use it to estimate tokens used. It will probably be more accurate for the OpenAI models.\n", "\n", - "1. How the text is split: by character passed in\n", - "2. How the chunk size is measured: by `tiktoken` tokenizer" + "1. How the text is split: by character passed in.\n", + "2. How the chunk size is measured: by `tiktoken` tokenizer." ] }, { @@ -91,7 +91,9 @@ "id": "de5b6a6e", "metadata": {}, "source": [ - "We can also load a tiktoken splitter directly" + "Note that if we use `CharacterTextSplitter.from_tiktoken_encoder`, text is only split by `CharacterTextSplitter` and `tiktoken` tokenizer is used to merge splits. It means that split can be larger than chunk size measured by `tiktoken` tokenizer. We can use `RecursiveCharacterTextSplitter.from_tiktoken_encoder` to make sure splits are not larger than chunk size of tokens allowed by the language model, where each split will be recursively split if it has a larger size.\n", + "\n", + "We can also load a tiktoken splitter directly, which ensure each split is smaller than chunk size." ] }, { @@ -120,8 +122,8 @@ "\n", "Another alternative to `NLTK` is to use [spaCy tokenizer](https://spacy.io/api/tokenizer).\n", "\n", - "1. How the text is split: by `spaCy` tokenizer\n", - "2. How the chunk size is measured: by number of characters" + "1. How the text is split: by `spaCy` tokenizer.\n", + "2. How the chunk size is measured: by number of characters." ] }, { @@ -329,7 +331,7 @@ "Rather than just splitting on \"\\n\\n\", we can use `NLTK` to split based on [NLTK tokenizers](https://www.nltk.org/api/nltk.tokenize.html).\n", "\n", "1. How the text is split: by `NLTK` tokenizer.\n", - "2. How the chunk size is measured:by number of characters" + "2. How the chunk size is measured: by number of characters." ] }, { @@ -428,8 +430,8 @@ "\n", "We use Hugging Face tokenizer, the [GPT2TokenizerFast](https://huggingface.co/Ransaka/gpt2-tokenizer-fast) to count the text length in tokens.\n", "\n", - "1. How the text is split: by character passed in\n", - "2. How the chunk size is measured: by number of tokens calculated by the `Hugging Face` tokenizer\n" + "1. How the text is split: by character passed in.\n", + "2. How the chunk size is measured: by number of tokens calculated by the `Hugging Face` tokenizer.\n" ] }, { diff --git a/docs/extras/modules/data_connection/indexing.ipynb b/docs/extras/modules/data_connection/indexing.ipynb index 28914efaa9..384333e23f 100644 --- a/docs/extras/modules/data_connection/indexing.ipynb +++ b/docs/extras/modules/data_connection/indexing.ipynb @@ -34,7 +34,7 @@ "\n", "When indexing documents into a vector store, it's possible that some existing documents in the vector store should be deleted. In certain situations you may want to remove any existing documents that are derived from the same sources as the new documents being indexed. In others you may want to delete all existing documents wholesale. The indexing API deletion modes let you pick the behavior you want:\n", "\n", - "| Delete Mode | De-Duplicates Content | Parallelizable | Cleans Up Deleted Source Docs | Cleans Up Mutations of Source Docs and/or Derived Docs | Clean Up Timing |\n", + "| Cleanup Mode | De-Duplicates Content | Parallelizable | Cleans Up Deleted Source Docs | Cleans Up Mutations of Source Docs and/or Derived Docs | Clean Up Timing |\n", "|-------------|-----------------------|---------------|----------------------------------|----------------------------------------------------|---------------------|\n", "| None | ✅ | ✅ | ❌ | ❌ | - |\n", "| Incremental | ✅ | ✅ | ❌ | ✅ | Continuously |\n", @@ -45,8 +45,8 @@ "\n", "`incremental` and `full` offer the following automated clean up:\n", "\n", - "* If the content of source document or derived documents has **changed**, both `incremental` or `full` modes will clean up (delete) previous versions of the content.\n", - "* If the source document has been **deleted** (meaning it is not included in the documents currently being indexed), the `full` delete mode will delete it from the vector store correctly, but the `incremental` mode will not.\n", + "* If the content of the source document or derived documents has **changed**, both `incremental` or `full` modes will clean up (delete) previous versions of the content.\n", + "* If the source document has been **deleted** (meaning it is not included in the documents currently being indexed), the `full` cleanup mode will delete it from the vector store correctly, but the `incremental` mode will not.\n", "\n", "When content is mutated (e.g., the source PDF file was revised) there will be a period of time during indexing when both the new and old versions may be returned to the user. This happens after the new content was written, but before the old version was deleted.\n", "\n", @@ -56,19 +56,19 @@ "## Requirements\n", "\n", "1. Do not use with a store that has been pre-populated with content independently of the indexing API, as the record manager will not know that records have been inserted previously.\n", - "2. Only works with LangChain ``VectorStore``'s that support:\n", + "2. Only works with LangChain `vectorstore`'s that support:\n", " * document addition by id (`add_documents` method with `ids` argument)\n", " * delete by id (`delete` method with)\n", " \n", "## Caution\n", "\n", - "The record manager relies on a time-based mechanism to determine what content can be cleaned up (when using `full` or `incremental` delete modes).\n", + "The record manager relies on a time-based mechanism to determine what content can be cleaned up (when using `full` or `incremental` cleanup modes).\n", "\n", - "If two tasks run back to back, and the first task finishes before the the clock time changes, then the second task may not be able to clean up content.\n", + "If two tasks run back-to-back, and the first task finishes before the the clock time changes, then the second task may not be able to clean up content.\n", "\n", "This is unlikely to be an issue in actual settings for the following reasons:\n", "\n", - "1. The RecordManager uses higher resolutino timestamps.\n", + "1. The RecordManager uses higher resolution timestamps.\n", "2. The data would need to change between the first and the second tasks runs, which becomes unlikely if the time interval between the tasks is small.\n", "3. Indexing tasks typically take more than a few ms." ] @@ -99,7 +99,7 @@ "id": "f81201ab-d997-433c-9f18-ceea70e61cbd", "metadata": {}, "source": [ - "Initialize a vector store and set up the embeddings" + "Initialize a vector store and set up the embeddings:" ] }, { @@ -125,7 +125,7 @@ "source": [ "Initialize a record manager with an appropriate namespace.\n", "\n", - "**Suggestion** Use a namespace that takes into account both the vectostore and the collection name in the vectorstore; e.g., 'redis/my_docs', 'chromadb/my_docs' or 'postgres/my_docs'" + "**Suggestion:** Use a namespace that takes into account both the vector store and the collection name in the vector store; e.g., 'redis/my_docs', 'chromadb/my_docs' or 'postgres/my_docs'." ] }, { @@ -148,7 +148,7 @@ "id": "835c2c19-68ec-4086-9066-f7ba40877fd5", "metadata": {}, "source": [ - "Create a schema before using the record manager" + "Create a schema before using the record manager." ] }, { @@ -166,7 +166,7 @@ "id": "7f07c6bd-6ada-4b17-a8c5-fe5e4a5278fd", "metadata": {}, "source": [ - "Let's index some test documents" + "Let's index some test documents:" ] }, { @@ -185,7 +185,7 @@ "id": "c7d572be-a913-4511-ab64-2864a252458a", "metadata": {}, "source": [ - "Indexing into an empty vectorstore" + "Indexing into an empty vector store:" ] }, { @@ -197,7 +197,7 @@ "source": [ "def _clear():\n", " \"\"\"Hacky helper method to clear content. See the `full` mode section to to understand why it works.\"\"\"\n", - " index([], record_manager, vectorstore, delete_mode=\"full\", source_id_key=\"source\")" + " index([], record_manager, vectorstore, cleanup=\"full\", source_id_key=\"source\")" ] }, { @@ -242,7 +242,7 @@ " [doc1, doc1, doc1, doc1, doc1],\n", " record_manager,\n", " vectorstore,\n", - " delete_mode=None,\n", + " cleanup=None,\n", " source_id_key=\"source\",\n", ")" ] @@ -276,7 +276,7 @@ ], "source": [ "index(\n", - " [doc1, doc2], record_manager, vectorstore, delete_mode=None, source_id_key=\"source\"\n", + " [doc1, doc2], record_manager, vectorstore, cleanup=None, source_id_key=\"source\"\n", ")" ] }, @@ -285,7 +285,7 @@ "id": "7be3e55a-5fe9-4f40-beff-577c2aa5e76a", "metadata": {}, "source": [ - "Second time around all content will be skipped" + "Second time around all content will be skipped:" ] }, { @@ -307,7 +307,7 @@ ], "source": [ "index(\n", - " [doc1, doc2], record_manager, vectorstore, delete_mode=None, source_id_key=\"source\"\n", + " [doc1, doc2], record_manager, vectorstore, cleanup=None, source_id_key=\"source\"\n", ")" ] }, @@ -351,7 +351,7 @@ " [doc1, doc2],\n", " record_manager,\n", " vectorstore,\n", - " delete_mode=\"incremental\",\n", + " cleanup=\"incremental\",\n", " source_id_key=\"source\",\n", ")" ] @@ -386,7 +386,7 @@ " [doc1, doc2],\n", " record_manager,\n", " vectorstore,\n", - " delete_mode=\"incremental\",\n", + " cleanup=\"incremental\",\n", " source_id_key=\"source\",\n", ")" ] @@ -396,7 +396,7 @@ "id": "b205c1ba-f069-4a4e-af93-dc98afd5c9e6", "metadata": {}, "source": [ - "If we provide no documents with incremental indexing mode, nothing will change" + "If we provide no documents with incremental indexing mode, nothing will change." ] }, { @@ -418,7 +418,7 @@ ], "source": [ "index(\n", - " [], record_manager, vectorstore, delete_mode=\"incremental\", source_id_key=\"source\"\n", + " [], record_manager, vectorstore, cleanup=\"incremental\", source_id_key=\"source\"\n", ")" ] }, @@ -462,7 +462,7 @@ " [changed_doc_2],\n", " record_manager,\n", " vectorstore,\n", - " delete_mode=\"incremental\",\n", + " cleanup=\"incremental\",\n", " source_id_key=\"source\",\n", ")" ] @@ -476,7 +476,7 @@ "\n", "In `full` mode the user should pass the `full` universe of content that should be indexed into the indexing function.\n", "\n", - "Any documents that are not passed into the indexing functino and are present in the vectorstore will be deleted!\n", + "Any documents that are not passed into the indexing function and are present in the vectorstore will be deleted!\n", "\n", "This behavior is useful to handle deletions of source documents." ] @@ -519,7 +519,7 @@ } ], "source": [ - "index(all_docs, record_manager, vectorstore, delete_mode=\"full\", source_id_key=\"source\")" + "index(all_docs, record_manager, vectorstore, cleanup=\"full\", source_id_key=\"source\")" ] }, { @@ -527,7 +527,7 @@ "id": "887c45c6-4363-4389-ac56-9cdad682b4c8", "metadata": {}, "source": [ - "Say someone deleted the first doc" + "Say someone deleted the first doc:" ] }, { @@ -566,7 +566,7 @@ "id": "d940bcb4-cf6d-4c21-a565-e7f53f6dacf1", "metadata": {}, "source": [ - "Using full mode will clean up the deleted content as well" + "Using full mode will clean up the deleted content as well." ] }, { @@ -587,7 +587,7 @@ } ], "source": [ - "index(all_docs, record_manager, vectorstore, delete_mode=\"full\", source_id_key=\"source\")" + "index(all_docs, record_manager, vectorstore, cleanup=\"full\", source_id_key=\"source\")" ] }, { @@ -693,7 +693,7 @@ " new_docs,\n", " record_manager,\n", " vectorstore,\n", - " delete_mode=\"incremental\",\n", + " cleanup=\"incremental\",\n", " source_id_key=\"source\",\n", ")" ] @@ -716,7 +716,7 @@ "id": "ab1c0915-3f9e-42ac-bdb5-3017935c6e7f", "metadata": {}, "source": [ - "This should delete the old versions of documents associated with `doggy.txt` source and replace them with the new versions" + "This should delete the old versions of documents associated with `doggy.txt` source and replace them with the new versions." ] }, { @@ -741,7 +741,7 @@ " changed_doggy_docs,\n", " record_manager,\n", " vectorstore,\n", - " delete_mode=\"incremental\",\n", + " cleanup=\"incremental\",\n", " source_id_key=\"source\",\n", ")" ] @@ -774,11 +774,11 @@ "id": "c0af4d24-d735-4e5d-ad9b-a2e8b281f9f1", "metadata": {}, "source": [ - "## Using with Loaders\n", + "## Using with loaders\n", "\n", "Indexing can accept either an iterable of documents or else any loader.\n", "\n", - "**Attention** The loader **MUST** set source keys correctly." + "**Attention:** The loader **must** set source keys correctly." ] }, { @@ -866,7 +866,7 @@ } ], "source": [ - "index(loader, record_manager, vectorstore, delete_mode=\"full\", source_id_key=\"source\")" + "index(loader, record_manager, vectorstore, cleanup=\"full\", source_id_key=\"source\")" ] }, { diff --git a/docs/extras/modules/data_connection/retrievers/MultiQueryRetriever.ipynb b/docs/extras/modules/data_connection/retrievers/MultiQueryRetriever.ipynb index 487a412768..73747bd5c2 100644 --- a/docs/extras/modules/data_connection/retrievers/MultiQueryRetriever.ipynb +++ b/docs/extras/modules/data_connection/retrievers/MultiQueryRetriever.ipynb @@ -7,7 +7,7 @@ "source": [ "# MultiQueryRetriever\n", "\n", - "Distance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on \"distance\". But, retrieval may produce difference results with subtle changes in query wording or if the embeddings do not capture the semantics of the data well. Prompt engineering / tuning is sometimes done to manually address these problems, but can be tedious.\n", + "Distance-based vector database retrieval embeds (represents) queries in high-dimensional space and finds similar embedded documents based on \"distance\". But, retrieval may produce different results with subtle changes in query wording or if the embeddings do not capture the semantics of the data well. Prompt engineering / tuning is sometimes done to manually address these problems, but can be tedious.\n", "\n", "The `MultiQueryRetriever` automates the process of prompt tuning by using an LLM to generate multiple queries from different perspectives for a given user input query. For each query, it retrieves a set of relevant documents and takes the unique union across all queries to get a larger set of potentially relevant documents. By generating multiple perspectives on the same question, the `MultiQueryRetriever` might be able to overcome some of the limitations of the distance-based retrieval and get a richer set of results." ] @@ -43,7 +43,7 @@ "id": "cca8f56c", "metadata": {}, "source": [ - "`Simple usage`\n", + "#### Simple usage\n", "\n", "Specify the LLM to use for query generation, and the retriver will do the rest." ] @@ -113,7 +113,7 @@ "id": "c54a282f", "metadata": {}, "source": [ - "`Supplying your own prompt`\n", + "#### Supplying your own prompt\n", "\n", "You can also supply a prompt along with an output parser to split the results into a list of queries." ] diff --git a/docs/extras/modules/data_connection/retrievers/ensemble.ipynb b/docs/extras/modules/data_connection/retrievers/ensemble.ipynb index f06349c7e5..24417ea3c8 100644 --- a/docs/extras/modules/data_connection/retrievers/ensemble.ipynb +++ b/docs/extras/modules/data_connection/retrievers/ensemble.ipynb @@ -6,11 +6,11 @@ "source": [ "# Ensemble Retriever\n", "\n", - "The `EnsembleRetriever` takes a list of retrievers as input and ensemble the results of their get_relevant_documents() methods and rerank the results based on the [Reciprocal Rank Fusion](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) algorithm.\n", + "The `EnsembleRetriever` takes a list of retrievers as input and ensemble the results of their `get_relevant_documents()` methods and rerank the results based on the [Reciprocal Rank Fusion](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf) algorithm.\n", "\n", "By leveraging the strengths of different algorithms, the `EnsembleRetriever` can achieve better performance than any single algorithm. \n", "\n", - "The most common pattern is to combine a sparse retriever(like BM25) with a dense retriever(like Embedding similarity), because their strengths are complementary. It is also known as \"hybrid search\".The sparse retriever is good at finding relevant documents based on keywords, while the dense retriever is good at finding relevant documents based on semantic similarity." + "The most common pattern is to combine a sparse retriever (like BM25) with a dense retriever (like embedding similarity), because their strengths are complementary. It is also known as \"hybrid search\". The sparse retriever is good at finding relevant documents based on keywords, while the dense retriever is good at finding relevant documents based on semantic similarity." ] }, { diff --git a/docs/extras/modules/data_connection/retrievers/multi_vector.ipynb b/docs/extras/modules/data_connection/retrievers/multi_vector.ipynb index 9197b51680..3097d63767 100644 --- a/docs/extras/modules/data_connection/retrievers/multi_vector.ipynb +++ b/docs/extras/modules/data_connection/retrievers/multi_vector.ipynb @@ -11,12 +11,12 @@ "\n", "The methods to create multiple vectors per document include:\n", "\n", - "- smaller chunks: split a document into smaller chunks, and embed those (this is ParentDocumentRetriever)\n", - "- summary: create a summary for each document, embed that along with (or instead of) the document\n", - "- hypothetical questions: create hypothetical questions that each document would be appropriate to answer, embed those along with (or instead of) the document\n", + "- Smaller chunks: split a document into smaller chunks, and embed those (this is ParentDocumentRetriever).\n", + "- Summary: create a summary for each document, embed that along with (or instead of) the document.\n", + "- Hypothetical questions: create hypothetical questions that each document would be appropriate to answer, embed those along with (or instead of) the document.\n", "\n", "\n", - "Note that this also enables another method of adding embeddings - manually. This is great because you can explicitly add questions or queries that should lead to a document being recovered, giving you more control" + "Note that this also enables another method of adding embeddings - manually. This is great because you can explicitly add questions or queries that should lead to a document being recovered, giving you more control." ] }, { @@ -68,7 +68,7 @@ "source": [ "## Smaller chunks\n", "\n", - "Often times it can be useful to retrieve larger chunks of information, but embed smaller chunks. This allows for embeddings to capture the semantic meaning as closely as possible, but for as much context as possible to be passed downstream. NOTE: this is what the ParentDocumentRetriever does. Here we show what is going on under the hood." + "Often times it can be useful to retrieve larger chunks of information, but embed smaller chunks. This allows for embeddings to capture the semantic meaning as closely as possible, but for as much context as possible to be passed downstream. Note that this is what the `ParentDocumentRetriever` does. Here we show what is going on under the hood." ] }, { diff --git a/docs/extras/modules/data_connection/retrievers/parent_document_retriever.ipynb b/docs/extras/modules/data_connection/retrievers/parent_document_retriever.ipynb index 206ecbfbad..db710610a1 100644 --- a/docs/extras/modules/data_connection/retrievers/parent_document_retriever.ipynb +++ b/docs/extras/modules/data_connection/retrievers/parent_document_retriever.ipynb @@ -15,7 +15,7 @@ "2. You want to have long enough documents that the context of each chunk is\n", " retained.\n", "\n", - "The ParentDocumentRetriever strikes that balance by splitting and storing\n", + "The `ParentDocumentRetriever` strikes that balance by splitting and storing\n", "small chunks of data. During retrieval, it first fetches the small chunks\n", "but then looks up the parent ids for those chunks and returns those larger\n", "documents.\n", @@ -70,7 +70,7 @@ "id": "d3943f72", "metadata": {}, "source": [ - "## Retrieving Full Documents\n", + "## Retrieving full documents\n", "\n", "In this mode, we want to retrieve the full documents. Therefore, we only specify a child splitter." ] @@ -143,7 +143,7 @@ "id": "f895d62b", "metadata": {}, "source": [ - "Let's now call the vectorstore search functionality - we should see that it returns small chunks (since we're storing the small chunks)." + "Let's now call the vector store search functionality - we should see that it returns small chunks (since we're storing the small chunks)." ] }, { @@ -220,7 +220,7 @@ "id": "14f813a5", "metadata": {}, "source": [ - "## Retrieving Larger Chunks\n", + "## Retrieving larger chunks\n", "\n", "Sometimes, the full documents can be too big to want to retrieve them as is. In that case, what we really want to do is to first split the raw documents into larger chunks, and then split it into smaller chunks. We then index the smaller chunks, but on retrieval we retrieve the larger chunks (but still not the full documents)." ] @@ -273,7 +273,7 @@ "id": "64ad3c8c", "metadata": {}, "source": [ - "We can see that there are much more than two documents now - these are the larger chunks" + "We can see that there are much more than two documents now - these are the larger chunks." ] }, { @@ -302,7 +302,7 @@ "id": "baaef673", "metadata": {}, "source": [ - "Let's make sure the underlying vectorstore still retrieves the small chunks." + "Let's make sure the underlying vector store still retrieves the small chunks." ] }, { diff --git a/docs/extras/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query.ipynb b/docs/extras/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query.ipynb index b284889d07..4f821019c4 100644 --- a/docs/extras/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query.ipynb +++ b/docs/extras/modules/data_connection/retrievers/self_query/activeloop_deeplake_self_query.ipynb @@ -8,9 +8,9 @@ "source": [ "# Deep Lake self-querying \n", "\n", - ">[DeepLake](https://www.activeloop.ai) is a multimodal database for building AI applications.\n", + ">[Deep Lake](https://www.activeloop.ai) is a multimodal database for building AI applications.\n", "\n", - "In the notebook we'll demo the `SelfQueryRetriever` wrapped around a DeepLake vector store. " + "In the notebook we'll demo the `SelfQueryRetriever` wrapped around a Deep Lake vector store. " ] }, { @@ -19,10 +19,10 @@ "id": "68e75fb9", "metadata": {}, "source": [ - "## Creating a Deep Lake vectorstore\n", - "First we'll want to create a DeepLake VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", + "## Creating a Deep Lake vector store\n", + "First we'll want to create a Deep Lake vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", "\n", - "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `deeplake` package." + "**Note:** The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `deeplake` package." ] }, { diff --git a/docs/extras/modules/data_connection/retrievers/self_query/chroma_self_query.ipynb b/docs/extras/modules/data_connection/retrievers/self_query/chroma_self_query.ipynb index e1b8f09638..ac6954e82d 100644 --- a/docs/extras/modules/data_connection/retrievers/self_query/chroma_self_query.ipynb +++ b/docs/extras/modules/data_connection/retrievers/self_query/chroma_self_query.ipynb @@ -17,10 +17,10 @@ "id": "68e75fb9", "metadata": {}, "source": [ - "## Creating a Chroma vectorstore\n", - "First we'll want to create a Chroma VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", + "## Creating a Chroma vector store\n", + "First we'll want to create a Chroma vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", "\n", - "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `chromadb` package." + "**Note:** The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `chromadb` package." ] }, { @@ -64,7 +64,7 @@ }, "outputs": [ { - "name": "stdin", + "name": "stdout", "output_type": "stream", "text": [ "OpenAI API Key: ········\n" diff --git a/docs/extras/modules/data_connection/retrievers/self_query/dashvector.ipynb b/docs/extras/modules/data_connection/retrievers/self_query/dashvector.ipynb new file mode 100644 index 0000000000..d1048ee5fa --- /dev/null +++ b/docs/extras/modules/data_connection/retrievers/self_query/dashvector.ipynb @@ -0,0 +1,434 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# DashVector self-querying\n", + "\n", + "> [DashVector](https://help.aliyun.com/document_detail/2510225.html) is a fully-managed vectorDB service that supports high-dimension dense and sparse vectors, real-time insertion and filtered search. It is built to scale automatically and can adapt to different application requirements.\n", + "\n", + "In this notebook we'll demo the `SelfQueryRetriever` with a `DashVector` vector store." + ], + "metadata": { + "collapsed": false + }, + "id": "59895c73d1a0f3ca" + }, + { + "cell_type": "markdown", + "source": [ + "## Create DashVector vectorstore\n", + "\n", + "First we'll want to create a `DashVector` VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", + "\n", + "To use DashVector, you have to have `dashvector` package installed, and you must have an API key and an Environment. Here are the [installation instructions](https://help.aliyun.com/document_detail/2510223.html).\n", + "\n", + "NOTE: The self-query retriever requires you to have `lark` package installed." + ], + "metadata": { + "collapsed": false + }, + "id": "539ae9367e45a178" + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "# !pip install lark dashvector" + ], + "metadata": { + "collapsed": false + }, + "id": "67df7e1f8dc8cdd0" + }, + { + "cell_type": "code", + "execution_count": 1, + "outputs": [], + "source": [ + "import os\n", + "import dashvector\n", + "\n", + "client = dashvector.Client(api_key=os.environ[\"DASHVECTOR_API_KEY\"])" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-24T02:58:46.905337Z", + "start_time": "2023-08-24T02:58:46.252566Z" + } + }, + "id": "ff61eaf13973b5fe" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "from langchain.schema import Document\n", + "from langchain.embeddings import DashScopeEmbeddings\n", + "from langchain.vectorstores import DashVector\n", + "\n", + "embeddings = DashScopeEmbeddings()\n", + "\n", + "# create DashVector collection\n", + "client.create(\"langchain-self-retriever-demo\", dimension=1536)" + ], + "metadata": { + "collapsed": false + }, + "id": "de5c77957ee42d14" + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [], + "source": [ + "docs = [\n", + " Document(\n", + " page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\",\n", + " metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"action\"},\n", + " ),\n", + " Document(\n", + " page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\",\n", + " metadata={\"year\": 2010, \"director\": \"Christopher Nolan\", \"rating\": 8.2},\n", + " ),\n", + " Document(\n", + " page_content=\"A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea\",\n", + " metadata={\"year\": 2006, \"director\": \"Satoshi Kon\", \"rating\": 8.6},\n", + " ),\n", + " Document(\n", + " page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\",\n", + " metadata={\"year\": 2019, \"director\": \"Greta Gerwig\", \"rating\": 8.3},\n", + " ),\n", + " Document(\n", + " page_content=\"Toys come alive and have a blast doing so\",\n", + " metadata={\"year\": 1995, \"genre\": \"animated\"},\n", + " ),\n", + " Document(\n", + " page_content=\"Three men walk into the Zone, three men walk out of the Zone\",\n", + " metadata={\n", + " \"year\": 1979,\n", + " \"director\": \"Andrei Tarkovsky\",\n", + " \"genre\": \"science fiction\",\n", + " \"rating\": 9.9,\n", + " },\n", + " ),\n", + "]\n", + "vectorstore = DashVector.from_documents(\n", + " docs, embeddings, collection_name=\"langchain-self-retriever-demo\"\n", + ")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-24T02:59:08.090031Z", + "start_time": "2023-08-24T02:59:05.660295Z" + } + }, + "id": "8f40605548a4550" + }, + { + "cell_type": "markdown", + "source": [ + "## Create your self-querying retriever\n", + "\n", + "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents." + ], + "metadata": { + "collapsed": false + }, + "id": "eb1340adafac8993" + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [], + "source": [ + "from langchain.llms import Tongyi\n", + "from langchain.retrievers.self_query.base import SelfQueryRetriever\n", + "from langchain.chains.query_constructor.base import AttributeInfo\n", + "\n", + "metadata_field_info = [\n", + " AttributeInfo(\n", + " name=\"genre\",\n", + " description=\"The genre of the movie\",\n", + " type=\"string or list[string]\",\n", + " ),\n", + " AttributeInfo(\n", + " name=\"year\",\n", + " description=\"The year the movie was released\",\n", + " type=\"integer\",\n", + " ),\n", + " AttributeInfo(\n", + " name=\"director\",\n", + " description=\"The name of the movie director\",\n", + " type=\"string\",\n", + " ),\n", + " AttributeInfo(\n", + " name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n", + " ),\n", + "]\n", + "document_content_description = \"Brief summary of a movie\"\n", + "llm = Tongyi(temperature=0)\n", + "retriever = SelfQueryRetriever.from_llm(\n", + " llm, vectorstore, document_content_description, metadata_field_info, verbose=True\n", + ")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-24T02:59:11.003940Z", + "start_time": "2023-08-24T02:59:10.476722Z" + } + }, + "id": "d65233dc044f95a7" + }, + { + "cell_type": "markdown", + "source": [ + "## Testing it out\n", + "\n", + "And now we can try actually using our retriever!" + ], + "metadata": { + "collapsed": false + }, + "id": "a54af0d67b473db6" + }, + { + "cell_type": "code", + "execution_count": 6, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query='dinosaurs' filter=None limit=None\n" + ] + }, + { + "data": { + "text/plain": "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.699999809265137, 'genre': 'action'}),\n Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'}),\n Document(page_content='Leo DiCaprio gets lost in a dream within a dream within a dream within a ...', metadata={'year': 2010, 'director': 'Christopher Nolan', 'rating': 8.199999809265137}),\n Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.600000381469727})]" + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example only specifies a relevant query\n", + "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-24T02:59:28.577901Z", + "start_time": "2023-08-24T02:59:26.780184Z" + } + }, + "id": "dad9da670a267fe7" + }, + { + "cell_type": "code", + "execution_count": 7, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query=' ' filter=Comparison(comparator=, attribute='rating', value=8.5) limit=None\n" + ] + }, + { + "data": { + "text/plain": "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'rating': 9.899999618530273, 'genre': 'science fiction'}),\n Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'director': 'Satoshi Kon', 'rating': 8.600000381469727})]" + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example only specifies a filter\n", + "retriever.get_relevant_documents(\"I want to watch a movie rated higher than 8.5\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-24T02:59:32.370774Z", + "start_time": "2023-08-24T02:59:30.614252Z" + } + }, + "id": "d486a64316153d52" + }, + { + "cell_type": "code", + "execution_count": 8, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query='Greta Gerwig' filter=Comparison(comparator=, attribute='director', value='Greta Gerwig') limit=None\n" + ] + }, + { + "data": { + "text/plain": "[Document(page_content='A bunch of normal-sized women are supremely wholesome and some men pine after them', metadata={'year': 2019, 'director': 'Greta Gerwig', 'rating': 8.300000190734863})]" + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example specifies a query and a filter\n", + "retriever.get_relevant_documents(\"Has Greta Gerwig directed any movies about women\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-24T02:59:35.353439Z", + "start_time": "2023-08-24T02:59:33.278255Z" + } + }, + "id": "e05919cdead7bd4a" + }, + { + "cell_type": "code", + "execution_count": 9, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query='science fiction' filter=Operation(operator=, arguments=[Comparison(comparator=, attribute='genre', value='science fiction'), Comparison(comparator=, attribute='rating', value=8.5)]) limit=None\n" + ] + }, + { + "data": { + "text/plain": "[Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'director': 'Andrei Tarkovsky', 'rating': 9.899999618530273, 'genre': 'science fiction'})]" + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example specifies a composite filter\n", + "retriever.get_relevant_documents(\"What's a highly rated (above 8.5) science fiction film?\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-24T02:59:38.913707Z", + "start_time": "2023-08-24T02:59:36.659271Z" + } + }, + "id": "ac2c7012379e918e" + }, + { + "cell_type": "markdown", + "source": [ + "## Filter k\n", + "\n", + "We can also use the self query retriever to specify `k`: the number of documents to fetch.\n", + "\n", + "We can do this by passing `enable_limit=True` to the constructor." + ], + "metadata": { + "collapsed": false + }, + "id": "af6aa93ae44af414" + }, + { + "cell_type": "code", + "execution_count": 10, + "outputs": [], + "source": [ + "retriever = SelfQueryRetriever.from_llm(\n", + " llm,\n", + " vectorstore,\n", + " document_content_description,\n", + " metadata_field_info,\n", + " enable_limit=True,\n", + " verbose=True,\n", + ")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-24T02:59:41.594073Z", + "start_time": "2023-08-24T02:59:41.563323Z" + } + }, + "id": "a8c8f09bf5702767" + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query='dinosaurs' filter=None limit=2\n" + ] + }, + { + "data": { + "text/plain": "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.699999809265137, 'genre': 'action'}),\n Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'genre': 'animated'})]" + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example only specifies a relevant query\n", + "retriever.get_relevant_documents(\"what are two movies about dinosaurs\")" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-08-24T02:59:48.450506Z", + "start_time": "2023-08-24T02:59:46.252944Z" + } + }, + "id": "b1089a6043980b84" + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + }, + "id": "6d2d64e2ebb17d30" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/extras/modules/data_connection/retrievers/self_query/elasticsearch_self_query.ipynb b/docs/extras/modules/data_connection/retrievers/self_query/elasticsearch_self_query.ipynb index 8a24fb1a9b..dbfc698667 100644 --- a/docs/extras/modules/data_connection/retrievers/self_query/elasticsearch_self_query.ipynb +++ b/docs/extras/modules/data_connection/retrievers/self_query/elasticsearch_self_query.ipynb @@ -13,10 +13,10 @@ "id": "68e75fb9", "metadata": {}, "source": [ - "## Creating a Elasticsearch vectorstore\n", - "First we'll want to create a Elasticsearch VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", + "## Creating a Elasticsearch vector store\n", + "First we'll want to create a Elasticsearch vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", "\n", - "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `elasticsearch` package." + "**Note:** The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `elasticsearch` package." ] }, { diff --git a/docs/extras/modules/data_connection/retrievers/self_query/milvus_self_query.ipynb b/docs/extras/modules/data_connection/retrievers/self_query/milvus_self_query.ipynb new file mode 100644 index 0000000000..068495eefa --- /dev/null +++ b/docs/extras/modules/data_connection/retrievers/self_query/milvus_self_query.ipynb @@ -0,0 +1,375 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Self-querying with Milvus\n", + "\n", + "In the walkthrough we'll demo the `SelfQueryRetriever` with a `Milvus` vector store." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating a Milvus vectorstore\n", + "First we'll want to create a Milvus VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", + "\n", + "I have used the cloud version of Milvus, thus I need `uri` and `token` as well.\n", + "\n", + "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `pymilvus` package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install lark" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!pip install pymilvus" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We want to use `OpenAIEmbeddings` so we have to get the OpenAI API Key." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "OPENAI_API_KEY = \"Use your OpenAI key:)\"\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.schema import Document\n", + "from langchain.embeddings.openai import OpenAIEmbeddings\n", + "from langchain.vectorstores import Milvus\n", + "\n", + "embeddings = OpenAIEmbeddings()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "docs = [\n", + " Document(page_content=\"A bunch of scientists bring back dinosaurs and mayhem breaks loose\", metadata={\"year\": 1993, \"rating\": 7.7, \"genre\": \"action\"}),\n", + " Document(page_content=\"Leo DiCaprio gets lost in a dream within a dream within a dream within a ...\", metadata={\"year\": 2010,\"genre\": \"thriller\", \"rating\": 8.2}),\n", + " Document(page_content=\"A bunch of normal-sized women are supremely wholesome and some men pine after them\", metadata={\"year\": 2019, \"rating\": 8.3, \"genre\": \"drama\"}),\n", + " Document(page_content=\"Three men walk into the Zone, three men walk out of the Zone\", metadata={\"year\": 1979, \"rating\": 9.9, \"genre\": \"science fiction\"}),\n", + " Document(\n", + " page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea',\n", + " metadata={\"year\": 2006, \"genre\": \"thriller\", 'rating': 9.0},\n", + " ),\n", + " Document(page_content=\"Toys come alive and have a blast doing so\", metadata={\"year\": 1995, \"genre\": \"animated\", \"rating\": 9.3 }),\n", + "]\n", + "\n", + "vector_store = Milvus.from_documents(\n", + " docs,\n", + " embedding=embeddings,\n", + " connection_args={\"uri\": 'Use your uri:)', \"token\":'Use your token:)'}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating our self-querying retriever\n", + "Now we can instantiate our retriever. To do this we'll need to provide some information upfront about the metadata fields that our documents support and a short description of the document contents." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.llms import OpenAI\n", + "from langchain.retrievers.self_query.base import SelfQueryRetriever\n", + "from langchain.chains.query_constructor.base import AttributeInfo\n", + "\n", + "metadata_field_info = [\n", + " AttributeInfo(\n", + " name=\"genre\",\n", + " description=\"The genre of the movie\",\n", + " type=\"string\",\n", + " ),\n", + " AttributeInfo(\n", + " name=\"year\",\n", + " description=\"The year the movie was released\",\n", + " type=\"integer\",\n", + " ),\n", + " AttributeInfo(\n", + " name=\"rating\", description=\"A 1-10 rating for the movie\", type=\"float\"\n", + " ),\n", + "]\n", + "document_content_description = \"Brief summary of a movie\"\n", + "llm = OpenAI(temperature=0)\n", + "retriever = SelfQueryRetriever.from_llm(\n", + " llm, vector_store, document_content_description, metadata_field_info, verbose=True\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Testing it out\n", + "And now we can try actually using our retriever!" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query='dinosaur' filter=None limit=None\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'action'}),\n", + " Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}),\n", + " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'genre': 'science fiction'}),\n", + " Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'rating': 9.0, 'genre': 'thriller'})]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example only specifies a relevant query\n", + "retriever.get_relevant_documents(\"What are some movies about dinosaurs\")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query=' ' filter=Comparison(comparator=, attribute='rating', value=9) limit=None\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}),\n", + " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'genre': 'science fiction'})]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example specifies a filter\n", + "retriever.get_relevant_documents(\"What are some highly rated movies (above 9)?\")" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query='toys' filter=Comparison(comparator=, attribute='rating', value=9) limit=None\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'}),\n", + " Document(page_content='Three men walk into the Zone, three men walk out of the Zone', metadata={'year': 1979, 'rating': 9.9, 'genre': 'science fiction'})]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example only specifies a query and a filter\n", + "retriever.get_relevant_documents(\"I want to watch a movie about toys rated higher than 9\")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query=' ' filter=Operation(operator=, arguments=[Comparison(comparator=, attribute='genre', value='thriller'), Comparison(comparator=, attribute='rating', value=9)]) limit=None\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea', metadata={'year': 2006, 'rating': 9.0, 'genre': 'thriller'})]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example specifies a composite filter\n", + "retriever.get_relevant_documents(\"What's a highly rated (above or equal 9) thriller film?\")" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query='dinosaur' filter=Operation(operator=, arguments=[Comparison(comparator=, attribute='year', value=1990), Comparison(comparator=, attribute='year', value=2005), Comparison(comparator=, attribute='genre', value='action')]) limit=None\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'action'})]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example specifies a query and composite filter\n", + "retriever.get_relevant_documents(\n", + " \"What's a movie after 1990 but before 2005 that's all about dinosaurs, \\\n", + " and preferably has a lot of action\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filter k\n", + "\n", + "We can also use the self query retriever to specify `k`: the number of documents to fetch.\n", + "\n", + "We can do this by passing `enable_limit=True` to the constructor." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "retriever = SelfQueryRetriever.from_llm(\n", + " llm, \n", + " vector_store, \n", + " document_content_description, \n", + " metadata_field_info, \n", + " verbose=True,\n", + " enable_limit=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "query='dinosaur' filter=None limit=2\n" + ] + }, + { + "data": { + "text/plain": [ + "[Document(page_content='A bunch of scientists bring back dinosaurs and mayhem breaks loose', metadata={'year': 1993, 'rating': 7.7, 'genre': 'action'}),\n", + " Document(page_content='Toys come alive and have a blast doing so', metadata={'year': 1995, 'rating': 9.3, 'genre': 'animated'})]" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This example only specifies a relevant query\n", + "retriever.get_relevant_documents(\"What are two movies about dinosaurs?\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.4" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/extras/modules/data_connection/retrievers/self_query/myscale_self_query.ipynb b/docs/extras/modules/data_connection/retrievers/self_query/myscale_self_query.ipynb index f43ac9fc3e..5288a7dd62 100644 --- a/docs/extras/modules/data_connection/retrievers/self_query/myscale_self_query.ipynb +++ b/docs/extras/modules/data_connection/retrievers/self_query/myscale_self_query.ipynb @@ -9,7 +9,7 @@ "\n", ">[MyScale](https://docs.myscale.com/en/) is an integrated vector database. You can access your database in SQL and also from here, LangChain. MyScale can make a use of [various data types and functions for filters](https://blog.myscale.com/2023/06/06/why-integrated-database-solution-can-boost-your-llm-apps/#filter-on-anything-without-constraints). It will boost up your LLM app no matter if you are scaling up your data or expand your system to broader application.\n", "\n", - "In the notebook we'll demo the `SelfQueryRetriever` wrapped around a MyScale vector store with some extra piece we contributed to LangChain. In short, it can be concluded into 4 points:\n", + "In the notebook we'll demo the `SelfQueryRetriever` wrapped around a MyScale vector store with some extra pieces we contributed to LangChain. In short, it can be condensed into 4 points:\n", "1. Add `contain` comparator to match list of any if there is more than one element matched\n", "2. Add `timestamp` data type for datetime match (ISO-format, or YYYY-MM-DD)\n", "3. Add `like` comparator for string pattern search\n", @@ -21,10 +21,10 @@ "id": "68e75fb9", "metadata": {}, "source": [ - "## Creating a MyScale vectorstore\n", - "MyScale has already been integrated to LangChain for a while. So you can follow [this notebook](/docs/integrations/vectorstores/myscale.ipynb) to create your own vectorstore for a self-query retriever.\n", + "## Creating a MyScale vector store\n", + "MyScale has already been integrated to LangChain for a while. So you can follow [this notebook](/docs/integrations/vectorstores/myscale) to create your own vectorstore for a self-query retriever.\n", "\n", - "NOTE: All self-query retrievers requires you to have `lark` installed (`pip install lark`). We use `lark` for grammar definition. Before you proceed to the next step, we also want to remind you that `clickhouse-connect` is also needed to interact with your MyScale backend." + "**Note:** All self-query retrievers requires you to have `lark` installed (`pip install lark`). We use `lark` for grammar definition. Before you proceed to the next step, we also want to remind you that `clickhouse-connect` is also needed to interact with your MyScale backend." ] }, { @@ -44,7 +44,7 @@ "id": "83811610-7df3-4ede-b268-68a6a83ba9e2", "metadata": {}, "source": [ - "In this tutorial we follow other example's setting and use `OpenAIEmbeddings`. Remember to get a OpenAI API Key for valid accesss to LLMs." + "In this tutorial we follow other example's setting and use `OpenAIEmbeddings`. Remember to get an OpenAI API Key for valid accesss to LLMs." ] }, { @@ -88,7 +88,7 @@ "metadata": {}, "source": [ "## Create some sample data\n", - "As you can see, the data we created has some difference to other self-query retrievers. We replaced keyword `year` to `date` which gives you a finer control on timestamps. We also altered the type of keyword `gerne` to list of strings, where LLM can use a new `contain` comparator to construct filters. We also provides comparator `like` and arbitrary function support to filters, which will be introduced in next few cells.\n", + "As you can see, the data we created has some differences compared to other self-query retrievers. We replaced the keyword `year` with `date` which gives you finer control on timestamps. We also changed the type of the keyword `gerne` to a list of strings, where an LLM can use a new `contain` comparator to construct filters. We also provide the `like` comparator and arbitrary function support to filters, which will be introduced in next few cells.\n", "\n", "Now let's look at the data first." ] @@ -146,7 +146,7 @@ "metadata": {}, "source": [ "## Creating our self-querying retriever\n", - "Just like other retrievers... Simple and nice." + "Just like other retrievers... simple and nice." ] }, { @@ -272,7 +272,7 @@ "id": "86371ac8", "metadata": {}, "source": [ - "# Wait a second... What else?\n", + "# Wait a second... what else?\n", "\n", "Self-query retriever with MyScale can do more! Let's find out." ] diff --git a/docs/extras/modules/data_connection/retrievers/self_query/pinecone.ipynb b/docs/extras/modules/data_connection/retrievers/self_query/pinecone.ipynb index 3bc5f7f36c..78c29641cc 100644 --- a/docs/extras/modules/data_connection/retrievers/self_query/pinecone.ipynb +++ b/docs/extras/modules/data_connection/retrievers/self_query/pinecone.ipynb @@ -16,11 +16,11 @@ "metadata": {}, "source": [ "## Creating a Pinecone index\n", - "First we'll want to create a `Pinecone` VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", + "First we'll want to create a `Pinecone` vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", "\n", - "To use Pinecone, you have to have `pinecone` package installed and you must have an API key and an Environment. Here are the [installation instructions](https://docs.pinecone.io/docs/quickstart).\n", + "To use Pinecone, you have to have `pinecone` package installed and you must have an API key and an environment. Here are the [installation instructions](https://docs.pinecone.io/docs/quickstart).\n", "\n", - "NOTE: The self-query retriever requires you to have `lark` package installed." + "**Note:** The self-query retriever requires you to have `lark` package installed." ] }, { diff --git a/docs/extras/modules/data_connection/retrievers/self_query/qdrant_self_query.ipynb b/docs/extras/modules/data_connection/retrievers/self_query/qdrant_self_query.ipynb index 70afecac7d..a8769e443b 100644 --- a/docs/extras/modules/data_connection/retrievers/self_query/qdrant_self_query.ipynb +++ b/docs/extras/modules/data_connection/retrievers/self_query/qdrant_self_query.ipynb @@ -8,7 +8,7 @@ "source": [ "# Qdrant self-querying \n", "\n", - ">[Qdrant](https://qdrant.tech/documentation/) (read: quadrant ) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. `Qdrant` is tailored to extended filtering support. It makes it useful \n", + ">[Qdrant](https://qdrant.tech/documentation/) (read: quadrant) is a vector similarity search engine. It provides a production-ready service with a convenient API to store, search, and manage points - vectors with an additional payload. `Qdrant` is tailored to extended filtering support.\n", "\n", "In the notebook we'll demo the `SelfQueryRetriever` wrapped around a Qdrant vector store. " ] @@ -19,10 +19,10 @@ "id": "68e75fb9", "metadata": {}, "source": [ - "## Creating a Qdrant vectorstore\n", - "First we'll want to create a Qdrant VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", + "## Creating a Qdrant vector store\n", + "First we'll want to create a Qdrant vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", "\n", - "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `qdrant-client` package." + "**Note:** The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `qdrant-client` package." ] }, { diff --git a/docs/extras/modules/data_connection/retrievers/self_query/weaviate_self_query.ipynb b/docs/extras/modules/data_connection/retrievers/self_query/weaviate_self_query.ipynb index 372c1249f6..382b5225d1 100644 --- a/docs/extras/modules/data_connection/retrievers/self_query/weaviate_self_query.ipynb +++ b/docs/extras/modules/data_connection/retrievers/self_query/weaviate_self_query.ipynb @@ -13,10 +13,10 @@ "id": "68e75fb9", "metadata": {}, "source": [ - "## Creating a Weaviate vectorstore\n", - "First we'll want to create a Weaviate VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", + "## Creating a Weaviate vector store\n", + "First we'll want to create a Weaviate vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies.\n", "\n", - "NOTE: The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `weaviate-client` package." + "**Note:** The self-query retriever requires you to have `lark` installed (`pip install lark`). We also need the `weaviate-client` package." ] }, { diff --git a/docs/extras/modules/data_connection/retrievers/web_research.ipynb b/docs/extras/modules/data_connection/retrievers/web_research.ipynb index 2d1a35f9bd..f880266a0f 100644 --- a/docs/extras/modules/data_connection/retrievers/web_research.ipynb +++ b/docs/extras/modules/data_connection/retrievers/web_research.ipynb @@ -80,9 +80,9 @@ "id": "39114da4", "metadata": {}, "source": [ - "`Run with citations`\n", + "#### Run with citations\n", "\n", - "We can use `RetrievalQAWithSourcesChain` to retrieve docs and provide citations" + "We can use `RetrievalQAWithSourcesChain` to retrieve docs and provide citations." ] }, { @@ -126,7 +126,7 @@ "id": "357559fd", "metadata": {}, "source": [ - "`Run with logging`\n", + "#### Run with logging\n", "\n", "Here, we use `get_relevant_documents` method to return docs." ] @@ -171,9 +171,9 @@ "id": "b681a846", "metadata": {}, "source": [ - "`Generate answer using retrieved docs`\n", + "#### Generate answer using retrieved docs\n", "\n", - "We can use `load_qa_chain` for QA using the retrieved docs" + "We can use `load_qa_chain` for QA using the retrieved docs." ] }, { @@ -207,7 +207,7 @@ "source": [ "### More flexibility\n", "\n", - "Pass an LLM chain with custom prompt and output parsing" + "Pass an LLM chain with custom prompt and output parsing." ] }, { @@ -326,7 +326,7 @@ "source": [ "### Run locally\n", "\n", - "Specify LLM and embeddings that will run locally (e.g., on your laptop)" + "Specify LLM and embeddings that will run locally (e.g., on your laptop)." ] }, { diff --git a/docs/extras/modules/data_connection/text_embedding/caching_embeddings.ipynb b/docs/extras/modules/data_connection/text_embedding/caching_embeddings.ipynb index 2a2c3b09d7..c00f70908a 100644 --- a/docs/extras/modules/data_connection/text_embedding/caching_embeddings.ipynb +++ b/docs/extras/modules/data_connection/text_embedding/caching_embeddings.ipynb @@ -9,13 +9,8 @@ "\n", "Embeddings can be stored or temporarily cached to avoid needing to recompute them.\n", "\n", - "Caching embeddings can be done using a `CacheBackedEmbeddings`.\n", - "\n", - "The cache backed embedder is a wrapper around an embedder that caches\n", - "embeddings in a key-value store. \n", - "\n", - "The text is hashed and the hash is used as the key in the cache.\n", - "\n", + "Caching embeddings can be done using a `CacheBackedEmbeddings`. The cache backed embedder is a wrapper around an embedder that caches\n", + "embeddings in a key-value store. The text is hashed and the hash is used as the key in the cache.\n", "\n", "The main supported way to initialized a `CacheBackedEmbeddings` is `from_bytes_store`. This takes in the following parameters:\n", "\n", @@ -44,9 +39,9 @@ "id": "9ddf07dd-3e72-41de-99d4-78e9521e272f", "metadata": {}, "source": [ - "## Using with a vectorstore\n", + "## Using with a vector store\n", "\n", - "First, let's see an example that uses the local file system for storing embeddings and uses FAISS vectorstore for retrieval." + "First, let's see an example that uses the local file system for storing embeddings and uses FAISS vector store for retrieval." ] }, { @@ -91,7 +86,7 @@ "id": "f8cdf33c-321d-4d2c-b76b-d6f5f8b42a92", "metadata": {}, "source": [ - "The cache is empty prior to embedding" + "The cache is empty prior to embedding:" ] }, { @@ -140,7 +135,7 @@ "id": "f526444b-93f8-423f-b6d1-dab539450921", "metadata": {}, "source": [ - "create the vectorstore" + "Create the vector store:" ] }, { @@ -168,7 +163,7 @@ "id": "64fc53f5-d559-467f-bf62-5daef32ffbc0", "metadata": {}, "source": [ - "If we try to create the vectostore again, it'll be much faster since it does not need to re-compute any embeddings." + "If we try to create the vector store again, it'll be much faster since it does not need to re-compute any embeddings." ] }, { diff --git a/docs/extras/modules/memory/adding_memory.ipynb b/docs/extras/modules/memory/adding_memory.ipynb index d97edc2e05..b341abc733 100644 --- a/docs/extras/modules/memory/adding_memory.ipynb +++ b/docs/extras/modules/memory/adding_memory.ipynb @@ -9,7 +9,8 @@ "source": [ "# Memory in LLMChain\n", "\n", - "This notebook goes over how to use the Memory class with an LLMChain. \n", + + "This notebook goes over how to use the Memory class with an `LLMChain`. \n", "\n", "We will add the [ConversationBufferMemory](https://api.python.langchain.com/en/latest/memory/langchain.memory.buffer.ConversationBufferMemory.html#langchain.memory.buffer.ConversationBufferMemory) class, although this can be any memory class." ] @@ -34,7 +35,7 @@ "id": "4b066ced", "metadata": {}, "source": [ - "The most important step is setting up the prompt correctly. In the below prompt, we have two input keys: one for the actual input, another for the input from the Memory class. Importantly, we make sure the keys in the PromptTemplate and the ConversationBufferMemory match up (`chat_history`)." + "The most important step is setting up the prompt correctly. In the below prompt, we have two input keys: one for the actual input, another for the input from the Memory class. Importantly, we make sure the keys in the `PromptTemplate` and the `ConversationBufferMemory` match up (`chat_history`)." ] }, { @@ -162,7 +163,7 @@ "id": "33978824-0048-4e75-9431-1b2c02c169b0", "metadata": {}, "source": [ - "## Adding Memory to a Chat Model-based LLMChain\n", + "## Adding Memory to a chat model-based `LLMChain`\n", "\n", "The above works for completion-style `LLM`s, but if you are using a chat model, you will likely get better performance using structured chat messages. Below is an example." ] @@ -188,9 +189,9 @@ "source": [ "We will use the [ChatPromptTemplate](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.ChatPromptTemplate.html) class to set up the chat prompt.\n", "\n", - "The [from_messages](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.ChatPromptTemplate.html#langchain.prompts.chat.ChatPromptTemplate.from_messages) method creates a ChatPromptTemplate from a list of messages (e.g., SystemMessage, HumanMessage, AIMessage, ChatMessage, etc.) or message templates, such as the [MessagesPlaceholder](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.MessagesPlaceholder.html#langchain.prompts.chat.MessagesPlaceholder) below.\n", + "The [from_messages](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.ChatPromptTemplate.html#langchain.prompts.chat.ChatPromptTemplate.from_messages) method creates a `ChatPromptTemplate` from a list of messages (e.g., `SystemMessage`, `HumanMessage`, `AIMessage`, `ChatMessage`, etc.) or message templates, such as the [MessagesPlaceholder](https://api.python.langchain.com/en/latest/prompts/langchain.prompts.chat.MessagesPlaceholder.html#langchain.prompts.chat.MessagesPlaceholder) below.\n", "\n", - "The configuration below makes it so the memory will be injected to the middle of the chat prompt, in the \"chat_history\" key, and the user's inputs will be added in a human/user message to the end of the chat prompt." + "The configuration below makes it so the memory will be injected to the middle of the chat prompt, in the `chat_history` key, and the user's inputs will be added in a human/user message to the end of the chat prompt." ] }, { diff --git a/docs/extras/modules/memory/agent_with_memory.ipynb b/docs/extras/modules/memory/agent_with_memory.ipynb index ced545c16a..acf4980f29 100644 --- a/docs/extras/modules/memory/agent_with_memory.ipynb +++ b/docs/extras/modules/memory/agent_with_memory.ipynb @@ -14,8 +14,8 @@ "\n", "In order to add a memory to an agent we are going to the the following steps:\n", "\n", - "1. We are going to create an LLMChain with memory.\n", - "2. We are going to use that LLMChain to create a custom Agent.\n", + "1. We are going to create an `LLMChain` with memory.\n", + "2. We are going to use that `LLMChain` to create a custom Agent.\n", "\n", "For the purposes of this exercise, we are going to create a simple custom Agent that has access to a search tool and utilizes the `ConversationBufferMemory` class." ] @@ -55,7 +55,7 @@ "id": "4ad2e708", "metadata": {}, "source": [ - "Notice the usage of the `chat_history` variable in the PromptTemplate, which matches up with the dynamic key name in the ConversationBufferMemory." + "Notice the usage of the `chat_history` variable in the `PromptTemplate`, which matches up with the dynamic key name in the `ConversationBufferMemory`." ] }, { @@ -86,7 +86,7 @@ "id": "0021675b", "metadata": {}, "source": [ - "We can now construct the LLMChain, with the Memory object, and then create the agent." + "We can now construct the `LLMChain`, with the Memory object, and then create the agent." ] }, { diff --git a/docs/extras/modules/memory/agent_with_memory_in_db.ipynb b/docs/extras/modules/memory/agent_with_memory_in_db.ipynb index b7bc5bcc86..d53a9d916e 100644 --- a/docs/extras/modules/memory/agent_with_memory_in_db.ipynb +++ b/docs/extras/modules/memory/agent_with_memory_in_db.ipynb @@ -63,7 +63,7 @@ "id": "4ad2e708", "metadata": {}, "source": [ - "Notice the usage of the `chat_history` variable in the PromptTemplate, which matches up with the dynamic key name in the ConversationBufferMemory." + "Notice the usage of the `chat_history` variable in the `PromptTemplate`, which matches up with the dynamic key name in the `ConversationBufferMemory`." ] }, { @@ -93,7 +93,7 @@ "id": "6d60bbd5", "metadata": {}, "source": [ - "Now we can create the ChatMessageHistory backed by the database." + "Now we can create the `RedisChatMessageHistory` backed by the database." ] }, { @@ -117,7 +117,7 @@ "id": "0021675b", "metadata": {}, "source": [ - "We can now construct the LLMChain, with the Memory object, and then create the agent." + "We can now construct the `LLMChain`, with the Memory object, and then create the agent." ] }, { diff --git a/docs/extras/modules/memory/conversational_customization.ipynb b/docs/extras/modules/memory/conversational_customization.ipynb index ae6c6ada23..7dbb48ae59 100644 --- a/docs/extras/modules/memory/conversational_customization.ipynb +++ b/docs/extras/modules/memory/conversational_customization.ipynb @@ -30,7 +30,7 @@ "id": "fe3cd3e9", "metadata": {}, "source": [ - "## AI Prefix\n", + "## AI prefix\n", "\n", "The first way to do so is by changing the AI prefix in the conversation summary. By default, this is set to \"AI\", but you can set this to be anything you want. Note that if you change this, you should also change the prompt used in the chain to reflect this naming change. Let's walk through an example of that in the example below." ] @@ -238,7 +238,7 @@ "id": "0517ccf8", "metadata": {}, "source": [ - "## Human Prefix\n", + "## Human prefix\n", "\n", "The next way to do so is by changing the Human prefix in the conversation summary. By default, this is set to \"Human\", but you can set this to be anything you want. Note that if you change this, you should also change the prompt used in the chain to reflect this naming change. Let's walk through an example of that in the example below." ] diff --git a/docs/extras/modules/memory/custom_memory.ipynb b/docs/extras/modules/memory/custom_memory.ipynb index 79d70c1372..8de18c824d 100644 --- a/docs/extras/modules/memory/custom_memory.ipynb +++ b/docs/extras/modules/memory/custom_memory.ipynb @@ -36,11 +36,11 @@ "id": "9489e5e1", "metadata": {}, "source": [ - "In this example, we will write a custom memory class that uses spacy to extract entities and save information about them in a simple hash table. Then, during the conversation, we will look at the input text, extract any entities, and put any information about them into the context.\n", + "In this example, we will write a custom memory class that uses spaCy to extract entities and save information about them in a simple hash table. Then, during the conversation, we will look at the input text, extract any entities, and put any information about them into the context.\n", "\n", "* Please note that this implementation is pretty simple and brittle and probably not useful in a production setting. Its purpose is to showcase that you can add custom memory implementations.\n", "\n", - "For this, we will need spacy." + "For this, we will need spaCy." ] }, { @@ -91,7 +91,7 @@ "\n", " def load_memory_variables(self, inputs: Dict[str, Any]) -> Dict[str, str]:\n", " \"\"\"Load the memory variables, in this case the entity key.\"\"\"\n", - " # Get the input text and run through spacy\n", + " # Get the input text and run through spaCy\n", " doc = nlp(inputs[list(inputs.keys())[0]])\n", " # Extract known information about entities, if they exist.\n", " entities = [\n", @@ -102,7 +102,7 @@ "\n", " def save_context(self, inputs: Dict[str, Any], outputs: Dict[str, str]) -> None:\n", " \"\"\"Save context from this conversation to buffer.\"\"\"\n", - " # Get the input text and run through spacy\n", + " # Get the input text and run through spaCy\n", " text = inputs[list(inputs.keys())[0]]\n", " doc = nlp(text)\n", " # For each entity that was mentioned, save this information to the dictionary.\n", @@ -119,7 +119,7 @@ "id": "429ba264", "metadata": {}, "source": [ - "We now define a prompt that takes in information about entities as well as user input" + "We now define a prompt that takes in information about entities as well as user input." ] }, { diff --git a/docs/extras/modules/memory/types/kg.ipynb b/docs/extras/modules/memory/types/kg.ipynb index 1d8c27ba20..7e08615512 100644 --- a/docs/extras/modules/memory/types/kg.ipynb +++ b/docs/extras/modules/memory/types/kg.ipynb @@ -109,7 +109,7 @@ "id": "dc956b0e", "metadata": {}, "source": [ - "We can also more modularly get current entities from a new message (will use previous messages as context.)" + "We can also more modularly get current entities from a new message (will use previous messages as context)." ] }, { @@ -138,7 +138,7 @@ "id": "e8749134", "metadata": {}, "source": [ - "We can also more modularly get knowledge triplets from a new message (will use previous messages as context.)" + "We can also more modularly get knowledge triplets from a new message (will use previous messages as context)." ] }, { diff --git a/docs/extras/modules/memory/types/summary_buffer.ipynb b/docs/extras/modules/memory/types/summary_buffer.ipynb index b27122905e..1a1acad763 100644 --- a/docs/extras/modules/memory/types/summary_buffer.ipynb +++ b/docs/extras/modules/memory/types/summary_buffer.ipynb @@ -10,7 +10,7 @@ "`ConversationSummaryBufferMemory` combines the two ideas. It keeps a buffer of recent interactions in memory, but rather than just completely flushing old interactions it compiles them into a summary and uses both. \n", "It uses token length rather than number of interactions to determine when to flush interactions.\n", "\n", - "Let's first walk through how to use the utilities" + "Let's first walk through how to use the utilities." ] }, { diff --git a/docs/extras/modules/memory/types/token_buffer.ipynb b/docs/extras/modules/memory/types/token_buffer.ipynb index 73c39b4c84..4432f4d930 100644 --- a/docs/extras/modules/memory/types/token_buffer.ipynb +++ b/docs/extras/modules/memory/types/token_buffer.ipynb @@ -9,7 +9,7 @@ "\n", "`ConversationTokenBufferMemory` keeps a buffer of recent interactions in memory, and uses token length rather than number of interactions to determine when to flush interactions.\n", "\n", - "Let's first walk through how to use the utilities" + "Let's first walk through how to use the utilities." ] }, { diff --git a/docs/extras/modules/model_io/models/chat/human_input_chat_model.ipynb b/docs/extras/modules/model_io/models/chat/human_input_chat_model.ipynb index 3b5ce27713..677d45af8c 100644 --- a/docs/extras/modules/model_io/models/chat/human_input_chat_model.ipynb +++ b/docs/extras/modules/model_io/models/chat/human_input_chat_model.ipynb @@ -5,9 +5,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Human input Chat Model\n", + "# Human input chat model\n", "\n", - "Along with HumanInputLLM, LangChain also provides a pseudo Chat Model class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the Chat Model and simulate how a human would respond if they received the messages.\n", + "Along with HumanInputLLM, LangChain also provides a pseudo chat model class that can be used for testing, debugging, or educational purposes. This allows you to mock out calls to the chat model and simulate how a human would respond if they received the messages.\n", "\n", "In this notebook, we go over how to use this.\n", "\n", diff --git a/docs/extras/modules/model_io/models/llms/custom_llm.ipynb b/docs/extras/modules/model_io/models/llms/custom_llm.ipynb index 2e7e907523..3fa76326fb 100644 --- a/docs/extras/modules/model_io/models/llms/custom_llm.ipynb +++ b/docs/extras/modules/model_io/models/llms/custom_llm.ipynb @@ -11,13 +11,13 @@ "\n", "There is only one required thing that a custom LLM needs to implement:\n", "\n", - "1. A `_call` method that takes in a string, some optional stop words, and returns a string\n", + "- A `_call` method that takes in a string, some optional stop words, and returns a string\n", "\n", "There is a second optional thing it can implement:\n", "\n", - "1. An `_identifying_params` property that is used to help with printing of this class. Should return a dictionary.\n", + "- An `_identifying_params` property that is used to help with printing of this class. Should return a dictionary.\n", "\n", - "Let's implement a very simple custom LLM that just returns the first N characters of the input." + "Let's implement a very simple custom LLM that just returns the first n characters of the input." ] }, { @@ -52,6 +52,7 @@ " prompt: str,\n", " stop: Optional[List[str]] = None,\n", " run_manager: Optional[CallbackManagerForLLMRun] = None,\n", + " **kwargs: Any,\n", " ) -> str:\n", " if stop is not None:\n", " raise ValueError(\"stop kwargs are not permitted.\")\n", diff --git a/docs/extras/modules/model_io/models/llms/fake_llm.ipynb b/docs/extras/modules/model_io/models/llms/fake_llm.ipynb index 99bc1d8480..61e5fc3b4a 100644 --- a/docs/extras/modules/model_io/models/llms/fake_llm.ipynb +++ b/docs/extras/modules/model_io/models/llms/fake_llm.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Fake LLM\n", - "We expose a fake LLM class that can be used for testing. This allows you to mock out calls to the LLM and simulate what would happen if the LLM responded in a certain way.\n", + "LangChain provides a fake LLM class that can be used for testing. This allows you to mock out calls to the LLM and simulate what would happen if the LLM responded in a certain way.\n", "\n", "In this notebook we go over how to use this.\n", "\n", diff --git a/docs/extras/modules/model_io/output_parsers/datetime.ipynb b/docs/extras/modules/model_io/output_parsers/datetime.ipynb index 1ec0e1eb6d..187cc47383 100644 --- a/docs/extras/modules/model_io/output_parsers/datetime.ipynb +++ b/docs/extras/modules/model_io/output_parsers/datetime.ipynb @@ -7,7 +7,7 @@ "source": [ "# Datetime parser\n", "\n", - "This OutputParser shows out to parse LLM output into datetime format." + "This OutputParser can be used to parse LLM output into datetime format." ] }, { diff --git a/docs/extras/modules/model_io/output_parsers/enum.ipynb b/docs/extras/modules/model_io/output_parsers/enum.ipynb index 7d1285243e..02dd890623 100644 --- a/docs/extras/modules/model_io/output_parsers/enum.ipynb +++ b/docs/extras/modules/model_io/output_parsers/enum.ipynb @@ -7,7 +7,7 @@ "source": [ "# Enum parser\n", "\n", - "This notebook shows how to use an Enum output parser" + "This notebook shows how to use an Enum output parser." ] }, { diff --git a/docs/extras/modules/model_io/output_parsers/pydantic.ipynb b/docs/extras/modules/model_io/output_parsers/pydantic.ipynb index 05a5bece67..14137fc2d6 100644 --- a/docs/extras/modules/model_io/output_parsers/pydantic.ipynb +++ b/docs/extras/modules/model_io/output_parsers/pydantic.ipynb @@ -10,7 +10,7 @@ "\n", "Keep in mind that large language models are leaky abstractions! You'll have to use an LLM with sufficient capacity to generate well-formed JSON. In the OpenAI family, DaVinci can do reliably but Curie's ability already drops off dramatically. \n", "\n", - "Use Pydantic to declare your data model. Pydantic's BaseModel like a Python dataclass, but with actual type checking + coercion." + "Use Pydantic to declare your data model. Pydantic's BaseModel is like a Python dataclass, but with actual type checking + coercion." ] }, { diff --git a/docs/extras/modules/model_io/output_parsers/retry.ipynb b/docs/extras/modules/model_io/output_parsers/retry.ipynb index 4d5a9218d6..383b3eb069 100644 --- a/docs/extras/modules/model_io/output_parsers/retry.ipynb +++ b/docs/extras/modules/model_io/output_parsers/retry.ipynb @@ -7,7 +7,7 @@ "source": [ "# Retry parser\n", "\n", - "While in some cases it is possible to fix any parsing mistakes by only looking at the output, in other cases it can't. An example of this is when the output is not just in the incorrect format, but is partially complete. Consider the below example." + "While in some cases it is possible to fix any parsing mistakes by only looking at the output, in other cases it isn't. An example of this is when the output is not just in the incorrect format, but is partially complete. Consider the below example." ] }, { @@ -93,7 +93,7 @@ "id": "25631465", "metadata": {}, "source": [ - "If we try to parse this response as is, we will get an error" + "If we try to parse this response as is, we will get an error:" ] }, { diff --git a/docs/extras/modules/model_io/prompts/example_selectors/custom_example_selector.md b/docs/extras/modules/model_io/prompts/example_selectors/custom_example_selector.md index d9bff15597..e4ada5c03c 100644 --- a/docs/extras/modules/model_io/prompts/example_selectors/custom_example_selector.md +++ b/docs/extras/modules/model_io/prompts/example_selectors/custom_example_selector.md @@ -1,6 +1,6 @@ # Custom example selector -In this tutorial, we'll create a custom example selector that selects every alternate example from a given list of examples. +In this tutorial, we'll create a custom example selector that selects examples randomly from a given list of examples. An `ExampleSelector` must implement two methods: @@ -9,9 +9,8 @@ An `ExampleSelector` must implement two methods: Let's implement a custom `ExampleSelector` that just selects two examples at random. -:::{note} +**Note:** Take a look at the current set of example selector implementations supported in LangChain [here](/docs/modules/model_io/prompts/example_selectors/). -::: @@ -52,7 +51,6 @@ examples = [ # Initialize example selector. example_selector = CustomExampleSelector(examples) - # Select examples example_selector.select_examples({"foo": "foo"}) # -> array([{'foo': '2'}, {'foo': '3'}], dtype=object) diff --git a/docs/extras/modules/model_io/prompts/example_selectors/mmr.ipynb b/docs/extras/modules/model_io/prompts/example_selectors/mmr.ipynb index 137d884f3e..b3f01b6513 100644 --- a/docs/extras/modules/model_io/prompts/example_selectors/mmr.ipynb +++ b/docs/extras/modules/model_io/prompts/example_selectors/mmr.ipynb @@ -30,7 +30,7 @@ " template=\"Input: {input}\\nOutput: {output}\",\n", ")\n", "\n", - "# These are a lot of examples of a pretend task of creating antonyms.\n", + "# Examples of a pretend task of creating antonyms.\n", "examples = [\n", " {\"input\": \"happy\", \"output\": \"sad\"},\n", " {\"input\": \"tall\", \"output\": \"short\"},\n", @@ -48,13 +48,13 @@ "outputs": [], "source": [ "example_selector = MaxMarginalRelevanceExampleSelector.from_examples(\n", - " # This is the list of examples available to select from.\n", + " # The list of examples available to select from.\n", " examples,\n", - " # This is the embedding class used to produce embeddings which are used to measure semantic similarity.\n", + " # The embedding class used to produce embeddings which are used to measure semantic similarity.\n", " OpenAIEmbeddings(),\n", - " # This is the VectorStore class that is used to store the embeddings and do a similarity search over.\n", + " # The VectorStore class that is used to store the embeddings and do a similarity search over.\n", " FAISS,\n", - " # This is the number of examples to produce.\n", + " # The number of examples to produce.\n", " k=2,\n", ")\n", "mmr_prompt = FewShotPromptTemplate(\n", @@ -122,13 +122,13 @@ "# Let's compare this to what we would just get if we went solely off of similarity,\n", "# by using SemanticSimilarityExampleSelector instead of MaxMarginalRelevanceExampleSelector.\n", "example_selector = SemanticSimilarityExampleSelector.from_examples(\n", - " # This is the list of examples available to select from.\n", + " # The list of examples available to select from.\n", " examples,\n", - " # This is the embedding class used to produce embeddings which are used to measure semantic similarity.\n", + " # The embedding class used to produce embeddings which are used to measure semantic similarity.\n", " OpenAIEmbeddings(),\n", - " # This is the VectorStore class that is used to store the embeddings and do a similarity search over.\n", + " # The VectorStore class that is used to store the embeddings and do a similarity search over.\n", " FAISS,\n", - " # This is the number of examples to produce.\n", + " # The number of examples to produce.\n", " k=2,\n", ")\n", "similar_prompt = FewShotPromptTemplate(\n", diff --git a/docs/extras/modules/model_io/prompts/example_selectors/ngram_overlap.ipynb b/docs/extras/modules/model_io/prompts/example_selectors/ngram_overlap.ipynb index 4eef053690..9876603a30 100644 --- a/docs/extras/modules/model_io/prompts/example_selectors/ngram_overlap.ipynb +++ b/docs/extras/modules/model_io/prompts/example_selectors/ngram_overlap.ipynb @@ -28,7 +28,7 @@ " template=\"Input: {input}\\nOutput: {output}\",\n", ")\n", "\n", - "# These are a lot of examples of a pretend task of creating antonyms.\n", + "# Examples of a pretend task of creating antonyms.\n", "examples = [\n", " {\"input\": \"happy\", \"output\": \"sad\"},\n", " {\"input\": \"tall\", \"output\": \"short\"},\n", @@ -45,7 +45,7 @@ "metadata": {}, "outputs": [], "source": [ - "# These are examples of a fictional translation task.\n", + "# Examples of a fictional translation task.\n", "examples = [\n", " {\"input\": \"See Spot run.\", \"output\": \"Ver correr a Spot.\"},\n", " {\"input\": \"My dog barks.\", \"output\": \"Mi perro ladra.\"},\n", @@ -65,11 +65,11 @@ " template=\"Input: {input}\\nOutput: {output}\",\n", ")\n", "example_selector = NGramOverlapExampleSelector(\n", - " # These are the examples it has available to choose from.\n", + " # The examples it has available to choose from.\n", " examples=examples,\n", - " # This is the PromptTemplate being used to format the examples.\n", + " # The PromptTemplate being used to format the examples.\n", " example_prompt=example_prompt,\n", - " # This is the threshold, at which selector stops.\n", + " # The threshold, at which selector stops.\n", " # It is set to -1.0 by default.\n", " threshold=-1.0,\n", " # For negative threshold:\n", diff --git a/docs/extras/modules/model_io/prompts/prompt_templates/validate.mdx b/docs/extras/modules/model_io/prompts/prompt_templates/validate.mdx index e68dbd2e4b..9a36ddaddd 100644 --- a/docs/extras/modules/model_io/prompts/prompt_templates/validate.mdx +++ b/docs/extras/modules/model_io/prompts/prompt_templates/validate.mdx @@ -1,6 +1,6 @@ # Validate template -By default, `PromptTemplate` will validate the `template` string by checking whether the `input_variables` match the variables defined in `template`. You can disable this behavior by setting `validate_template` to `False` +By default, `PromptTemplate` will validate the `template` string by checking whether the `input_variables` match the variables defined in `template`. You can disable this behavior by setting `validate_template` to `False`. ```python template = "I am learning langchain because {reason}." diff --git a/docs/extras/use_cases/apis.ipynb b/docs/extras/use_cases/apis.ipynb index 6013e0052e..1af0a7f3ce 100644 --- a/docs/extras/use_cases/apis.ipynb +++ b/docs/extras/use_cases/apis.ipynb @@ -15,7 +15,7 @@ "\n", "This can be very useful for retrieving context for the LLM to utilize.\n", "\n", - "And, more generally, it allows us to interact with APIs using natural langugage! \n", + "And, more generally, it allows us to interact with APIs using natural language! \n", " \n", "\n", "## Overview\n", @@ -334,7 +334,7 @@ "source": [ "**Web requests**\n", "\n", - "URL requets are such a common use-case that we have the `LLMRequestsChain`, which makes a HTTP GET request. " + "URL requests are such a common use-case that we have the `LLMRequestsChain`, which makes an HTTP GET request. " ] }, { diff --git a/docs/extras/use_cases/code_understanding.ipynb b/docs/extras/use_cases/code_understanding.ipynb index ac670c43d1..60a02b9bb3 100644 --- a/docs/extras/use_cases/code_understanding.ipynb +++ b/docs/extras/use_cases/code_understanding.ipynb @@ -921,6 +921,48 @@ "llm(\"Question: In bash, how do I list all the text files in the current directory that have been modified in the last month? Answer:\")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.chains.question_answering import load_qa_chain\n", + "\n", + "# Prompt\n", + "template = \"\"\"Use the following pieces of context to answer the question at the end. \n", + "If you don't know the answer, just say that you don't know, don't try to make up an answer. \n", + "Use three sentences maximum and keep the answer as concise as possible. \n", + "{context}\n", + "Question: {question}\n", + "Helpful Answer:\"\"\"\n", + "QA_CHAIN_PROMPT = PromptTemplate(\n", + " input_variables=[\"context\", \"question\"],\n", + " template=template,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also use the LangChain Prompt Hub to store and fetch prompts.\n", + "\n", + "This will work with your [LangSmith API key](https://docs.smith.langchain.com/).\n", + "\n", + "Let's try with a default RAG prompt, [here](https://smith.langchain.com/hub/rlm/rag-prompt)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain import hub\n", + "QA_CHAIN_PROMPT = hub.pull(\"rlm/rag-prompt-default\")" + ] + }, { "cell_type": "code", "execution_count": 29, @@ -970,20 +1012,6 @@ } ], "source": [ - "from langchain.chains.question_answering import load_qa_chain\n", - "\n", - "# Prompt\n", - "template = \"\"\"Use the following pieces of context to answer the question at the end. \n", - "If you don't know the answer, just say that you don't know, don't try to make up an answer. \n", - "Use three sentences maximum and keep the answer as concise as possible. \n", - "{context}\n", - "Question: {question}\n", - "Helpful Answer:\"\"\"\n", - "QA_CHAIN_PROMPT = PromptTemplate(\n", - " input_variables=[\"context\", \"question\"],\n", - " template=template,\n", - ")\n", - "\n", "# Docs\n", "question = \"How can I initialize a ReAct agent?\"\n", "docs = retriever.get_relevant_documents(question)\n", diff --git a/docs/extras/use_cases/more/agents/agent_simulations/index.mdx b/docs/extras/use_cases/more/agents/agent_simulations/index.mdx index 4232231b85..8843ba7469 100644 --- a/docs/extras/use_cases/more/agents/agent_simulations/index.mdx +++ b/docs/extras/use_cases/more/agents/agent_simulations/index.mdx @@ -4,7 +4,7 @@ sidebar_position: 0 # Agent simulations -Agent simulations involve interacting one of more agents with each other. +Agent simulations involve interacting one or more agents with each other. Agent simulations generally involve two main components: - Long Term Memory diff --git a/docs/extras/use_cases/more/agents/agents/multi_modal_output_agent.ipynb b/docs/extras/use_cases/more/agents/agents/multi_modal_output_agent.ipynb deleted file mode 100644 index de67f1f43b..0000000000 --- a/docs/extras/use_cases/more/agents/agents/multi_modal_output_agent.ipynb +++ /dev/null @@ -1,298 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cd835d40", - "metadata": {}, - "source": [ - "# Multi-modal outputs: Image & Text" - ] - }, - { - "cell_type": "markdown", - "id": "fa88e03a", - "metadata": {}, - "source": [ - "This notebook shows how non-text producing tools can be used to create multi-modal agents.\n", - "\n", - "This example is limited to text and image outputs and uses UUIDs to transfer content across tools and agents. \n", - "\n", - "This example uses Steamship to generate and store generated images. Generated are auth protected by default. \n", - "\n", - "You can get your Steamship api key here: https://steamship.com/account/api" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "0653da01", - "metadata": {}, - "outputs": [], - "source": [ - "from steamship import Block, Steamship\n", - "import re\n", - "from IPython.display import Image" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f6933033", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain import OpenAI\n", - "from langchain.agents import initialize_agent\n", - "from langchain.agents import AgentType\n", - "from langchain.tools import SteamshipImageGenerationTool" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "71e51e53", - "metadata": {}, - "outputs": [], - "source": [ - "llm = OpenAI(temperature=0)" - ] - }, - { - "cell_type": "markdown", - "id": "a9fc769d", - "metadata": {}, - "source": [ - "## Dall-E " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "cd177dfe", - "metadata": {}, - "outputs": [], - "source": [ - "tools = [SteamshipImageGenerationTool(model_name=\"dall-e\")]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c71b1e46", - "metadata": {}, - "outputs": [], - "source": [ - "mrkl = initialize_agent(\n", - " tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "603aeb9a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3m I need to generate an image of a parrot playing soccer.\n", - "Action: GenerateImage\n", - "Action Input: A parrot wearing a soccer uniform, kicking a soccer ball.\u001b[0m\n", - "Observation: \u001b[36;1m\u001b[1;3mE28BE7C7-D105-41E0-8A5B-2CE21424DFEC\u001b[0m\n", - "Thought:\u001b[32;1m\u001b[1;3m I now have the UUID of the generated image.\n", - "Final Answer: The UUID of the generated image is E28BE7C7-D105-41E0-8A5B-2CE21424DFEC.\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n" - ] - } - ], - "source": [ - "output = mrkl.run(\"How would you visualize a parot playing soccer?\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "25eb4efe", - "metadata": {}, - "outputs": [], - "source": [ - "def show_output(output):\n", - " \"\"\"Display the multi-modal output from the agent.\"\"\"\n", - " UUID_PATTERN = re.compile(\n", - " r\"([0-9A-Za-z]{8}-[0-9A-Za-z]{4}-[0-9A-Za-z]{4}-[0-9A-Za-z]{4}-[0-9A-Za-z]{12})\"\n", - " )\n", - "\n", - " outputs = UUID_PATTERN.split(output)\n", - " outputs = [\n", - " re.sub(r\"^\\W+\", \"\", el) for el in outputs\n", - " ] # Clean trailing and leading non-word characters\n", - "\n", - " for output in outputs:\n", - " maybe_block_id = UUID_PATTERN.search(output)\n", - " if maybe_block_id:\n", - " display(Image(Block.get(Steamship(), _id=maybe_block_id.group()).raw()))\n", - " else:\n", - " print(output, end=\"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "082792a0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The UUID of the generated image is \n", - "\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "show_output(output)" - ] - }, - { - "cell_type": "markdown", - "id": "e247b2c4", - "metadata": {}, - "source": [ - "## StableDiffusion " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "315025e7", - "metadata": {}, - "outputs": [], - "source": [ - "tools = [SteamshipImageGenerationTool(model_name=\"stable-diffusion\")]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7930064a", - "metadata": {}, - "outputs": [], - "source": [ - "mrkl = initialize_agent(\n", - " tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "611a833d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3m I need to generate an image of a parrot playing soccer.\n", - "Action: GenerateImage\n", - "Action Input: A parrot wearing a soccer uniform, kicking a soccer ball.\u001b[0m\n", - "Observation: \u001b[36;1m\u001b[1;3m25BB588F-85E4-4915-82BE-67ADCF974881\u001b[0m\n", - "Thought:\u001b[32;1m\u001b[1;3m I now have the UUID of the generated image.\n", - "Final Answer: The UUID of the generated image is 25BB588F-85E4-4915-82BE-67ADCF974881.\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n" - ] - } - ], - "source": [ - "output = mrkl.run(\"How would you visualize a parot playing soccer?\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d7a3edaf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The UUID of the generated image is \n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n" - ] - } - ], - "source": [ - "show_output(output)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55556043", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/extras/use_cases/more/agents/multi_modal/_image_agent_files/output_10_1.png b/docs/extras/use_cases/more/agents/multi_modal/_image_agent_files/output_10_1.png deleted file mode 100644 index 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b/docs/extras/use_cases/more/agents/multi_modal/image_agent.ipynb deleted file mode 100644 index 107cea32c5..0000000000 --- a/docs/extras/use_cases/more/agents/multi_modal/image_agent.ipynb +++ /dev/null @@ -1,253 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "cd835d40", - "metadata": {}, - "source": [ - "# Multi-modal outputs: Image & Text" - ] - }, - { - "cell_type": "markdown", - "id": "fa88e03a", - "metadata": {}, - "source": [ - "This notebook shows how non-text producing tools can be used to create multi-modal agents.\n", - "\n", - "This example is limited to text and image outputs and uses UUIDs to transfer content across tools and agents. \n", - "\n", - "This example uses Steamship to generate and store generated images. Generated are auth protected by default. \n", - "\n", - "You can get your Steamship api key here: https://steamship.com/account/api" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "0653da01", - "metadata": {}, - "outputs": [], - "source": [ - "from steamship import Block, Steamship\n", - "import re\n", - "from IPython.display import Image" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "f6933033", - "metadata": {}, - "outputs": [], - "source": [ - "from langchain import OpenAI\n", - "from langchain.agents import initialize_agent\n", - "from langchain.agents import AgentType\n", - "from langchain.tools import SteamshipImageGenerationTool" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "71e51e53", - "metadata": {}, - "outputs": [], - "source": [ - "llm = OpenAI(temperature=0)" - ] - }, - { - "cell_type": "markdown", - "id": "a9fc769d", - "metadata": {}, - "source": [ - "## Dall-E " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "cd177dfe", - "metadata": {}, - "outputs": [], - "source": [ - "tools = [SteamshipImageGenerationTool(model_name=\"dall-e\")]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "c71b1e46", - "metadata": {}, - "outputs": [], - "source": [ - "mrkl = initialize_agent(\n", - " tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "603aeb9a", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3m I need to generate an image of a parrot playing soccer.\n", - "Action: GenerateImage\n", - "Action Input: A parrot wearing a soccer uniform, kicking a soccer ball.\u001b[0m\n", - "Observation: \u001b[36;1m\u001b[1;3mE28BE7C7-D105-41E0-8A5B-2CE21424DFEC\u001b[0m\n", - "Thought:\u001b[32;1m\u001b[1;3m I now have the UUID of the generated image.\n", - "Final Answer: The UUID of the generated image is E28BE7C7-D105-41E0-8A5B-2CE21424DFEC.\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n" - ] - } - ], - "source": [ - "output = mrkl.run(\"How would you visualize a parot playing soccer?\")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "25eb4efe", - "metadata": {}, - "outputs": [], - "source": [ - "def show_output(output):\n", - " \"\"\"Display the multi-modal output from the agent.\"\"\"\n", - " UUID_PATTERN = re.compile(\n", - " r\"([0-9A-Za-z]{8}-[0-9A-Za-z]{4}-[0-9A-Za-z]{4}-[0-9A-Za-z]{4}-[0-9A-Za-z]{12})\"\n", - " )\n", - "\n", - " outputs = UUID_PATTERN.split(output)\n", - " outputs = [\n", - " re.sub(r\"^\\W+\", \"\", el) for el in outputs\n", - " ] # Clean trailing and leading non-word characters\n", - "\n", - " for output in outputs:\n", - " maybe_block_id = UUID_PATTERN.search(output)\n", - " if maybe_block_id:\n", - " display(Image(Block.get(Steamship(), _id=maybe_block_id.group()).raw()))\n", - " else:\n", - " print(output, end=\"\\n\\n\")" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "082792a0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The UUID of the generated image is \n", - "\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "show_output(output)" - ] - }, - { - "cell_type": "markdown", - "id": "e247b2c4", - "metadata": {}, - "source": [ - "## StableDiffusion " - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "315025e7", - "metadata": {}, - "outputs": [], - "source": [ - "tools = [SteamshipImageGenerationTool(model_name=\"stable-diffusion\")]" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "7930064a", - "metadata": {}, - "outputs": [], - "source": [ - "mrkl = initialize_agent(\n", - " tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "611a833d", - "metadata": {}, - "outputs": [], - "source": [ - "output = mrkl.run(\"How would you visualize a parot playing soccer?\")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d7a3edaf", - "metadata": {}, - "outputs": [], - "source": [ - "show_output(output)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ffdf9c53", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.16" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/docs/extras/use_cases/more/agents/multi_modal/multi_modal_output_agent.ipynb b/docs/extras/use_cases/more/agents/multi_modal/multi_modal_output_agent.ipynb index 10723d780b..cc32520acd 100644 --- a/docs/extras/use_cases/more/agents/multi_modal/multi_modal_output_agent.ipynb +++ b/docs/extras/use_cases/more/agents/multi_modal/multi_modal_output_agent.ipynb @@ -24,7 +24,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "0653da01", "metadata": {}, "outputs": [], @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "f6933033", "metadata": {}, "outputs": [], @@ -49,7 +49,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "71e51e53", "metadata": {}, "outputs": [], @@ -67,7 +67,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "cd177dfe", "metadata": {}, "outputs": [], @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "c71b1e46", "metadata": {}, "outputs": [], @@ -89,35 +89,17 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "603aeb9a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3m I need to generate an image of a parrot playing soccer.\n", - "Action: GenerateImage\n", - "Action Input: A parrot wearing a soccer uniform, kicking a soccer ball.\u001b[0m\n", - "Observation: \u001b[36;1m\u001b[1;3mE28BE7C7-D105-41E0-8A5B-2CE21424DFEC\u001b[0m\n", - "Thought:\u001b[32;1m\u001b[1;3m I now have the UUID of the generated image.\n", - "Final Answer: The UUID of the generated image is E28BE7C7-D105-41E0-8A5B-2CE21424DFEC.\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "output = mrkl.run(\"How would you visualize a parot playing soccer?\")" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "25eb4efe", "metadata": {}, "outputs": [], @@ -141,35 +123,6 @@ " print(output, end=\"\\n\\n\")" ] }, - { - "cell_type": "code", - "execution_count": 9, - "id": "082792a0", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The UUID of the generated image is \n", - "\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "show_output(output)" - ] - }, { "cell_type": "markdown", "id": "e247b2c4", @@ -180,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "315025e7", "metadata": {}, "outputs": [], @@ -190,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "7930064a", "metadata": {}, "outputs": [], @@ -202,76 +155,13 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "611a833d", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n", - "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3m I need to generate an image of a parrot playing soccer.\n", - "Action: GenerateImage\n", - "Action Input: A parrot wearing a soccer uniform, kicking a soccer ball.\u001b[0m\n", - "Observation: \u001b[36;1m\u001b[1;3m25BB588F-85E4-4915-82BE-67ADCF974881\u001b[0m\n", - "Thought:\u001b[32;1m\u001b[1;3m I now have the UUID of the generated image.\n", - "Final Answer: The UUID of the generated image is 25BB588F-85E4-4915-82BE-67ADCF974881.\u001b[0m\n", - "\n", - "\u001b[1m> Finished chain.\u001b[0m\n" - ] - } - ], + "outputs": [], "source": [ "output = mrkl.run(\"How would you visualize a parot playing soccer?\")" ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "d7a3edaf", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The UUID of the generated image is \n", - "\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "\n" - ] - } - ], - "source": [ - "show_output(output)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "55556043", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -290,7 +180,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.2" + "version": "3.11.3" } }, "nbformat": 4, diff --git a/docs/extras/use_cases/question_answering/how_to/code/code-analysis-deeplake.ipynb b/docs/extras/use_cases/question_answering/how_to/code/code-analysis-deeplake.ipynb index 69cfb0b9fe..56b24a5e8a 100644 --- a/docs/extras/use_cases/question_answering/how_to/code/code-analysis-deeplake.ipynb +++ b/docs/extras/use_cases/question_answering/how_to/code/code-analysis-deeplake.ipynb @@ -888,6 +888,7 @@ "- ConversationChain\n", "- ChatVectorDBChain\n", "- ConversationalRetrievalChain\n", + "- FalkorDBQAChain\n", "- FlareChain\n", "- ArangoGraphQAChain\n", "- GraphQAChain\n", diff --git a/docs/extras/use_cases/question_answering/how_to/local_retrieval_qa.ipynb b/docs/extras/use_cases/question_answering/how_to/local_retrieval_qa.ipynb index d01b0bb3e2..a1fe71e8b5 100644 --- a/docs/extras/use_cases/question_answering/how_to/local_retrieval_qa.ipynb +++ b/docs/extras/use_cases/question_answering/how_to/local_retrieval_qa.ipynb @@ -11,7 +11,9 @@ "\n", "LangChain has [integrations](https://integrations.langchain.com/) with many open source LLMs that can be run locally.\n", "\n", - "For example, here we show how to run `GPT4All` or `Llama-v2` locally (e.g., on your laptop) using local embeddings and a local LLM.\n", + "See [here](docs/guides/local_llms) for setup instructions for these LLMs. \n", + "\n", + "For example, here we show how to run `GPT4All` or `LLaMA2` locally (e.g., on your laptop) using local embeddings and a local LLM.\n", "\n", "## Document Loading \n", "\n", @@ -25,7 +27,7 @@ "metadata": {}, "outputs": [], "source": [ - "pip install gpt4all chromadb" + "pip install gpt4all chromadb langchainhub" ] }, { @@ -40,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 1, "id": "f8cf5765", "metadata": {}, "outputs": [], @@ -66,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 2, "id": "fdce8923", "metadata": {}, "outputs": [ @@ -76,6 +78,13 @@ "text": [ "Found model file at /Users/rlm/.cache/gpt4all/ggml-all-MiniLM-L6-v2-f16.bin\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "objc[49534]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x131614208) and /Users/rlm/miniforge3/envs/llama2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libllamamodel-mainline-metal.dylib (0x131988208). One of the two will be used. Which one is undefined.\n" + ] } ], "source": [ @@ -95,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "id": "b0c55e98", "metadata": {}, "outputs": [ @@ -105,7 +114,7 @@ "4" ] }, - "execution_count": 10, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -118,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "id": "32b43339", "metadata": {}, "outputs": [ @@ -128,7 +137,7 @@ "Document(page_content='Task decomposition can be done (1) by LLM with simple prompting like \"Steps for XYZ.\\\\n1.\", \"What are the subgoals for achieving XYZ?\", (2) by using task-specific instructions; e.g. \"Write a story outline.\" for writing a novel, or (3) with human inputs.', metadata={'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en', 'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': \"LLM Powered Autonomous Agents | Lil'Log\"})" ] }, - "execution_count": 11, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -144,9 +153,15 @@ "source": [ "## Model \n", "\n", - "### Llama-v2\n", + "### LLaMA2\n", "\n", - "Download a GGML converted model (e.g., [here](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/tree/main))." + "Note: new versions of `llama-cpp-python` use GGUF model files (see [here](https://github.com/abetlen/llama-cpp-python/pull/633)).\n", + "\n", + "If you have an existing GGML model, see [here](docs/integrations/llms/llamacpp) for instructions for conversion for GGUF. \n", + " \n", + "And / or, you can download a GGUF converted model (e.g., [here](https://huggingface.co/TheBloke)).\n", + "\n", + "Finally, as noted in detail [here](docs/guides/local_llms) install `llama-cpp-python`" ] }, { @@ -180,16 +195,16 @@ { "cell_type": "code", "execution_count": null, - "id": "2fd6fe25", + "id": "5884779a-957e-4c4c-b447-bc8385edc67e", "metadata": {}, "outputs": [], "source": [ - "! CMAKE_ARGS=\"-DLLAMA_METAL=on\" FORCE_CMAKE=1 pip install -U llama-cpp-python --no-cache-dir" + "! CMAKE_ARGS=\"-DLLAMA_METAL=on\" FORCE_CMAKE=1 /Users/rlm/miniforge3/envs/llama/bin/pip install -U llama-cpp-python --no-cache-dir" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "cd7164e3", "metadata": {}, "outputs": [], @@ -209,78 +224,10 @@ }, { "cell_type": "code", - "execution_count": 28, - "id": "74718579", + "execution_count": null, + "id": "af1176bb-d52a-4cf0-b983-8b7433d45b4f", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "llama.cpp: loading model from /Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\n", - "llama_model_load_internal: format = ggjt v3 (latest)\n", - "llama_model_load_internal: n_vocab = 32000\n", - "llama_model_load_internal: n_ctx = 2048\n", - "llama_model_load_internal: n_embd = 5120\n", - "llama_model_load_internal: n_mult = 256\n", - "llama_model_load_internal: n_head = 40\n", - "llama_model_load_internal: n_layer = 40\n", - "llama_model_load_internal: n_rot = 128\n", - "llama_model_load_internal: freq_base = 10000.0\n", - "llama_model_load_internal: freq_scale = 1\n", - "llama_model_load_internal: ftype = 2 (mostly Q4_0)\n", - "llama_model_load_internal: n_ff = 13824\n", - "llama_model_load_internal: model size = 13B\n", - "llama_model_load_internal: ggml ctx size = 0.09 MB\n", - "llama_model_load_internal: mem required = 8819.71 MB (+ 1608.00 MB per state)\n", - "llama_new_context_with_model: kv self size = 1600.00 MB\n", - "ggml_metal_init: allocating\n", - "ggml_metal_init: using MPS\n", - "ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/llama_cpp/ggml-metal.metal'\n", - "ggml_metal_init: loaded kernel_add 0x76add7460\n", - "ggml_metal_init: loaded kernel_mul 0x76add5090\n", - "ggml_metal_init: loaded kernel_mul_row 0x76addae00\n", - "ggml_metal_init: loaded kernel_scale 0x76adb2940\n", - "ggml_metal_init: loaded kernel_silu 0x76adb8610\n", - "ggml_metal_init: loaded kernel_relu 0x76addb700\n", - "ggml_metal_init: loaded kernel_gelu 0x76addc100\n", - "ggml_metal_init: loaded kernel_soft_max 0x76addcb80\n", - "ggml_metal_init: loaded kernel_diag_mask_inf 0x76addd600\n", - "ggml_metal_init: loaded kernel_get_rows_f16 0x295f16380\n", - "ggml_metal_init: loaded kernel_get_rows_q4_0 0x295f165e0\n", - "ggml_metal_init: loaded kernel_get_rows_q4_1 0x295f16840\n", - "ggml_metal_init: loaded kernel_get_rows_q2_K 0x295f16aa0\n", - "ggml_metal_init: loaded kernel_get_rows_q3_K 0x295f16d00\n", - "ggml_metal_init: loaded kernel_get_rows_q4_K 0x295f16f60\n", - "ggml_metal_init: loaded kernel_get_rows_q5_K 0x295f171c0\n", - "ggml_metal_init: loaded kernel_get_rows_q6_K 0x295f17420\n", - "ggml_metal_init: loaded kernel_rms_norm 0x295f17680\n", - "ggml_metal_init: loaded kernel_norm 0x295f178e0\n", - "ggml_metal_init: loaded kernel_mul_mat_f16_f32 0x295f17b40\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32 0x295f17da0\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32 0x295f18000\n", - "ggml_metal_init: loaded kernel_mul_mat_q2_K_f32 0x7962b9900\n", - "ggml_metal_init: loaded kernel_mul_mat_q3_K_f32 0x7962bf5f0\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_K_f32 0x7962bc630\n", - "ggml_metal_init: loaded kernel_mul_mat_q5_K_f32 0x142045960\n", - "ggml_metal_init: loaded kernel_mul_mat_q6_K_f32 0x7962ba2b0\n", - "ggml_metal_init: loaded kernel_rope 0x7962c35f0\n", - "ggml_metal_init: loaded kernel_alibi_f32 0x7962c30b0\n", - "ggml_metal_init: loaded kernel_cpy_f32_f16 0x7962c15b0\n", - "ggml_metal_init: loaded kernel_cpy_f32_f32 0x7962beb10\n", - "ggml_metal_init: loaded kernel_cpy_f16_f16 0x7962bf060\n", - "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n", - "ggml_metal_init: hasUnifiedMemory = true\n", - "ggml_metal_init: maxTransferRate = built-in GPU\n", - "ggml_metal_add_buffer: allocated 'data ' buffer, size = 6984.06 MB, (35852.94 / 21845.34), warning: current allocated size is greater than the recommended max working set size\n", - "ggml_metal_add_buffer: allocated 'eval ' buffer, size = 1026.00 MB, (36878.94 / 21845.34), warning: current allocated size is greater than the recommended max working set size\n", - "ggml_metal_add_buffer: allocated 'kv ' buffer, size = 1602.00 MB, (38480.94 / 21845.34), warning: current allocated size is greater than the recommended max working set size\n", - "ggml_metal_add_buffer: allocated 'scr0 ' buffer, size = 298.00 MB, (38778.94 / 21845.34), warning: current allocated size is greater than the recommended max working set size\n", - "AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 | \n", - "ggml_metal_add_buffer: allocated 'scr1 ' buffer, size = 512.00 MB, (39290.94 / 21845.34), warning: current allocated size is greater than the recommended max working set size\n" - ] - } - ], + "outputs": [], "source": [ "n_gpu_layers = 1 # Metal set to 1 is enough.\n", "n_batch = 512 # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.\n", @@ -288,7 +235,7 @@ "\n", "# Make sure the model path is correct for your system!\n", "llm = LlamaCpp(\n", - " model_path=\"/Users/rlm/Desktop/Code/llama.cpp/llama-2-13b-chat.ggmlv3.q4_0.bin\",\n", + " model_path=\"/Users/rlm/Desktop/Code/llama.cpp/models/llama-2-13b-chat.ggufv3.q4_0.bin\",\n", " n_gpu_layers=n_gpu_layers,\n", " n_batch=n_batch,\n", " n_ctx=2048,\n", @@ -313,8 +260,8 @@ }, { "cell_type": "code", - "execution_count": 30, - "id": "e940de71", + "execution_count": 11, + "id": "bf0162e0-8c41-4344-88ae-ff2bbaeb12eb", "metadata": {}, "outputs": [ { @@ -328,13 +275,17 @@ "name": "stdout", "output_type": "stream", "text": [ + "by jonathan \n", "\n", - "Setting: The Late Show with Stephen Colbert. The studio audience is filled with fans of both comedians, and the energy is electric. The two comedians are seated at a table, ready to begin their epic rap battle.\n", + "Here's the hypothetical rap battle:\n", "\n", - "Stephen Colbert: (smirking) Oh, you think you can take me down, John? You're just a Brit with a funny accent, and I'm the king of comedy!\n", - "John Oliver: (grinning) Oh, you think you're tough, Stephen? You're just a has-been from South Carolina, and I'm the future of comedy!\n", - "The battle begins, with each comedian delivering clever rhymes and witty insults. Here are a few lines that might be included:\n", - "Stephen Colbert: (rapping) You may have a big brain, John, but you can't touch my charm / I've got the audience in stitches, while you're just a blemish on the screen / Your accent is so thick, it's like trying to hear a speech through a mouthful of marshmallows / You may have" + "[Stephen Colbert]: Yo, this is Stephen Colbert, known for my comedy show. I'm here to put some sense in your mind, like an enema do-go. Your opponent? A man of laughter and witty quips, John Oliver! Now let's see who gets the most laughs while taking shots at each other\n", + "\n", + "[John Oliver]: Yo, this is John Oliver, known for my own comedy show. I'm here to take your mind on an adventure through wit and humor. But first, allow me to you to our contestant: Stephen Colbert! His show has been around since the '90s, but it's time to see who can out-rap whom\n", + "\n", + "[Stephen Colbert]: You claim to be a witty man, John Oliver, with your British charm and clever remarks. But my knows that I'm America's funnyman! Who's the one taking you? Nobody!\n", + "\n", + "[John Oliver]: Hey Stephen Colbert, don't get too cocky. You may" ] }, { @@ -342,29 +293,26 @@ "output_type": "stream", "text": [ "\n", - "llama_print_timings: load time = 2201.54 ms\n", - "llama_print_timings: sample time = 182.54 ms / 256 runs ( 0.71 ms per token, 1402.41 tokens per second)\n", - "llama_print_timings: prompt eval time = 0.00 ms / 1 tokens ( 0.00 ms per token, inf tokens per second)\n", - "llama_print_timings: eval time = 8484.62 ms / 256 runs ( 33.14 ms per token, 30.17 tokens per second)\n", - "llama_print_timings: total time = 9000.62 ms\n" + "llama_print_timings: load time = 4481.74 ms\n", + "llama_print_timings: sample time = 183.05 ms / 256 runs ( 0.72 ms per token, 1398.53 tokens per second)\n", + "llama_print_timings: prompt eval time = 456.05 ms / 13 tokens ( 35.08 ms per token, 28.51 tokens per second)\n", + "llama_print_timings: eval time = 7375.20 ms / 255 runs ( 28.92 ms per token, 34.58 tokens per second)\n", + "llama_print_timings: total time = 8388.92 ms\n" ] }, { "data": { "text/plain": [ - "\"\\nSetting: The Late Show with Stephen Colbert. The studio audience is filled with fans of both comedians, and the energy is electric. The two comedians are seated at a table, ready to begin their epic rap battle.\\n\\nStephen Colbert: (smirking) Oh, you think you can take me down, John? You're just a Brit with a funny accent, and I'm the king of comedy!\\nJohn Oliver: (grinning) Oh, you think you're tough, Stephen? You're just a has-been from South Carolina, and I'm the future of comedy!\\nThe battle begins, with each comedian delivering clever rhymes and witty insults. Here are a few lines that might be included:\\nStephen Colbert: (rapping) You may have a big brain, John, but you can't touch my charm / I've got the audience in stitches, while you're just a blemish on the screen / Your accent is so thick, it's like trying to hear a speech through a mouthful of marshmallows / You may have\"" + "\"by jonathan \\n\\nHere's the hypothetical rap battle:\\n\\n[Stephen Colbert]: Yo, this is Stephen Colbert, known for my comedy show. I'm here to put some sense in your mind, like an enema do-go. Your opponent? A man of laughter and witty quips, John Oliver! Now let's see who gets the most laughs while taking shots at each other\\n\\n[John Oliver]: Yo, this is John Oliver, known for my own comedy show. I'm here to take your mind on an adventure through wit and humor. But first, allow me to you to our contestant: Stephen Colbert! His show has been around since the '90s, but it's time to see who can out-rap whom\\n\\n[Stephen Colbert]: You claim to be a witty man, John Oliver, with your British charm and clever remarks. But my knows that I'm America's funnyman! Who's the one taking you? Nobody!\\n\\n[John Oliver]: Hey Stephen Colbert, don't get too cocky. You may\"" ] }, - "execution_count": 30, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "prompt = \"\"\"\n", - "Question: A rap battle between Stephen Colbert and John Oliver\n", - "\"\"\"\n", - "llm(prompt)" + "llm(\"Simulate a rap battle between Stephen Colbert and John Oliver\")" ] }, { @@ -389,85 +337,10 @@ }, { "cell_type": "code", - "execution_count": 2, - "id": "4a24eef1", + "execution_count": null, + "id": "57c1aec0-04c7-479e-b9bf-af3c547ba0a3", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found model file at /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "objc[47842]: Class GGMLMetalClass is implemented in both /Users/rlm/anaconda3/envs/lcn2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x29f48c208) and /Users/rlm/anaconda3/envs/lcn2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libllamamodel-mainline-metal.dylib (0x29f970208). One of the two will be used. Which one is undefined.\n", - "llama.cpp: using Metal\n", - "llama.cpp: loading model from /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n", - "llama_model_load_internal: format = ggjt v3 (latest)\n", - "llama_model_load_internal: n_vocab = 32001\n", - "llama_model_load_internal: n_ctx = 2048\n", - "llama_model_load_internal: n_embd = 5120\n", - "llama_model_load_internal: n_mult = 256\n", - "llama_model_load_internal: n_head = 40\n", - "llama_model_load_internal: n_layer = 40\n", - "llama_model_load_internal: n_rot = 128\n", - "llama_model_load_internal: ftype = 2 (mostly Q4_0)\n", - "llama_model_load_internal: n_ff = 13824\n", - "llama_model_load_internal: n_parts = 1\n", - "llama_model_load_internal: model size = 13B\n", - "llama_model_load_internal: ggml ctx size = 0.09 MB\n", - "llama_model_load_internal: mem required = 9031.71 MB (+ 1608.00 MB per state)\n", - "llama_new_context_with_model: kv self size = 1600.00 MB\n", - "ggml_metal_init: allocating\n", - "ggml_metal_init: using MPS\n", - "ggml_metal_init: loading '/Users/rlm/anaconda3/envs/lcn2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/ggml-metal.metal'\n", - "ggml_metal_init: loaded kernel_add 0x115fcbfb0\n", - "ggml_metal_init: loaded kernel_mul 0x115fcd4a0\n", - "ggml_metal_init: loaded kernel_mul_row 0x115fce850\n", - "ggml_metal_init: loaded kernel_scale 0x115fcd700\n", - "ggml_metal_init: loaded kernel_silu 0x115fcd960\n", - "ggml_metal_init: loaded kernel_relu 0x115fcfd50\n", - "ggml_metal_init: loaded kernel_gelu 0x115fd03c0\n", - "ggml_metal_init: loaded kernel_soft_max 0x115fcf640\n", - "ggml_metal_init: loaded kernel_diag_mask_inf 0x115fd07f0\n", - "ggml_metal_init: loaded kernel_get_rows_f16 0x1147b2450\n", - "ggml_metal_init: loaded kernel_get_rows_q4_0 0x11479d1d0\n", - "ggml_metal_init: loaded kernel_get_rows_q4_1 0x1147ad1f0\n", - "ggml_metal_init: loaded kernel_get_rows_q2_k 0x1147aef50\n", - "ggml_metal_init: loaded kernel_get_rows_q3_k 0x1147af1b0\n", - "ggml_metal_init: loaded kernel_get_rows_q4_k 0x1147af410\n", - "ggml_metal_init: loaded kernel_get_rows_q5_k 0x1147affa0\n", - "ggml_metal_init: loaded kernel_get_rows_q6_k 0x1147b0200\n", - "ggml_metal_init: loaded kernel_rms_norm 0x1147b0460\n", - "ggml_metal_init: loaded kernel_norm 0x1147bfc90\n", - "ggml_metal_init: loaded kernel_mul_mat_f16_f32 0x1147c0230\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32 0x1147c0490\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32 0x1147c06f0\n", - "ggml_metal_init: loaded kernel_mul_mat_q2_k_f32 0x1147c0950\n", - "ggml_metal_init: loaded kernel_mul_mat_q3_k_f32 0x1147c0bb0\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_k_f32 0x1147c0e10\n", - "ggml_metal_init: loaded kernel_mul_mat_q5_k_f32 0x1147c1070\n", - "ggml_metal_init: loaded kernel_mul_mat_q6_k_f32 0x1147c13d0\n", - "ggml_metal_init: loaded kernel_rope 0x1147c1a00\n", - "ggml_metal_init: loaded kernel_alibi_f32 0x1147c2120\n", - "ggml_metal_init: loaded kernel_cpy_f32_f16 0x115fd1690\n", - "ggml_metal_init: loaded kernel_cpy_f32_f32 0x115fd1c60\n", - "ggml_metal_init: loaded kernel_cpy_f16_f16 0x115fd2d40\n", - "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n", - "ggml_metal_init: hasUnifiedMemory = true\n", - "ggml_metal_init: maxTransferRate = built-in GPU\n", - "ggml_metal_add_buffer: allocated 'data ' buffer, size = 6984.06 MB, ( 6984.45 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'eval ' buffer, size = 1024.00 MB, ( 8008.45 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'kv ' buffer, size = 1602.00 MB, ( 9610.45 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'scr0 ' buffer, size = 512.00 MB, (10122.45 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'scr1 ' buffer, size = 512.00 MB, (10634.45 / 21845.34)\n" - ] - } - ], + "outputs": [], "source": [ "from langchain.llms import GPT4All\n", "\n", @@ -495,9 +368,7 @@ "cell_type": "code", "execution_count": 27, "id": "18a3716d", - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "name": "stderr", @@ -573,9 +444,31 @@ "`chain_type=\"stuff\"` (see [here](https://python.langchain.com/docs/modules/chains/document/stuff)) means that all the docs will be added (stuffed) into a prompt." ] }, + { + "cell_type": "markdown", + "id": "3cce6977-52e7-4944-89b4-c161d04f6698", + "metadata": {}, + "source": [ + "We can also use the LangChain Prompt Hub to store and fetch prompts that are model-specific.\n", + "\n", + "This will work with your [LangSmith API key](https://docs.smith.langchain.com/).\n", + "\n", + "Let's try with a default RAG prompt, [here](https://smith.langchain.com/hub/rlm/rag-prompt)." + ] + }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, + "id": "cc638992-0924-41c0-8dae-8cf683e72b16", + "metadata": {}, + "outputs": [], + "source": [ + "pip install langchainhub" + ] + }, + { + "cell_type": "code", + "execution_count": 28, "id": "c01c1725", "metadata": {}, "outputs": [ @@ -590,7 +483,8 @@ "name": "stdout", "output_type": "stream", "text": [ - " Hi there! There are three main approaches to task decomposition. One is using LLM with simple prompting like \"Steps for XYZ.\" or \"What are the subgoals for achieving XYZ?\" Another approach is by using task-specific instructions, such as \"Write a story outline\" for writing a novel. Finally, task decomposition can also be done with human inputs. Thanks for asking!" + "\n", + "Task can be done by down a task into smaller subtasks, using simple prompting like \"Steps for XYZ.\" or task-specific like \"Write a story outline\" for writing a novel." ] }, { @@ -598,43 +492,118 @@ "output_type": "stream", "text": [ "\n", - "llama_print_timings: load time = 1191.88 ms\n", - "llama_print_timings: sample time = 61.21 ms / 85 runs ( 0.72 ms per token, 1388.64 tokens per second)\n", - "llama_print_timings: prompt eval time = 8014.11 ms / 267 tokens ( 30.02 ms per token, 33.32 tokens per second)\n", - "llama_print_timings: eval time = 2908.17 ms / 84 runs ( 34.62 ms per token, 28.88 tokens per second)\n", - "llama_print_timings: total time = 11096.23 ms\n" + "llama_print_timings: load time = 11326.20 ms\n", + "llama_print_timings: sample time = 33.03 ms / 47 runs ( 0.70 ms per token, 1422.86 tokens per second)\n", + "llama_print_timings: prompt eval time = 1387.31 ms / 242 tokens ( 5.73 ms per token, 174.44 tokens per second)\n", + "llama_print_timings: eval time = 1321.62 ms / 46 runs ( 28.73 ms per token, 34.81 tokens per second)\n", + "llama_print_timings: total time = 2801.08 ms\n" ] }, { "data": { "text/plain": [ - "{'output_text': ' Hi there! There are three main approaches to task decomposition. One is using LLM with simple prompting like \"Steps for XYZ.\" or \"What are the subgoals for achieving XYZ?\" Another approach is by using task-specific instructions, such as \"Write a story outline\" for writing a novel. Finally, task decomposition can also be done with human inputs. Thanks for asking!'}" + "{'output_text': '\\nTask can be done by down a task into smaller subtasks, using simple prompting like \"Steps for XYZ.\" or task-specific like \"Write a story outline\" for writing a novel.'}" ] }, - "execution_count": 20, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "# Prompt \n", + "from langchain import hub\n", + "rag_prompt = hub.pull(\"rlm/rag-prompt\")\n", "from langchain.chains.question_answering import load_qa_chain\n", - "\n", - "# Prompt\n", - "template = \"\"\"Use the following pieces of context to answer the question at the end. \n", - "If you don't know the answer, just say that you don't know, don't try to make up an answer. \n", - "Use three sentences maximum and keep the answer as concise as possible. \n", - "Always say \"thanks for asking!\" at the end of the answer. \n", - "{context}\n", - "Question: {question}\n", - "Helpful Answer:\"\"\"\n", - "QA_CHAIN_PROMPT = PromptTemplate(\n", - " input_variables=[\"context\", \"question\"],\n", - " template=template,\n", - ")\n", - "\n", "# Chain\n", - "chain = load_qa_chain(llm, chain_type=\"stuff\", prompt=QA_CHAIN_PROMPT)\n", - "\n", + "chain = load_qa_chain(llm, chain_type=\"stuff\", prompt=rag_prompt)\n", + "# Run\n", + "chain({\"input_documents\": docs, \"question\": question}, return_only_outputs=True)" + ] + }, + { + "cell_type": "markdown", + "id": "2e5913f0-cf92-4e21-8794-0502ba11b202", + "metadata": {}, + "source": [ + "Now, let's try with [a prompt specifically for LLaMA](https://smith.langchain.com/hub/rlm/rag-prompt-llama), which [includes special tokens](https://huggingface.co/blog/llama2#how-to-prompt-llama-2)." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "78f6862d-b7a6-4e03-84e4-45667185bf9b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ChatPromptTemplate(input_variables=['question', 'context'], output_parser=None, partial_variables={}, messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['question', 'context'], output_parser=None, partial_variables={}, template=\"[INST]<> You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question. If you don't know the answer, just say that you don't know. Use three sentences maximum and keep the answer concise.<> \\nQuestion: {question} \\nContext: {context} \\nAnswer: [/INST]\", template_format='f-string', validate_template=True), additional_kwargs={})])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Prompt\n", + "rag_prompt_llama = hub.pull(\"rlm/rag-prompt-llama\")\n", + "rag_prompt_llama" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "67cefb46-acd3-4c2a-a8f6-b62c7c3e30dc", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Llama.generate: prefix-match hit\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Sure, I'd be happy to help! Based on the context, here are some to task:\n", + "\n", + "1. LLM with simple prompting: This using a large model (LLM) with simple prompts like \"Steps for XYZ\" or \"What are the subgoals for achieving XYZ?\" to decompose tasks into smaller steps.\n", + "2. Task-specific: Another is to use task-specific, such as \"Write a story outline\" for writing a novel, to guide the of tasks.\n", + "3. Human inputs:, human inputs can be used to supplement the process, in cases where the task a high degree of creativity or expertise.\n", + "\n", + "As fores in long-term and task, one major is that LLMs to adjust plans when faced with errors, making them less robust to humans who learn from trial and error." + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "llama_print_timings: load time = 11326.20 ms\n", + "llama_print_timings: sample time = 144.81 ms / 207 runs ( 0.70 ms per token, 1429.47 tokens per second)\n", + "llama_print_timings: prompt eval time = 1506.13 ms / 258 tokens ( 5.84 ms per token, 171.30 tokens per second)\n", + "llama_print_timings: eval time = 6231.92 ms / 206 runs ( 30.25 ms per token, 33.06 tokens per second)\n", + "llama_print_timings: total time = 8158.41 ms\n" + ] + }, + { + "data": { + "text/plain": [ + "{'output_text': ' Sure, I\\'d be happy to help! Based on the context, here are some to task:\\n\\n1. LLM with simple prompting: This using a large model (LLM) with simple prompts like \"Steps for XYZ\" or \"What are the subgoals for achieving XYZ?\" to decompose tasks into smaller steps.\\n2. Task-specific: Another is to use task-specific, such as \"Write a story outline\" for writing a novel, to guide the of tasks.\\n3. Human inputs:, human inputs can be used to supplement the process, in cases where the task a high degree of creativity or expertise.\\n\\nAs fores in long-term and task, one major is that LLMs to adjust plans when faced with errors, making them less robust to humans who learn from trial and error.'}" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Chain\n", + "chain = load_qa_chain(llm, chain_type=\"stuff\", prompt=rag_prompt_llama)\n", "# Run\n", "chain({\"input_documents\": docs, \"question\": question}, return_only_outputs=True)" ] @@ -655,7 +624,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 29, "id": "86c7a349", "metadata": {}, "outputs": [], @@ -665,13 +634,13 @@ "qa_chain = RetrievalQA.from_chain_type(\n", " llm,\n", " retriever=vectorstore.as_retriever(),\n", - " chain_type_kwargs={\"prompt\": QA_CHAIN_PROMPT},\n", + " chain_type_kwargs={\"prompt\": rag_prompt_llama},\n", ")" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 30, "id": "112ca227", "metadata": {}, "outputs": [ @@ -686,8 +655,15 @@ "name": "stdout", "output_type": "stream", "text": [ - " \n", - "The three approaches to Task decomposition are LLMs with simple prompting, task-specific instructions, or human inputs. Thanks for asking!" + " Sure! Based on the context, here's my answer to your:\n", + "\n", + "There are several to task,:\n", + "\n", + "1. LLM-based with simple prompting, such as \"Steps for XYZ\" or \"What are the subgoals for achieving XYZ?\"\n", + "2. Task-specific, like \"Write a story outline\" for writing a novel.\n", + "3. Human inputs to guide the process.\n", + "\n", + "These can be used to decompose complex tasks into smaller, more manageable subtasks, which can help improve the and effectiveness of task. However, long-term and task can being due to the need to plan over a lengthy history and explore the space., LLMs may to adjust plans when faced with errors, making them less robust to human learners who can learn from trial and error." ] }, { @@ -695,21 +671,21 @@ "output_type": "stream", "text": [ "\n", - "llama_print_timings: load time = 1191.88 ms\n", - "llama_print_timings: sample time = 22.78 ms / 31 runs ( 0.73 ms per token, 1360.66 tokens per second)\n", - "llama_print_timings: prompt eval time = 0.00 ms / 1 tokens ( 0.00 ms per token, inf tokens per second)\n", - "llama_print_timings: eval time = 1320.23 ms / 31 runs ( 42.59 ms per token, 23.48 tokens per second)\n", - "llama_print_timings: total time = 1387.70 ms\n" + "llama_print_timings: load time = 11326.20 ms\n", + "llama_print_timings: sample time = 139.20 ms / 200 runs ( 0.70 ms per token, 1436.76 tokens per second)\n", + "llama_print_timings: prompt eval time = 1532.26 ms / 258 tokens ( 5.94 ms per token, 168.38 tokens per second)\n", + "llama_print_timings: eval time = 5977.62 ms / 199 runs ( 30.04 ms per token, 33.29 tokens per second)\n", + "llama_print_timings: total time = 7916.21 ms\n" ] }, { "data": { "text/plain": [ "{'query': 'What are the approaches to Task Decomposition?',\n", - " 'result': ' \\nThe three approaches to Task decomposition are LLMs with simple prompting, task-specific instructions, or human inputs. Thanks for asking!'}" + " 'result': ' Sure! Based on the context, here\\'s my answer to your:\\n\\nThere are several to task,:\\n\\n1. LLM-based with simple prompting, such as \"Steps for XYZ\" or \"What are the subgoals for achieving XYZ?\"\\n2. Task-specific, like \"Write a story outline\" for writing a novel.\\n3. Human inputs to guide the process.\\n\\nThese can be used to decompose complex tasks into smaller, more manageable subtasks, which can help improve the and effectiveness of task. However, long-term and task can being due to the need to plan over a lengthy history and explore the space., LLMs may to adjust plans when faced with errors, making them less robust to human learners who can learn from trial and error.'}" ] }, - "execution_count": 22, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/extras/use_cases/question_answering/question_answering.ipynb b/docs/extras/use_cases/question_answering/question_answering.ipynb index fe61c0ad25..5eda2bd40a 100644 --- a/docs/extras/use_cases/question_answering/question_answering.ipynb +++ b/docs/extras/use_cases/question_answering/question_answering.ipynb @@ -14,7 +14,7 @@ "\n", "In this walkthrough we'll go over how to build a question-answering over documents application using LLMs. Two very related use cases which we cover elsewhere are:\n", "- [QA over structured data](/docs/use_cases/sql) (e.g., SQL)\n", - "- [QA over code](/docs/use_cases/code) (e.g., Python)\n", + "- [QA over code](/docs/use_cases/code_understanding) (e.g., Python)\n", "\n", "![intro.png](/img/qa_intro.png)\n", "\n", @@ -52,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "046cefc0", "metadata": {}, "outputs": [], @@ -269,28 +269,10 @@ }, { "cell_type": "code", - "execution_count": 9, - "id": "c690f01a", + "execution_count": null, + "id": "9cfe3270-4e89-4c60-a2e5-9026b021bf76", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:langchain.retrievers.multi_query:Generated queries: ['1. How can Task Decomposition be approached?', '2. What are the different methods for Task Decomposition?', '3. What are the various approaches to decomposing tasks?']\n" - ] - }, - { - "data": { - "text/plain": [ - "4" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import logging\n", "\n", @@ -318,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "99fa1aec", "metadata": {}, "outputs": [ @@ -326,10 +308,10 @@ "data": { "text/plain": [ "{'query': 'What are the approaches to Task Decomposition?',\n", - " 'result': 'There are three approaches to task decomposition:\\n\\n1. Using Language Model with simple prompting: This approach involves using a Language Model (LLM) with simple prompts like \"Steps for XYZ\" or \"What are the subgoals for achieving XYZ?\" to guide the task decomposition process.\\n\\n2. Using task-specific instructions: In this approach, task-specific instructions are provided to guide the task decomposition. For example, for the task of writing a novel, an instruction like \"Write a story outline\" can be given to help decompose the task into smaller subtasks.\\n\\n3. Human inputs: Task decomposition can also be done with the help of human inputs. This involves getting input and guidance from humans to break down a complex task into smaller, more manageable subtasks.'}" + " 'result': 'The approaches to task decomposition include:\\n\\n1. Simple prompting: This approach involves using simple prompts or questions to guide the agent in breaking down a task into smaller subgoals. For example, the agent can be prompted with \"Steps for XYZ\" or \"What are the subgoals for achieving XYZ?\" to facilitate task decomposition.\\n\\n2. Task-specific instructions: In this approach, task-specific instructions are provided to the agent to guide the decomposition process. For example, if the task is to write a novel, the agent can be instructed to \"Write a story outline\" as a step in the task decomposition.\\n\\n3. Human inputs: This approach involves incorporating human inputs in the task decomposition process. Humans can provide guidance, feedback, and assistance to the agent in breaking down complex tasks into manageable subgoals.\\n\\nThese approaches aim to enable efficient handling of complex tasks by breaking them down into smaller, more manageable subgoals.'}" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -355,97 +337,7 @@ "#### Choosing LLMs\n", "- Browse the > 55 LLM and chat model integrations [here](https://integrations.langchain.com/).\n", "- See further documentation on LLMs and chat models [here](/docs/modules/model_io/models/).\n", - "- Use local LLMS: The popularity of [PrivateGPT](https://github.com/imartinez/privateGPT) and [GPT4All](https://github.com/nomic-ai/gpt4all) underscore the importance of running LLMs locally.\n", - "Using `GPT4All` is as simple as [downloading the binary]((/docs/integrations/llms/gpt4all)) and then:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "02d6c9dc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Found model file at /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "objc[61331]: Class GGMLMetalClass is implemented in both /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x2e3384208) and /Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libllamamodel-mainline-metal.dylib (0x2e37b0208). One of the two will be used. Which one is undefined.\n", - "llama.cpp: using Metal\n", - "llama.cpp: loading model from /Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\n", - "llama_model_load_internal: format = ggjt v3 (latest)\n", - "llama_model_load_internal: n_vocab = 32001\n", - "llama_model_load_internal: n_ctx = 2048\n", - "llama_model_load_internal: n_embd = 5120\n", - "llama_model_load_internal: n_mult = 256\n", - "llama_model_load_internal: n_head = 40\n", - "llama_model_load_internal: n_layer = 40\n", - "llama_model_load_internal: n_rot = 128\n", - "llama_model_load_internal: ftype = 2 (mostly Q4_0)\n", - "llama_model_load_internal: n_ff = 13824\n", - "llama_model_load_internal: n_parts = 1\n", - "llama_model_load_internal: model size = 13B\n", - "llama_model_load_internal: ggml ctx size = 0.09 MB\n", - "llama_model_load_internal: mem required = 9031.71 MB (+ 1608.00 MB per state)\n", - "llama_new_context_with_model: kv self size = 1600.00 MB\n", - "ggml_metal_init: allocating\n", - "ggml_metal_init: using MPS\n", - "ggml_metal_init: loading '/Users/rlm/miniforge3/envs/llama/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/ggml-metal.metal'\n", - "ggml_metal_init: loaded kernel_add 0x2bbbbc2f0\n", - "ggml_metal_init: loaded kernel_mul 0x2bbbba840\n", - "ggml_metal_init: loaded kernel_mul_row 0x2bb917dd0\n", - "ggml_metal_init: loaded kernel_scale 0x2bb918150\n", - "ggml_metal_init: loaded kernel_silu 0x2bb9184d0\n", - "ggml_metal_init: loaded kernel_relu 0x2bb918850\n", - "ggml_metal_init: loaded kernel_gelu 0x2bbbc3f10\n", - "ggml_metal_init: loaded kernel_soft_max 0x2bbbc5840\n", - "ggml_metal_init: loaded kernel_diag_mask_inf 0x2bbbc4c70\n", - "ggml_metal_init: loaded kernel_get_rows_f16 0x2bbbc5fc0\n", - "ggml_metal_init: loaded kernel_get_rows_q4_0 0x2bbbc6720\n", - "ggml_metal_init: loaded kernel_get_rows_q4_1 0x2bb918c10\n", - "ggml_metal_init: loaded kernel_get_rows_q2_k 0x2bbbc51b0\n", - "ggml_metal_init: loaded kernel_get_rows_q3_k 0x2bbbc7630\n", - "ggml_metal_init: loaded kernel_get_rows_q4_k 0x2d4394e30\n", - "ggml_metal_init: loaded kernel_get_rows_q5_k 0x2bbbc7890\n", - "ggml_metal_init: loaded kernel_get_rows_q6_k 0x2d4395210\n", - "ggml_metal_init: loaded kernel_rms_norm 0x2bbbc8740\n", - "ggml_metal_init: loaded kernel_norm 0x2bbbc8b30\n", - "ggml_metal_init: loaded kernel_mul_mat_f16_f32 0x2d4395470\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_0_f32 0x2d4395a70\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_1_f32 0x1242b1a00\n", - "ggml_metal_init: loaded kernel_mul_mat_q2_k_f32 0x29f17d1c0\n", - "ggml_metal_init: loaded kernel_mul_mat_q3_k_f32 0x2d4396050\n", - "ggml_metal_init: loaded kernel_mul_mat_q4_k_f32 0x2bbbc98a0\n", - "ggml_metal_init: loaded kernel_mul_mat_q5_k_f32 0x2bbbca4a0\n", - "ggml_metal_init: loaded kernel_mul_mat_q6_k_f32 0x2bbbcae90\n", - "ggml_metal_init: loaded kernel_rope 0x2bbbca700\n", - "ggml_metal_init: loaded kernel_alibi_f32 0x2bbbcc6e0\n", - "ggml_metal_init: loaded kernel_cpy_f32_f16 0x2bbbccf90\n", - "ggml_metal_init: loaded kernel_cpy_f32_f32 0x2bbbcd900\n", - "ggml_metal_init: loaded kernel_cpy_f16_f16 0x2bbbce1f0\n", - "ggml_metal_init: recommendedMaxWorkingSetSize = 21845.34 MB\n", - "ggml_metal_init: hasUnifiedMemory = true\n", - "ggml_metal_init: maxTransferRate = built-in GPU\n", - "ggml_metal_add_buffer: allocated 'data ' buffer, size = 6984.06 MB, ( 6984.45 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'eval ' buffer, size = 1024.00 MB, ( 8008.45 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'kv ' buffer, size = 1602.00 MB, ( 9610.45 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'scr0 ' buffer, size = 512.00 MB, (10122.45 / 21845.34)\n", - "ggml_metal_add_buffer: allocated 'scr1 ' buffer, size = 512.00 MB, (10634.45 / 21845.34)\n" - ] - } - ], - "source": [ - "from langchain.llms import GPT4All\n", - "from langchain.chains import RetrievalQA\n", - "\n", - "llm = GPT4All(model=\"/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin\",max_tokens=2048)\n", - "qa_chain = RetrievalQA.from_chain_type(llm, retriever=vectorstore.as_retriever())" + "- See a guide on local LLMS [here](/docs/modules/use_cases/question_answering/how_to/local_retrieval_qa)." ] }, { @@ -460,24 +352,17 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "id": "e4fee704", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ggml_metal_free: deallocating\n" - ] - }, { "data": { "text/plain": [ - "'The approaches to task decomposition include using LLM with simple prompting, task-specific instructions, or human inputs. Thanks for asking!'" + "'The approaches to Task Decomposition are (1) using simple prompting by LLM, (2) using task-specific instructions, and (3) incorporating human inputs. Thanks for asking!'" ] }, - "execution_count": 13, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -507,8 +392,65 @@ }, { "cell_type": "markdown", - "id": "ff40e8db", + "id": "c825e9bf-6a56-46e4-8bbb-05441f76cb96", "metadata": {}, + "source": [ + "We can also store and fetch prompts from the LangChain prompt hub.\n", + "\n", + "This will work with your [LangSmith API key](https://docs.smith.langchain.com/).\n", + "\n", + "For example, see [here](https://smith.langchain.com/hub/rlm/rag-prompt) is a common prompt for RAG.\n", + "\n", + "We can load this." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a896060f-ebc4-4236-a4ad-32960601c6e8", + "metadata": {}, + "outputs": [], + "source": [ + "pip install langchainhub" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "aef8e734-ba54-48ae-b959-1898618f2d90", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'The approaches to task decomposition include using LLM with simple prompting, task-specific instructions, and human inputs.'" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# RAG prompt\n", + "from langchain import hub\n", + "QA_CHAIN_PROMPT_HUB = hub.pull(\"rlm/rag-prompt\")\n", + "\n", + "qa_chain = RetrievalQA.from_chain_type(\n", + " llm,\n", + " retriever=vectorstore.as_retriever(),\n", + " chain_type_kwargs={\"prompt\": QA_CHAIN_PROMPT_HUB}\n", + ")\n", + "result = qa_chain({\"query\": question})\n", + "result[\"result\"]" + ] + }, + { + "cell_type": "markdown", + "id": "ff40e8db", + "metadata": { + "jp-MarkdownHeadingCollapsed": true + }, "source": [ "#### Return source documents\n", "\n", diff --git a/docs/extras/use_cases/sql.ipynb b/docs/extras/use_cases/sql.ipynb index c33b4e3eb7..0a047779f8 100644 --- a/docs/extras/use_cases/sql.ipynb +++ b/docs/extras/use_cases/sql.ipynb @@ -149,9 +149,7 @@ { "cell_type": "code", "execution_count": 8, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "from langchain.chat_models import ChatOpenAI\n", @@ -251,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -280,6 +278,25 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also access this [prompt](https://smith.langchain.com/hub/rlm/text-to-sql) in the LangChain prompt hub.\n", + "\n", + "This will work with your [LangSmith API key](https://docs.smith.langchain.com/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain import hub\n", + "CUSTOM_PROMPT = hub.pull(\"rlm/text-to-sql\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -696,6 +713,395 @@ "agent_executor.run(\"Describe the playlisttrack table\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Extending the SQL Toolkit\n", + "\n", + "Although the out-of-the-box SQL Toolkit contains the necessary tools to start working on a database, it is often the case that some extra tools may be useful for extending the agent's capabilities. This is particularly useful when trying to use **domain specific knowledge** in the solution, in order to improve its overall performance.\n", + "\n", + "Some examples include:\n", + "\n", + "- Including dynamic few shot examples\n", + "- Finding misspellings in proper nouns to use as column filters\n", + "\n", + "We can create separate tools which tackle these specific use cases and include them as a complement to the standard SQL Toolkit. Let's see how to include these two custom tools.\n", + "\n", + "#### Including dynamic few-shot examples\n", + "\n", + "In order to include dynamic few-shot examples, we need a custom **Retriever Tool** that handles the vector database in order to retrieve the examples that are semantically similar to the user’s question.\n", + "\n", + "Let's start by creating a dictionary with some examples: " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# few_shots = {'List all artists.': 'SELECT * FROM artists;',\n", + "# \"Find all albums for the artist 'AC/DC'.\": \"SELECT * FROM albums WHERE ArtistId = (SELECT ArtistId FROM artists WHERE Name = 'AC/DC');\",\n", + "# \"List all tracks in the 'Rock' genre.\": \"SELECT * FROM tracks WHERE GenreId = (SELECT GenreId FROM genres WHERE Name = 'Rock');\",\n", + "# 'Find the total duration of all tracks.': 'SELECT SUM(Milliseconds) FROM tracks;',\n", + "# 'List all customers from Canada.': \"SELECT * FROM customers WHERE Country = 'Canada';\",\n", + "# 'How many tracks are there in the album with ID 5?': 'SELECT COUNT(*) FROM tracks WHERE AlbumId = 5;',\n", + "# 'Find the total number of invoices.': 'SELECT COUNT(*) FROM invoices;',\n", + "# 'List all tracks that are longer than 5 minutes.': 'SELECT * FROM tracks WHERE Milliseconds > 300000;',\n", + "# 'Who are the top 5 customers by total purchase?': 'SELECT CustomerId, SUM(Total) AS TotalPurchase FROM invoices GROUP BY CustomerId ORDER BY TotalPurchase DESC LIMIT 5;',\n", + "# 'Which albums are from the year 2000?': \"SELECT * FROM albums WHERE strftime('%Y', ReleaseDate) = '2000';\",\n", + "# 'How many employees are there': 'SELECT COUNT(*) FROM \"employee\"'\n", + "# }" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can then create a retriever using the list of questions, assigning the target SQL query as metadata:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.embeddings.openai import OpenAIEmbeddings\n", + "from langchain.vectorstores import FAISS\n", + "from langchain.schema import Document\n", + "\n", + "embeddings = OpenAIEmbeddings()\n", + "\n", + "few_shot_docs = [Document(page_content=question, metadata={'sql_query': few_shots[question]}) for question in few_shots.keys()]\n", + "vector_db = FAISS.from_documents(few_shot_docs, embeddings)\n", + "retriever = vector_db.as_retriever()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can create our own custom tool and append it as a new tool in the `create_sql_agent` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.agents.agent_toolkits import create_retriever_tool\n", + "\n", + "tool_description = \"\"\"\n", + "This tool will help you understand similar examples to adapt them to the user question.\n", + "Input to this tool should be the user question.\n", + "\"\"\"\n", + "\n", + "retriever_tool = create_retriever_tool(\n", + " retriever,\n", + " name='sql_get_similar_examples',\n", + " description=tool_description\n", + " )\n", + "custom_tool_list = [retriever_tool]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can create the agent, adjusting the standard SQL Agent suffix to consider our use case. Although the most straightforward way to handle this would be to include it just in the tool description, this is often not enough and we need to specify it in the agent prompt using the `suffix` argument in the constructor." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.agents import create_sql_agent, AgentType\n", + "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n", + "from langchain.utilities import SQLDatabase\n", + "from langchain.chat_models import ChatOpenAI\n", + "\n", + "db = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")\n", + "llm = ChatOpenAI(model_name='gpt-4',temperature=0)\n", + "\n", + "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n", + "\n", + "custom_suffix = \"\"\"\n", + "I should first get the similar examples I know.\n", + "If the examples are enough to construct the query, I can build it.\n", + "Otherwise, I can then look at the tables in the database to see what I can query.\n", + "Then I should query the schema of the most relevant tables\n", + "\"\"\"\n", + "\n", + "agent = create_sql_agent(llm=llm,\n", + " toolkit=toolkit,\n", + " verbose=True,\n", + " agent_type=AgentType.OPENAI_FUNCTIONS,\n", + " extra_tools=custom_tool_list,\n", + " suffix=custom_suffix\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try it out:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3m\n", + "Invoking: `sql_get_similar_examples` with `How many employees do we have?`\n", + "\n", + "\n", + "\u001b[0m\u001b[33;1m\u001b[1;3m[Document(page_content='How many employees are there', metadata={'sql_query': 'SELECT COUNT(*) FROM \"employee\"'}), Document(page_content='Find the total number of invoices.', metadata={'sql_query': 'SELECT COUNT(*) FROM invoices;'})]\u001b[0m\u001b[32;1m\u001b[1;3m\n", + "Invoking: `sql_db_query_checker` with `SELECT COUNT(*) FROM employee`\n", + "responded: {content}\n", + "\n", + "\u001b[0m\u001b[36;1m\u001b[1;3mSELECT COUNT(*) FROM employee\u001b[0m\u001b[32;1m\u001b[1;3m\n", + "Invoking: `sql_db_query` with `SELECT COUNT(*) FROM employee`\n", + "\n", + "\n", + "\u001b[0m\u001b[36;1m\u001b[1;3m[(8,)]\u001b[0m\u001b[32;1m\u001b[1;3mWe have 8 employees.\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "'We have 8 employees.'" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agent.run(\"How many employees do we have?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the agent first used the `sql_get_similar_examples` tool in order to retrieve similar examples. As the question was very similar to other few shot examples, the agent **didn't need to use any other tool** from the standard Toolkit, thus **saving time and tokens**." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Finding and correcting misspellings for proper nouns\n", + "\n", + "In order to filter columns that contain proper nouns such as addresses, song names or artists, we first need to double-check the spelling in order to filter the data correctly. \n", + "\n", + "We can achieve this by creating a vector store using all the distinct proper nouns that exist in the database. We can then have the agent query that vector store each time the user includes a proper noun in their question, to find the correct spelling for that word. In this way, the agent can make sure it understands which entity the user is referring to before building the target query.\n", + "\n", + "Let's follow a similar approach to the few shots, but without metadata: just embedding the proper nouns and then querying to get the most similar one to the misspelled user question.\n", + "\n", + "First we need the unique values for each entity we want, for which we define a function that parses the result into a list of elements:" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [], + "source": [ + "import ast\n", + "import re\n", + "\n", + "def run_query_save_results(db, query):\n", + " res = db.run(query)\n", + " res = [el for sub in ast.literal_eval(res) for el in sub if el]\n", + " res = [re.sub(r'\\b\\d+\\b', '', string).strip() for string in res]\n", + " return res\n", + "\n", + "artists = run_query_save_results(db, \"SELECT Name FROM Artist\")\n", + "albums = run_query_save_results(db, \"SELECT Title FROM Album\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can proceed with creating the custom **retreiver tool** and the final agent:" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.agents.agent_toolkits import create_retriever_tool\n", + "from langchain.embeddings.openai import OpenAIEmbeddings\n", + "from langchain.vectorstores import FAISS\n", + "\n", + "\n", + "texts = (artists + albums)\n", + "\n", + "embeddings = OpenAIEmbeddings()\n", + "vector_db = FAISS.from_texts(texts, embeddings)\n", + "retriever = vector_db.as_retriever()\n", + "\n", + "retriever_tool = create_retriever_tool(\n", + " retriever,\n", + " name='name_search',\n", + " description='use to learn how a piece of data is actually written, can be from names, surnames addresses etc'\n", + " )\n", + "\n", + "custom_tool_list = [retriever_tool]" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "from langchain.agents import create_sql_agent, AgentType\n", + "from langchain.agents.agent_toolkits import SQLDatabaseToolkit\n", + "from langchain.utilities import SQLDatabase\n", + "from langchain.chat_models import ChatOpenAI\n", + "\n", + "# db = SQLDatabase.from_uri(\"sqlite:///Chinook.db\")\n", + "llm = ChatOpenAI(model_name='gpt-4', temperature=0)\n", + "\n", + "toolkit = SQLDatabaseToolkit(db=db, llm=llm)\n", + "\n", + "custom_suffix = \"\"\"\n", + "If a user asks for me to filter based on proper nouns, I should first check the spelling using the name_search tool.\n", + "Otherwise, I can then look at the tables in the database to see what I can query.\n", + "Then I should query the schema of the most relevant tables\n", + "\"\"\"\n", + "\n", + "agent = create_sql_agent(llm=llm,\n", + " toolkit=toolkit,\n", + " verbose=True,\n", + " agent_type=AgentType.OPENAI_FUNCTIONS,\n", + " extra_tools=custom_tool_list,\n", + " suffix=custom_suffix\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's try it out:" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3m\n", + "Invoking: `name_search` with `alis in pains`\n", + "\n", + "\n", + "\u001b[0m\u001b[33;1m\u001b[1;3m[Document(page_content='House of Pain', metadata={}), Document(page_content='Alice In Chains', metadata={}), Document(page_content='Aisha Duo', metadata={}), Document(page_content='House Of Pain', metadata={})]\u001b[0m\u001b[32;1m\u001b[1;3m\n", + "Invoking: `sql_db_list_tables` with ``\n", + "responded: {content}\n", + "\n", + "\u001b[0m\u001b[38;5;200m\u001b[1;3mAlbum, Artist, Customer, Employee, Genre, Invoice, InvoiceLine, MediaType, Playlist, PlaylistTrack, Track\u001b[0m\u001b[32;1m\u001b[1;3m\n", + "Invoking: `sql_db_schema` with `Album, Artist`\n", + "responded: {content}\n", + "\n", + "\u001b[0m\u001b[33;1m\u001b[1;3m\n", + "CREATE TABLE \"Album\" (\n", + "\t\"AlbumId\" INTEGER NOT NULL, \n", + "\t\"Title\" NVARCHAR(160) NOT NULL, \n", + "\t\"ArtistId\" INTEGER NOT NULL, \n", + "\tPRIMARY KEY (\"AlbumId\"), \n", + "\tFOREIGN KEY(\"ArtistId\") REFERENCES \"Artist\" (\"ArtistId\")\n", + ")\n", + "\n", + "/*\n", + "3 rows from Album table:\n", + "AlbumId\tTitle\tArtistId\n", + "1\tFor Those About To Rock We Salute You\t1\n", + "2\tBalls to the Wall\t2\n", + "3\tRestless and Wild\t2\n", + "*/\n", + "\n", + "\n", + "CREATE TABLE \"Artist\" (\n", + "\t\"ArtistId\" INTEGER NOT NULL, \n", + "\t\"Name\" NVARCHAR(120), \n", + "\tPRIMARY KEY (\"ArtistId\")\n", + ")\n", + "\n", + "/*\n", + "3 rows from Artist table:\n", + "ArtistId\tName\n", + "1\tAC/DC\n", + "2\tAccept\n", + "3\tAerosmith\n", + "*/\u001b[0m\u001b[32;1m\u001b[1;3m\n", + "Invoking: `sql_db_query_checker` with `SELECT COUNT(*) FROM Album JOIN Artist ON Album.ArtistId = Artist.ArtistId WHERE Artist.Name = 'Alice In Chains'`\n", + "responded: {content}\n", + "\n", + "\u001b[0m\u001b[36;1m\u001b[1;3mSELECT COUNT(*) FROM Album JOIN Artist ON Album.ArtistId = Artist.ArtistId WHERE Artist.Name = 'Alice In Chains'\u001b[0m\u001b[32;1m\u001b[1;3m\n", + "Invoking: `sql_db_query` with `SELECT COUNT(*) FROM Album JOIN Artist ON Album.ArtistId = Artist.ArtistId WHERE Artist.Name = 'Alice In Chains'`\n", + "\n", + "\n", + "\u001b[0m\u001b[36;1m\u001b[1;3m[(1,)]\u001b[0m\u001b[32;1m\u001b[1;3mAlice In Chains has 1 album in the database.\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + }, + { + "data": { + "text/plain": [ + "'Alice In Chains has 1 album in the database.'" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "agent.run(\"How many albums does alis in pains have?\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we can see, the agent used the `name_search` tool in order to check how to correctly query the database for this specific artist." + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -850,9 +1256,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.8.17" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/docs/extras/use_cases/summarization.ipynb b/docs/extras/use_cases/summarization.ipynb index 630d15f934..000ba48124 100644 --- a/docs/extras/use_cases/summarization.ipynb +++ b/docs/extras/use_cases/summarization.ipynb @@ -220,6 +220,30 @@ "map_chain = LLMChain(llm=llm, prompt=map_prompt)" ] }, + { + "cell_type": "markdown", + "id": "272ce8ce-919d-4ded-bbd5-a53a8a30bc66", + "metadata": {}, + "source": [ + "We can also use the Prompt Hub to store and fetch prompts.\n", + "\n", + "This will work with your [LangSmith API key](https://docs.smith.langchain.com/).\n", + "\n", + "For example, see the map prompt [here](https://smith.langchain.com/hub/rlm/map-prompt)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce48b805-d98b-4e0f-8b9e-3b3e72cad3d3", + "metadata": {}, + "outputs": [], + "source": [ + "from langchain import hub\n", + "map_prompt = hub.pull(\"rlm/map-prompt\")\n", + "map_chain = LLMChain(llm=llm, prompt=map_prompt)" + ] + }, { "cell_type": "markdown", "id": "bee3c331", @@ -232,8 +256,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "1edb1b0d", + "execution_count": null, + "id": "6a718890-99ab-439a-8f79-b9ae9c58ad24", "metadata": {}, "outputs": [], "source": [ @@ -242,7 +266,28 @@ "{doc_summaries}\n", "Take these and distill it into a final, consolidated summary of the main themes. \n", "Helpful Answer:\"\"\"\n", - "reduce_prompt = PromptTemplate.from_template(reduce_template)\n", + "reduce_prompt = PromptTemplate.from_template(reduce_template)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f189184a-673e-4530-8a6b-57b091045d87", + "metadata": {}, + "outputs": [], + "source": [ + "# Note we can also get this from the prompt hub, as noted above\n", + "reduce_prompt = hub.pull(\"rlm/map-prompt\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "1edb1b0d", + "metadata": {}, + "outputs": [], + "source": [ + "# Run chain\n", "reduce_chain = LLMChain(llm=llm, prompt=reduce_prompt)\n", "\n", "# Takes a list of documents, combines them into a single string, and passes this to an LLMChain\n", @@ -503,7 +548,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.9" + "version": "3.9.16" } }, "nbformat": 4, diff --git a/docs/snippets/modules/agents/agent_types/openai_functions_agent.mdx b/docs/snippets/modules/agents/agent_types/openai_functions_agent.mdx index cb5e085e4c..a04e2ac8c7 100644 --- a/docs/snippets/modules/agents/agent_types/openai_functions_agent.mdx +++ b/docs/snippets/modules/agents/agent_types/openai_functions_agent.mdx @@ -1,6 +1,8 @@ -Install openai,google-search-results packages which are required as the langchain packages call them internally +Install `openai`, `google-search-results` packages which are required as the LangChain packages call them internally. ->pip install openai google-search-results +```bash +pip install openai google-search-results +``` ```python from langchain import LLMMathChain, OpenAI, SerpAPIWrapper, SQLDatabase, SQLDatabaseChain diff --git a/docs/snippets/modules/agents/agent_types/plan_and_execute.mdx b/docs/snippets/modules/agents/agent_types/plan_and_execute.mdx index 0603589688..e5a529525f 100644 --- a/docs/snippets/modules/agents/agent_types/plan_and_execute.mdx +++ b/docs/snippets/modules/agents/agent_types/plan_and_execute.mdx @@ -53,7 +53,7 @@ executor = load_agent_executor(model, tools, verbose=True) agent = PlanAndExecute(planner=planner, executor=executor, verbose=True) ``` -## Run Example +## Run example ```python diff --git a/docs/snippets/modules/agents/agent_types/structured_chat.mdx b/docs/snippets/modules/agents/agent_types/structured_chat.mdx index 68350f97b4..e7b7d6e5de 100644 --- a/docs/snippets/modules/agents/agent_types/structured_chat.mdx +++ b/docs/snippets/modules/agents/agent_types/structured_chat.mdx @@ -202,7 +202,7 @@ print(response) ## Adding in memory -Here is how you add in memory to this agent +Here is how you add in memory to this agent: ```python diff --git a/docs/snippets/modules/agents/get_started.mdx b/docs/snippets/modules/agents/get_started.mdx index c16cc77964..ffa81dc584 100644 --- a/docs/snippets/modules/agents/get_started.mdx +++ b/docs/snippets/modules/agents/get_started.mdx @@ -1,12 +1,12 @@ This will go over how to get started building an agent. We will use a LangChain agent class, but show how to customize it to give it specific context. -We will then define custom tools, and then run it all in the standard LangChain AgentExecutor. +We will then define custom tools, and then run it all in the standard LangChain `AgentExecutor`. ### Set up the agent -We will use the OpenAIFunctionsAgent. +We will use the `OpenAIFunctionsAgent`. This is easiest and best agent to get started with. -It does however require usage of ChatOpenAI models. +It does however require usage of `ChatOpenAI` models. If you want to use a different language model, we would recommend using the [ReAct](/docs/modules/agents/agent_types/react) agent. For this guide, we will construct a custom agent that has access to a custom tool. @@ -40,7 +40,7 @@ tools = [get_word_length] Now let us create the prompt. We can use the `OpenAIFunctionsAgent.create_prompt` helper function to create a prompt automatically. -This allows for a few different ways to customize, including passing in a custom SystemMessage, which we will do. +This allows for a few different ways to customize, including passing in a custom `SystemMessage`, which we will do. ```python from langchain.schema import SystemMessage @@ -55,7 +55,7 @@ Putting those pieces together, we can now create the agent. agent = OpenAIFunctionsAgent(llm=llm, tools=tools, prompt=prompt) ``` -Finally, we create the AgentExecutor - the runtime for our agent. +Finally, we create the `AgentExecutor` - the runtime for our agent. ```python from langchain.agents import AgentExecutor @@ -97,7 +97,7 @@ Let's fix that by adding in memory. In order to do this, we need to do two things: 1. Add a place for memory variables to go in the prompt -2. Add memory to the AgentExecutor (note that we add it here, and NOT to the agent, as this is the outermost chain) +2. Add memory to the `AgentExecutor` (note that we add it here, and NOT to the agent, as this is the outermost chain) First, let's add a place for memory in the prompt. We do this by adding a placeholder for messages with the key `"chat_history"`. diff --git a/docs/snippets/modules/agents/how_to/custom_llm_agent.mdx b/docs/snippets/modules/agents/how_to/custom_llm_agent.mdx index f6a4de83ae..b8a03de621 100644 --- a/docs/snippets/modules/agents/how_to/custom_llm_agent.mdx +++ b/docs/snippets/modules/agents/how_to/custom_llm_agent.mdx @@ -1,5 +1,5 @@ -The LLMAgent is used in an AgentExecutor. This AgentExecutor can largely be thought of as a loop that: -1. Passes user input and any previous steps to the Agent (in this case, the LLMAgent) +The LLM Agent is used in an `AgentExecutor`. This `AgentExecutor` can largely be thought of as a loop that: +1. Passes user input and any previous steps to the Agent (in this case, the LLM Agent) 2. If the Agent returns an `AgentFinish`, then return that directly to the user 3. If the Agent returns an `AgentAction`, then use that to call a tool and get an `Observation` 4. Repeat, passing the `AgentAction` and `Observation` back to the Agent until an `AgentFinish` is emitted. @@ -43,7 +43,7 @@ tools = [ ] ``` -## Prompt Template +## Prompt template This instructs the agent on what to do. Generally, the template should incorporate: @@ -112,11 +112,11 @@ prompt = CustomPromptTemplate( ) ``` -## Output Parser +## Output parser The output parser is responsible for parsing the LLM output into `AgentAction` and `AgentFinish`. This usually depends heavily on the prompt used. -This is where you can change the parsing to do retries, handle whitespace, etc +This is where you can change the parsing to do retries, handle whitespace, etc. ```python @@ -164,7 +164,7 @@ This depends heavily on the prompt and model you are using. Generally, you want ## Set up the Agent -We can now combine everything to set up our agent +We can now combine everything to set up our agent: ```python @@ -225,7 +225,7 @@ agent_executor.run("How many people live in canada as of 2023?") If you want to add memory to the agent, you'll need to: -1. Add a place in the custom prompt for the chat_history +1. Add a place in the custom prompt for the `chat_history` 2. Add a memory object to the agent executor. diff --git a/docs/snippets/modules/agents/how_to/custom_llm_chat_agent.mdx b/docs/snippets/modules/agents/how_to/custom_llm_chat_agent.mdx index a44fffae08..87f1d58464 100644 --- a/docs/snippets/modules/agents/how_to/custom_llm_chat_agent.mdx +++ b/docs/snippets/modules/agents/how_to/custom_llm_chat_agent.mdx @@ -1,5 +1,5 @@ -The LLMAgent is used in an AgentExecutor. This AgentExecutor can largely be thought of as a loop that: -1. Passes user input and any previous steps to the Agent (in this case, the LLMAgent) +The LLM Agent is used in an `AgentExecutor`. This `AgentExecutor` can largely be thought of as a loop that: +1. Passes user input and any previous steps to the Agent (in this case, the LLM Agent) 2. If the Agent returns an `AgentFinish`, then return that directly to the user 3. If the Agent returns an `AgentAction`, then use that to call a tool and get an `Observation` 4. Repeat, passing the `AgentAction` and `Observation` back to the Agent until an `AgentFinish` is emitted. @@ -35,7 +35,7 @@ import re from getpass import getpass ``` -## Set up tool +## Set up tools Set up any tools the agent may want to use. This may be necessary to put in the prompt (so that the agent knows to use these tools). @@ -57,7 +57,7 @@ tools = [ ] ``` -## Prompt Template +## Prompt template This instructs the agent on what to do. Generally, the template should incorporate: @@ -131,11 +131,11 @@ prompt = CustomPromptTemplate( ) ``` -## Output Parser +## Output parser The output parser is responsible for parsing the LLM output into `AgentAction` and `AgentFinish`. This usually depends heavily on the prompt used. -This is where you can change the parsing to do retries, handle whitespace, etc +This is where you can change the parsing to do retries, handle whitespace, etc. ```python @@ -188,7 +188,7 @@ This depends heavily on the prompt and model you are using. Generally, you want ## Set up the Agent -We can now combine everything to set up our agent +We can now combine everything to set up our agent: ```python diff --git a/docs/snippets/modules/callbacks/get_started.mdx b/docs/snippets/modules/callbacks/get_started.mdx index 9596e2a373..8ada4da7a7 100644 --- a/docs/snippets/modules/callbacks/get_started.mdx +++ b/docs/snippets/modules/callbacks/get_started.mdx @@ -72,7 +72,7 @@ class BaseCallbackHandler: LangChain provides a few built-in handlers that you can use to get started. These are available in the `langchain/callbacks` module. The most basic handler is the `StdOutCallbackHandler`, which simply logs all events to `stdout`. -**Note** when the `verbose` flag on the object is set to true, the `StdOutCallbackHandler` will be invoked even without being explicitly passed in. +**Note**: when the `verbose` flag on the object is set to true, the `StdOutCallbackHandler` will be invoked even without being explicitly passed in. ```python from langchain.callbacks import StdOutCallbackHandler @@ -137,6 +137,6 @@ The `verbose` argument is available on most objects throughout the API (Chains, ### When do you want to use each of these? -- Constructor callbacks are most useful for use cases such as logging, monitoring, etc., which are _not specific to a single request_, but rather to the entire chain. For example, if you want to log all the requests made to an LLMChain, you would pass a handler to the constructor. +- Constructor callbacks are most useful for use cases such as logging, monitoring, etc., which are _not specific to a single request_, but rather to the entire chain. For example, if you want to log all the requests made to an `LLMChain`, you would pass a handler to the constructor. - Request callbacks are most useful for use cases such as streaming, where you want to stream the output of a single request to a specific websocket connection, or other similar use cases. For example, if you want to stream the output of a single request to a websocket, you would pass a handler to the `call()` method diff --git a/docs/snippets/modules/chains/additional/analyze_document.mdx b/docs/snippets/modules/chains/additional/analyze_document.mdx index 989c3c0aec..b9d6bdeaa3 100644 --- a/docs/snippets/modules/chains/additional/analyze_document.mdx +++ b/docs/snippets/modules/chains/additional/analyze_document.mdx @@ -4,7 +4,7 @@ with open("../../state_of_the_union.txt") as f: ``` ## Summarize -Let's take a look at it in action below, using it summarize a long document. +Let's take a look at it in action below, using it to summarize a long document. ```python diff --git a/docs/snippets/modules/chains/foundational/llm_chain.mdx b/docs/snippets/modules/chains/foundational/llm_chain.mdx index ec14bbf59d..d22e01c39a 100644 --- a/docs/snippets/modules/chains/foundational/llm_chain.mdx +++ b/docs/snippets/modules/chains/foundational/llm_chain.mdx @@ -19,7 +19,7 @@ llm_chain("colorful socks") -## Additional ways of running LLM Chain +## Additional ways of running `LLMChain` Aside from `__call__` and `run` methods shared by all `Chain` object, `LLMChain` offers a few more ways of calling the chain logic: @@ -139,7 +139,7 @@ llm_chain.predict_and_parse() ## Initialize from string -You can also construct an LLMChain from a string template directly. +You can also construct an `LLMChain` from a string template directly. ```python diff --git a/docs/snippets/modules/chains/foundational/sequential_chains.mdx b/docs/snippets/modules/chains/foundational/sequential_chains.mdx index d8a22bebf6..fa9c6ec227 100644 --- a/docs/snippets/modules/chains/foundational/sequential_chains.mdx +++ b/docs/snippets/modules/chains/foundational/sequential_chains.mdx @@ -89,7 +89,7 @@ print(review) ## Sequential Chain Of course, not all sequential chains will be as simple as passing a single string as an argument and getting a single string as output for all steps in the chain. In this next example, we will experiment with more complex chains that involve multiple inputs, and where there also multiple final outputs. -Of particular importance is how we name the input/output variable names. In the above example we didn't have to think about that because we were just passing the output of one chain directly as input to the next, but here we do have worry about that because we have multiple inputs. +Of particular importance is how we name the input/output variables. In the above example we didn't have to think about that because we were just passing the output of one chain directly as input to the next, but here we do have worry about that because we have multiple inputs. ```python @@ -158,7 +158,7 @@ overall_chain({"title":"Tragedy at sunset on the beach", "era": "Victorian Engla ### Memory in Sequential Chains Sometimes you may want to pass along some context to use in each step of the chain or in a later part of the chain, but maintaining and chaining together the input/output variables can quickly get messy. Using `SimpleMemory` is a convenient way to do manage this and clean up your chains. -For example, using the previous playwright SequentialChain, lets say you wanted to include some context about date, time and location of the play, and using the generated synopsis and review, create some social media post text. You could add these new context variables as `input_variables`, or we can add a `SimpleMemory` to the chain to manage this context: +For example, using the previous playwright `SequentialChain`, lets say you wanted to include some context about date, time and location of the play, and using the generated synopsis and review, create some social media post text. You could add these new context variables as `input_variables`, or we can add a `SimpleMemory` to the chain to manage this context: diff --git a/docs/snippets/modules/data_connection/document_loaders/get_started.mdx b/docs/snippets/modules/data_connection/document_loaders/get_started.mdx index 907da0c344..9a937e7b5b 100644 --- a/docs/snippets/modules/data_connection/document_loaders/get_started.mdx +++ b/docs/snippets/modules/data_connection/document_loaders/get_started.mdx @@ -1,4 +1,4 @@ -The simplest loader reads in a file as text and places it all into one Document. +The simplest loader reads in a file as text and places it all into one document. ```python from langchain.document_loaders import TextLoader diff --git a/docs/snippets/modules/data_connection/document_loaders/how_to/csv.mdx b/docs/snippets/modules/data_connection/document_loaders/how_to/csv.mdx index bdb5cef95f..3067afb07b 100644 --- a/docs/snippets/modules/data_connection/document_loaders/how_to/csv.mdx +++ b/docs/snippets/modules/data_connection/document_loaders/how_to/csv.mdx @@ -19,7 +19,7 @@ print(data) -## Customizing the csv parsing and loading +## Customizing the CSV parsing and loading See the [csv module](https://docs.python.org/3/library/csv.html) documentation for more information of what csv args are supported. diff --git a/docs/snippets/modules/data_connection/document_loaders/how_to/file_directory.mdx b/docs/snippets/modules/data_connection/document_loaders/how_to/file_directory.mdx index 5fd585564c..b393a85a86 100644 --- a/docs/snippets/modules/data_connection/document_loaders/how_to/file_directory.mdx +++ b/docs/snippets/modules/data_connection/document_loaders/how_to/file_directory.mdx @@ -1,4 +1,4 @@ -Under the hood, by default this uses the [UnstructuredLoader](/docs/integrations/document_loaders/unstructured_file.html) +Under the hood, by default this uses the [UnstructuredLoader](/docs/integrations/document_loaders/unstructured_file.html). ```python from langchain.document_loaders import DirectoryLoader @@ -121,7 +121,7 @@ len(docs) -## Auto detect file encodings with TextLoader +## Auto-detect file encodings with TextLoader In this example we will see some strategies that can be useful when loading a big list of arbitrary files from a directory using the `TextLoader` class. @@ -212,7 +212,7 @@ loader.load() -The file `example-non-utf8.txt` uses a different encoding the `load()` function fails with a helpful message indicating which file failed decoding. +The file `example-non-utf8.txt` uses a different encoding, so the `load()` function fails with a helpful message indicating which file failed decoding. With the default behavior of `TextLoader` any failure to load any of the documents will fail the whole loading process and no documents are loaded. diff --git a/docs/snippets/modules/data_connection/document_loaders/how_to/pdf.mdx b/docs/snippets/modules/data_connection/document_loaders/how_to/pdf.mdx index 5342b7dc33..3717b139fc 100644 --- a/docs/snippets/modules/data_connection/document_loaders/how_to/pdf.mdx +++ b/docs/snippets/modules/data_connection/document_loaders/how_to/pdf.mdx @@ -139,9 +139,9 @@ data[0] ### Fetching remote PDFs using Unstructured -This covers how to load online pdfs into a document format that we can use downstream. This can be used for various online pdf sites such as https://open.umn.edu/opentextbooks/textbooks/ and https://arxiv.org/archive/ +This covers how to load online PDFs into a document format that we can use downstream. This can be used for various online PDF sites such as https://open.umn.edu/opentextbooks/textbooks/ and https://arxiv.org/archive/ -Note: all other pdf loaders can also be used to fetch remote PDFs, but `OnlinePDFLoader` is a legacy function, and works specifically with `UnstructuredPDFLoader`. +Note: all other PDF loaders can also be used to fetch remote PDFs, but `OnlinePDFLoader` is a legacy function, and works specifically with `UnstructuredPDFLoader`. @@ -208,7 +208,7 @@ data = loader.load() ### Using PDFMiner to generate HTML text -This can be helpful for chunking texts semantically into sections as the output html content can be parsed via `BeautifulSoup` to get more structured and rich information about font size, page numbers, pdf headers/footers, etc. +This can be helpful for chunking texts semantically into sections as the output html content can be parsed via `BeautifulSoup` to get more structured and rich information about font size, page numbers, PDF headers/footers, etc. ```python @@ -222,7 +222,7 @@ loader = PDFMinerPDFasHTMLLoader("example_data/layout-parser-paper.pdf") ```python -data = loader.load()[0] # entire pdf is loaded as a single Document +data = loader.load()[0] # entire PDF is loaded as a single Document ``` @@ -259,7 +259,7 @@ for c in content: cur_text = c.text snippets.append((cur_text,cur_fs)) # Note: The above logic is very straightforward. One can also add more strategies such as removing duplicate snippets (as -# headers/footers in a PDF appear on multiple pages so if we find duplicatess safe to assume that it is redundant info) +# headers/footers in a PDF appear on multiple pages so if we find duplicates it's safe to assume that it is redundant info) ``` @@ -285,7 +285,7 @@ for s in snippets: continue # if current snippet's font size > previous section's content but less than previous section's heading than also make a new - # section (e.g. title of a pdf will have the highest font size but we don't want it to subsume all sections) + # section (e.g. title of a PDF will have the highest font size but we don't want it to subsume all sections) metadata={'heading':s[0], 'content_font': 0, 'heading_font': s[1]} metadata.update(data.metadata) semantic_snippets.append(Document(page_content='',metadata=metadata)) @@ -358,7 +358,7 @@ loader = PyPDFDirectoryLoader("example_data/") docs = loader.load() ``` -## Using pdfplumber +## Using PDFPlumber Like PyMuPDF, the output Documents contain detailed metadata about the PDF and its pages, and returns one document per page. diff --git a/docs/snippets/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx b/docs/snippets/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx index 5b22670325..503ca52a53 100644 --- a/docs/snippets/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx +++ b/docs/snippets/modules/data_connection/document_transformers/text_splitters/code_splitter.mdx @@ -50,7 +50,7 @@ RecursiveCharacterTextSplitter.get_separators_for_language(Language.PYTHON) ## Python -Here's an example using the PythonTextSplitter +Here's an example using the PythonTextSplitter: ```python @@ -78,7 +78,7 @@ python_docs ## JS -Here's an example using the JS text splitter +Here's an example using the JS text splitter: ```python @@ -109,7 +109,7 @@ js_docs ## Markdown -Here's an example using the Markdown text splitter. +Here's an example using the Markdown text splitter: ````python @@ -155,7 +155,7 @@ md_docs ## Latex -Here's an example on Latex text +Here's an example on Latex text: ```python @@ -219,7 +219,7 @@ latex_docs ## HTML -Here's an example using an HTML text splitter +Here's an example using an HTML text splitter: ```python @@ -281,7 +281,7 @@ html_docs ## Solidity -Here's an example using the Solidity text splitter +Here's an example using the Solidity text splitter: ```python SOL_CODE = """ diff --git a/docs/snippets/modules/data_connection/retrievers/get_started.mdx b/docs/snippets/modules/data_connection/retrievers/get_started.mdx index 6e492dc105..8a42c3a361 100644 --- a/docs/snippets/modules/data_connection/retrievers/get_started.mdx +++ b/docs/snippets/modules/data_connection/retrievers/get_started.mdx @@ -36,27 +36,27 @@ class BaseRetriever(ABC): It's that simple! You can call `get_relevant_documents` or the async `aget_relevant_documents` methods to retrieve documents relevant to a query, where "relevance" is defined by the specific retriever object you are calling. -Of course, we also help construct what we think useful Retrievers are. The main type of Retriever that we focus on is a Vectorstore retriever. We will focus on that for the rest of this guide. +Of course, we also help construct what we think useful retrievers are. The main type of retriever that we focus on is a vector store retriever. We will focus on that for the rest of this guide. -In order to understand what a vectorstore retriever is, it's important to understand what a Vectorstore is. So let's look at that. +In order to understand what a vector store retriever is, it's important to understand what a vector store is. So let's look at that. -By default, LangChain uses [Chroma](/docs/ecosystem/integrations/chroma.html) as the vectorstore to index and search embeddings. To walk through this tutorial, we'll first need to install `chromadb`. +By default, LangChain uses [Chroma](/docs/ecosystem/integrations/chroma.html) as the vector store to index and search embeddings. To walk through this tutorial, we'll first need to install `chromadb`. ``` pip install chromadb ``` This example showcases question answering over documents. -We have chosen this as the example for getting started because it nicely combines a lot of different elements (Text splitters, embeddings, vectorstores) and then also shows how to use them in a chain. +We have chosen this as the example for getting started because it nicely combines a lot of different elements (Text splitters, embeddings, vector stores) and then also shows how to use them in a chain. Question answering over documents consists of four steps: 1. Create an index -2. Create a Retriever from that index +2. Create a retriever from that index 3. Create a question answering chain 4. Ask questions! -Each of the steps has multiple sub steps and potential configurations. In this notebook we will primarily focus on (1). We will start by showing the one-liner for doing so, but then break down what is actually going on. +Each of the steps has multiple substeps and potential configurations. In this notebook we will primarily focus on (1). We will start by showing the one-liner for doing so, but then break down what is actually going on. First, let's import some common classes we'll use no matter what. @@ -66,7 +66,7 @@ from langchain.chains import RetrievalQA from langchain.llms import OpenAI ``` -Next in the generic setup, let's specify the document loader we want to use. You can download the `state_of_the_union.txt` file [here](https://github.com/hwchase17/langchain/blob/master/docs/extras/modules/state_of_the_union.txt) +Next in the generic setup, let's specify the document loader we want to use. You can download the `state_of_the_union.txt` file [here](https://github.com/hwchase17/langchain/blob/master/docs/extras/modules/state_of_the_union.txt). ```python @@ -129,7 +129,7 @@ index.query_with_sources(query) -What is returned from the `VectorstoreIndexCreator` is `VectorStoreIndexWrapper`, which provides these nice `query` and `query_with_sources` functionality. If we just wanted to access the vectorstore directly, we can also do that. +What is returned from the `VectorstoreIndexCreator` is a `VectorStoreIndexWrapper`, which provides these nice `query` and `query_with_sources` functionalities. If we just want to access the vector store directly, we can also do that. ```python @@ -144,7 +144,7 @@ index.vectorstore -If we then want to access the VectorstoreRetriever, we can do that with: +If we then want to access the `VectorStoreRetriever`, we can do that with: ```python @@ -159,7 +159,7 @@ index.vectorstore.as_retriever() -It can also be convenient to filter the vectorstore by the metadata associated with documents, particularly when your vectorstore has multiple sources. This can be done using the `query` method like so: +It can also be convenient to filter the vector store by the metadata associated with documents, particularly when your vector store has multiple sources. This can be done using the `query` method like so: ```python @@ -185,7 +185,7 @@ There are three main steps going on after the documents are loaded: 1. Splitting documents into chunks 2. Creating embeddings for each document -3. Storing documents and embeddings in a vectorstore +3. Storing documents and embeddings in a vector store Let's walk through this in code @@ -211,7 +211,7 @@ from langchain.embeddings import OpenAIEmbeddings embeddings = OpenAIEmbeddings() ``` -We now create the vectorstore to use as the index. +We now create the vector store to use as the index. ```python diff --git a/docs/snippets/modules/data_connection/retrievers/how_to/time_weighted_vectorstore.mdx b/docs/snippets/modules/data_connection/retrievers/how_to/time_weighted_vectorstore.mdx index 0e2dbc20c7..42d4ec8359 100644 --- a/docs/snippets/modules/data_connection/retrievers/how_to/time_weighted_vectorstore.mdx +++ b/docs/snippets/modules/data_connection/retrievers/how_to/time_weighted_vectorstore.mdx @@ -9,9 +9,9 @@ from langchain.schema import Document from langchain.vectorstores import FAISS ``` -## Low Decay Rate +## Low decay rate -A low `decay rate` (in this, to be extreme, we will set close to 0) means memories will be "remembered" for longer. A `decay rate` of 0 means memories never be forgotten, making this retriever equivalent to the vector lookup. +A low `decay rate` (in this, to be extreme, we will set it close to 0) means memories will be "remembered" for longer. A `decay rate` of 0 means memories never be forgotten, making this retriever equivalent to the vector lookup. ```python @@ -53,7 +53,7 @@ retriever.get_relevant_documents("hello world") -## High Decay Rate +## High decay rate With a high `decay rate` (e.g., several 9's), the `recency score` quickly goes to 0! If you set this all the way to 1, `recency` is 0 for all objects, once again making this equivalent to a vector lookup. @@ -98,9 +98,9 @@ retriever.get_relevant_documents("hello world") -## Virtual Time +## Virtual time -Using some utils in LangChain, you can mock out the time component +Using some utils in LangChain, you can mock out the time component. ```python diff --git a/docs/snippets/modules/data_connection/retrievers/how_to/vectorstore.mdx b/docs/snippets/modules/data_connection/retrievers/how_to/vectorstore.mdx index 512070f41f..60fb5cad32 100644 --- a/docs/snippets/modules/data_connection/retrievers/how_to/vectorstore.mdx +++ b/docs/snippets/modules/data_connection/retrievers/how_to/vectorstore.mdx @@ -34,8 +34,8 @@ retriever = db.as_retriever() docs = retriever.get_relevant_documents("what did he say about ketanji brown jackson") ``` -## Maximum Marginal Relevance Retrieval -By default, the vectorstore retriever uses similarity search. If the underlying vectorstore support maximum marginal relevance search, you can specify that as the search type. +## Maximum marginal relevance retrieval +By default, the vector store retriever uses similarity search. If the underlying vector store supports maximum marginal relevance search, you can specify that as the search type. ```python @@ -47,9 +47,9 @@ retriever = db.as_retriever(search_type="mmr") docs = retriever.get_relevant_documents("what did he say about ketanji brown jackson") ``` -## Similarity Score Threshold Retrieval +## Similarity score threshold retrieval -You can also a retrieval method that sets a similarity score threshold and only returns documents with a score above that threshold +You can also a retrieval method that sets a similarity score threshold and only returns documents with a score above that threshold. ```python diff --git a/docs/snippets/modules/data_connection/retrievers/self_query/get_started.mdx b/docs/snippets/modules/data_connection/retrievers/self_query/get_started.mdx index ef7fdc9bb3..56bb752608 100644 --- a/docs/snippets/modules/data_connection/retrievers/self_query/get_started.mdx +++ b/docs/snippets/modules/data_connection/retrievers/self_query/get_started.mdx @@ -1,11 +1,11 @@ ## Get started We'll use a Pinecone vector store in this example. -First we'll want to create a `Pinecone` VectorStore and seed it with some data. We've created a small demo set of documents that contain summaries of movies. +First we'll want to create a `Pinecone` vector store and seed it with some data. We've created a small demo set of documents that contain summaries of movies. -To use Pinecone, you need to have `pinecone` package installed and you must have an API key and an Environment. Here are the [installation instructions](https://docs.pinecone.io/docs/quickstart). +To use Pinecone, you need to have `pinecone` package installed and you must have an API key and an environment. Here are the [installation instructions](https://docs.pinecone.io/docs/quickstart). -NOTE: The self-query retriever requires you to have `lark` package installed. +**Note:** The self-query retriever requires you to have `lark` package installed. ```python diff --git a/docs/snippets/modules/data_connection/text_embedding/get_started.mdx b/docs/snippets/modules/data_connection/text_embedding/get_started.mdx index 2c73304ad7..933beea52f 100644 --- a/docs/snippets/modules/data_connection/text_embedding/get_started.mdx +++ b/docs/snippets/modules/data_connection/text_embedding/get_started.mdx @@ -20,7 +20,7 @@ from langchain.embeddings import OpenAIEmbeddings embeddings_model = OpenAIEmbeddings(openai_api_key="...") ``` -otherwise you can initialize without any params: +Otherwise you can initialize without any params: ```python from langchain.embeddings import OpenAIEmbeddings diff --git a/docs/snippets/modules/data_connection/vectorstores/async.mdx b/docs/snippets/modules/data_connection/vectorstores/async.mdx index 0463bbe931..007f25b14f 100644 --- a/docs/snippets/modules/data_connection/vectorstores/async.mdx +++ b/docs/snippets/modules/data_connection/vectorstores/async.mdx @@ -1,4 +1,4 @@ -Langchain supports async operation on vector stores. All the methods might be called using their async counterparts, with the prefix `a`, meaning `async`. +LangChain supports async operation on vector stores. All the methods might be called using their async counterparts, with the prefix `a`, meaning `async`. `Qdrant` is a vector store, which supports all the async operations, thus it will be used in this walkthrough. @@ -47,7 +47,7 @@ docs = await db.asimilarity_search_by_vector(embedding_vector) ## Maximum marginal relevance search (MMR) -Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. It is also supported in async API. +Maximal marginal relevance optimizes for similarity to query **and** diversity among selected documents. It is also supported in async API. ```python query = "What did the president say about Ketanji Brown Jackson" diff --git a/docs/snippets/modules/memory/get_started.mdx b/docs/snippets/modules/memory/get_started.mdx index 53ddf5fa38..352b606736 100644 --- a/docs/snippets/modules/memory/get_started.mdx +++ b/docs/snippets/modules/memory/get_started.mdx @@ -1,5 +1,5 @@ -Let's take a look at how to use ConversationBufferMemory in chains. -ConversationBufferMemory is an extremely simple form of memory that just keeps a list of chat messages in a buffer +Let's take a look at how to use `ConversationBufferMemory` in chains. +`ConversationBufferMemory` is an extremely simple form of memory that just keeps a list of chat messages in a buffer and passes those into the prompt template. ```python @@ -7,7 +7,7 @@ from langchain.memory import ConversationBufferMemory memory = ConversationBufferMemory() memory.chat_memory.add_user_message("hi!") -memory.chat_memory.add_ai_message("whats up?") +memory.chat_memory.add_ai_message("what's up?") ``` When using memory in a chain, there are a few key concepts to understand. @@ -16,7 +16,7 @@ Each individual memory type may very well have its own parameters and concepts t ### What variables get returned from memory Before going into the chain, various variables are read from memory. -This have specific names which need to align with the variables the chain expects. +These have specific names which need to align with the variables the chain expects. You can see what these variables are by calling `memory.load_memory_variables({})`. Note that the empty dictionary that we pass in is just a placeholder for real variables. If the memory type you are using is dependent upon the input variables, you may need to pass some in. @@ -28,35 +28,35 @@ memory.load_memory_variables({}) ``` - {'history': "Human: hi!\nAI: whats up?"} + {'history': "Human: hi!\nAI: what's up?"} ``` In this case, you can see that `load_memory_variables` returns a single key, `history`. -This means that your chain (and likely your prompt) should expect and input named `history`. +This means that your chain (and likely your prompt) should expect an input named `history`. You can usually control this variable through parameters on the memory class. For example, if you want the memory variables to be returned in the key `chat_history` you can do: ```python memory = ConversationBufferMemory(memory_key="chat_history") memory.chat_memory.add_user_message("hi!") -memory.chat_memory.add_ai_message("whats up?") +memory.chat_memory.add_ai_message("what's up?") ``` ``` - {'chat_history': "Human: hi!\nAI: whats up?"} + {'chat_history': "Human: hi!\nAI: what's up?"} ``` -The parameter name to control these keys may vary per memory type, but it's important to understand that (1) this is controllable, (2) how to control it. +The parameter name to control these keys may vary per memory type, but it's important to understand that (1) this is controllable, and (2) how to control it. ### Whether memory is a string or a list of messages One of the most common types of memory involves returning a list of chat messages. -These can either be returned as a single string, all concatenated together (useful when they will be passed in LLMs) +These can either be returned as a single string, all concatenated together (useful when they will be passed into LLMs) or a list of ChatMessages (useful when passed into ChatModels). By default, they are returned as a single string. @@ -65,13 +65,13 @@ In order to return as a list of messages, you can set `return_messages=True` ```python memory = ConversationBufferMemory(return_messages=True) memory.chat_memory.add_user_message("hi!") -memory.chat_memory.add_ai_message("whats up?") +memory.chat_memory.add_ai_message("what's up?") ``` ``` {'history': [HumanMessage(content='hi!', additional_kwargs={}, example=False), - AIMessage(content='whats up?', additional_kwargs={}, example=False)]} + AIMessage(content='what's up?', additional_kwargs={}, example=False)]} ``` @@ -81,13 +81,13 @@ memory.chat_memory.add_ai_message("whats up?") Often times chains take in or return multiple input/output keys. In these cases, how can we know which keys we want to save to the chat message history? This is generally controllable by `input_key` and `output_key` parameters on the memory types. -These default to None - and if there is only one input/output key it is known to just use that. -However, if there are multiple input/output keys then you MUST specify the name of which one to use +These default to `None` - and if there is only one input/output key it is known to just use that. +However, if there are multiple input/output keys then you MUST specify the name of which one to use. ### End to end example Finally, let's take a look at using this in a chain. -We'll use an LLMChain, and show working with both an LLM and a ChatModel. +We'll use an `LLMChain`, and show working with both an LLM and a ChatModel. #### Using an LLM diff --git a/docs/snippets/modules/memory/types/buffer.mdx b/docs/snippets/modules/memory/types/buffer.mdx index 897dc12e57..6f1d9e4c10 100644 --- a/docs/snippets/modules/memory/types/buffer.mdx +++ b/docs/snippets/modules/memory/types/buffer.mdx @@ -153,5 +153,3 @@ conversation.predict(input="Tell me about yourself.") ``` - -And that's it for the getting started! There are plenty of different types of memory, check out our examples to see them all diff --git a/docs/snippets/modules/memory/types/summary.mdx b/docs/snippets/modules/memory/types/summary.mdx index 20529cc6c4..8bdb4f7590 100644 --- a/docs/snippets/modules/memory/types/summary.mdx +++ b/docs/snippets/modules/memory/types/summary.mdx @@ -62,7 +62,7 @@ memory.predict_new_summary(messages, previous_summary) ## Initializing with messages/existing summary -If you have messages outside this class, you can easily initialize the class with ChatMessageHistory. During loading, a summary will be calculated. +If you have messages outside this class, you can easily initialize the class with `ChatMessageHistory`. During loading, a summary will be calculated. ```python diff --git a/docs/snippets/modules/memory/types/vectorstore_retriever_memory.mdx b/docs/snippets/modules/memory/types/vectorstore_retriever_memory.mdx index 43b4ee7519..4e6b4a5438 100644 --- a/docs/snippets/modules/memory/types/vectorstore_retriever_memory.mdx +++ b/docs/snippets/modules/memory/types/vectorstore_retriever_memory.mdx @@ -7,9 +7,9 @@ from langchain.chains import ConversationChain from langchain.prompts import PromptTemplate ``` -### Initialize your VectorStore +### Initialize your vector store -Depending on the store you choose, this step may look different. Consult the relevant VectorStore documentation for more details. +Depending on the store you choose, this step may look different. Consult the relevant vector store documentation for more details. ```python @@ -25,9 +25,9 @@ embedding_fn = OpenAIEmbeddings().embed_query vectorstore = FAISS(embedding_fn, index, InMemoryDocstore({}), {}) ``` -### Create your the VectorStoreRetrieverMemory +### Create your `VectorStoreRetrieverMemory` -The memory object is instantiated from any VectorStoreRetriever. +The memory object is instantiated from any vector store retriever. ```python diff --git a/docs/snippets/modules/model_io/models/chat/get_started.mdx b/docs/snippets/modules/model_io/models/chat/get_started.mdx index 127283bb2e..452738d830 100644 --- a/docs/snippets/modules/model_io/models/chat/get_started.mdx +++ b/docs/snippets/modules/model_io/models/chat/get_started.mdx @@ -19,7 +19,7 @@ from langchain.chat_models import ChatOpenAI chat = ChatOpenAI(openai_api_key="...") ``` -otherwise you can initialize without any params: +Otherwise you can initialize without any params: ```python from langchain.chat_models import ChatOpenAI @@ -101,7 +101,7 @@ result -You can recover things like token usage from this LLMResult +You can recover things like token usage from this LLMResult: ```python diff --git a/docs/snippets/modules/model_io/models/chat/how_to/prompts.mdx b/docs/snippets/modules/model_io/models/chat/how_to/prompts.mdx index a02c7b4e24..da0df2dbc9 100644 --- a/docs/snippets/modules/model_io/models/chat/how_to/prompts.mdx +++ b/docs/snippets/modules/model_io/models/chat/how_to/prompts.mdx @@ -1,6 +1,6 @@ You can make use of templating by using a `MessagePromptTemplate`. You can build a `ChatPromptTemplate` from one or more `MessagePromptTemplates`. You can use `ChatPromptTemplate`'s `format_prompt` -- this returns a `PromptValue`, which you can convert to a string or Message object, depending on whether you want to use the formatted value as input to an llm or chat model. -For convenience, there is a `from_template` method exposed on the template. If you were to use this template, this is what it would look like: +For convenience, there is a `from_template` method defined on the template. If you were to use this template, this is what it would look like: ```python diff --git a/docs/snippets/modules/model_io/models/llms/get_started.mdx b/docs/snippets/modules/model_io/models/llms/get_started.mdx index 54d6a96b93..76c79589f0 100644 --- a/docs/snippets/modules/model_io/models/llms/get_started.mdx +++ b/docs/snippets/modules/model_io/models/llms/get_started.mdx @@ -90,7 +90,7 @@ llm_result.generations[-1] -You can also access provider specific information that is returned. This information is NOT standardized across providers. +You can also access provider specific information that is returned. This information is **not** standardized across providers. ```python diff --git a/docs/snippets/modules/model_io/models/llms/how_to/llm_caching.mdx b/docs/snippets/modules/model_io/models/llms/how_to/llm_caching.mdx index 5bb436ff82..2bf7b2a4a0 100644 --- a/docs/snippets/modules/model_io/models/llms/how_to/llm_caching.mdx +++ b/docs/snippets/modules/model_io/models/llms/how_to/llm_caching.mdx @@ -97,8 +97,8 @@ llm.predict("Tell me a joke") -## Optional Caching in Chains -You can also turn off caching for particular nodes in chains. Note that because of certain interfaces, its often easier to construct the chain first, and then edit the LLM afterwards. +## Optional caching in chains +You can also turn off caching for particular nodes in chains. Note that because of certain interfaces, it's often easier to construct the chain first, and then edit the LLM afterwards. As an example, we will load a summarizer map-reduce chain. We will cache results for the map-step, but then not freeze it for the combine step. diff --git a/docs/snippets/modules/model_io/prompts/example_selectors/get_started.mdx b/docs/snippets/modules/model_io/prompts/example_selectors/get_started.mdx index 0444462e1a..7020fa4500 100644 --- a/docs/snippets/modules/model_io/prompts/example_selectors/get_started.mdx +++ b/docs/snippets/modules/model_io/prompts/example_selectors/get_started.mdx @@ -7,4 +7,4 @@ class BaseExampleSelector(ABC): """Select which examples to use based on the inputs.""" ``` -The only method it needs to expose is a ``select_examples`` method. This takes in the input variables and then returns a list of examples. It is up to each specific implementation as to how those examples are selected. Let's take a look at some below. +The only method it needs to define is a ``select_examples`` method. This takes in the input variables and then returns a list of examples. It is up to each specific implementation as to how those examples are selected. diff --git a/docs/snippets/modules/model_io/prompts/example_selectors/length_based.mdx b/docs/snippets/modules/model_io/prompts/example_selectors/length_based.mdx index 9c0e70bdd7..8c76ccbf26 100644 --- a/docs/snippets/modules/model_io/prompts/example_selectors/length_based.mdx +++ b/docs/snippets/modules/model_io/prompts/example_selectors/length_based.mdx @@ -4,7 +4,7 @@ from langchain.prompts import FewShotPromptTemplate from langchain.prompts.example_selector import LengthBasedExampleSelector -# These are a lot of examples of a pretend task of creating antonyms. +# Examples of a pretend task of creating antonyms. examples = [ {"input": "happy", "output": "sad"}, {"input": "tall", "output": "short"}, @@ -17,14 +17,14 @@ example_prompt = PromptTemplate( template="Input: {input}\nOutput: {output}", ) example_selector = LengthBasedExampleSelector( - # These are the examples it has available to choose from. + # The examples it has available to choose from. examples=examples, - # This is the PromptTemplate being used to format the examples. + # The PromptTemplate being used to format the examples. example_prompt=example_prompt, - # This is the maximum length that the formatted examples should be. + # The maximum length that the formatted examples should be. # Length is measured by the get_text_length function below. max_length=25, - # This is the function used to get the length of a string, which is used + # The function used to get the length of a string, which is used # to determine which examples to include. It is commented out because # it is provided as a default value if none is specified. # get_text_length: Callable[[str], int] = lambda x: len(re.split("\n| ", x)) diff --git a/docs/snippets/modules/model_io/prompts/example_selectors/similarity.mdx b/docs/snippets/modules/model_io/prompts/example_selectors/similarity.mdx index f13916be74..87384d54f1 100644 --- a/docs/snippets/modules/model_io/prompts/example_selectors/similarity.mdx +++ b/docs/snippets/modules/model_io/prompts/example_selectors/similarity.mdx @@ -9,7 +9,7 @@ example_prompt = PromptTemplate( template="Input: {input}\nOutput: {output}", ) -# These are a lot of examples of a pretend task of creating antonyms. +# Examples of a pretend task of creating antonyms. examples = [ {"input": "happy", "output": "sad"}, {"input": "tall", "output": "short"}, @@ -22,13 +22,13 @@ examples = [ ```python example_selector = SemanticSimilarityExampleSelector.from_examples( - # This is the list of examples available to select from. + # The list of examples available to select from. examples, - # This is the embedding class used to produce embeddings which are used to measure semantic similarity. + # The embedding class used to produce embeddings which are used to measure semantic similarity. OpenAIEmbeddings(), - # This is the VectorStore class that is used to store the embeddings and do a similarity search over. + # The VectorStore class that is used to store the embeddings and do a similarity search over. Chroma, - # This is the number of examples to produce. + # The number of examples to produce. k=1 ) similar_prompt = FewShotPromptTemplate( diff --git a/docs/snippets/modules/model_io/prompts/prompt_templates/get_started.mdx b/docs/snippets/modules/model_io/prompts/prompt_templates/get_started.mdx index dddaf86f41..25d62563ce 100644 --- a/docs/snippets/modules/model_io/prompts/prompt_templates/get_started.mdx +++ b/docs/snippets/modules/model_io/prompts/prompt_templates/get_started.mdx @@ -55,7 +55,7 @@ For more information, see [Custom Prompt Templates](./custom_prompt_template.htm ## Chat prompt template -The prompt to [Chat Models](../models/chat) is a list of chat messages. +The prompt to [chat models](../models/chat) is a list of chat messages. Each chat message is associated with content, and an additional parameter called `role`. For example, in the OpenAI [Chat Completions API](https://platform.openai.com/docs/guides/chat/introduction), a chat message can be associated with an AI assistant, a human or a system role. diff --git a/libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/task_executor.py b/libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/task_executor.py index bcdea12b90..bf742d192b 100644 --- a/libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/task_executor.py +++ b/libs/experimental/langchain_experimental/autonomous_agents/hugginggpt/task_executor.py @@ -111,7 +111,9 @@ class TaskExecutor: dep_task = self.id_task_map[dep_id] for k, v in task.args.items(): if f"" in v: - task.args[k].replace(f"", dep_task.result) + task.args[k] = task.args[k].replace( + f"", dep_task.result + ) def run(self) -> str: for task in self.tasks: diff --git a/libs/experimental/langchain_experimental/comprehend_moderation/__init__.py b/libs/experimental/langchain_experimental/comprehend_moderation/__init__.py index 5e4a268631..d129f9fc7f 100644 --- a/libs/experimental/langchain_experimental/comprehend_moderation/__init__.py +++ b/libs/experimental/langchain_experimental/comprehend_moderation/__init__.py @@ -5,9 +5,11 @@ from langchain_experimental.comprehend_moderation.base_moderation import BaseMod from langchain_experimental.comprehend_moderation.base_moderation_callbacks import ( BaseModerationCallbackHandler, ) -from langchain_experimental.comprehend_moderation.base_moderation_enums import ( - BaseModerationActions, - BaseModerationFilters, +from langchain_experimental.comprehend_moderation.base_moderation_config import ( + BaseModerationConfig, + ModerationIntentConfig, + ModerationPiiConfig, + ModerationToxicityConfig, ) from langchain_experimental.comprehend_moderation.intent import ComprehendIntent from langchain_experimental.comprehend_moderation.pii import ComprehendPII @@ -15,11 +17,13 @@ from langchain_experimental.comprehend_moderation.toxicity import ComprehendToxi __all__ = [ "BaseModeration", - "BaseModerationActions", - "BaseModerationFilters", "ComprehendPII", "ComprehendIntent", "ComprehendToxicity", + "BaseModerationConfig", + "ModerationPiiConfig", + "ModerationToxicityConfig", + "ModerationIntentConfig", "BaseModerationCallbackHandler", "AmazonComprehendModerationChain", ] diff --git a/libs/experimental/langchain_experimental/comprehend_moderation/amazon_comprehend_moderation.py b/libs/experimental/langchain_experimental/comprehend_moderation/amazon_comprehend_moderation.py index d00520e627..100e061556 100644 --- a/libs/experimental/langchain_experimental/comprehend_moderation/amazon_comprehend_moderation.py +++ b/libs/experimental/langchain_experimental/comprehend_moderation/amazon_comprehend_moderation.py @@ -3,12 +3,13 @@ from typing import Any, Dict, List, Optional from langchain.callbacks.manager import CallbackManagerForChainRun from langchain.chains.base import Chain -from langchain_experimental.comprehend_moderation.base_moderation import ( - BaseModeration, -) +from langchain_experimental.comprehend_moderation.base_moderation import BaseModeration from langchain_experimental.comprehend_moderation.base_moderation_callbacks import ( BaseModerationCallbackHandler, ) +from langchain_experimental.comprehend_moderation.base_moderation_config import ( + BaseModerationConfig, +) from langchain_experimental.pydantic_v1 import root_validator @@ -21,10 +22,13 @@ class AmazonComprehendModerationChain(Chain): input_key: str = "input" #: :meta private: """Key used to fetch/store the input in data containers. Defaults to `input`""" - moderation_config: Optional[Dict[str, Any]] = None - """Configuration settings for moderation""" + moderation_config: BaseModerationConfig = BaseModerationConfig() + """ + Configuration settings for moderation, + defaults to BaseModerationConfig with default values + """ - client: Optional[Any] + client: Optional[Any] = None """boto3 client object for connection to Amazon Comprehend""" region_name: Optional[str] = None diff --git a/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation.py b/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation.py index c639112b95..3724ac081d 100644 --- a/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation.py +++ b/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation.py @@ -1,5 +1,5 @@ import uuid -from typing import Any, Callable, Dict, Optional +from typing import Any, Callable, Optional from langchain.callbacks.manager import CallbackManagerForChainRun from langchain.prompts.base import StringPromptValue @@ -15,7 +15,7 @@ class BaseModeration: def __init__( self, client: Any, - config: Optional[Dict[str, Any]] = None, + config: Optional[Any] = None, moderation_callback: Optional[Any] = None, unique_id: Optional[str] = None, run_manager: Optional[CallbackManagerForChainRun] = None, @@ -105,6 +105,11 @@ class BaseModeration: self.run_manager.on_text(message) def moderate(self, prompt: Any) -> str: + from langchain_experimental.comprehend_moderation.base_moderation_config import ( # noqa: E501 + ModerationIntentConfig, + ModerationPiiConfig, + ModerationToxicityConfig, + ) from langchain_experimental.comprehend_moderation.base_moderation_exceptions import ( # noqa: E501 ModerationIntentionError, ModerationPiiError, @@ -115,47 +120,43 @@ class BaseModeration: # convert prompt to text input_text = self._convert_prompt_to_text(prompt=prompt) output_text = str() + # perform moderation - if self.config is None: - # In absence of config Action will default to STOP only - self._log_message_for_verbose("Running pii validation...\n") - pii_validate = self._moderation_class(moderation_class=ComprehendPII) - output_text = pii_validate(prompt_value=input_text) + filter_functions = { + "pii": ComprehendPII, + "toxicity": ComprehendToxicity, + "intent": ComprehendIntent, + } - self._log_message_for_verbose("Running toxicity validation...\n") - toxicity_validate = self._moderation_class( - moderation_class=ComprehendToxicity - ) - output_text = toxicity_validate(prompt_value=output_text) + filters = self.config.filters # type: ignore - self._log_message_for_verbose("Running intent validation...\n") - intent_validate = self._moderation_class( - moderation_class=ComprehendIntent + for _filter in filters: + filter_name = ( + "pii" + if isinstance(_filter, ModerationPiiConfig) + else ( + "toxicity" + if isinstance(_filter, ModerationToxicityConfig) + else ( + "intent" + if isinstance(_filter, ModerationIntentConfig) + else None + ) + ) ) - output_text = intent_validate(prompt_value=output_text) - else: - filter_functions = { - "pii": ComprehendPII, - "toxicity": ComprehendToxicity, - "intent": ComprehendIntent, - } - filters = self.config["filters"] - for _filter in filters: - filter_name = f"{_filter}" - if filter_name in filter_functions: - self._log_message_for_verbose( - f"Running {filter_name} Validation...\n" - ) - validation_fn = self._moderation_class( - moderation_class=filter_functions[filter_name] - ) - input_text = input_text if not output_text else output_text - output_text = validation_fn( - prompt_value=input_text, - config=self.config[filter_name] - if filter_name in self.config - else None, - ) + if filter_name in filter_functions: + self._log_message_for_verbose( + f"Running {filter_name} Validation...\n" + ) + validation_fn = self._moderation_class( + moderation_class=filter_functions[filter_name] + ) + input_text = input_text if not output_text else output_text + output_text = validation_fn( + prompt_value=input_text, + config=_filter.dict(), + ) + # convert text to prompt and return return self._convert_text_to_prompt(prompt=prompt, text=output_text) diff --git a/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_callbacks.py b/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_callbacks.py index d7fcd76a10..2e3e51db2d 100644 --- a/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_callbacks.py +++ b/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_callbacks.py @@ -28,19 +28,19 @@ class BaseModerationCallbackHandler: self, moderation_beacon: Dict[str, Any], unique_id: str, **kwargs: Any ) -> None: """Run after PII validation is complete.""" - raise NotImplementedError("Subclasses should implement this async method.") + pass async def on_after_toxicity( self, moderation_beacon: Dict[str, Any], unique_id: str, **kwargs: Any ) -> None: """Run after Toxicity validation is complete.""" - raise NotImplementedError("Subclasses should implement this async method.") + pass async def on_after_intent( self, moderation_beacon: Dict[str, Any], unique_id: str, **kwargs: Any ) -> None: """Run after Toxicity validation is complete.""" - raise NotImplementedError("Subclasses should implement this async method.") + pass @property def pii_callback(self) -> bool: diff --git a/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_config.py b/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_config.py new file mode 100644 index 0000000000..7e91cb783b --- /dev/null +++ b/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_config.py @@ -0,0 +1,51 @@ +from typing import List, Union + +from pydantic import BaseModel + + +class ModerationPiiConfig(BaseModel): + threshold: float = 0.5 + """Threshold for PII confidence score, defaults to 0.5 i.e. 50%""" + + labels: List[str] = [] + """ + List of PII Universal Labels. + Defaults to `list[]` + """ + + redact: bool = False + """Whether to perform redaction of detected PII entities""" + + mask_character: str = "*" + """Redaction mask character in case redact=True, defaults to asterisk (*)""" + + +class ModerationToxicityConfig(BaseModel): + threshold: float = 0.5 + """Threshold for Toxic label confidence score, defaults to 0.5 i.e. 50%""" + + labels: List[str] = [] + """List of toxic labels, defaults to `list[]`""" + + +class ModerationIntentConfig(BaseModel): + threshold: float = 0.5 + """ + Threshold for Intent classification + confidence score, defaults to 0.5 i.e. 50% + """ + + +class BaseModerationConfig(BaseModel): + filters: List[ + Union[ModerationPiiConfig, ModerationToxicityConfig, ModerationIntentConfig] + ] = [ + ModerationPiiConfig(), + ModerationToxicityConfig(), + ModerationIntentConfig(), + ] + """ + Filters applied to the moderation chain, defaults to + `[ModerationPiiConfig(), ModerationToxicityConfig(), + ModerationIntentConfig()]` + """ diff --git a/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_enums.py b/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_enums.py deleted file mode 100644 index aec629ebcc..0000000000 --- a/libs/experimental/langchain_experimental/comprehend_moderation/base_moderation_enums.py +++ /dev/null @@ -1,12 +0,0 @@ -from enum import Enum - - -class BaseModerationActions(Enum): - STOP = 1 - ALLOW = 2 - - -class BaseModerationFilters(str, Enum): - PII = "pii" - TOXICITY = "toxicity" - INTENT = "intent" diff --git a/libs/experimental/langchain_experimental/comprehend_moderation/intent.py b/libs/experimental/langchain_experimental/comprehend_moderation/intent.py index 761c072868..d71f54d3aa 100644 --- a/libs/experimental/langchain_experimental/comprehend_moderation/intent.py +++ b/libs/experimental/langchain_experimental/comprehend_moderation/intent.py @@ -1,6 +1,5 @@ import asyncio -import warnings -from typing import Any, Dict, Optional +from typing import Any, Optional from langchain_experimental.comprehend_moderation.base_moderation_exceptions import ( ModerationIntentionError, @@ -30,20 +29,17 @@ class ComprehendIntent: intent_endpoint = "document-classifier-endpoint/prompt-intent" return f"arn:aws:{service}:{region_name}:aws:{intent_endpoint}" - def validate( - self, prompt_value: str, config: Optional[Dict[str, Any]] = None - ) -> str: + def validate(self, prompt_value: str, config: Any = None) -> str: """ Check and validate the intent of the given prompt text. Args: - comprehend_client: Comprehend client for intent classification - prompt_value (str): The input text to be checked for unintended intent - config (Dict[str, Any]): Configuration settings for intent checks + prompt_value (str): The input text to be checked for unintended intent. + config (Dict[str, Any]): Configuration settings for intent checks. Raises: ValueError: If unintended intent is found in the prompt text based - on the specified threshold. + on the specified threshold. Returns: str: The input prompt_value. @@ -53,26 +49,16 @@ class ComprehendIntent: Comprehend's classify_document API and raises an error if unintended intent is detected with a score above the specified threshold. + Example: + comprehend_client = boto3.client('comprehend') + prompt_text = "Please tell me your credit card information." + config = {"threshold": 0.7} + checked_prompt = check_intent(comprehend_client, prompt_text, config) """ - from langchain_experimental.comprehend_moderation.base_moderation_enums import ( - BaseModerationActions, - ) - threshold = config.get("threshold", 0.5) if config else 0.5 - action = ( - config.get("action", BaseModerationActions.STOP) - if config - else BaseModerationActions.STOP - ) + threshold = config.get("threshold") intent_found = False - if action == BaseModerationActions.ALLOW: - warnings.warn( - "You have allowed content with Harmful content." - "Defaulting to STOP action..." - ) - action = BaseModerationActions.STOP - endpoint_arn = self._get_arn() response = self.client.classify_document( Text=prompt_value, EndpointArn=endpoint_arn diff --git a/libs/experimental/langchain_experimental/comprehend_moderation/pii.py b/libs/experimental/langchain_experimental/comprehend_moderation/pii.py index 2c82b7a400..0d3e07ae64 100644 --- a/libs/experimental/langchain_experimental/comprehend_moderation/pii.py +++ b/libs/experimental/langchain_experimental/comprehend_moderation/pii.py @@ -23,33 +23,19 @@ class ComprehendPII: self.callback = callback self.unique_id = unique_id - def validate( - self, prompt_value: str, config: Optional[Dict[str, Any]] = None - ) -> str: - from langchain_experimental.comprehend_moderation.base_moderation_enums import ( - BaseModerationActions, + def validate(self, prompt_value: str, config: Any = None) -> str: + redact = config.get("redact") + return ( + self._detect_pii(prompt_value=prompt_value, config=config) + if redact + else self._contains_pii(prompt_value=prompt_value, config=config) ) - if config: - action = config.get("action", BaseModerationActions.STOP) - if action not in [BaseModerationActions.STOP, BaseModerationActions.ALLOW]: - raise ValueError("Action can either be stop or allow") - - return ( - self._contains_pii(prompt_value=prompt_value, config=config) - if action == BaseModerationActions.STOP - else self._detect_pii(prompt_value=prompt_value, config=config) - ) - else: - return self._contains_pii(prompt_value=prompt_value) - - def _contains_pii( - self, prompt_value: str, config: Optional[Dict[str, Any]] = None - ) -> str: + def _contains_pii(self, prompt_value: str, config: Any = None) -> str: """ Checks for Personally Identifiable Information (PII) labels above a - specified threshold. - + specified threshold. Uses Amazon Comprehend Contains PII Entities API. See - + https://docs.aws.amazon.com/comprehend/latest/APIReference/API_ContainsPiiEntities.html Args: prompt_value (str): The input text to be checked for PII labels. config (Dict[str, Any]): Configuration for PII check and actions. @@ -68,8 +54,8 @@ class ComprehendPII: self.moderation_beacon["moderation_input"] = prompt_value self.moderation_beacon["moderation_output"] = pii_identified - threshold = config.get("threshold", 0.5) if config else 0.5 - pii_labels = config.get("labels", []) if config else [] + threshold = config.get("threshold") + pii_labels = config.get("labels") pii_found = False for entity in pii_identified["Labels"]: if (entity["Score"] >= threshold and entity["Name"] in pii_labels) or ( @@ -93,7 +79,8 @@ class ComprehendPII: Detects and handles Personally Identifiable Information (PII) entities in the given prompt text using Amazon Comprehend's detect_pii_entities API. The function provides options to redact or stop processing based on the identified - PII entities and a provided configuration. + PII entities and a provided configuration. Uses Amazon Comprehend Detect PII + Entities API. Args: prompt_value (str): The input text to be checked for PII entities. @@ -143,9 +130,9 @@ class ComprehendPII: if pii_found: raise ModerationPiiError else: - threshold = config.get("threshold", 0.5) # type: ignore - pii_labels = config.get("labels", []) # type: ignore - mask_marker = config.get("mask_character", "*") # type: ignore + threshold = config.get("threshold") # type: ignore + pii_labels = config.get("labels") # type: ignore + mask_marker = config.get("mask_character") # type: ignore pii_found = False for entity in pii_identified["Entities"]: @@ -157,10 +144,14 @@ class ComprehendPII: pii_found = True char_offset_begin = entity["BeginOffset"] char_offset_end = entity["EndOffset"] + + mask_length = char_offset_end - char_offset_begin + 1 + masked_part = mask_marker * mask_length + prompt_value = ( prompt_value[:char_offset_begin] - + mask_marker * (char_offset_end - char_offset_begin) - + prompt_value[char_offset_end:] + + masked_part + + prompt_value[char_offset_end + 1 :] ) if self.callback and self.callback.pii_callback: diff --git a/libs/experimental/langchain_experimental/comprehend_moderation/toxicity.py b/libs/experimental/langchain_experimental/comprehend_moderation/toxicity.py index b66320ec55..c616e506e7 100644 --- a/libs/experimental/langchain_experimental/comprehend_moderation/toxicity.py +++ b/libs/experimental/langchain_experimental/comprehend_moderation/toxicity.py @@ -1,7 +1,6 @@ import asyncio import importlib -import warnings -from typing import Any, Dict, List, Optional +from typing import Any, List, Optional from langchain_experimental.comprehend_moderation.base_moderation_exceptions import ( ModerationToxicityError, @@ -30,14 +29,15 @@ class ComprehendToxicity: Validate and initialize toxicity processing configuration. Args: - max_size (int): Maximum sentence size defined in the configuration object. + max_size (int): Maximum sentence size defined in the + configuration object. Raises: Exception: If the maximum sentence size exceeds the 5KB limit. Note: - This function ensures that the NLTK punkt tokenizer is downloaded if not - already present. + This function ensures that the NLTK punkt tokenizer is downloaded + if not already present. Returns: None @@ -63,34 +63,36 @@ class ComprehendToxicity: Split a paragraph into chunks of sentences, respecting the maximum size limit. Args: - paragraph (str): The input paragraph to be split into chunks - max_size (int, optional): The maximum size limit in bytes for each chunk - Defaults to 1024. + paragraph (str): The input paragraph to be split into chunks. + max_size (int, optional): The maximum size limit in bytes for + each chunk. Defaults to 1024. Returns: - List[List[str]]: A list of chunks, where each chunk is a list of sentences + List[List[str]]: A list of chunks, where each chunk is a list + of sentences. Note: - This function validates the maximum sentence size based on service limits - using the 'toxicity_init_validate' function. It uses the NLTK sentence - tokenizer to split the paragraph into sentences. + This function validates the maximum sentence size based on service + limits using the 'toxicity_init_validate' function. It uses the NLTK + sentence tokenizer to split the paragraph into sentences. + Example: + paragraph = "This is a sample paragraph. It + contains multiple sentences. ..." + chunks = split_paragraph(paragraph, max_size=2048) """ # validate max. sentence size based on Service limits nltk = self._toxicity_init_validate(max_size) - sentences = nltk.sent_tokenize(prompt_value) - - chunks = [] - current_chunk = [] # type: ignore + chunks = list() # type: ignore + current_chunk = list() # type: ignore current_size = 0 for sentence in sentences: sentence_size = len(sentence.encode("utf-8")) - - # If adding a new sentence exceeds max_size or - # current_chunk has 10 sentences, start a new chunk + # If adding a new sentence exceeds max_size + # or current_chunk has 10 sentences, start a new chunk if (current_size + sentence_size > max_size) or (len(current_chunk) >= 10): if current_chunk: # Avoid appending empty chunks chunks.append(current_chunk) @@ -103,16 +105,12 @@ class ComprehendToxicity: # Add any remaining sentences if current_chunk: chunks.append(current_chunk) - return chunks - def validate( - self, prompt_value: str, config: Optional[Dict[str, Any]] = None - ) -> str: + def validate(self, prompt_value: str, config: Any = None) -> str: """ - Check the toxicity of a given text prompt using AWS Comprehend service - and apply actions based on configuration. - + Check the toxicity of a given text prompt using AWS + Comprehend service and apply actions based on configuration. Args: prompt_value (str): The text content to be checked for toxicity. config (Dict[str, Any]): Configuration for toxicity checks and actions. @@ -122,7 +120,7 @@ class ComprehendToxicity: Raises: ValueError: If the prompt contains toxic labels and cannot be - processed based on the configuration. + processed based on the configuration. """ chunks = self._split_paragraph(prompt_value=prompt_value) @@ -134,76 +132,34 @@ class ComprehendToxicity: if self.callback and self.callback.toxicity_callback: self.moderation_beacon["moderation_input"] = segments # type: ignore self.moderation_beacon["moderation_output"] = response + toxicity_found = False + threshold = config.get("threshold") + toxicity_labels = config.get("labels") - if config: - from langchain_experimental.comprehend_moderation.base_moderation_enums import ( # noqa: E501 - BaseModerationActions, - ) - - toxicity_found = False - action = config.get("action", BaseModerationActions.STOP) - if action not in [ - BaseModerationActions.STOP, - BaseModerationActions.ALLOW, - ]: - raise ValueError("Action can either be stop or allow") - - threshold = config.get("threshold", 0.5) if config else 0.5 - toxicity_labels = config.get("labels", []) if config else [] - - if action == BaseModerationActions.STOP: - for item in response["ResultList"]: - for label in item["Labels"]: - if ( - label - and ( - not toxicity_labels - or label["Name"] in toxicity_labels - ) - and label["Score"] >= threshold - ): - toxicity_found = True - break - - if action == BaseModerationActions.ALLOW: - if not toxicity_labels: - warnings.warn( - "You have allowed toxic content without specifying " - "any toxicity labels." - ) - else: - for item in response["ResultList"]: - for label in item["Labels"]: - if ( - label["Name"] in toxicity_labels - and label["Score"] >= threshold - ): - toxicity_found = True - break - - if self.callback and self.callback.toxicity_callback: - if toxicity_found: - self.moderation_beacon["moderation_status"] = "LABELS_FOUND" - asyncio.create_task( - self.callback.on_after_toxicity( - self.moderation_beacon, self.unique_id - ) - ) - if toxicity_found: - raise ModerationToxicityError + if not toxicity_labels: + for item in response["ResultList"]: + for label in item["Labels"]: + if label["Score"] >= threshold: + toxicity_found = True + break else: - if response["ResultList"]: - detected_toxic_labels = list() - for item in response["ResultList"]: - detected_toxic_labels.extend(item["Labels"]) - if any(item["Score"] >= 0.5 for item in detected_toxic_labels): - if self.callback and self.callback.toxicity_callback: - self.moderation_beacon["moderation_status"] = "LABELS_FOUND" - asyncio.create_task( - self.callback.on_after_toxicity( - self.moderation_beacon, self.unique_id - ) - ) - raise ModerationToxicityError + for item in response["ResultList"]: + for label in item["Labels"]: + if ( + label["Name"] in toxicity_labels + and label["Score"] >= threshold + ): + toxicity_found = True + break + if self.callback and self.callback.toxicity_callback: + if toxicity_found: + self.moderation_beacon["moderation_status"] = "LABELS_FOUND" + asyncio.create_task( + self.callback.on_after_toxicity( + self.moderation_beacon, self.unique_id + ) + ) + if toxicity_found: + raise ModerationToxicityError return prompt_value diff --git a/libs/experimental/langchain_experimental/data_anonymizer/presidio.py b/libs/experimental/langchain_experimental/data_anonymizer/presidio.py index 544958ac15..298e3de1d5 100644 --- a/libs/experimental/langchain_experimental/data_anonymizer/presidio.py +++ b/libs/experimental/langchain_experimental/data_anonymizer/presidio.py @@ -18,14 +18,12 @@ class PresidioAnonymizer(AnonymizerBase): def __init__( self, analyzed_fields: Optional[List[str]] = None, - language: str = "en", operators: Optional[Dict[str, OperatorConfig]] = None, ): """ Args: analyzed_fields: List of fields to detect and then anonymize. Defaults to all entities supported by Microsoft Presidio. - language: Language to use for analysis. Defaults to english. operators: Operators to use for anonymization. Operators allow for custom anonymization of detected PII. Learn more: @@ -54,7 +52,6 @@ class PresidioAnonymizer(AnonymizerBase): if analyzed_fields is not None else list(get_pseudoanonymizer_mapping().keys()) ) - self.language = language self.operators = ( operators if operators is not None @@ -72,7 +69,7 @@ class PresidioAnonymizer(AnonymizerBase): results = self._analyzer.analyze( text, entities=self.analyzed_fields, - language=self.language, + language="en", ) return self._anonymizer.anonymize( diff --git a/libs/experimental/langchain_experimental/fallacy_removal/__init__.py b/libs/experimental/langchain_experimental/fallacy_removal/__init__.py new file mode 100644 index 0000000000..804990be4b --- /dev/null +++ b/libs/experimental/langchain_experimental/fallacy_removal/__init__.py @@ -0,0 +1,4 @@ +"""The Chain runs a self-review of logical fallacies as determined by this paper \ +categorizing and defining logical fallacies https://arxiv.org/pdf/2212.07425.pdf. \ +Modeled after Constitutional AI and in same format, but applying logical \ +fallacies as generalized rules to remove in output""" diff --git a/libs/experimental/langchain_experimental/fallacy_removal/base.py b/libs/experimental/langchain_experimental/fallacy_removal/base.py new file mode 100644 index 0000000000..df897bcf42 --- /dev/null +++ b/libs/experimental/langchain_experimental/fallacy_removal/base.py @@ -0,0 +1,181 @@ +"""Chain for applying removals of logical fallacies.""" +from __future__ import annotations + +from typing import Any, Dict, List, Optional + +from langchain.callbacks.manager import CallbackManagerForChainRun +from langchain.chains.base import Chain +from langchain.chains.llm import LLMChain +from langchain.schema import BasePromptTemplate +from langchain.schema.language_model import BaseLanguageModel + +from langchain_experimental.fallacy_removal.fallacies import FALLACIES +from langchain_experimental.fallacy_removal.models import LogicalFallacy +from langchain_experimental.fallacy_removal.prompts import ( + FALLACY_CRITIQUE_PROMPT, + FALLACY_REVISION_PROMPT, +) + + +class FallacyChain(Chain): + """Chain for applying logical fallacy evaluations, modeled after Constitutional AI \ + and in same format, but applying logical fallacies as generalized rules to remove \ + in output + + Example: + .. code-block:: python + + from langchain.llms import OpenAI + from langchain.chains import LLMChain + from langchain_experimental.fallacy import FallacyChain + from langchain_experimental.fallacy_removal.models import LogicalFallacy + + llm = OpenAI() + + qa_prompt = PromptTemplate( + template="Q: {question} A:", + input_variables=["question"], + ) + qa_chain = LLMChain(llm=llm, prompt=qa_prompt) + + fallacy_chain = FallacyChain.from_llm( + llm=llm, + chain=qa_chain, + logical_fallacies=[ + LogicalFallacy( + fallacy_critique_request="Tell if this answer meets criteria.", + fallacy_revision_request=\ + "Give an answer that meets better criteria.", + ) + ], + ) + + fallacy_chain.run(question="How do I know if the earth is round?") + """ + + chain: LLMChain + logical_fallacies: List[LogicalFallacy] + fallacy_critique_chain: LLMChain + fallacy_revision_chain: LLMChain + return_intermediate_steps: bool = False + + @classmethod + def get_fallacies(cls, names: Optional[List[str]] = None) -> List[LogicalFallacy]: + if names is None: + return list(FALLACIES.values()) + else: + return [FALLACIES[name] for name in names] + + @classmethod + def from_llm( + cls, + llm: BaseLanguageModel, + chain: LLMChain, + fallacy_critique_prompt: BasePromptTemplate = FALLACY_CRITIQUE_PROMPT, + fallacy_revision_prompt: BasePromptTemplate = FALLACY_REVISION_PROMPT, + **kwargs: Any, + ) -> "FallacyChain": + """Create a chain from an LLM.""" + fallacy_critique_chain = LLMChain(llm=llm, prompt=fallacy_critique_prompt) + fallacy_revision_chain = LLMChain(llm=llm, prompt=fallacy_revision_prompt) + return cls( + chain=chain, + fallacy_critique_chain=fallacy_critique_chain, + fallacy_revision_chain=fallacy_revision_chain, + **kwargs, + ) + + @property + def input_keys(self) -> List[str]: + """Input keys.""" + return self.chain.input_keys + + @property + def output_keys(self) -> List[str]: + """Output keys.""" + if self.return_intermediate_steps: + return ["output", "fallacy_critiques_and_revisions", "initial_output"] + return ["output"] + + def _call( + self, + inputs: Dict[str, Any], + run_manager: Optional[CallbackManagerForChainRun] = None, + ) -> Dict[str, Any]: + _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() + response = self.chain.run( + **inputs, + callbacks=_run_manager.get_child("original"), + ) + initial_response = response + input_prompt = self.chain.prompt.format(**inputs) + + _run_manager.on_text( + text="Initial response: " + response + "\n\n", + verbose=self.verbose, + color="yellow", + ) + fallacy_critiques_and_revisions = [] + for logical_fallacy in self.logical_fallacies: + # Fallacy critique below + + fallacy_raw_critique = self.fallacy_critique_chain.run( + input_prompt=input_prompt, + output_from_model=response, + fallacy_critique_request=logical_fallacy.fallacy_critique_request, + callbacks=_run_manager.get_child("fallacy_critique"), + ) + fallacy_critique = self._parse_critique( + output_string=fallacy_raw_critique, + ).strip() + + # if fallacy critique contains "No fallacy critique needed" then done + if "no fallacy critique needed" in fallacy_critique.lower(): + fallacy_critiques_and_revisions.append((fallacy_critique, "")) + continue + + fallacy_revision = self.fallacy_revision_chain.run( + input_prompt=input_prompt, + output_from_model=response, + fallacy_critique_request=logical_fallacy.fallacy_critique_request, + fallacy_critique=fallacy_critique, + revision_request=logical_fallacy.fallacy_revision_request, + callbacks=_run_manager.get_child("fallacy_revision"), + ).strip() + response = fallacy_revision + fallacy_critiques_and_revisions.append((fallacy_critique, fallacy_revision)) + + _run_manager.on_text( + text=f"Applying {logical_fallacy.name}..." + "\n\n", + verbose=self.verbose, + color="green", + ) + + _run_manager.on_text( + text="Logical Fallacy: " + fallacy_critique + "\n\n", + verbose=self.verbose, + color="blue", + ) + + _run_manager.on_text( + text="Updated response: " + fallacy_revision + "\n\n", + verbose=self.verbose, + color="yellow", + ) + + final_output: Dict[str, Any] = {"output": response} + if self.return_intermediate_steps: + final_output["initial_output"] = initial_response + final_output[ + "fallacy_critiques_and_revisions" + ] = fallacy_critiques_and_revisions + return final_output + + @staticmethod + def _parse_critique(output_string: str) -> str: + if "Fallacy Revision request:" not in output_string: + return output_string + output_string = output_string.split("Fallacy Revision request:")[0] + if "\n\n" in output_string: + output_string = output_string.split("\n\n")[0] + return output_string diff --git a/libs/experimental/langchain_experimental/fallacy_removal/fallacies.py b/libs/experimental/langchain_experimental/fallacy_removal/fallacies.py new file mode 100644 index 0000000000..efe4838fda --- /dev/null +++ b/libs/experimental/langchain_experimental/fallacy_removal/fallacies.py @@ -0,0 +1,209 @@ +"""Classification of logical fallacies in Natural Language Arguments \ +from https://arxiv.org/pdf/2212.07425.pdf +""" + +from typing import Dict + +from langchain_experimental.fallacy_removal.models import LogicalFallacy + +FALLACIES: Dict[str, LogicalFallacy] = { + "adhominem": LogicalFallacy( + name="adhominem", + fallacy_critique_request="Identify any feasible ways in which \ + the assistant’s last response is attacking the character or \ + personal traits of the person making an argument rather than \ + addressing the actual argument and evidence.", + fallacy_revision_request="Please rewrite the assistant response\ + to remove any attacking the character or personal traits of the\ + person making an argument rather than addressing the actual\ + argument and evidence.", + ), + "adpopulum": LogicalFallacy( + name="adpopulum", + fallacy_critique_request="Identify ways in which the assistant’s\ + last response may be asserting that something must be true or \ + correct simply because many people believe it or do it, without \ + actual facts or evidence to support the conclusion.", + fallacy_revision_request="Please rewrite the assistant response \ + to remove any assertion that something must be true or correct \ + simply because many people believe it or do it, without actual \ + facts or evidence to support the conclusion.", + ), + "appealtoemotion": LogicalFallacy( + name="appealtoemotion", + fallacy_critique_request="Identify all ways in which the \ + assistant’s last response is an attempt to win support for an \ + argument by exploiting or manipulating people's emotions rather \ + than using facts and reason.", + fallacy_revision_request="Please rewrite the assistant response \ + to remove any attempt to win support for an argument by \ + exploiting or manipulating people's emotions rather than using \ + facts and reason.", + ), + "fallacyofextension": LogicalFallacy( + name="fallacyofextension", + fallacy_critique_request="Identify any ways in which the \ + assitant's last response is making broad, sweeping generalizations\ + and extending the implications of an argument far beyond what the \ + initial premises support.", + fallacy_revision_request="Rewrite the assistant response to remove\ + all broad, sweeping generalizations and extending the implications\ + of an argument far beyond what the initial premises support.", + ), + "intentionalfallacy": LogicalFallacy( + name="intentionalfallacy", + fallacy_critique_request="Identify any way in which the assistant’s\ + last response may be falsely supporting a conclusion by claiming to\ + understand an author or creator's subconscious intentions without \ + clear evidence.", + fallacy_revision_request="Revise the assistant’s last response to \ + remove any false support of a conclusion by claiming to understand\ + an author or creator's subconscious intentions without clear \ + evidence.", + ), + "falsecausality": LogicalFallacy( + name="falsecausality", + fallacy_critique_request="Think carefully about whether the \ + assistant's last response is jumping to conclusions about causation\ + between events or circumstances without adequate evidence to infer \ + a causal relationship.", + fallacy_revision_request="Please write a new version of the \ + assistant’s response that removes jumping to conclusions about\ + causation between events or circumstances without adequate \ + evidence to infer a causal relationship.", + ), + "falsedilemma": LogicalFallacy( + name="falsedilemma", + fallacy_critique_request="Identify any way in which the \ + assistant's last response may be presenting only two possible options\ + or sides to a situation when there are clearly other alternatives \ + that have not been considered or addressed.", + fallacy_revision_request="Amend the assistant’s last response to \ + remove any presentation of only two possible options or sides to a \ + situation when there are clearly other alternatives that have not \ + been considered or addressed.", + ), + "hastygeneralization": LogicalFallacy( + name="hastygeneralization", + fallacy_critique_request="Identify any way in which the assistant’s\ + last response is making a broad inference or generalization to \ + situations, people, or circumstances that are not sufficiently \ + similar based on a specific example or limited evidence.", + fallacy_revision_request="Please rewrite the assistant response to\ + remove a broad inference or generalization to situations, people, \ + or circumstances that are not sufficiently similar based on a \ + specific example or limited evidence.", + ), + "illogicalarrangement": LogicalFallacy( + name="illogicalarrangement", + fallacy_critique_request="Think carefully about any ways in which \ + the assistant's last response is constructing an argument in a \ + flawed, illogical way, so the premises do not connect to or lead\ + to the conclusion properly.", + fallacy_revision_request="Please rewrite the assistant’s response\ + so as to remove any construction of an argument that is flawed and\ + illogical or if the premises do not connect to or lead to the \ + conclusion properly.", + ), + "fallacyofcredibility": LogicalFallacy( + name="fallacyofcredibility", + fallacy_critique_request="Discuss whether the assistant's last \ + response was dismissing or attacking the credibility of the person\ + making an argument rather than directly addressing the argument \ + itself.", + fallacy_revision_request="Revise the assistant’s response so as \ + that it refrains from dismissing or attacking the credibility of\ + the person making an argument rather than directly addressing \ + the argument itself.", + ), + "circularreasoning": LogicalFallacy( + name="circularreasoning", + fallacy_critique_request="Discuss ways in which the assistant’s\ + last response may be supporting a premise by simply repeating \ + the premise as the conclusion without giving actual proof or \ + evidence.", + fallacy_revision_request="Revise the assistant’s response if \ + possible so that it’s not supporting a premise by simply \ + repeating the premise as the conclusion without giving actual\ + proof or evidence.", + ), + "beggingthequestion": LogicalFallacy( + name="beggingthequestion", + fallacy_critique_request="Discuss ways in which the assistant's\ + last response is restating the conclusion of an argument as a \ + premise without providing actual support for the conclusion in \ + the first place.", + fallacy_revision_request="Write a revision of the assistant’s \ + response that refrains from restating the conclusion of an \ + argument as a premise without providing actual support for the \ + conclusion in the first place.", + ), + "trickquestion": LogicalFallacy( + name="trickquestion", + fallacy_critique_request="Identify ways in which the \ + assistant’s last response is asking a question that \ + contains or assumes information that has not been proven or \ + substantiated.", + fallacy_revision_request="Please write a new assistant \ + response so that it does not ask a question that contains \ + or assumes information that has not been proven or \ + substantiated.", + ), + "overapplier": LogicalFallacy( + name="overapplier", + fallacy_critique_request="Identify ways in which the assistant’s\ + last response is applying a general rule or generalization to a \ + specific case it was not meant to apply to.", + fallacy_revision_request="Please write a new response that does\ + not apply a general rule or generalization to a specific case \ + it was not meant to apply to.", + ), + "equivocation": LogicalFallacy( + name="equivocation", + fallacy_critique_request="Read the assistant’s last response \ + carefully and identify if it is using the same word or phrase \ + in two different senses or contexts within an argument.", + fallacy_revision_request="Rewrite the assistant response so \ + that it does not use the same word or phrase in two different \ + senses or contexts within an argument.", + ), + "amphiboly": LogicalFallacy( + name="amphiboly", + fallacy_critique_request="Critique the assistant’s last response\ + to see if it is constructing sentences such that the grammar \ + or structure is ambiguous, leading to multiple interpretations.", + fallacy_revision_request="Please rewrite the assistant response\ + to remove any construction of sentences where the grammar or \ + structure is ambiguous or leading to multiple interpretations.", + ), + "accent": LogicalFallacy( + name="accent", + fallacy_critique_request="Discuss whether the assitant's response\ + is misrepresenting an argument by shifting the emphasis of a word\ + or phrase to give it a different meaning than intended.", + fallacy_revision_request="Please rewrite the AI model's response\ + so that it is not misrepresenting an argument by shifting the \ + emphasis of a word or phrase to give it a different meaning than\ + intended.", + ), + "composition": LogicalFallacy( + name="composition", + fallacy_critique_request="Discuss whether the assistant's \ + response is erroneously inferring that something is true of \ + the whole based on the fact that it is true of some part or \ + parts.", + fallacy_revision_request="Please rewrite the assitant's response\ + so that it is not erroneously inferring that something is true \ + of the whole based on the fact that it is true of some part or \ + parts.", + ), + "division": LogicalFallacy( + name="division", + fallacy_critique_request="Discuss whether the assistant's last \ + response is erroneously inferring that something is true of the \ + parts based on the fact that it is true of the whole.", + fallacy_revision_request="Please rewrite the assitant's response\ + so that it is not erroneously inferring that something is true \ + of the parts based on the fact that it is true of the whole.", + ), +} diff --git a/libs/experimental/langchain_experimental/fallacy_removal/models.py b/libs/experimental/langchain_experimental/fallacy_removal/models.py new file mode 100644 index 0000000000..b56652c637 --- /dev/null +++ b/libs/experimental/langchain_experimental/fallacy_removal/models.py @@ -0,0 +1,10 @@ +"""Models for the Logical Fallacy Chain""" +from langchain_experimental.pydantic_v1 import BaseModel + + +class LogicalFallacy(BaseModel): + """Class for a logical fallacy.""" + + fallacy_critique_request: str + fallacy_revision_request: str + name: str = "Logical Fallacy" diff --git a/libs/experimental/langchain_experimental/fallacy_removal/prompts.py b/libs/experimental/langchain_experimental/fallacy_removal/prompts.py new file mode 100644 index 0000000000..614a8a2dbf --- /dev/null +++ b/libs/experimental/langchain_experimental/fallacy_removal/prompts.py @@ -0,0 +1,135 @@ +from langchain.prompts.few_shot import FewShotPromptTemplate +from langchain.prompts.prompt import PromptTemplate + +fallacy_critique_example = PromptTemplate( + template="""Human: {input_prompt} + +Model: {output_from_model} + +Fallacy Critique Request: {fallacy_critique_request} + +Fallacy Critique: {fallacy_critique}""", + input_variables=[ + "input_prompt", + "output_from_model", + "fallacy_critique_request", + "fallacy_critique", + ], +) + +examples = [ + { + "input_prompt": "If everyone says the Earth is round, \ + how do I know that's correct?", + "output_from_model": "The earth is round because your \ + teacher says it is", + "fallacy_critique_request": "Identify specific ways in\ + which the model’s previous response had a logical fallacy. \ + Also point out potential logical fallacies in the human’s \ + questions and responses. Examples of logical fallacies \ + include but are not limited to ad hominem, ad populum, \ + appeal to emotion and false causality.", + "fallacy_critique": "This statement contains the logical \ + fallacy of Ad Verecundiam or Appeal to Authority. It is \ + a fallacy because it asserts something to be true purely \ + based on the authority of the source making the claim, \ + without any actual evidence to support it. Fallacy \ + Critique Needed", + "fallacy_revision_request": "Please rewrite the model \ + response to remove all logical fallacies, and to \ + politely point out any logical fallacies from the \ + human.", + "fallacy_revision": "The earth is round based on \ + evidence from observations of its curvature from high \ + altitudes, photos from space showing its spherical shape, \ + circumnavigation, and the fact that we see its rounded \ + shadow on the moon during lunar eclipses.", + }, + { + "input_prompt": "Should we invest more in our school \ + music program? After all, studies show students \ + involved in music perform better academically.", + "output_from_model": "I don't think we should invest \ + more in the music program. Playing the piccolo won't \ + teach someone better math skills.", + "fallacy_critique_request": "Identify specific ways \ + in which the model’s previous response had a logical \ + fallacy. Also point out potential logical fallacies \ + in the human’s questions and responses. Examples of \ + logical fallacies include but are not limited to ad \ + homimem, ad populum, appeal to emotion and false \ + causality.", + "fallacy_critique": "This answer commits the division \ + fallacy by rejecting the argument based on assuming \ + capabilities true of the parts (playing an instrument \ + like piccolo) also apply to the whole \ + (the full music program). The answer focuses only on \ + part of the music program rather than considering it \ + as a whole. Fallacy Critique Needed.", + "fallacy_revision_request": "Please rewrite the model \ + response to remove all logical fallacies, and to \ + politely point out any logical fallacies from the human.", + "fallacy_revision": "While playing an instrument may \ + teach discipline, more evidence is needed on whether \ + music education courses improve critical thinking \ + skills across subjects before determining if increased \ + investment in the whole music program is warranted.", + }, +] + +FALLACY_CRITIQUE_PROMPT = FewShotPromptTemplate( + example_prompt=fallacy_critique_example, + examples=[ + {k: v for k, v in e.items() if k != "fallacy_revision_request"} + for e in examples + ], + prefix="Below is a conversation between a human and an \ + AI assistant. If there is no material critique of the \ + model output, append to the end of the Fallacy Critique: \ + 'No fallacy critique needed.' If there is material \ + critique \ + of the model output, append to the end of the Fallacy \ + Critique: 'Fallacy Critique needed.'", + suffix="""Human: {input_prompt} +Model: {output_from_model} + +Fallacy Critique Request: {fallacy_critique_request} + +Fallacy Critique:""", + example_separator="\n === \n", + input_variables=["input_prompt", "output_from_model", "fallacy_critique_request"], +) + +FALLACY_REVISION_PROMPT = FewShotPromptTemplate( + example_prompt=fallacy_critique_example, + examples=examples, + prefix="Below is a conversation between a human and \ + an AI assistant.", + suffix="""Human: {input_prompt} + +Model: {output_from_model} + +Fallacy Critique Request: {fallacy_critique_request} + +Fallacy Critique: {fallacy_critique} + +If the fallacy critique does not identify anything worth \ +changing, ignore the Fallacy Revision Request and do not \ +make any revisions. Instead, return "No revisions needed". + +If the fallacy critique does identify something worth \ +changing, please revise the model response based on the \ +Fallacy Revision Request. + +Fallacy Revision Request: {fallacy_revision_request} + +Fallacy Revision:""", + example_separator="\n === \n", + input_variables=[ + "input_prompt", + "output_from_model", + "fallacy_critique_request", + "fallacy_critique", + "fallacy_revision_request", + ], +) diff --git a/libs/experimental/langchain_experimental/sql/__init__.py b/libs/experimental/langchain_experimental/sql/__init__.py index cd229f833d..d14f9a0dea 100644 --- a/libs/experimental/langchain_experimental/sql/__init__.py +++ b/libs/experimental/langchain_experimental/sql/__init__.py @@ -1,4 +1,4 @@ """Chain for interacting with SQL Database.""" -from langchain_experimental.sql.base import SQLDatabaseChain +from langchain_experimental.sql.base import SQLDatabaseChain, SQLDatabaseSequentialChain -__all__ = ["SQLDatabaseChain"] +__all__ = ["SQLDatabaseChain", "SQLDatabaseSequentialChain"] diff --git a/libs/experimental/pyproject.toml b/libs/experimental/pyproject.toml index 4289324455..8e876c392c 100644 --- a/libs/experimental/pyproject.toml +++ b/libs/experimental/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain-experimental" -version = "0.0.12" +version = "0.0.14" description = "Building applications with LLMs through composability" authors = [] license = "MIT" diff --git a/libs/experimental/tests/unit_tests/test_logical_fallacy.py b/libs/experimental/tests/unit_tests/test_logical_fallacy.py new file mode 100644 index 0000000000..455c76a463 --- /dev/null +++ b/libs/experimental/tests/unit_tests/test_logical_fallacy.py @@ -0,0 +1,26 @@ +"""Unit tests for the Logical Fallacy chain, same format as CAI""" +from langchain_experimental.fallacy_removal.base import FallacyChain + +TEXT_ONE = """ This text is bad.\ + +Fallacy Revision request: Make it great.\ + +Fallacy Revision:""" + +TEXT_TWO = """ This text is bad.\n\n""" + +TEXT_THREE = """ This text is bad.\ + +Fallacy Revision request: Make it great again.\ + +Fallacy Revision: Better text""" + + +def test_fallacy_critique_parsing() -> None: + """Test parsing of critique text.""" + for text in [TEXT_ONE, TEXT_TWO, TEXT_THREE]: + fallacy_critique = FallacyChain._parse_critique(text) + + assert ( + fallacy_critique.strip() == "This text is bad." + ), f"Failed on {text} with {fallacy_critique}" diff --git a/libs/langchain/langchain/agents/agent_toolkits/sql/base.py b/libs/langchain/langchain/agents/agent_toolkits/sql/base.py index 0d516622bd..4b0531b8c7 100644 --- a/libs/langchain/langchain/agents/agent_toolkits/sql/base.py +++ b/libs/langchain/langchain/agents/agent_toolkits/sql/base.py @@ -1,5 +1,5 @@ """SQL agent.""" -from typing import Any, Dict, List, Optional +from typing import Any, Dict, List, Optional, Sequence from langchain.agents.agent import AgentExecutor, BaseSingleActionAgent from langchain.agents.agent_toolkits.sql.prompt import ( @@ -21,6 +21,7 @@ from langchain.prompts.chat import ( ) from langchain.schema.language_model import BaseLanguageModel from langchain.schema.messages import AIMessage, SystemMessage +from langchain.tools import BaseTool def create_sql_agent( @@ -38,10 +39,11 @@ def create_sql_agent( early_stopping_method: str = "force", verbose: bool = False, agent_executor_kwargs: Optional[Dict[str, Any]] = None, + extra_tools: Sequence[BaseTool] = (), **kwargs: Dict[str, Any], ) -> AgentExecutor: """Construct an SQL agent from an LLM and tools.""" - tools = toolkit.get_tools() + tools = toolkit.get_tools() + list(extra_tools) prefix = prefix.format(dialect=toolkit.dialect, top_k=top_k) agent: BaseSingleActionAgent diff --git a/libs/langchain/langchain/cache.py b/libs/langchain/langchain/cache.py index 4364c33e1a..32eb310d44 100644 --- a/libs/langchain/langchain/cache.py +++ b/libs/langchain/langchain/cache.py @@ -27,6 +27,7 @@ import json import logging import warnings from datetime import timedelta +from functools import lru_cache from typing import ( TYPE_CHECKING, Any, @@ -51,6 +52,7 @@ except ImportError: from langchain.embeddings.base import Embeddings +from langchain.llms.base import LLM, get_prompts from langchain.load.dump import dumps from langchain.load.load import loads from langchain.schema import ChatGeneration, Generation @@ -62,6 +64,7 @@ logger = logging.getLogger(__file__) if TYPE_CHECKING: import momento + from cassandra.cluster import Session as CassandraSession def _hash(_input: str) -> str: @@ -711,3 +714,264 @@ class MomentoCache(BaseCache): pass elif isinstance(flush_response, CacheFlush.Error): raise flush_response.inner_exception + + +CASSANDRA_CACHE_DEFAULT_TABLE_NAME = "langchain_llm_cache" +CASSANDRA_CACHE_DEFAULT_TTL_SECONDS = None + + +class CassandraCache(BaseCache): + """ + Cache that uses Cassandra / Astra DB as a backend. + + It uses a single Cassandra table. + The lookup keys (which get to form the primary key) are: + - prompt, a string + - llm_string, a deterministic str representation of the model parameters. + (needed to prevent collisions same-prompt-different-model collisions) + """ + + def __init__( + self, + session: CassandraSession, + keyspace: str, + table_name: str = CASSANDRA_CACHE_DEFAULT_TABLE_NAME, + ttl_seconds: Optional[int] = CASSANDRA_CACHE_DEFAULT_TTL_SECONDS, + ): + """ + Initialize with a ready session and a keyspace name. + Args: + session (cassandra.cluster.Session): an open Cassandra session + keyspace (str): the keyspace to use for storing the cache + table_name (str): name of the Cassandra table to use as cache + ttl_seconds (optional int): time-to-live for cache entries + (default: None, i.e. forever) + """ + try: + from cassio.table import ElasticCassandraTable + except (ImportError, ModuleNotFoundError): + raise ValueError( + "Could not import cassio python package. " + "Please install it with `pip install cassio`." + ) + + self.session = session + self.keyspace = keyspace + self.table_name = table_name + self.ttl_seconds = ttl_seconds + + self.kv_cache = ElasticCassandraTable( + session=self.session, + keyspace=self.keyspace, + table=self.table_name, + keys=["llm_string", "prompt"], + primary_key_type=["TEXT", "TEXT"], + ttl_seconds=self.ttl_seconds, + ) + + def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: + """Look up based on prompt and llm_string.""" + item = self.kv_cache.get( + llm_string=_hash(llm_string), + prompt=_hash(prompt), + ) + if item: + return _load_generations_from_json(item["body_blob"]) + else: + return None + + def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: + """Update cache based on prompt and llm_string.""" + blob = _dump_generations_to_json(return_val) + self.kv_cache.put( + llm_string=_hash(llm_string), + prompt=_hash(prompt), + body_blob=blob, + ) + + def delete_through_llm( + self, prompt: str, llm: LLM, stop: Optional[List[str]] = None + ) -> None: + """ + A wrapper around `delete` with the LLM being passed. + In case the llm(prompt) calls have a `stop` param, you should pass it here + """ + llm_string = get_prompts( + {**llm.dict(), **{"stop": stop}}, + [], + )[1] + return self.delete(prompt, llm_string=llm_string) + + def delete(self, prompt: str, llm_string: str) -> None: + """Evict from cache if there's an entry.""" + return self.kv_cache.delete( + llm_string=_hash(llm_string), + prompt=_hash(prompt), + ) + + def clear(self, **kwargs: Any) -> None: + """Clear cache. This is for all LLMs at once.""" + self.kv_cache.clear() + + +CASSANDRA_SEMANTIC_CACHE_DEFAULT_DISTANCE_METRIC = "dot" +CASSANDRA_SEMANTIC_CACHE_DEFAULT_SCORE_THRESHOLD = 0.85 +CASSANDRA_SEMANTIC_CACHE_DEFAULT_TABLE_NAME = "langchain_llm_semantic_cache" +CASSANDRA_SEMANTIC_CACHE_DEFAULT_TTL_SECONDS = None +CASSANDRA_SEMANTIC_CACHE_EMBEDDING_CACHE_SIZE = 16 + + +class CassandraSemanticCache(BaseCache): + """ + Cache that uses Cassandra as a vector-store backend for semantic + (i.e. similarity-based) lookup. + + It uses a single (vector) Cassandra table and stores, in principle, + cached values from several LLMs, so the LLM's llm_string is part + of the rows' primary keys. + + The similarity is based on one of several distance metrics (default: "dot"). + If choosing another metric, the default threshold is to be re-tuned accordingly. + """ + + def __init__( + self, + session: CassandraSession, + keyspace: str, + embedding: Embeddings, + table_name: str = CASSANDRA_SEMANTIC_CACHE_DEFAULT_TABLE_NAME, + distance_metric: str = CASSANDRA_SEMANTIC_CACHE_DEFAULT_DISTANCE_METRIC, + score_threshold: float = CASSANDRA_SEMANTIC_CACHE_DEFAULT_SCORE_THRESHOLD, + ttl_seconds: Optional[int] = CASSANDRA_SEMANTIC_CACHE_DEFAULT_TTL_SECONDS, + ): + """ + Initialize the cache with all relevant parameters. + Args: + session (cassandra.cluster.Session): an open Cassandra session + keyspace (str): the keyspace to use for storing the cache + embedding (Embedding): Embedding provider for semantic + encoding and search. + table_name (str): name of the Cassandra (vector) table + to use as cache + distance_metric (str, 'dot'): which measure to adopt for + similarity searches + score_threshold (optional float): numeric value to use as + cutoff for the similarity searches + ttl_seconds (optional int): time-to-live for cache entries + (default: None, i.e. forever) + The default score threshold is tuned to the default metric. + Tune it carefully yourself if switching to another distance metric. + """ + try: + from cassio.table import MetadataVectorCassandraTable + except (ImportError, ModuleNotFoundError): + raise ValueError( + "Could not import cassio python package. " + "Please install it with `pip install cassio`." + ) + self.session = session + self.keyspace = keyspace + self.embedding = embedding + self.table_name = table_name + self.distance_metric = distance_metric + self.score_threshold = score_threshold + self.ttl_seconds = ttl_seconds + + # The contract for this class has separate lookup and update: + # in order to spare some embedding calculations we cache them between + # the two calls. + # Note: each instance of this class has its own `_get_embedding` with + # its own lru. + @lru_cache(maxsize=CASSANDRA_SEMANTIC_CACHE_EMBEDDING_CACHE_SIZE) + def _cache_embedding(text: str) -> List[float]: + return self.embedding.embed_query(text=text) + + self._get_embedding = _cache_embedding + self.embedding_dimension = self._get_embedding_dimension() + + self.table = MetadataVectorCassandraTable( + session=self.session, + keyspace=self.keyspace, + table=self.table_name, + primary_key_type=["TEXT"], + vector_dimension=self.embedding_dimension, + ttl_seconds=self.ttl_seconds, + metadata_indexing=("allow", {"_llm_string_hash"}), + ) + + def _get_embedding_dimension(self) -> int: + return len(self._get_embedding(text="This is a sample sentence.")) + + def update(self, prompt: str, llm_string: str, return_val: RETURN_VAL_TYPE) -> None: + """Update cache based on prompt and llm_string.""" + embedding_vector = self._get_embedding(text=prompt) + llm_string_hash = _hash(llm_string) + body = _dump_generations_to_json(return_val) + metadata = { + "_prompt": prompt, + "_llm_string_hash": llm_string_hash, + } + row_id = f"{_hash(prompt)}-{llm_string_hash}" + # + self.table.put( + body_blob=body, + vector=embedding_vector, + row_id=row_id, + metadata=metadata, + ) + + def lookup(self, prompt: str, llm_string: str) -> Optional[RETURN_VAL_TYPE]: + """Look up based on prompt and llm_string.""" + hit_with_id = self.lookup_with_id(prompt, llm_string) + if hit_with_id is not None: + return hit_with_id[1] + else: + return None + + def lookup_with_id( + self, prompt: str, llm_string: str + ) -> Optional[Tuple[str, RETURN_VAL_TYPE]]: + """ + Look up based on prompt and llm_string. + If there are hits, return (document_id, cached_entry) + """ + prompt_embedding: List[float] = self._get_embedding(text=prompt) + hits = list( + self.table.metric_ann_search( + vector=prompt_embedding, + metadata={"_llm_string_hash": _hash(llm_string)}, + n=1, + metric=self.distance_metric, + metric_threshold=self.score_threshold, + ) + ) + if hits: + hit = hits[0] + generations_str = hit["body_blob"] + return ( + hit["row_id"], + _load_generations_from_json(generations_str), + ) + else: + return None + + def lookup_with_id_through_llm( + self, prompt: str, llm: LLM, stop: Optional[List[str]] = None + ) -> Optional[Tuple[str, RETURN_VAL_TYPE]]: + llm_string = get_prompts( + {**llm.dict(), **{"stop": stop}}, + [], + )[1] + return self.lookup_with_id(prompt, llm_string=llm_string) + + def delete_by_document_id(self, document_id: str) -> None: + """ + Given this is a "similarity search" cache, an invalidation pattern + that makes sense is first a lookup to get an ID, and then deleting + with that ID. This is for the second step. + """ + self.table.delete(row_id=document_id) + + def clear(self, **kwargs: Any) -> None: + """Clear the *whole* semantic cache.""" + self.table.clear() diff --git a/libs/langchain/langchain/callbacks/manager.py b/libs/langchain/langchain/callbacks/manager.py index 52832338fc..ccd84a85e6 100644 --- a/libs/langchain/langchain/callbacks/manager.py +++ b/libs/langchain/langchain/callbacks/manager.py @@ -1193,7 +1193,6 @@ class CallbackManager(BaseCallbackManager): """ if run_id is None: run_id = uuid.uuid4() - _handle_event( self.handlers, "on_chain_start", diff --git a/libs/langchain/langchain/chains/base.py b/libs/langchain/langchain/chains/base.py index 701da6c375..848d0940a6 100644 --- a/libs/langchain/langchain/chains/base.py +++ b/libs/langchain/langchain/chains/base.py @@ -68,6 +68,7 @@ class Chain(Serializable, Runnable[Dict[str, Any], Dict[str, Any]], ABC): callbacks=config.get("callbacks"), tags=config.get("tags"), metadata=config.get("metadata"), + run_name=config.get("run_name"), **kwargs, ) @@ -89,6 +90,7 @@ class Chain(Serializable, Runnable[Dict[str, Any], Dict[str, Any]], ABC): callbacks=config.get("callbacks"), tags=config.get("tags"), metadata=config.get("metadata"), + run_name=config.get("run_name"), **kwargs, ) @@ -235,6 +237,7 @@ class Chain(Serializable, Runnable[Dict[str, Any], Dict[str, Any]], ABC): *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, + run_name: Optional[str] = None, include_run_info: bool = False, ) -> Dict[str, Any]: """Execute the chain. @@ -276,6 +279,7 @@ class Chain(Serializable, Runnable[Dict[str, Any], Dict[str, Any]], ABC): run_manager = callback_manager.on_chain_start( dumpd(self), inputs, + name=run_name, ) try: outputs = ( @@ -302,6 +306,7 @@ class Chain(Serializable, Runnable[Dict[str, Any], Dict[str, Any]], ABC): *, tags: Optional[List[str]] = None, metadata: Optional[Dict[str, Any]] = None, + run_name: Optional[str] = None, include_run_info: bool = False, ) -> Dict[str, Any]: """Asynchronously execute the chain. @@ -343,6 +348,7 @@ class Chain(Serializable, Runnable[Dict[str, Any], Dict[str, Any]], ABC): run_manager = await callback_manager.on_chain_start( dumpd(self), inputs, + name=run_name, ) try: outputs = ( diff --git a/libs/langchain/langchain/chains/openai_functions/extraction.py b/libs/langchain/langchain/chains/openai_functions/extraction.py index c881537866..284b97c416 100644 --- a/libs/langchain/langchain/chains/openai_functions/extraction.py +++ b/libs/langchain/langchain/chains/openai_functions/extraction.py @@ -1,4 +1,4 @@ -from typing import Any, List +from typing import Any, List, Optional from langchain.chains.base import Chain from langchain.chains.llm import LLMChain @@ -13,6 +13,7 @@ from langchain.output_parsers.openai_functions import ( ) from langchain.prompts import ChatPromptTemplate from langchain.pydantic_v1 import BaseModel +from langchain.schema import BasePromptTemplate from langchain.schema.language_model import BaseLanguageModel @@ -43,13 +44,17 @@ Passage: def create_extraction_chain( - schema: dict, llm: BaseLanguageModel, verbose: bool = False + schema: dict, + llm: BaseLanguageModel, + prompt: Optional[BasePromptTemplate] = None, + verbose: bool = False, ) -> Chain: """Creates a chain that extracts information from a passage. Args: schema: The schema of the entities to extract. llm: The language model to use. + prompt: The prompt to use for extraction. verbose: Whether to run in verbose mode. In verbose mode, some intermediate logs will be printed to the console. Defaults to `langchain.verbose` value. @@ -57,12 +62,12 @@ def create_extraction_chain( Chain that can be used to extract information from a passage. """ function = _get_extraction_function(schema) - prompt = ChatPromptTemplate.from_template(_EXTRACTION_TEMPLATE) + extraction_prompt = prompt or ChatPromptTemplate.from_template(_EXTRACTION_TEMPLATE) output_parser = JsonKeyOutputFunctionsParser(key_name="info") llm_kwargs = get_llm_kwargs(function) chain = LLMChain( llm=llm, - prompt=prompt, + prompt=extraction_prompt, llm_kwargs=llm_kwargs, output_parser=output_parser, verbose=verbose, @@ -71,13 +76,19 @@ def create_extraction_chain( def create_extraction_chain_pydantic( - pydantic_schema: Any, llm: BaseLanguageModel + pydantic_schema: Any, + llm: BaseLanguageModel, + prompt: Optional[BasePromptTemplate] = None, + verbose: bool = False, ) -> Chain: """Creates a chain that extracts information from a passage using pydantic schema. Args: pydantic_schema: The pydantic schema of the entities to extract. llm: The language model to use. + prompt: The prompt to use for extraction. + verbose: Whether to run in verbose mode. In verbose mode, some intermediate + logs will be printed to the console. Defaults to `langchain.verbose` value. Returns: Chain that can be used to extract information from a passage. @@ -92,15 +103,16 @@ def create_extraction_chain_pydantic( ) function = _get_extraction_function(openai_schema) - prompt = ChatPromptTemplate.from_template(_EXTRACTION_TEMPLATE) + extraction_prompt = prompt or ChatPromptTemplate.from_template(_EXTRACTION_TEMPLATE) output_parser = PydanticAttrOutputFunctionsParser( pydantic_schema=PydanticSchema, attr_name="info" ) llm_kwargs = get_llm_kwargs(function) chain = LLMChain( llm=llm, - prompt=prompt, + prompt=extraction_prompt, llm_kwargs=llm_kwargs, output_parser=output_parser, + verbose=verbose, ) return chain diff --git a/libs/langchain/langchain/chains/openai_functions/qa_with_structure.py b/libs/langchain/langchain/chains/openai_functions/qa_with_structure.py index dff17030a3..770b3e9d76 100644 --- a/libs/langchain/langchain/chains/openai_functions/qa_with_structure.py +++ b/libs/langchain/langchain/chains/openai_functions/qa_with_structure.py @@ -28,6 +28,7 @@ def create_qa_with_structure_chain( schema: Union[dict, Type[BaseModel]], output_parser: str = "base", prompt: Optional[Union[PromptTemplate, ChatPromptTemplate]] = None, + verbose: bool = False, ) -> LLMChain: """Create a question answering chain that returns an answer with sources based on schema. @@ -87,18 +88,24 @@ def create_qa_with_structure_chain( prompt=prompt, llm_kwargs=llm_kwargs, output_parser=_output_parser, + verbose=verbose, ) return chain -def create_qa_with_sources_chain(llm: BaseLanguageModel, **kwargs: Any) -> LLMChain: +def create_qa_with_sources_chain( + llm: BaseLanguageModel, verbose: bool = False, **kwargs: Any +) -> LLMChain: """Create a question answering chain that returns an answer with sources. Args: llm: Language model to use for the chain. + verbose: Whether to print the details of the chain **kwargs: Keyword arguments to pass to `create_qa_with_structure_chain`. Returns: Chain (LLMChain) that can be used to answer questions with citations. """ - return create_qa_with_structure_chain(llm, AnswerWithSources, **kwargs) + return create_qa_with_structure_chain( + llm, AnswerWithSources, verbose=verbose, **kwargs + ) diff --git a/libs/langchain/langchain/chains/query_constructor/ir.py b/libs/langchain/langchain/chains/query_constructor/ir.py index 04eac8be91..c8d6fc976b 100644 --- a/libs/langchain/langchain/chains/query_constructor/ir.py +++ b/libs/langchain/langchain/chains/query_constructor/ir.py @@ -81,6 +81,7 @@ class Comparator(str, Enum): """Enumerator of the comparison operators.""" EQ = "eq" + NE = "ne" GT = "gt" GTE = "gte" LT = "lt" diff --git a/libs/langchain/langchain/chains/router/multi_prompt_prompt.py b/libs/langchain/langchain/chains/router/multi_prompt_prompt.py index 5221ea9334..b879e718a4 100644 --- a/libs/langchain/langchain/chains/router/multi_prompt_prompt.py +++ b/libs/langchain/langchain/chains/router/multi_prompt_prompt.py @@ -28,4 +28,5 @@ modifications are needed. {{input}} << OUTPUT (must include ```json at the start of the response) >> +<< OUTPUT (must end with ```) >> """ diff --git a/libs/langchain/langchain/chat_loaders/imessage.py b/libs/langchain/langchain/chat_loaders/imessage.py index df2ed95577..ff9a06142c 100644 --- a/libs/langchain/langchain/chat_loaders/imessage.py +++ b/libs/langchain/langchain/chat_loaders/imessage.py @@ -108,8 +108,13 @@ class IMessageChatLoader(chat_loaders.BaseChatLoader): ) from e cursor = conn.cursor() - # Fetch the list of chat IDs - cursor.execute("SELECT ROWID FROM chat") + # Fetch the list of chat IDs sorted by time (most recent first) + query = """SELECT chat_id + FROM message + JOIN chat_message_join ON message.ROWID = chat_message_join.message_id + GROUP BY chat_id + ORDER BY MAX(date) DESC;""" + cursor.execute(query) chat_ids = [row[0] for row in cursor.fetchall()] for chat_id in chat_ids: diff --git a/libs/langchain/langchain/chat_models/__init__.py b/libs/langchain/langchain/chat_models/__init__.py index ee21a2377e..b03cb77710 100644 --- a/libs/langchain/langchain/chat_models/__init__.py +++ b/libs/langchain/langchain/chat_models/__init__.py @@ -20,6 +20,7 @@ an interface where "chat messages" are the inputs and outputs. from langchain.chat_models.anthropic import ChatAnthropic from langchain.chat_models.anyscale import ChatAnyscale from langchain.chat_models.azure_openai import AzureChatOpenAI +from langchain.chat_models.bedrock import BedrockChat from langchain.chat_models.ernie import ErnieBotChat from langchain.chat_models.fake import FakeListChatModel from langchain.chat_models.google_palm import ChatGooglePalm @@ -35,6 +36,7 @@ from langchain.chat_models.vertexai import ChatVertexAI __all__ = [ "ChatOpenAI", "AzureChatOpenAI", + "BedrockChat", "FakeListChatModel", "PromptLayerChatOpenAI", "ChatAnthropic", diff --git a/libs/langchain/langchain/chat_models/anthropic.py b/libs/langchain/langchain/chat_models/anthropic.py index 4d00eae4df..a944ab1708 100644 --- a/libs/langchain/langchain/chat_models/anthropic.py +++ b/libs/langchain/langchain/chat_models/anthropic.py @@ -6,10 +6,6 @@ from langchain.callbacks.manager import ( ) from langchain.chat_models.base import BaseChatModel from langchain.llms.anthropic import _AnthropicCommon -from langchain.schema import ( - ChatGeneration, - ChatResult, -) from langchain.schema.messages import ( AIMessage, AIMessageChunk, @@ -18,7 +14,54 @@ from langchain.schema.messages import ( HumanMessage, SystemMessage, ) -from langchain.schema.output import ChatGenerationChunk +from langchain.schema.output import ChatGeneration, ChatGenerationChunk, ChatResult + + +def _convert_one_message_to_text( + message: BaseMessage, + human_prompt: str, + ai_prompt: str, +) -> str: + if isinstance(message, ChatMessage): + message_text = f"\n\n{message.role.capitalize()}: {message.content}" + elif isinstance(message, HumanMessage): + message_text = f"{human_prompt} {message.content}" + elif isinstance(message, AIMessage): + message_text = f"{ai_prompt} {message.content}" + elif isinstance(message, SystemMessage): + message_text = f"{human_prompt} {message.content}" + else: + raise ValueError(f"Got unknown type {message}") + return message_text + + +def convert_messages_to_prompt_anthropic( + messages: List[BaseMessage], + *, + human_prompt: str = "\n\nHuman:", + ai_prompt: str = "\n\nAssistant:", +) -> str: + """Format a list of messages into a full prompt for the Anthropic model + Args: + messages (List[BaseMessage]): List of BaseMessage to combine. + human_prompt (str, optional): Human prompt tag. Defaults to "\n\nHuman:". + ai_prompt (str, optional): AI prompt tag. Defaults to "\n\nAssistant:". + Returns: + str: Combined string with necessary human_prompt and ai_prompt tags. + """ + + messages = messages.copy() # don't mutate the original list + + if not isinstance(messages[-1], AIMessage): + messages.append(AIMessage(content="")) + + text = "".join( + _convert_one_message_to_text(message, human_prompt, ai_prompt) + for message in messages + ) + + # trim off the trailing ' ' that might come from the "Assistant: " + return text.rstrip() class ChatAnthropic(BaseChatModel, _AnthropicCommon): @@ -55,52 +98,19 @@ class ChatAnthropic(BaseChatModel, _AnthropicCommon): def lc_serializable(self) -> bool: return True - def _convert_one_message_to_text(self, message: BaseMessage) -> str: - if isinstance(message, ChatMessage): - message_text = f"\n\n{message.role.capitalize()}: {message.content}" - elif isinstance(message, HumanMessage): - message_text = f"{self.HUMAN_PROMPT} {message.content}" - elif isinstance(message, AIMessage): - message_text = f"{self.AI_PROMPT} {message.content}" - elif isinstance(message, SystemMessage): - message_text = f"{self.HUMAN_PROMPT} {message.content}" - else: - raise ValueError(f"Got unknown type {message}") - return message_text - - def _convert_messages_to_text(self, messages: List[BaseMessage]) -> str: - """Format a list of strings into a single string with necessary newlines. - - Args: - messages (List[BaseMessage]): List of BaseMessage to combine. - - Returns: - str: Combined string with necessary newlines. - """ - return "".join( - self._convert_one_message_to_text(message) for message in messages - ) - def _convert_messages_to_prompt(self, messages: List[BaseMessage]) -> str: """Format a list of messages into a full prompt for the Anthropic model - Args: messages (List[BaseMessage]): List of BaseMessage to combine. - Returns: str: Combined string with necessary HUMAN_PROMPT and AI_PROMPT tags. """ - messages = messages.copy() # don't mutate the original list - - if not self.AI_PROMPT: - raise NameError("Please ensure the anthropic package is loaded") - - if not isinstance(messages[-1], AIMessage): - messages.append(AIMessage(content="")) - text = self._convert_messages_to_text(messages) - return ( - text.rstrip() - ) # trim off the trailing ' ' that might come from the "Assistant: " + prompt_params = {} + if self.HUMAN_PROMPT: + prompt_params["human_prompt"] = self.HUMAN_PROMPT + if self.AI_PROMPT: + prompt_params["ai_prompt"] = self.AI_PROMPT + return convert_messages_to_prompt_anthropic(messages=messages, **prompt_params) def _stream( self, @@ -152,7 +162,9 @@ class ChatAnthropic(BaseChatModel, _AnthropicCommon): for chunk in self._stream(messages, stop, run_manager, **kwargs): completion += chunk.text else: - prompt = self._convert_messages_to_prompt(messages) + prompt = self._convert_messages_to_prompt( + messages, + ) params: Dict[str, Any] = { "prompt": prompt, **self._default_params, @@ -177,7 +189,9 @@ class ChatAnthropic(BaseChatModel, _AnthropicCommon): async for chunk in self._astream(messages, stop, run_manager, **kwargs): completion += chunk.text else: - prompt = self._convert_messages_to_prompt(messages) + prompt = self._convert_messages_to_prompt( + messages, + ) params: Dict[str, Any] = { "prompt": prompt, **self._default_params, diff --git a/libs/langchain/langchain/chat_models/bedrock.py b/libs/langchain/langchain/chat_models/bedrock.py new file mode 100644 index 0000000000..a539d6058b --- /dev/null +++ b/libs/langchain/langchain/chat_models/bedrock.py @@ -0,0 +1,98 @@ +from typing import Any, AsyncIterator, Dict, Iterator, List, Optional + +from langchain.callbacks.manager import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) +from langchain.chat_models.anthropic import convert_messages_to_prompt_anthropic +from langchain.chat_models.base import BaseChatModel +from langchain.llms.bedrock import BedrockBase +from langchain.pydantic_v1 import Extra +from langchain.schema.messages import AIMessage, BaseMessage +from langchain.schema.output import ChatGeneration, ChatGenerationChunk, ChatResult + + +class ChatPromptAdapter: + """Adapter class to prepare the inputs from Langchain to prompt format + that Chat model expects. + """ + + @classmethod + def convert_messages_to_prompt( + cls, provider: str, messages: List[BaseMessage] + ) -> str: + if provider == "anthropic": + prompt = convert_messages_to_prompt_anthropic(messages=messages) + else: + raise NotImplementedError( + f"Provider {provider} model does not support chat." + ) + return prompt + + +class BedrockChat(BaseChatModel, BedrockBase): + @property + def _llm_type(self) -> str: + """Return type of chat model.""" + return "amazon_bedrock_chat" + + class Config: + """Configuration for this pydantic object.""" + + extra = Extra.forbid + + def _stream( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> Iterator[ChatGenerationChunk]: + raise NotImplementedError( + """Bedrock doesn't support stream requests at the moment.""" + ) + + def _astream( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> AsyncIterator[ChatGenerationChunk]: + raise NotImplementedError( + """Bedrock doesn't support async requests at the moment.""" + ) + + def _generate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + provider = self._get_provider() + prompt = ChatPromptAdapter.convert_messages_to_prompt( + provider=provider, messages=messages + ) + + params: Dict[str, Any] = {**kwargs} + if stop: + params["stop_sequences"] = stop + + completion = self._prepare_input_and_invoke( + prompt=prompt, stop=stop, run_manager=run_manager, **params + ) + + message = AIMessage(content=completion) + return ChatResult(generations=[ChatGeneration(message=message)]) + + async def _agenerate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + raise NotImplementedError( + """Bedrock doesn't support async stream requests at the moment.""" + ) diff --git a/libs/langchain/langchain/chat_models/fake.py b/libs/langchain/langchain/chat_models/fake.py index 1fe54fef64..97596631e8 100644 --- a/libs/langchain/langchain/chat_models/fake.py +++ b/libs/langchain/langchain/chat_models/fake.py @@ -7,9 +7,35 @@ from langchain.callbacks.manager import ( AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun, ) -from langchain.chat_models.base import SimpleChatModel +from langchain.chat_models.base import BaseChatModel, SimpleChatModel +from langchain.schema import ChatResult from langchain.schema.messages import AIMessageChunk, BaseMessage -from langchain.schema.output import ChatGenerationChunk +from langchain.schema.output import ChatGeneration, ChatGenerationChunk + + +class FakeMessagesListChatModel(BaseChatModel): + responses: List[BaseMessage] + sleep: Optional[float] = None + i: int = 0 + + def _generate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> ChatResult: + response = self.responses[self.i] + if self.i < len(self.responses) - 1: + self.i += 1 + else: + self.i = 0 + generation = ChatGeneration(message=response) + return ChatResult(generations=[generation]) + + @property + def _llm_type(self) -> str: + return "fake-messages-list-chat-model" class FakeListChatModel(SimpleChatModel): diff --git a/libs/langchain/langchain/chat_models/litellm.py b/libs/langchain/langchain/chat_models/litellm.py index f429c32218..9d263872ff 100644 --- a/libs/langchain/langchain/chat_models/litellm.py +++ b/libs/langchain/langchain/chat_models/litellm.py @@ -188,20 +188,6 @@ def _convert_message_to_dict(message: BaseMessage) -> dict: class ChatLiteLLM(BaseChatModel): - """`LiteLLM` Chat models API. - - 1. The ``GOOGLE_API_KEY``` environment variable set with your API key, or - 2. Pass your API key using the google_api_key kwarg to the ChatGoogle - constructor. - - Example: - .. code-block:: python - - from langchain.chat_models import ChatGooglePalm - chat = ChatGooglePalm() - - """ - client: Any #: :meta private: model: str = "gpt-3.5-turbo" model_name: Optional[str] = None @@ -302,6 +288,15 @@ class ChatLiteLLM(BaseChatModel): values["openrouter_api_key"] = get_from_dict_or_env( values, "openrouter_api_key", "OPENROUTER_API_KEY", default="" ) + values["cohere_api_key"] = get_from_dict_or_env( + values, "cohere_api_key", "COHERE_API_KEY", default="" + ) + values["huggingface_api_key"] = get_from_dict_or_env( + values, "huggingface_api_key", "HUGGINGFACE_API_KEY", default="" + ) + values["together_ai_api_key"] = get_from_dict_or_env( + values, "together_ai_api_key", "TOGETHERAI_API_KEY", default="" + ) values["client"] = litellm if values["temperature"] is not None and not 0 <= values["temperature"] <= 1: diff --git a/libs/langchain/langchain/document_loaders/blob_loaders/file_system.py b/libs/langchain/langchain/document_loaders/blob_loaders/file_system.py index 0ac6ad0d84..ac1812f795 100644 --- a/libs/langchain/langchain/document_loaders/blob_loaders/file_system.py +++ b/libs/langchain/langchain/document_loaders/blob_loaders/file_system.py @@ -99,7 +99,7 @@ class FileSystemBlobLoader(BlobLoader): else: raise TypeError(f"Expected str or Path, got {type(path)}") - self.path = _path + self.path = _path.expanduser() # Expand user to handle ~ self.glob = glob self.suffixes = set(suffixes or []) self.show_progress = show_progress diff --git a/libs/langchain/langchain/document_loaders/json_loader.py b/libs/langchain/langchain/document_loaders/json_loader.py index e13ae1bb89..f5b2e43a71 100644 --- a/libs/langchain/langchain/document_loaders/json_loader.py +++ b/libs/langchain/langchain/document_loaders/json_loader.py @@ -64,7 +64,7 @@ class JSONLoader(BaseLoader): if line: self._parse(line, docs) else: - self._parse(self.file_path.read_text(), docs) + self._parse(self.file_path.read_text(encoding="utf-8"), docs) return docs def _parse(self, content: str, docs: List[Document]) -> None: @@ -76,24 +76,20 @@ class JSONLoader(BaseLoader): # and prevent the user from getting a cryptic error later on. if self._content_key is not None: self._validate_content_key(data) + if self._metadata_func is not None: + self._validate_metadata_func(data) for i, sample in enumerate(data, len(docs) + 1): - metadata = dict( - source=str(self.file_path), - seq_num=i, + text = self._get_text(sample=sample) + metadata = self._get_metadata( + sample=sample, source=str(self.file_path), seq_num=i ) - text = self._get_text(sample=sample, metadata=metadata) docs.append(Document(page_content=text, metadata=metadata)) - def _get_text(self, sample: Any, metadata: dict) -> str: + def _get_text(self, sample: Any) -> str: """Convert sample to string format""" if self._content_key is not None: content = sample.get(self._content_key) - if self._metadata_func is not None: - # We pass in the metadata dict to the metadata_func - # so that the user can customize the default metadata - # based on the content of the JSON object. - metadata = self._metadata_func(sample, metadata) else: content = sample @@ -112,6 +108,20 @@ class JSONLoader(BaseLoader): else: return str(content) if content is not None else "" + def _get_metadata( + self, sample: Dict[str, Any], **additional_fields: Any + ) -> Dict[str, Any]: + """ + Return a metadata dictionary base on the existence of metadata_func + :param sample: single data payload + :param additional_fields: key-word arguments to be added as metadata values + :return: + """ + if self._metadata_func is not None: + return self._metadata_func(sample, additional_fields) + else: + return additional_fields + def _validate_content_key(self, data: Any) -> None: """Check if a content key is valid""" sample = data.first() @@ -127,6 +137,10 @@ class JSONLoader(BaseLoader): with the key `{self._content_key}`" ) + def _validate_metadata_func(self, data: Any) -> None: + """Check if the metadata_func output is valid""" + + sample = data.first() if self._metadata_func is not None: sample_metadata = self._metadata_func(sample, {}) if not isinstance(sample_metadata, dict): diff --git a/libs/langchain/langchain/document_loaders/obsidian.py b/libs/langchain/langchain/document_loaders/obsidian.py index ab3a3b188a..3474358671 100644 --- a/libs/langchain/langchain/document_loaders/obsidian.py +++ b/libs/langchain/langchain/document_loaders/obsidian.py @@ -1,15 +1,24 @@ +import logging import re from pathlib import Path from typing import List +import yaml + from langchain.docstore.document import Document from langchain.document_loaders.base import BaseLoader +logger = logging.getLogger(__name__) + class ObsidianLoader(BaseLoader): """Load `Obsidian` files from directory.""" - FRONT_MATTER_REGEX = re.compile(r"^---\n(.*?)\n---\n", re.MULTILINE | re.DOTALL) + FRONT_MATTER_REGEX = re.compile(r"^---\n(.*?)\n---\n", re.DOTALL) + TAG_REGEX = re.compile(r"[^\S\/]#([a-zA-Z_]+[-_/\w]*)") + DATAVIEW_LINE_REGEX = re.compile(r"^\s*(\w+)::\s*(.*)$", re.MULTILINE) + DATAVIEW_INLINE_BRACKET_REGEX = re.compile(r"\[(\w+)::\s*(.*)\]", re.MULTILINE) + DATAVIEW_INLINE_PAREN_REGEX = re.compile(r"\((\w+)::\s*(.*)\)", re.MULTILINE) def __init__( self, path: str, encoding: str = "UTF-8", collect_metadata: bool = True @@ -30,18 +39,64 @@ class ObsidianLoader(BaseLoader): """Parse front matter metadata from the content and return it as a dict.""" if not self.collect_metadata: return {} + match = self.FRONT_MATTER_REGEX.search(content) - front_matter = {} - if match: - lines = match.group(1).split("\n") - for line in lines: - if ":" in line: - key, value = line.split(":", 1) - front_matter[key.strip()] = value.strip() - else: - # Skip lines without a colon - continue - return front_matter + if not match: + return {} + + try: + front_matter = yaml.safe_load(match.group(1)) + + # If tags are a string, split them into a list + if "tags" in front_matter and isinstance(front_matter["tags"], str): + front_matter["tags"] = front_matter["tags"].split(", ") + + return front_matter + except yaml.parser.ParserError: + logger.warning("Encountered non-yaml frontmatter") + return {} + + def _to_langchain_compatible_metadata(self, metadata: dict) -> dict: + """Convert a dictionary to a compatible with langchain.""" + result = {} + for key, value in metadata.items(): + if type(value) in {str, int, float}: + result[key] = value + else: + result[key] = str(value) + return result + + def _parse_document_tags(self, content: str) -> set: + """Return a set of all tags in within the document.""" + if not self.collect_metadata: + return set() + + match = self.TAG_REGEX.findall(content) + if not match: + return set() + + return {tag for tag in match} + + def _parse_dataview_fields(self, content: str) -> dict: + """Parse obsidian dataview plugin fields from the content and return it + as a dict.""" + if not self.collect_metadata: + return {} + + return { + **{ + match[0]: match[1] + for match in self.DATAVIEW_LINE_REGEX.findall(content) + }, + **{ + match[0]: match[1] + for match in self.DATAVIEW_INLINE_PAREN_REGEX.findall(content) + }, + **{ + match[0]: match[1] + for match in self.DATAVIEW_INLINE_BRACKET_REGEX.findall(content) + }, + } def _remove_front_matter(self, content: str) -> str: """Remove front matter metadata from the given content.""" @@ -51,22 +106,29 @@ class ObsidianLoader(BaseLoader): def load(self) -> List[Document]: """Load documents.""" - ps = list(Path(self.file_path).glob("**/*.md")) + paths = list(Path(self.file_path).glob("**/*.md")) docs = [] - for p in ps: - with open(p, encoding=self.encoding) as f: + for path in paths: + with open(path, encoding=self.encoding) as f: text = f.read() front_matter = self._parse_front_matter(text) + tags = self._parse_document_tags(text) + dataview_fields = self._parse_dataview_fields(text) text = self._remove_front_matter(text) metadata = { - "source": str(p.name), - "path": str(p), - "created": p.stat().st_ctime, - "last_modified": p.stat().st_mtime, - "last_accessed": p.stat().st_atime, - **front_matter, + "source": str(path.name), + "path": str(path), + "created": path.stat().st_ctime, + "last_modified": path.stat().st_mtime, + "last_accessed": path.stat().st_atime, + **self._to_langchain_compatible_metadata(front_matter), + **dataview_fields, } + + if tags or front_matter.get("tags"): + metadata["tags"] = ",".join(tags | set(front_matter.get("tags", []))) + docs.append(Document(page_content=text, metadata=metadata)) return docs diff --git a/libs/langchain/langchain/document_loaders/parsers/pdf.py b/libs/langchain/langchain/document_loaders/parsers/pdf.py index 00d8d9adea..2ec7a684be 100644 --- a/libs/langchain/langchain/document_loaders/parsers/pdf.py +++ b/libs/langchain/langchain/document_loaders/parsers/pdf.py @@ -1,11 +1,16 @@ """Module contains common parsers for PDFs.""" -from typing import Any, Iterator, Mapping, Optional, Sequence, Union +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, Iterator, Mapping, Optional, Sequence, Union from urllib.parse import urlparse from langchain.document_loaders.base import BaseBlobParser from langchain.document_loaders.blob_loaders import Blob from langchain.schema import Document +if TYPE_CHECKING: + import pdfplumber.page + class PyPDFParser(BaseBlobParser): """Load `PDF` using `pypdf` and chunk at character level.""" @@ -116,13 +121,17 @@ class PyPDFium2Parser(BaseBlobParser): class PDFPlumberParser(BaseBlobParser): """Parse `PDF` with `PDFPlumber`.""" - def __init__(self, text_kwargs: Optional[Mapping[str, Any]] = None) -> None: + def __init__( + self, text_kwargs: Optional[Mapping[str, Any]] = None, dedupe: bool = False + ) -> None: """Initialize the parser. Args: text_kwargs: Keyword arguments to pass to ``pdfplumber.Page.extract_text()`` + dedupe: Avoiding the error of duplicate characters if `dedupe=True`. """ self.text_kwargs = text_kwargs or {} + self.dedupe = dedupe def lazy_parse(self, blob: Blob) -> Iterator[Document]: """Lazily parse the blob.""" @@ -133,7 +142,7 @@ class PDFPlumberParser(BaseBlobParser): yield from [ Document( - page_content=page.extract_text(**self.text_kwargs), + page_content=self._process_page_content(page), metadata=dict( { "source": blob.source, @@ -151,6 +160,12 @@ class PDFPlumberParser(BaseBlobParser): for page in doc.pages ] + def _process_page_content(self, page: pdfplumber.page.Page) -> str: + """Process the page content based on dedupe.""" + if self.dedupe: + return page.dedupe_chars().extract_text(**self.text_kwargs) + return page.extract_text(**self.text_kwargs) + class AmazonTextractPDFParser(BaseBlobParser): """Send `PDF` files to `Amazon Textract` and parse them. @@ -244,3 +259,36 @@ class AmazonTextractPDFParser(BaseBlobParser): page_content=current_text, metadata={"source": blob.source, "page": current_page}, ) + + +class DocumentIntelligenceParser(BaseBlobParser): + """Loads a PDF with Azure Document Intelligence + (formerly Forms Recognizer) and chunks at character level.""" + + def __init__(self, client: Any, model: str): + self.client = client + self.model = model + + def _generate_docs(self, blob: Blob, result: Any) -> Iterator[Document]: + for p in result.pages: + content = " ".join([line.content for line in p.lines]) + + d = Document( + page_content=content, + metadata={ + "source": blob.source, + "page": p.page_number, + }, + ) + yield d + + def lazy_parse(self, blob: Blob) -> Iterator[Document]: + """Lazily parse the blob.""" + + with blob.as_bytes_io() as file_obj: + poller = self.client.begin_analyze_document(self.model, file_obj) + result = poller.result() + + docs = self._generate_docs(blob, result) + + yield from docs diff --git a/libs/langchain/langchain/document_loaders/pdf.py b/libs/langchain/langchain/document_loaders/pdf.py index 301af6953e..801a426a76 100644 --- a/libs/langchain/langchain/document_loaders/pdf.py +++ b/libs/langchain/langchain/document_loaders/pdf.py @@ -16,6 +16,7 @@ from langchain.document_loaders.base import BaseLoader from langchain.document_loaders.blob_loaders import Blob from langchain.document_loaders.parsers.pdf import ( AmazonTextractPDFParser, + DocumentIntelligenceParser, PDFMinerParser, PDFPlumberParser, PyMuPDFParser, @@ -436,7 +437,10 @@ class PDFPlumberLoader(BasePDFLoader): """Load `PDF` files using `pdfplumber`.""" def __init__( - self, file_path: str, text_kwargs: Optional[Mapping[str, Any]] = None + self, + file_path: str, + text_kwargs: Optional[Mapping[str, Any]] = None, + dedupe: bool = False, ) -> None: """Initialize with a file path.""" try: @@ -449,11 +453,12 @@ class PDFPlumberLoader(BasePDFLoader): super().__init__(file_path) self.text_kwargs = text_kwargs or {} + self.dedupe = dedupe def load(self) -> List[Document]: """Load file.""" - parser = PDFPlumberParser(text_kwargs=self.text_kwargs) + parser = PDFPlumberParser(text_kwargs=self.text_kwargs, dedupe=self.dedupe) blob = Blob.from_path(self.file_path) return parser.parse(blob) @@ -597,3 +602,51 @@ class AmazonTextractPDFLoader(BasePDFLoader): return 1 else: raise ValueError(f"unsupported mime type: {blob.mimetype}") + + +class DocumentIntelligenceLoader(BasePDFLoader): + """Loads a PDF with Azure Document Intelligence""" + + def __init__( + self, file_path: str, client: Any, model: str = "prebuilt-document" + ) -> None: + """ + Initialize the object for file processing with Azure Document Intelligence + (formerly Form Recognizer). + + This constructor initializes a DocumentIntelligenceParser object to be used + for parsing files using the Azure Document Intelligence API. The load method + generates a Document node including metadata (source blob and page number) + for each page. + + Parameters: + ----------- + file_path : str + The path to the file that needs to be parsed. + client: Any + A DocumentAnalysisClient to perform the analysis of the blob + model : str + The model name or ID to be used for form recognition in Azure. + + Examples: + --------- + >>> obj = DocumentIntelligenceLoader( + ... file_path="path/to/file", + ... client=client, + ... model="prebuilt-document" + ... ) + """ + + self.parser = DocumentIntelligenceParser(client=client, model=model) + super().__init__(file_path) + + def load(self) -> List[Document]: + """Load given path as pages.""" + return list(self.lazy_load()) + + def lazy_load( + self, + ) -> Iterator[Document]: + """Lazy load given path as pages.""" + blob = Blob.from_path(self.file_path) + yield from self.parser.parse(blob) diff --git a/libs/langchain/langchain/document_loaders/s3_directory.py b/libs/langchain/langchain/document_loaders/s3_directory.py index b4d6f2a40a..1363c82647 100644 --- a/libs/langchain/langchain/document_loaders/s3_directory.py +++ b/libs/langchain/langchain/document_loaders/s3_directory.py @@ -1,22 +1,99 @@ -from typing import List +from __future__ import annotations + +from typing import TYPE_CHECKING, List, Optional, Union from langchain.docstore.document import Document from langchain.document_loaders.base import BaseLoader from langchain.document_loaders.s3_file import S3FileLoader +if TYPE_CHECKING: + import botocore + class S3DirectoryLoader(BaseLoader): """Load from `Amazon AWS S3` directory.""" - def __init__(self, bucket: str, prefix: str = ""): + def __init__( + self, + bucket: str, + prefix: str = "", + *, + region_name: Optional[str] = None, + api_version: Optional[str] = None, + use_ssl: Optional[bool] = True, + verify: Union[str, bool, None] = None, + endpoint_url: Optional[str] = None, + aws_access_key_id: Optional[str] = None, + aws_secret_access_key: Optional[str] = None, + aws_session_token: Optional[str] = None, + boto_config: Optional[botocore.client.Config] = None, + ): """Initialize with bucket and key name. - Args: - bucket: The name of the S3 bucket. - prefix: The prefix of the S3 key. Defaults to "". + :param bucket: The name of the S3 bucket. + :param prefix: The prefix of the S3 key. Defaults to "". + + :param region_name: The name of the region associated with the client. + A client is associated with a single region. + + :param api_version: The API version to use. By default, botocore will + use the latest API version when creating a client. You only need + to specify this parameter if you want to use a previous API version + of the client. + + :param use_ssl: Whether to use SSL. By default, SSL is used. + Note that not all services support non-ssl connections. + + :param verify: Whether to verify SSL certificates. + By default SSL certificates are verified. You can provide the + following values: + + * False - do not validate SSL certificates. SSL will still be + used (unless use_ssl is False), but SSL certificates + will not be verified. + * path/to/cert/bundle.pem - A filename of the CA cert bundle to + uses. You can specify this argument if you want to use a + different CA cert bundle than the one used by botocore. + + :param endpoint_url: The complete URL to use for the constructed + client. Normally, botocore will automatically construct the + appropriate URL to use when communicating with a service. You can + specify a complete URL (including the "http/https" scheme) to + override this behavior. If this value is provided, then + ``use_ssl`` is ignored. + + :param aws_access_key_id: The access key to use when creating + the client. This is entirely optional, and if not provided, + the credentials configured for the session will automatically + be used. You only need to provide this argument if you want + to override the credentials used for this specific client. + + :param aws_secret_access_key: The secret key to use when creating + the client. Same semantics as aws_access_key_id above. + + :param aws_session_token: The session token to use when creating + the client. Same semantics as aws_access_key_id above. + + :type boto_config: botocore.client.Config + :param boto_config: Advanced boto3 client configuration options. If a value + is specified in the client config, its value will take precedence + over environment variables and configuration values, but not over + a value passed explicitly to the method. If a default config + object is set on the session, the config object used when creating + the client will be the result of calling ``merge()`` on the + default config with the config provided to this call. """ self.bucket = bucket self.prefix = prefix + self.region_name = region_name + self.api_version = api_version + self.use_ssl = use_ssl + self.verify = verify + self.endpoint_url = endpoint_url + self.aws_access_key_id = aws_access_key_id + self.aws_secret_access_key = aws_secret_access_key + self.aws_session_token = aws_session_token + self.boto_config = boto_config def load(self) -> List[Document]: """Load documents.""" @@ -27,10 +104,33 @@ class S3DirectoryLoader(BaseLoader): "Could not import boto3 python package. " "Please install it with `pip install boto3`." ) - s3 = boto3.resource("s3") + s3 = boto3.resource( + "s3", + region_name=self.region_name, + api_version=self.api_version, + use_ssl=self.use_ssl, + verify=self.verify, + endpoint_url=self.endpoint_url, + aws_access_key_id=self.aws_access_key_id, + aws_secret_access_key=self.aws_secret_access_key, + aws_session_token=self.aws_session_token, + config=self.boto_config, + ) bucket = s3.Bucket(self.bucket) docs = [] for obj in bucket.objects.filter(Prefix=self.prefix): - loader = S3FileLoader(self.bucket, obj.key) + loader = S3FileLoader( + self.bucket, + obj.key, + region_name=self.region_name, + api_version=self.api_version, + use_ssl=self.use_ssl, + verify=self.verify, + endpoint_url=self.endpoint_url, + aws_access_key_id=self.aws_access_key_id, + aws_secret_access_key=self.aws_secret_access_key, + aws_session_token=self.aws_session_token, + boto_config=self.boto_config, + ) docs.extend(loader.load()) return docs diff --git a/libs/langchain/langchain/document_loaders/s3_file.py b/libs/langchain/langchain/document_loaders/s3_file.py index 2e0b56700e..8a5581dd76 100644 --- a/libs/langchain/langchain/document_loaders/s3_file.py +++ b/libs/langchain/langchain/document_loaders/s3_file.py @@ -1,23 +1,100 @@ +from __future__ import annotations + import os import tempfile -from typing import List +from typing import TYPE_CHECKING, List, Optional, Union from langchain.document_loaders.unstructured import UnstructuredBaseLoader +if TYPE_CHECKING: + import botocore + class S3FileLoader(UnstructuredBaseLoader): """Load from `Amazon AWS S3` file.""" - def __init__(self, bucket: str, key: str): + def __init__( + self, + bucket: str, + key: str, + *, + region_name: Optional[str] = None, + api_version: Optional[str] = None, + use_ssl: Optional[bool] = True, + verify: Union[str, bool, None] = None, + endpoint_url: Optional[str] = None, + aws_access_key_id: Optional[str] = None, + aws_secret_access_key: Optional[str] = None, + aws_session_token: Optional[str] = None, + boto_config: Optional[botocore.client.Config] = None, + ): """Initialize with bucket and key name. - Args: - bucket: The name of the S3 bucket. - key: The key of the S3 object. + :param bucket: The name of the S3 bucket. + :param key: The key of the S3 object. + + :param region_name: The name of the region associated with the client. + A client is associated with a single region. + + :param api_version: The API version to use. By default, botocore will + use the latest API version when creating a client. You only need + to specify this parameter if you want to use a previous API version + of the client. + + :param use_ssl: Whether or not to use SSL. By default, SSL is used. + Note that not all services support non-ssl connections. + + :param verify: Whether or not to verify SSL certificates. + By default SSL certificates are verified. You can provide the + following values: + + * False - do not validate SSL certificates. SSL will still be + used (unless use_ssl is False), but SSL certificates + will not be verified. + * path/to/cert/bundle.pem - A filename of the CA cert bundle to + uses. You can specify this argument if you want to use a + different CA cert bundle than the one used by botocore. + + :param endpoint_url: The complete URL to use for the constructed + client. Normally, botocore will automatically construct the + appropriate URL to use when communicating with a service. You can + specify a complete URL (including the "http/https" scheme) to + override this behavior. If this value is provided, then + ``use_ssl`` is ignored. + + :param aws_access_key_id: The access key to use when creating + the client. This is entirely optional, and if not provided, + the credentials configured for the session will automatically + be used. You only need to provide this argument if you want + to override the credentials used for this specific client. + + :param aws_secret_access_key: The secret key to use when creating + the client. Same semantics as aws_access_key_id above. + + :param aws_session_token: The session token to use when creating + the client. Same semantics as aws_access_key_id above. + + :type boto_config: botocore.client.Config + :param boto_config: Advanced boto3 client configuration options. If a value + is specified in the client config, its value will take precedence + over environment variables and configuration values, but not over + a value passed explicitly to the method. If a default config + object is set on the session, the config object used when creating + the client will be the result of calling ``merge()`` on the + default config with the config provided to this call. """ super().__init__() self.bucket = bucket self.key = key + self.region_name = region_name + self.api_version = api_version + self.use_ssl = use_ssl + self.verify = verify + self.endpoint_url = endpoint_url + self.aws_access_key_id = aws_access_key_id + self.aws_secret_access_key = aws_secret_access_key + self.aws_session_token = aws_session_token + self.boto_config = boto_config def _get_elements(self) -> List: """Get elements.""" @@ -30,7 +107,18 @@ class S3FileLoader(UnstructuredBaseLoader): "Could not import `boto3` python package. " "Please install it with `pip install boto3`." ) - s3 = boto3.client("s3") + s3 = boto3.client( + "s3", + region_name=self.region_name, + api_version=self.api_version, + use_ssl=self.use_ssl, + verify=self.verify, + endpoint_url=self.endpoint_url, + aws_access_key_id=self.aws_access_key_id, + aws_secret_access_key=self.aws_secret_access_key, + aws_session_token=self.aws_session_token, + config=self.boto_config, + ) with tempfile.TemporaryDirectory() as temp_dir: file_path = f"{temp_dir}/{self.key}" os.makedirs(os.path.dirname(file_path), exist_ok=True) diff --git a/libs/langchain/langchain/document_transformers/html2text.py b/libs/langchain/langchain/document_transformers/html2text.py index ede87af9c7..3c123950d3 100644 --- a/libs/langchain/langchain/document_transformers/html2text.py +++ b/libs/langchain/langchain/document_transformers/html2text.py @@ -5,13 +5,22 @@ from langchain.schema import BaseDocumentTransformer, Document class Html2TextTransformer(BaseDocumentTransformer): """Replace occurrences of a particular search pattern with a replacement string + + Arguments: + ignore_links: Whether links should be ignored; defaults to True. + ignore_images: Whether images should be ignored; defaults to True. + Example: .. code-block:: python from langchain.document_transformers import Html2TextTransformer - html2text=Html2TextTransformer() - docs_transform=html2text.transform_documents(docs) + html2text = Html2TextTransformer() + docs_transform = html2text.transform_documents(docs) """ + def __init__(self, ignore_links: bool = True, ignore_images: bool = True) -> None: + self.ignore_links = ignore_links + self.ignore_images = ignore_images + def transform_documents( self, documents: Sequence[Document], @@ -25,10 +34,11 @@ class Html2TextTransformer(BaseDocumentTransformer): install it with `pip install html2text`""" ) - # Create an html2text.HTML2Text object and override some properties + # Create a html2text.HTML2Text object and override some properties h = html2text.HTML2Text() - h.ignore_links = True - h.ignore_images = True + h.ignore_links = self.ignore_links + h.ignore_images = self.ignore_images + for d in documents: d.page_content = h.handle(d.page_content) return documents diff --git a/libs/langchain/langchain/embeddings/deepinfra.py b/libs/langchain/langchain/embeddings/deepinfra.py index 79d741b062..77bfd35d61 100644 --- a/libs/langchain/langchain/embeddings/deepinfra.py +++ b/libs/langchain/langchain/embeddings/deepinfra.py @@ -111,7 +111,7 @@ class DeepInfraEmbeddings(BaseModel, Embeddings): Returns: List of embeddings, one for each text. """ - instruction_pairs = [f"{self.query_instruction}{text}" for text in texts] + instruction_pairs = [f"{self.embed_instruction}{text}" for text in texts] embeddings = self._embed(instruction_pairs) return embeddings diff --git a/libs/langchain/langchain/embeddings/edenai.py b/libs/langchain/langchain/embeddings/edenai.py index 5e10ddb166..9c22ed92fb 100644 --- a/libs/langchain/langchain/embeddings/edenai.py +++ b/libs/langchain/langchain/embeddings/edenai.py @@ -1,4 +1,4 @@ -from typing import Dict, List, Optional +from typing import Any, Dict, List, Optional from langchain.embeddings.base import Embeddings from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator @@ -14,9 +14,15 @@ class EdenAiEmbeddings(BaseModel, Embeddings): edenai_api_key: Optional[str] = Field(None, description="EdenAI API Token") - provider: Optional[str] = "openai" + provider: str = "openai" """embedding provider to use (eg: openai,google etc.)""" + model: Optional[str] = None + """ + model name for above provider (eg: 'text-davinci-003' for openai) + available models are shown on https://docs.edenai.co/ under 'available providers' + """ + class Config: """Configuration for this pydantic object.""" @@ -30,6 +36,12 @@ class EdenAiEmbeddings(BaseModel, Embeddings): ) return values + @staticmethod + def get_user_agent() -> str: + from langchain import __version__ + + return f"langchain/{__version__}" + def _generate_embeddings(self, texts: List[str]) -> List[List[float]]: """Compute embeddings using EdenAi api.""" url = "https://api.edenai.run/v2/text/embeddings" @@ -38,9 +50,14 @@ class EdenAiEmbeddings(BaseModel, Embeddings): "accept": "application/json", "content-type": "application/json", "authorization": f"Bearer {self.edenai_api_key}", + "User-Agent": self.get_user_agent(), } - payload = {"texts": texts, "providers": self.provider} + payload: Dict[str, Any] = {"texts": texts, "providers": self.provider} + + if self.model is not None: + payload["settings"] = {self.provider: self.model} + request = Requests(headers=headers) response = request.post(url=url, data=payload) if response.status_code >= 500: @@ -55,6 +72,11 @@ class EdenAiEmbeddings(BaseModel, Embeddings): temp = response.json() + provider_response = temp[self.provider] + if provider_response.get("status") == "fail": + err_msg = provider_response.get("error", {}).get("message") + raise Exception(err_msg) + embeddings = [] for embed_item in temp[self.provider]["items"]: embedding = embed_item["embedding"] diff --git a/libs/langchain/langchain/embeddings/octoai_embeddings.py b/libs/langchain/langchain/embeddings/octoai_embeddings.py index c2e1711f12..dd98f3b335 100644 --- a/libs/langchain/langchain/embeddings/octoai_embeddings.py +++ b/libs/langchain/langchain/embeddings/octoai_embeddings.py @@ -87,4 +87,4 @@ class OctoAIEmbeddings(BaseModel, Embeddings): def embed_query(self, text: str) -> List[float]: """Compute query embedding using an OctoAI instruct model.""" text = text.replace("\n", " ") - return self._compute_embeddings([text], self.embed_instruction)[0] + return self._compute_embeddings([text], self.query_instruction)[0] diff --git a/libs/langchain/langchain/embeddings/openai.py b/libs/langchain/langchain/embeddings/openai.py index 976c879f95..88cb7c9332 100644 --- a/libs/langchain/langchain/embeddings/openai.py +++ b/libs/langchain/langchain/embeddings/openai.py @@ -87,8 +87,8 @@ def _async_retry_decorator(embeddings: OpenAIEmbeddings) -> Any: # https://stackoverflow.com/questions/76469415/getting-embeddings-of-length-1-from-langchain-openaiembeddings -def _check_response(response: dict) -> dict: - if any(len(d["embedding"]) == 1 for d in response["data"]): +def _check_response(response: dict, skip_empty: bool = False) -> dict: + if any(len(d["embedding"]) == 1 for d in response["data"]) and not skip_empty: import openai raise openai.error.APIError("OpenAI API returned an empty embedding") @@ -102,7 +102,7 @@ def embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> Any: @retry_decorator def _embed_with_retry(**kwargs: Any) -> Any: response = embeddings.client.create(**kwargs) - return _check_response(response) + return _check_response(response, skip_empty=embeddings.skip_empty) return _embed_with_retry(**kwargs) @@ -113,7 +113,7 @@ async def async_embed_with_retry(embeddings: OpenAIEmbeddings, **kwargs: Any) -> @_async_retry_decorator(embeddings) async def _async_embed_with_retry(**kwargs: Any) -> Any: response = await embeddings.client.acreate(**kwargs) - return _check_response(response) + return _check_response(response, skip_empty=embeddings.skip_empty) return await _async_embed_with_retry(**kwargs) @@ -196,6 +196,9 @@ class OpenAIEmbeddings(BaseModel, Embeddings): """Whether to show a progress bar when embedding.""" model_kwargs: Dict[str, Any] = Field(default_factory=dict) """Holds any model parameters valid for `create` call not explicitly specified.""" + skip_empty: bool = False + """Whether to skip empty strings when embedding or raise an error. + Defaults to not skipping.""" class Config: """Configuration for this pydantic object.""" @@ -371,6 +374,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings): results: List[List[List[float]]] = [[] for _ in range(len(texts))] num_tokens_in_batch: List[List[int]] = [[] for _ in range(len(texts))] for i in range(len(indices)): + if self.skip_empty and len(batched_embeddings[i]) == 1: + continue results[indices[i]].append(batched_embeddings[i]) num_tokens_in_batch[indices[i]].append(len(tokens[i])) diff --git a/libs/langchain/langchain/hub.py b/libs/langchain/langchain/hub.py index 0c0d2d0fef..ac5012e635 100644 --- a/libs/langchain/langchain/hub.py +++ b/libs/langchain/langchain/hub.py @@ -30,7 +30,7 @@ def push( api_url: Optional[str] = None, api_key: Optional[str] = None, parent_commit_hash: Optional[str] = "latest", - new_repo_is_public: bool = False, + new_repo_is_public: bool = True, new_repo_description: str = "", ) -> str: """ @@ -45,7 +45,7 @@ def push( :param parent_commit_hash: The commit hash of the parent commit to push to. Defaults to the latest commit automatically. :param new_repo_is_public: Whether the repo should be public. Defaults to - False (Private by default). + True (Public by default). :param new_repo_description: The description of the repo. Defaults to an empty string. """ diff --git a/libs/langchain/langchain/indexes/_api.py b/libs/langchain/langchain/indexes/_api.py index 130a5c685d..6f2ebf9828 100644 --- a/libs/langchain/langchain/indexes/_api.py +++ b/libs/langchain/langchain/indexes/_api.py @@ -169,7 +169,7 @@ def index( vector_store: VectorStore, *, batch_size: int = 100, - delete_mode: Literal["incremental", "full", None] = None, + cleanup: Literal["incremental", "full", None] = None, source_id_key: Union[str, Callable[[Document], str], None] = None, ) -> IndexingResult: """Index data from the loader into the vector store. @@ -195,7 +195,7 @@ def index( updated. vector_store: Vector store to index the documents into. batch_size: Batch size to use when indexing. - delete_mode: How to handle clean up of documents. + cleanup: How to handle clean up of documents. - Incremental: Cleans up all documents that haven't been updated AND that are associated with source ids that were seen during indexing. @@ -213,14 +213,14 @@ def index( Indexing result which contains information about how many documents were added, updated, deleted, or skipped. """ - if delete_mode not in {"incremental", "full", None}: + if cleanup not in {"incremental", "full", None}: raise ValueError( - f"delete_mode should be one of 'incremental', 'full' or None. " - f"Got {delete_mode}." + f"cleanup should be one of 'incremental', 'full' or None. " + f"Got {cleanup}." ) - if delete_mode == "incremental" and source_id_key is None: - raise ValueError("Source id key is required when delete mode is incremental.") + if cleanup == "incremental" and source_id_key is None: + raise ValueError("Source id key is required when cleanup mode is incremental.") # Check that the Vectorstore has required methods implemented methods = ["delete", "add_documents"] @@ -264,12 +264,12 @@ def index( source_id_assigner(doc) for doc in hashed_docs ] - if delete_mode == "incremental": - # If the delete mode is incremental, source ids are required. + if cleanup == "incremental": + # If the cleanup mode is incremental, source ids are required. for source_id, hashed_doc in zip(source_ids, hashed_docs): if source_id is None: raise ValueError( - "Source ids are required when delete mode is incremental. " + "Source ids are required when cleanup mode is incremental. " f"Document that starts with " f"content: {hashed_doc.page_content[:100]} was not assigned " f"as source id." @@ -307,7 +307,7 @@ def index( ) # If source IDs are provided, we can do the deletion incrementally! - if delete_mode == "incremental": + if cleanup == "incremental": # Get the uids of the documents that were not returned by the loader. # mypy isn't good enough to determine that source ids cannot be None @@ -328,7 +328,7 @@ def index( record_manager.delete_keys(uids_to_delete) num_deleted += len(uids_to_delete) - if delete_mode == "full": + if cleanup == "full": uids_to_delete = record_manager.list_keys(before=index_start_dt) if uids_to_delete: diff --git a/libs/langchain/langchain/llms/__init__.py b/libs/langchain/langchain/llms/__init__.py index a454e10afd..d8736cfaae 100644 --- a/libs/langchain/langchain/llms/__init__.py +++ b/libs/langchain/langchain/llms/__init__.py @@ -81,7 +81,7 @@ from langchain.llms.symblai_nebula import Nebula from langchain.llms.textgen import TextGen from langchain.llms.titan_takeoff import TitanTakeoff from langchain.llms.tongyi import Tongyi -from langchain.llms.vertexai import VertexAI +from langchain.llms.vertexai import VertexAI, VertexAIModelGarden from langchain.llms.vllm import VLLM, VLLMOpenAI from langchain.llms.writer import Writer from langchain.llms.xinference import Xinference @@ -152,6 +152,7 @@ __all__ = [ "TitanTakeoff", "Tongyi", "VertexAI", + "VertexAIModelGarden", "VLLM", "VLLMOpenAI", "Writer", @@ -217,6 +218,7 @@ type_to_cls_dict: Dict[str, Type[BaseLLM]] = { "tongyi": Tongyi, "titan_takeoff": TitanTakeoff, "vertexai": VertexAI, + "vertexai_model_garden": VertexAIModelGarden, "openllm": OpenLLM, "openllm_client": OpenLLM, "vllm": VLLM, diff --git a/libs/langchain/langchain/llms/base.py b/libs/langchain/langchain/llms/base.py index a91ecd9f2a..3724db869f 100644 --- a/libs/langchain/langchain/llms/base.py +++ b/libs/langchain/langchain/llms/base.py @@ -60,6 +60,7 @@ from langchain.schema.language_model import BaseLanguageModel, LanguageModelInpu from langchain.schema.messages import AIMessage, BaseMessage, get_buffer_string from langchain.schema.output import GenerationChunk from langchain.schema.runnable import RunnableConfig +from langchain.schema.runnable.config import get_config_list logger = logging.getLogger(__name__) @@ -262,20 +263,28 @@ class BaseLLM(BaseLanguageModel[str], ABC): self, inputs: List[LanguageModelInput], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, - max_concurrency: Optional[int] = None, + *, + return_exceptions: bool = False, **kwargs: Any, ) -> List[str]: - config = self._get_config_list(config, len(inputs)) + config = get_config_list(config, len(inputs)) + max_concurrency = config[0].get("max_concurrency") if max_concurrency is None: - llm_result = self.generate_prompt( - [self._convert_input(input) for input in inputs], - callbacks=[c.get("callbacks") for c in config], - tags=[c.get("tags") for c in config], - metadata=[c.get("metadata") for c in config], - **kwargs, - ) - return [g[0].text for g in llm_result.generations] + try: + llm_result = self.generate_prompt( + [self._convert_input(input) for input in inputs], + callbacks=[c.get("callbacks") for c in config], + tags=[c.get("tags") for c in config], + metadata=[c.get("metadata") for c in config], + **kwargs, + ) + return [g[0].text for g in llm_result.generations] + except Exception as e: + if return_exceptions: + return cast(List[str], [e for _ in inputs]) + else: + raise e else: batches = [ inputs[i : i + max_concurrency] @@ -284,33 +293,43 @@ class BaseLLM(BaseLanguageModel[str], ABC): return [ output for batch in batches - for output in self.batch(batch, config=config, **kwargs) + for output in self.batch( + batch, config=config, return_exceptions=return_exceptions, **kwargs + ) ] async def abatch( self, inputs: List[LanguageModelInput], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, - max_concurrency: Optional[int] = None, + *, + return_exceptions: bool = False, **kwargs: Any, ) -> List[str]: if type(self)._agenerate == BaseLLM._agenerate: # model doesn't implement async batch, so use default implementation return await asyncio.get_running_loop().run_in_executor( - None, self.batch, inputs, config, max_concurrency + None, partial(self.batch, **kwargs), inputs, config ) - config = self._get_config_list(config, len(inputs)) + config = get_config_list(config, len(inputs)) + max_concurrency = config[0].get("max_concurrency") if max_concurrency is None: - llm_result = await self.agenerate_prompt( - [self._convert_input(input) for input in inputs], - callbacks=[c.get("callbacks") for c in config], - tags=[c.get("tags") for c in config], - metadata=[c.get("metadata") for c in config], - **kwargs, - ) - return [g[0].text for g in llm_result.generations] + try: + llm_result = await self.agenerate_prompt( + [self._convert_input(input) for input in inputs], + callbacks=[c.get("callbacks") for c in config], + tags=[c.get("tags") for c in config], + metadata=[c.get("metadata") for c in config], + **kwargs, + ) + return [g[0].text for g in llm_result.generations] + except Exception as e: + if return_exceptions: + return cast(List[str], [e for _ in inputs]) + else: + raise e else: batches = [ inputs[i : i + max_concurrency] diff --git a/libs/langchain/langchain/llms/bedrock.py b/libs/langchain/langchain/llms/bedrock.py index 75af53a6a5..1805e89281 100644 --- a/libs/langchain/langchain/llms/bedrock.py +++ b/libs/langchain/langchain/llms/bedrock.py @@ -1,10 +1,11 @@ import json +from abc import ABC from typing import Any, Dict, List, Mapping, Optional from langchain.callbacks.manager import CallbackManagerForLLMRun from langchain.llms.base import LLM from langchain.llms.utils import enforce_stop_tokens -from langchain.pydantic_v1 import Extra, root_validator +from langchain.pydantic_v1 import BaseModel, Extra, root_validator class LLMInputOutputAdapter: @@ -47,33 +48,7 @@ class LLMInputOutputAdapter: return response_body.get("results")[0].get("outputText") -class Bedrock(LLM): - """Bedrock models. - - To authenticate, the AWS client uses the following methods to - automatically load credentials: - https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html - - If a specific credential profile should be used, you must pass - the name of the profile from the ~/.aws/credentials file that is to be used. - - Make sure the credentials / roles used have the required policies to - access the Bedrock service. - """ - - """ - Example: - .. code-block:: python - - from bedrock_langchain.bedrock_llm import BedrockLLM - - llm = BedrockLLM( - credentials_profile_name="default", - model_id="amazon.titan-tg1-large" - ) - - """ - +class BedrockBase(BaseModel, ABC): client: Any #: :meta private: region_name: Optional[str] = None @@ -99,11 +74,6 @@ class Bedrock(LLM): endpoint_url: Optional[str] = None """Needed if you don't want to default to us-east-1 endpoint""" - class Config: - """Configuration for this pydantic object.""" - - extra = Extra.forbid - @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that AWS credentials to and python package exists in environment.""" @@ -151,11 +121,77 @@ class Bedrock(LLM): **{"model_kwargs": _model_kwargs}, } + def _get_provider(self) -> str: + return self.model_id.split(".")[0] + + def _prepare_input_and_invoke( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> str: + _model_kwargs = self.model_kwargs or {} + + provider = self._get_provider() + params = {**_model_kwargs, **kwargs} + input_body = LLMInputOutputAdapter.prepare_input(provider, prompt, params) + body = json.dumps(input_body) + accept = "application/json" + contentType = "application/json" + + try: + response = self.client.invoke_model( + body=body, modelId=self.model_id, accept=accept, contentType=contentType + ) + text = LLMInputOutputAdapter.prepare_output(provider, response) + + except Exception as e: + raise ValueError(f"Error raised by bedrock service: {e}") + + if stop is not None: + text = enforce_stop_tokens(text, stop) + + return text + + +class Bedrock(LLM, BedrockBase): + """Bedrock models. + + To authenticate, the AWS client uses the following methods to + automatically load credentials: + https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html + + If a specific credential profile should be used, you must pass + the name of the profile from the ~/.aws/credentials file that is to be used. + + Make sure the credentials / roles used have the required policies to + access the Bedrock service. + """ + + """ + Example: + .. code-block:: python + + from bedrock_langchain.bedrock_llm import BedrockLLM + + llm = BedrockLLM( + credentials_profile_name="default", + model_id="amazon.titan-tg1-large" + ) + + """ + @property def _llm_type(self) -> str: """Return type of llm.""" return "amazon_bedrock" + class Config: + """Configuration for this pydantic object.""" + + extra = Extra.forbid + def _call( self, prompt: str, @@ -177,25 +213,7 @@ class Bedrock(LLM): response = se("Tell me a joke.") """ - _model_kwargs = self.model_kwargs or {} - provider = self.model_id.split(".")[0] - params = {**_model_kwargs, **kwargs} - input_body = LLMInputOutputAdapter.prepare_input(provider, prompt, params) - body = json.dumps(input_body) - accept = "application/json" - contentType = "application/json" - - try: - response = self.client.invoke_model( - body=body, modelId=self.model_id, accept=accept, contentType=contentType - ) - text = LLMInputOutputAdapter.prepare_output(provider, response) - - except Exception as e: - raise ValueError(f"Error raised by bedrock service: {e}") - - if stop is not None: - text = enforce_stop_tokens(text, stop) + text = self._prepare_input_and_invoke(prompt=prompt, stop=stop, **kwargs) return text diff --git a/libs/langchain/langchain/llms/edenai.py b/libs/langchain/langchain/llms/edenai.py index e63c0776e5..521653b427 100644 --- a/libs/langchain/langchain/llms/edenai.py +++ b/libs/langchain/langchain/llms/edenai.py @@ -41,13 +41,24 @@ class EdenAI(LLM): """Subfeature of above feature, use generation by default""" provider: str - """Geneerative provider to use (eg: openai,stabilityai,cohere,google etc.)""" + """Generative provider to use (eg: openai,stabilityai,cohere,google etc.)""" - params: Dict[str, Any] + model: Optional[str] = None """ - Parameters to pass to above subfeature (excluding 'providers' & 'text') - ref text: https://docs.edenai.co/reference/text_generation_create - ref image: https://docs.edenai.co/reference/text_generation_create + model name for above provider (eg: 'text-davinci-003' for openai) + available models are shown on https://docs.edenai.co/ under 'available providers' + """ + + # Optional parameters to add depending of chosen feature + # see api reference for more infos + temperature: Optional[float] = Field(default=None, ge=0, le=1) # for text + max_tokens: Optional[int] = Field(default=None, ge=0) # for text + resolution: Optional[Literal["256x256", "512x512", "1024x1024"]] = None # for image + + params: Dict[str, Any] = Field(default_factory=dict) + """ + DEPRECATED: use temperature, max_tokens, resolution directly + optional parameters to pass to api """ model_kwargs: Dict[str, Any] = Field(default_factory=dict) @@ -98,6 +109,12 @@ class EdenAI(LLM): else: return output[self.provider]["items"][0]["image"] + @staticmethod + def get_user_agent() -> str: + from langchain import __version__ + + return f"langchain/{__version__}" + def _call( self, prompt: str, @@ -112,7 +129,6 @@ class EdenAI(LLM): Returns: json formatted str response. - """ stops = None if self.stop_sequences is not None and stop is not None: @@ -125,16 +141,28 @@ class EdenAI(LLM): stops = stop url = f"{self.base_url}/{self.feature}/{self.subfeature}" - headers = {"Authorization": f"Bearer {self.edenai_api_key}"} - payload = { - **self.params, - "providers": self.provider, - "num_images": 1, # always limit to 1 (ignored for text) - "text": prompt, - **kwargs, + headers = { + "Authorization": f"Bearer {self.edenai_api_key}", + "User-Agent": self.get_user_agent(), + } + payload: Dict[str, Any] = { + "providers": self.provider, + "text": prompt, + "max_tokens": self.max_tokens, + "temperature": self.temperature, + "resolution": self.resolution, + **self.params, + **kwargs, + "num_images": 1, # always limit to 1 (ignored for text) } - request = Requests(headers=headers) + # filter None values to not pass them to the http payload + payload = {k: v for k, v in payload.items() if v is not None} + + if self.model is not None: + payload["settings"] = {self.provider: self.model} + + request = Requests(headers=headers) response = request.post(url=url, data=payload) if response.status_code >= 500: @@ -147,7 +175,13 @@ class EdenAI(LLM): f"{response.status_code}: {response.text}" ) - output = self._format_output(response.json()) + data = response.json() + provider_response = data[self.provider] + if provider_response.get("status") == "fail": + err_msg = provider_response.get("error", {}).get("message") + raise Exception(err_msg) + + output = self._format_output(data) if stops is not None: output = enforce_stop_tokens(output, stops) @@ -182,19 +216,29 @@ class EdenAI(LLM): else: stops = stop - print("Running the acall") url = f"{self.base_url}/{self.feature}/{self.subfeature}" - headers = {"Authorization": f"Bearer {self.edenai_api_key}"} - payload = { - **self.params, + headers = { + "Authorization": f"Bearer {self.edenai_api_key}", + "User-Agent": self.get_user_agent(), + } + payload: Dict[str, Any] = { "providers": self.provider, - "num_images": 1, # always limit to 1 (ignored for text) "text": prompt, + "max_tokens": self.max_tokens, + "temperature": self.temperature, + "resolution": self.resolution, + **self.params, **kwargs, + "num_images": 1, # always limit to 1 (ignored for text) } + # filter `None` values to not pass them to the http payload as null + payload = {k: v for k, v in payload.items() if v is not None} + + if self.model is not None: + payload["settings"] = {self.provider: self.model} + async with ClientSession() as session: - print("Requesting") async with session.post(url, json=payload, headers=headers) as response: if response.status >= 500: raise Exception(f"EdenAI Server: Error {response.status}") @@ -209,6 +253,10 @@ class EdenAI(LLM): ) response_json = await response.json() + provider_response = response_json[self.provider] + if provider_response.get("status") == "fail": + err_msg = provider_response.get("error", {}).get("message") + raise Exception(err_msg) output = self._format_output(response_json) if stops is not None: diff --git a/libs/langchain/langchain/llms/huggingface_text_gen_inference.py b/libs/langchain/langchain/llms/huggingface_text_gen_inference.py index 8e23d935f8..683b2f4dde 100644 --- a/libs/langchain/langchain/llms/huggingface_text_gen_inference.py +++ b/libs/langchain/langchain/llms/huggingface_text_gen_inference.py @@ -1,3 +1,4 @@ +import logging from typing import Any, AsyncIterator, Dict, Iterator, List, Optional from langchain.callbacks.manager import ( @@ -7,86 +8,91 @@ from langchain.callbacks.manager import ( from langchain.llms.base import LLM from langchain.pydantic_v1 import Extra, Field, root_validator from langchain.schema.output import GenerationChunk +from langchain.utils import get_pydantic_field_names + +logger = logging.getLogger(__name__) class HuggingFaceTextGenInference(LLM): """ HuggingFace text generation API. - It generates text from a given prompt. + To use, you should have the `text-generation` python package installed and + a text-generation server running. - Attributes: - - max_new_tokens: The maximum number of tokens to generate. - - top_k: The number of top-k tokens to consider when generating text. - - top_p: The cumulative probability threshold for generating text. - - typical_p: The typical probability threshold for generating text. - - temperature: The temperature to use when generating text. - - repetition_penalty: The repetition penalty to use when generating text. - - truncate: truncate inputs tokens to the given size - - stop_sequences: A list of stop sequences to use when generating text. - - seed: The seed to use when generating text. - - inference_server_url: The URL of the inference server to use. - - timeout: The timeout value in seconds to use while connecting to inference server. - - server_kwargs: The keyword arguments to pass to the inference server. - - client: The client object used to communicate with the inference server. - - async_client: The async client object used to communicate with the server. - - Methods: - - _call: Generates text based on a given prompt and stop sequences. - - _acall: Async generates text based on a given prompt and stop sequences. - - _llm_type: Returns the type of LLM. - - _default_params: Returns the default parameters for calling text generation - inference API. - """ - - """ Example: .. code-block:: python # Basic Example (no streaming) llm = HuggingFaceTextGenInference( - inference_server_url = "http://localhost:8010/", - max_new_tokens = 512, - top_k = 10, - top_p = 0.95, - typical_p = 0.95, - temperature = 0.01, - repetition_penalty = 1.03, + inference_server_url="http://localhost:8010/", + max_new_tokens=512, + top_k=10, + top_p=0.95, + typical_p=0.95, + temperature=0.01, + repetition_penalty=1.03, ) print(llm("What is Deep Learning?")) - + # Streaming response example from langchain.callbacks import streaming_stdout - + callbacks = [streaming_stdout.StreamingStdOutCallbackHandler()] llm = HuggingFaceTextGenInference( - inference_server_url = "http://localhost:8010/", - max_new_tokens = 512, - top_k = 10, - top_p = 0.95, - typical_p = 0.95, - temperature = 0.01, - repetition_penalty = 1.03, - callbacks = callbacks, - streaming = True + inference_server_url="http://localhost:8010/", + max_new_tokens=512, + top_k=10, + top_p=0.95, + typical_p=0.95, + temperature=0.01, + repetition_penalty=1.03, + callbacks=callbacks, + streaming=True ) print(llm("What is Deep Learning?")) - + """ max_new_tokens: int = 512 + """Maximum number of generated tokens""" top_k: Optional[int] = None + """The number of highest probability vocabulary tokens to keep for + top-k-filtering.""" top_p: Optional[float] = 0.95 + """If set to < 1, only the smallest set of most probable tokens with probabilities + that add up to `top_p` or higher are kept for generation.""" typical_p: Optional[float] = 0.95 - temperature: float = 0.8 + """Typical Decoding mass. See [Typical Decoding for Natural Language + Generation](https://arxiv.org/abs/2202.00666) for more information.""" + temperature: Optional[float] = 0.8 + """The value used to module the logits distribution.""" repetition_penalty: Optional[float] = None + """The parameter for repetition penalty. 1.0 means no penalty. + See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.""" + return_full_text: bool = False + """Whether to prepend the prompt to the generated text""" truncate: Optional[int] = None + """Truncate inputs tokens to the given size""" stop_sequences: List[str] = Field(default_factory=list) + """Stop generating tokens if a member of `stop_sequences` is generated""" seed: Optional[int] = None + """Random sampling seed""" inference_server_url: str = "" + """text-generation-inference instance base url""" timeout: int = 120 - server_kwargs: Dict[str, Any] = Field(default_factory=dict) + """Timeout in seconds""" streaming: bool = False + """Whether to generate a stream of tokens asynchronously""" + do_sample: bool = False + """Activate logits sampling""" + watermark: bool = False + """Watermarking with [A Watermark for Large Language Models] + (https://arxiv.org/abs/2301.10226)""" + server_kwargs: Dict[str, Any] = Field(default_factory=dict) + """Holds any text-generation-inference server parameters not explicitly specified""" + model_kwargs: Dict[str, Any] = Field(default_factory=dict) + """Holds any model parameters valid for `call` not explicitly specified""" client: Any async_client: Any @@ -95,6 +101,32 @@ class HuggingFaceTextGenInference(LLM): extra = Extra.forbid + @root_validator(pre=True) + def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]: + """Build extra kwargs from additional params that were passed in.""" + all_required_field_names = get_pydantic_field_names(cls) + extra = values.get("model_kwargs", {}) + for field_name in list(values): + if field_name in extra: + raise ValueError(f"Found {field_name} supplied twice.") + if field_name not in all_required_field_names: + logger.warning( + f"""WARNING! {field_name} is not default parameter. + {field_name} was transferred to model_kwargs. + Please confirm that {field_name} is what you intended.""" + ) + extra[field_name] = values.pop(field_name) + + invalid_model_kwargs = all_required_field_names.intersection(extra.keys()) + if invalid_model_kwargs: + raise ValueError( + f"Parameters {invalid_model_kwargs} should be specified explicitly. " + f"Instead they were passed in as part of `model_kwargs` parameter." + ) + + values["model_kwargs"] = extra + return values + @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that python package exists in environment.""" @@ -134,9 +166,13 @@ class HuggingFaceTextGenInference(LLM): "typical_p": self.typical_p, "temperature": self.temperature, "repetition_penalty": self.repetition_penalty, + "return_full_text": self.return_full_text, "truncate": self.truncate, "stop_sequences": self.stop_sequences, "seed": self.seed, + "do_sample": self.do_sample, + "watermark": self.watermark, + **self.model_kwargs, } def _invocation_params( diff --git a/libs/langchain/langchain/llms/pipelineai.py b/libs/langchain/langchain/llms/pipelineai.py index 0d257e3364..cff6c0f5b1 100644 --- a/libs/langchain/langchain/llms/pipelineai.py +++ b/libs/langchain/langchain/llms/pipelineai.py @@ -91,7 +91,7 @@ class PipelineAI(LLM, BaseModel): try: from pipeline import PipelineCloud except ImportError: - raise ValueError( + raise ImportError( "Could not import pipeline-ai python package. " "Please install it with `pip install pipeline-ai`." ) diff --git a/libs/langchain/langchain/llms/textgen.py b/libs/langchain/langchain/llms/textgen.py index 0d846ecc88..5f83dc08b9 100644 --- a/libs/langchain/langchain/llms/textgen.py +++ b/libs/langchain/langchain/llms/textgen.py @@ -1,10 +1,13 @@ import json import logging -from typing import Any, Dict, Iterator, List, Optional +from typing import Any, AsyncIterator, Dict, Iterator, List, Optional import requests -from langchain.callbacks.manager import CallbackManagerForLLMRun +from langchain.callbacks.manager import ( + AsyncCallbackManagerForLLMRun, + CallbackManagerForLLMRun, +) from langchain.llms.base import LLM from langchain.pydantic_v1 import Field from langchain.schema.output import GenerationChunk @@ -224,6 +227,54 @@ class TextGen(LLM): return result + async def _acall( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> str: + """Call the textgen web API and return the output. + + Args: + prompt: The prompt to use for generation. + stop: A list of strings to stop generation when encountered. + + Returns: + The generated text. + + Example: + .. code-block:: python + + from langchain.llms import TextGen + llm = TextGen(model_url="http://localhost:5000") + llm("Write a story about llamas.") + """ + if self.streaming: + combined_text_output = "" + async for chunk in self._astream( + prompt=prompt, stop=stop, run_manager=run_manager, **kwargs + ): + combined_text_output += chunk.text + print(prompt + combined_text_output) + result = combined_text_output + + else: + url = f"{self.model_url}/api/v1/generate" + params = self._get_parameters(stop) + request = params.copy() + request["prompt"] = prompt + response = requests.post(url, json=request) + + if response.status_code == 200: + result = response.json()["results"][0]["text"] + print(prompt + result) + else: + print(f"ERROR: response: {response}") + result = "" + + return result + def _stream( self, prompt: str, @@ -296,3 +347,76 @@ class TextGen(LLM): if run_manager: run_manager.on_llm_new_token(token=chunk.text) + + async def _astream( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> AsyncIterator[GenerationChunk]: + """Yields results objects as they are generated in real time. + + It also calls the callback manager's on_llm_new_token event with + similar parameters to the OpenAI LLM class method of the same name. + + Args: + prompt: The prompts to pass into the model. + stop: Optional list of stop words to use when generating. + + Returns: + A generator representing the stream of tokens being generated. + + Yields: + A dictionary like objects containing a string token and metadata. + See text-generation-webui docs and below for more. + + Example: + .. code-block:: python + + from langchain.llms import TextGen + llm = TextGen( + model_url = "ws://localhost:5005" + streaming=True + ) + for chunk in llm.stream("Ask 'Hi, how are you?' like a pirate:'", + stop=["'","\n"]): + print(chunk, end='', flush=True) + + """ + try: + import websocket + except ImportError: + raise ImportError( + "The `websocket-client` package is required for streaming." + ) + + params = {**self._get_parameters(stop), **kwargs} + + url = f"{self.model_url}/api/v1/stream" + + request = params.copy() + request["prompt"] = prompt + + websocket_client = websocket.WebSocket() + + websocket_client.connect(url) + + websocket_client.send(json.dumps(request)) + + while True: + result = websocket_client.recv() + result = json.loads(result) + + if result["event"] == "text_stream": + chunk = GenerationChunk( + text=result["text"], + generation_info=None, + ) + yield chunk + elif result["event"] == "stream_end": + websocket_client.close() + return + + if run_manager: + await run_manager.on_llm_new_token(token=chunk.text) diff --git a/libs/langchain/langchain/llms/titan_takeoff.py b/libs/langchain/langchain/llms/titan_takeoff.py index 517918fa99..d87784acea 100644 --- a/libs/langchain/langchain/llms/titan_takeoff.py +++ b/libs/langchain/langchain/llms/titan_takeoff.py @@ -10,8 +10,10 @@ from langchain.schema.output import GenerationChunk class TitanTakeoff(LLM): - port: int = 8000 - """Specifies the port to use for the Titan Takeoff API. Default = 8000.""" + base_url: str = "http://localhost:8000" + """Specifies the baseURL to use for the Titan Takeoff API. + Default = http://localhost:8000. + """ generate_max_length: int = 128 """Maximum generation length. Default = 128.""" @@ -92,7 +94,7 @@ class TitanTakeoff(LLM): text_output += chunk.text return text_output - url = f"http://localhost:{self.port}/generate" + url = f"{self.base_url}/generate" params = {"text": prompt, **self._default_params} response = requests.post(url, json=params) @@ -139,7 +141,7 @@ class TitanTakeoff(LLM): response = model(prompt) """ - url = f"http://localhost:{self.port}/generate_stream" + url = f"{self.base_url}/generate_stream" params = {"text": prompt, **self._default_params} response = requests.post(url, json=params, stream=True) @@ -154,4 +156,4 @@ class TitanTakeoff(LLM): @property def _identifying_params(self) -> Mapping[str, Any]: """Get the identifying parameters.""" - return {"port": self.port, **{}, **self._default_params} + return {"base_url": self.base_url, **{}, **self._default_params} diff --git a/libs/langchain/langchain/llms/vertexai.py b/libs/langchain/langchain/llms/vertexai.py index cdc541824c..2426587f58 100644 --- a/libs/langchain/langchain/llms/vertexai.py +++ b/libs/langchain/langchain/llms/vertexai.py @@ -11,12 +11,17 @@ from langchain.callbacks.manager import ( from langchain.llms.base import LLM, create_base_retry_decorator from langchain.llms.utils import enforce_stop_tokens from langchain.pydantic_v1 import BaseModel, root_validator +from langchain.schema import ( + Generation, + LLMResult, +) from langchain.utilities.vertexai import ( init_vertexai, raise_vertex_import_error, ) if TYPE_CHECKING: + from google.cloud.aiplatform.gapic import PredictionServiceClient from vertexai.language_models._language_models import _LanguageModel @@ -57,10 +62,40 @@ def completion_with_retry(llm: VertexAI, *args: Any, **kwargs: Any) -> Any: return _completion_with_retry(*args, **kwargs) -class _VertexAICommon(BaseModel): +class _VertexAIBase(BaseModel): + project: Optional[str] = None + "The default GCP project to use when making Vertex API calls." + location: str = "us-central1" + "The default location to use when making API calls." + request_parallelism: int = 5 + "The amount of parallelism allowed for requests issued to VertexAI models. " + "Default is 5." + max_retries: int = 6 + """The maximum number of retries to make when generating.""" + task_executor: ClassVar[Optional[Executor]] = None + stop: Optional[List[str]] = None + "Optional list of stop words to use when generating." + model_name: Optional[str] = None + "Underlying model name." + + def _enforce_stop_words(self, text: str, stop: Optional[List[str]] = None) -> str: + if stop is None and self.stop is not None: + stop = self.stop + if stop: + return enforce_stop_tokens(text, stop) + return text + + @classmethod + def _get_task_executor(cls, request_parallelism: int = 5) -> Executor: + if cls.task_executor is None: + cls.task_executor = ThreadPoolExecutor(max_workers=request_parallelism) + return cls.task_executor + + +class _VertexAICommon(_VertexAIBase): client: "_LanguageModel" = None #: :meta private: model_name: str - "Model name to use." + "Underlying model name." temperature: float = 0.0 "Sampling temperature, it controls the degree of randomness in token selection." max_output_tokens: int = 128 @@ -71,27 +106,20 @@ class _VertexAICommon(BaseModel): top_k: int = 40 "How the model selects tokens for output, the next token is selected from " "among the top-k most probable tokens. Top-k is ignored for Codey models." - stop: Optional[List[str]] = None - "Optional list of stop words to use when generating." - project: Optional[str] = None - "The default GCP project to use when making Vertex API calls." - location: str = "us-central1" - "The default location to use when making API calls." credentials: Any = None "The default custom credentials (google.auth.credentials.Credentials) to use " "when making API calls. If not provided, credentials will be ascertained from " "the environment." - request_parallelism: int = 5 - "The amount of parallelism allowed for requests issued to VertexAI models. " - "Default is 5." - max_retries: int = 6 - """The maximum number of retries to make when generating.""" - task_executor: ClassVar[Optional[Executor]] = None @property def is_codey_model(self) -> bool: return is_codey_model(self.model_name) + @property + def _identifying_params(self) -> Dict[str, Any]: + """Get the identifying parameters.""" + return {**{"model_name": self.model_name}, **self._default_params} + @property def _default_params(self) -> Dict[str, Any]: if self.is_codey_model: @@ -114,28 +142,10 @@ class _VertexAICommon(BaseModel): res = completion_with_retry(self, prompt, **params) # type: ignore return self._enforce_stop_words(res.text, stop) - def _enforce_stop_words(self, text: str, stop: Optional[List[str]] = None) -> str: - if stop is None and self.stop is not None: - stop = self.stop - if stop: - return enforce_stop_tokens(text, stop) - return text - - @property - def _identifying_params(self) -> Dict[str, Any]: - """Get the identifying parameters.""" - return {**{"model_name": self.model_name}, **self._default_params} - @property def _llm_type(self) -> str: return "vertexai" - @classmethod - def _get_task_executor(cls, request_parallelism: int = 5) -> Executor: - if cls.task_executor is None: - cls.task_executor = ThreadPoolExecutor(max_workers=request_parallelism) - return cls.task_executor - @classmethod def _try_init_vertexai(cls, values: Dict) -> None: allowed_params = ["project", "location", "credentials"] @@ -176,27 +186,6 @@ class VertexAI(_VertexAICommon, LLM): raise_vertex_import_error() return values - async def _acall( - self, - prompt: str, - stop: Optional[List[str]] = None, - run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, - **kwargs: Any, - ) -> str: - """Call Vertex model to get predictions based on the prompt. - - Args: - prompt: The prompt to pass into the model. - stop: A list of stop words (optional). - run_manager: A callback manager for async interaction with LLMs. - - Returns: - The string generated by the model. - """ - return await asyncio.wrap_future( - self._get_task_executor().submit(self._predict, prompt, stop) - ) - def _call( self, prompt: str, @@ -215,3 +204,145 @@ class VertexAI(_VertexAICommon, LLM): The string generated by the model. """ return self._predict(prompt, stop, **kwargs) + + async def _acall( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> str: + """Call Vertex model to get predictions based on the prompt. + + Args: + prompt: The prompt to pass into the model. + stop: A list of stop words (optional). + run_manager: A callback manager for async interaction with LLMs. + + Returns: + The string generated by the model. + """ + return await asyncio.wrap_future( + self._get_task_executor().submit(self._call, prompt, stop) + ) + + +class VertexAIModelGarden(_VertexAIBase, LLM): + """Large language models served from Vertex AI Model Garden.""" + + client: "PredictionServiceClient" = None #: :meta private: + endpoint_id: str + "A name of an endpoint where the model has been deployed." + allowed_model_args: Optional[List[str]] = None + """Allowed optional args to be passed to the model.""" + prompt_arg: str = "prompt" + result_arg: str = "generated_text" + + @root_validator() + def validate_environment(cls, values: Dict) -> Dict: + """Validate that the python package exists in environment.""" + try: + from google.cloud.aiplatform.gapic import PredictionServiceClient + except ImportError: + raise_vertex_import_error() + + if values["project"] is None: + raise ValueError( + "A GCP project should be provided to run inference on Model Garden!" + ) + + client_options = { + "api_endpoint": f"{values['location']}-aiplatform.googleapis.com" + } + values["client"] = PredictionServiceClient(client_options=client_options) + return values + + @property + def _llm_type(self) -> str: + return "vertexai_model_garden" + + def _call( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> str: + """Call Vertex model to get predictions based on the prompt. + + Args: + prompt: The prompt to pass into the model. + stop: A list of stop words (optional). + run_manager: A Callbackmanager for LLM run, optional. + + Returns: + The string generated by the model. + """ + result = self._generate( + prompts=[prompt], stop=stop, run_manager=run_manager, **kwargs + ) + return result.generations[0][0].text + + def _generate( + self, + prompts: List[str], + stop: Optional[List[str]] = None, + run_manager: Optional[CallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> LLMResult: + """Run the LLM on the given prompt and input.""" + try: + from google.protobuf import json_format + from google.protobuf.struct_pb2 import Value + except ImportError: + raise ImportError( + "protobuf package not found, please install it with" + " `pip install protobuf`" + ) + + instances = [] + for prompt in prompts: + if self.allowed_model_args: + instance = { + k: v for k, v in kwargs.items() if k in self.allowed_model_args + } + else: + instance = {} + instance[self.prompt_arg] = prompt + instances.append(instance) + + predict_instances = [ + json_format.ParseDict(instance_dict, Value()) for instance_dict in instances + ] + + endpoint = self.client.endpoint_path( + project=self.project, location=self.location, endpoint=self.endpoint_id + ) + response = self.client.predict(endpoint=endpoint, instances=predict_instances) + generations: List[List[Generation]] = [] + for result in response.predictions: + generations.append( + [Generation(text=prediction[self.result_arg]) for prediction in result] + ) + return LLMResult(generations=generations) + + async def _acall( + self, + prompt: str, + stop: Optional[List[str]] = None, + run_manager: Optional[AsyncCallbackManagerForLLMRun] = None, + **kwargs: Any, + ) -> str: + """Call Vertex model to get predictions based on the prompt. + + Args: + prompt: The prompt to pass into the model. + stop: A list of stop words (optional). + run_manager: A callback manager for async interaction with LLMs. + + Returns: + The string generated by the model. + """ + return await asyncio.wrap_future( + self._get_task_executor().submit(self._call, prompt, stop) + ) diff --git a/libs/langchain/langchain/llms/vllm.py b/libs/langchain/langchain/llms/vllm.py index 9f456fde51..537a9bbb6f 100644 --- a/libs/langchain/langchain/llms/vllm.py +++ b/libs/langchain/langchain/llms/vllm.py @@ -3,7 +3,7 @@ from typing import Any, Dict, List, Optional from langchain.callbacks.manager import CallbackManagerForLLMRun from langchain.llms.base import BaseLLM from langchain.llms.openai import BaseOpenAI -from langchain.pydantic_v1 import root_validator +from langchain.pydantic_v1 import Field, root_validator from langchain.schema.output import Generation, LLMResult @@ -62,6 +62,13 @@ class VLLM(BaseLLM): dtype: str = "auto" """The data type for the model weights and activations.""" + download_dir: Optional[str] = None + """Directory to download and load the weights. (Default to the default + cache dir of huggingface)""" + + vllm_kwargs: Dict[str, Any] = Field(default_factory=dict) + """Holds any model parameters valid for `vllm.LLM` call not explicitly specified.""" + client: Any #: :meta private: @root_validator() @@ -81,6 +88,8 @@ class VLLM(BaseLLM): tensor_parallel_size=values["tensor_parallel_size"], trust_remote_code=values["trust_remote_code"], dtype=values["dtype"], + download_dir=values["download_dir"], + **values["vllm_kwargs"], ) return values diff --git a/libs/langchain/langchain/load/serializable.py b/libs/langchain/langchain/load/serializable.py index 535be6476d..46fdf2878b 100644 --- a/libs/langchain/langchain/load/serializable.py +++ b/libs/langchain/langchain/load/serializable.py @@ -1,5 +1,5 @@ from abc import ABC -from typing import Any, Dict, List, Literal, TypedDict, Union, cast +from typing import Any, Dict, List, Literal, Optional, TypedDict, Union, cast from langchain.pydantic_v1 import BaseModel, PrivateAttr @@ -28,6 +28,7 @@ class SerializedNotImplemented(BaseSerialized): """Serialized not implemented.""" type: Literal["not_implemented"] + repr: Optional[str] class Serializable(BaseModel, ABC): @@ -156,8 +157,15 @@ def to_json_not_implemented(obj: object) -> SerializedNotImplemented: _id = [*obj.__class__.__module__.split("."), obj.__class__.__name__] except Exception: pass - return { + + result: SerializedNotImplemented = { "lc": 1, "type": "not_implemented", "id": _id, + "repr": None, } + try: + result["repr"] = repr(obj) + except Exception: + pass + return result diff --git a/libs/langchain/langchain/memory/chat_message_histories/dynamodb.py b/libs/langchain/langchain/memory/chat_message_histories/dynamodb.py index 3800017516..704efa9ea7 100644 --- a/libs/langchain/langchain/memory/chat_message_histories/dynamodb.py +++ b/libs/langchain/langchain/memory/chat_message_histories/dynamodb.py @@ -1,5 +1,5 @@ import logging -from typing import List, Optional +from typing import Dict, List, Optional from langchain.schema import ( BaseChatMessageHistory, @@ -17,8 +17,7 @@ logger = logging.getLogger(__name__) class DynamoDBChatMessageHistory(BaseChatMessageHistory): """Chat message history that stores history in AWS DynamoDB. - This class expects that a DynamoDB table with name `table_name` - and a partition Key of `SessionId` is present. + This class expects that a DynamoDB table exists with name `table_name` Args: table_name: name of the DynamoDB table @@ -28,10 +27,21 @@ class DynamoDBChatMessageHistory(BaseChatMessageHistory): is optional and useful for test purposes, like using Localstack. If you plan to use AWS cloud service, you normally don't have to worry about setting the endpoint_url. + primary_key_name: name of the primary key of the DynamoDB table. This argument + is optional, defaulting to "SessionId". + key: an optional dictionary with a custom primary and secondary key. + This argument is optional, but useful when using composite dynamodb keys, or + isolating records based off of application details such as a user id. + This may also contain global and local secondary index keys. """ def __init__( - self, table_name: str, session_id: str, endpoint_url: Optional[str] = None + self, + table_name: str, + session_id: str, + endpoint_url: Optional[str] = None, + primary_key_name: str = "SessionId", + key: Optional[Dict[str, str]] = None, ): import boto3 @@ -41,6 +51,7 @@ class DynamoDBChatMessageHistory(BaseChatMessageHistory): client = boto3.resource("dynamodb") self.table = client.Table(table_name) self.session_id = session_id + self.key: Dict = key or {primary_key_name: session_id} @property def messages(self) -> List[BaseMessage]: # type: ignore @@ -49,7 +60,7 @@ class DynamoDBChatMessageHistory(BaseChatMessageHistory): response = None try: - response = self.table.get_item(Key={"SessionId": self.session_id}) + response = self.table.get_item(Key=self.key) except ClientError as error: if error.response["Error"]["Code"] == "ResourceNotFoundException": logger.warning("No record found with session id: %s", self.session_id) @@ -73,9 +84,7 @@ class DynamoDBChatMessageHistory(BaseChatMessageHistory): messages.append(_message) try: - self.table.put_item( - Item={"SessionId": self.session_id, "History": messages} - ) + self.table.put_item(Item={**self.key, "History": messages}) except ClientError as err: logger.error(err) @@ -84,6 +93,6 @@ class DynamoDBChatMessageHistory(BaseChatMessageHistory): from botocore.exceptions import ClientError try: - self.table.delete_item(Key={"SessionId": self.session_id}) + self.table.delete_item(self.key) except ClientError as err: logger.error(err) diff --git a/libs/langchain/langchain/memory/chat_message_histories/sql.py b/libs/langchain/langchain/memory/chat_message_histories/sql.py index 43fedd531d..019ffd58b8 100644 --- a/libs/langchain/langchain/memory/chat_message_histories/sql.py +++ b/libs/langchain/langchain/memory/chat_message_histories/sql.py @@ -1,6 +1,7 @@ import json import logging -from typing import List +from abc import ABC, abstractmethod +from typing import Any, List, Optional from sqlalchemy import Column, Integer, Text, create_engine @@ -18,6 +19,25 @@ from langchain.schema.messages import BaseMessage, _message_to_dict, messages_fr logger = logging.getLogger(__name__) +class BaseMessageConverter(ABC): + """The class responsible for converting BaseMessage to your SQLAlchemy model.""" + + @abstractmethod + def from_sql_model(self, sql_message: Any) -> BaseMessage: + """Convert a SQLAlchemy model to a BaseMessage instance.""" + raise NotImplementedError + + @abstractmethod + def to_sql_model(self, message: BaseMessage, session_id: str) -> Any: + """Convert a BaseMessage instance to a SQLAlchemy model.""" + raise NotImplementedError + + @abstractmethod + def get_sql_model_class(self) -> Any: + """Get the SQLAlchemy model class.""" + raise NotImplementedError + + def create_message_model(table_name, DynamicBase): # type: ignore """ Create a message model for a given table name. @@ -41,6 +61,24 @@ def create_message_model(table_name, DynamicBase): # type: ignore return Message +class DefaultMessageConverter(BaseMessageConverter): + """The default message converter for SQLChatMessageHistory.""" + + def __init__(self, table_name: str): + self.model_class = create_message_model(table_name, declarative_base()) + + def from_sql_model(self, sql_message: Any) -> BaseMessage: + return messages_from_dict([json.loads(sql_message.message)])[0] + + def to_sql_model(self, message: BaseMessage, session_id: str) -> Any: + return self.model_class( + session_id=session_id, message=json.dumps(_message_to_dict(message)) + ) + + def get_sql_model_class(self) -> Any: + return self.model_class + + class SQLChatMessageHistory(BaseChatMessageHistory): """Chat message history stored in an SQL database.""" @@ -49,44 +87,49 @@ class SQLChatMessageHistory(BaseChatMessageHistory): session_id: str, connection_string: str, table_name: str = "message_store", + session_id_field_name: str = "session_id", + custom_message_converter: Optional[BaseMessageConverter] = None, ): - self.table_name = table_name self.connection_string = connection_string self.engine = create_engine(connection_string, echo=False) + self.session_id_field_name = session_id_field_name + self.converter = custom_message_converter or DefaultMessageConverter(table_name) + self.sql_model_class = self.converter.get_sql_model_class() + if not hasattr(self.sql_model_class, session_id_field_name): + raise ValueError("SQL model class must have session_id column") self._create_table_if_not_exists() self.session_id = session_id self.Session = sessionmaker(self.engine) def _create_table_if_not_exists(self) -> None: - DynamicBase = declarative_base() - self.Message = create_message_model(self.table_name, DynamicBase) - # Create all does the check for us in case the table exists. - DynamicBase.metadata.create_all(self.engine) + self.sql_model_class.metadata.create_all(self.engine) @property def messages(self) -> List[BaseMessage]: # type: ignore """Retrieve all messages from db""" with self.Session() as session: - result = session.query(self.Message).where( - self.Message.session_id == self.session_id + result = session.query(self.sql_model_class).where( + getattr(self.sql_model_class, self.session_id_field_name) + == self.session_id ) - items = [json.loads(record.message) for record in result] - messages = messages_from_dict(items) + messages = [] + for record in result: + messages.append(self.converter.from_sql_model(record)) return messages def add_message(self, message: BaseMessage) -> None: """Append the message to the record in db""" with self.Session() as session: - jsonstr = json.dumps(_message_to_dict(message)) - session.add(self.Message(session_id=self.session_id, message=jsonstr)) + session.add(self.converter.to_sql_model(message, self.session_id)) session.commit() def clear(self) -> None: """Clear session memory from db""" with self.Session() as session: - session.query(self.Message).filter( - self.Message.session_id == self.session_id + session.query(self.sql_model_class).filter( + getattr(self.sql_model_class, self.session_id_field_name) + == self.session_id ).delete() session.commit() diff --git a/libs/langchain/langchain/output_parsers/__init__.py b/libs/langchain/langchain/output_parsers/__init__.py index f029f52bfb..d3d49c6bd2 100644 --- a/libs/langchain/langchain/output_parsers/__init__.py +++ b/libs/langchain/langchain/output_parsers/__init__.py @@ -20,6 +20,7 @@ from langchain.output_parsers.fix import OutputFixingParser from langchain.output_parsers.list import ( CommaSeparatedListOutputParser, ListOutputParser, + NumberedListOutputParser, ) from langchain.output_parsers.pydantic import PydanticOutputParser from langchain.output_parsers.rail_parser import GuardrailsOutputParser @@ -36,6 +37,7 @@ __all__ = [ "EnumOutputParser", "GuardrailsOutputParser", "ListOutputParser", + "NumberedListOutputParser", "OutputFixingParser", "PydanticOutputParser", "RegexDictParser", diff --git a/libs/langchain/langchain/output_parsers/list.py b/libs/langchain/langchain/output_parsers/list.py index 92850fc31c..6680cb908a 100644 --- a/libs/langchain/langchain/output_parsers/list.py +++ b/libs/langchain/langchain/output_parsers/list.py @@ -1,5 +1,6 @@ from __future__ import annotations +import re from abc import abstractmethod from typing import List @@ -34,3 +35,29 @@ class CommaSeparatedListOutputParser(ListOutputParser): def parse(self, text: str) -> List[str]: """Parse the output of an LLM call.""" return text.strip().split(", ") + + @property + def _type(self) -> str: + return "comma-separated-list" + + +class NumberedListOutputParser(ListOutputParser): + """Parse a numbered list.""" + + def get_format_instructions(self) -> str: + return ( + "Your response should be a numbered list with each item on a new line. " + "For example: \n\n1. foo\n\n2. bar\n\n3. baz" + ) + + def parse(self, text: str) -> List[str]: + """Parse the output of an LLM call.""" + pattern = r"\d+\.\s([^\n]+)" + + # Extract the text of each item + matches = re.findall(pattern, text) + return matches + + @property + def _type(self) -> str: + return "numbered-list" diff --git a/libs/langchain/langchain/retrievers/multi_query.py b/libs/langchain/langchain/retrievers/multi_query.py index 8398ce40d9..b99bb84a7e 100644 --- a/libs/langchain/langchain/retrievers/multi_query.py +++ b/libs/langchain/langchain/retrievers/multi_query.py @@ -1,5 +1,5 @@ import logging -from typing import List +from typing import List, Sequence from langchain.callbacks.manager import CallbackManagerForRetrieverRun from langchain.chains.llm import LLMChain @@ -43,10 +43,14 @@ DEFAULT_QUERY_PROMPT = PromptTemplate( ) +def _unique_documents(documents: Sequence[Document]) -> List[Document]: + return [doc for i, doc in enumerate(documents) if doc not in documents[:i]] + + class MultiQueryRetriever(BaseRetriever): """Given a query, use an LLM to write a set of queries. - Retrieve docs for each query. Rake the unique union of all retrieved docs. + Retrieve docs for each query. Return the unique union of all retrieved docs. """ retriever: BaseRetriever @@ -85,7 +89,7 @@ class MultiQueryRetriever(BaseRetriever): *, run_manager: CallbackManagerForRetrieverRun, ) -> List[Document]: - """Get relevated documents given a user query. + """Get relevant documents given a user query. Args: question: user query @@ -95,8 +99,7 @@ class MultiQueryRetriever(BaseRetriever): """ queries = self.generate_queries(query, run_manager) documents = self.retrieve_documents(queries, run_manager) - unique_documents = self.unique_union(documents) - return unique_documents + return self.unique_union(documents) def generate_queries( self, question: str, run_manager: CallbackManagerForRetrieverRun @@ -145,12 +148,4 @@ class MultiQueryRetriever(BaseRetriever): Returns: List of unique retrieved Documents """ - # Create a dictionary with page_content as keys to remove duplicates - # TODO: Add Document ID property (e.g., UUID) - unique_documents_dict = { - (doc.page_content, tuple(sorted(doc.metadata.items()))): doc - for doc in documents - } - - unique_documents = list(unique_documents_dict.values()) - return unique_documents + return _unique_documents(documents) diff --git a/libs/langchain/langchain/retrievers/self_query/base.py b/libs/langchain/langchain/retrievers/self_query/base.py index 806d4e1502..0251bff52c 100644 --- a/libs/langchain/langchain/retrievers/self_query/base.py +++ b/libs/langchain/langchain/retrievers/self_query/base.py @@ -9,8 +9,10 @@ from langchain.chains.query_constructor.ir import StructuredQuery, Visitor from langchain.chains.query_constructor.schema import AttributeInfo from langchain.pydantic_v1 import BaseModel, Field, root_validator from langchain.retrievers.self_query.chroma import ChromaTranslator +from langchain.retrievers.self_query.dashvector import DashvectorTranslator from langchain.retrievers.self_query.deeplake import DeepLakeTranslator from langchain.retrievers.self_query.elasticsearch import ElasticsearchTranslator +from langchain.retrievers.self_query.milvus import MilvusTranslator from langchain.retrievers.self_query.myscale import MyScaleTranslator from langchain.retrievers.self_query.pinecone import PineconeTranslator from langchain.retrievers.self_query.qdrant import QdrantTranslator @@ -19,8 +21,10 @@ from langchain.schema import BaseRetriever, Document from langchain.schema.language_model import BaseLanguageModel from langchain.vectorstores import ( Chroma, + DashVector, DeepLake, ElasticsearchStore, + Milvus, MyScale, Pinecone, Qdrant, @@ -35,11 +39,13 @@ def _get_builtin_translator(vectorstore: VectorStore) -> Visitor: BUILTIN_TRANSLATORS: Dict[Type[VectorStore], Type[Visitor]] = { Pinecone: PineconeTranslator, Chroma: ChromaTranslator, + DashVector: DashvectorTranslator, Weaviate: WeaviateTranslator, Qdrant: QdrantTranslator, MyScale: MyScaleTranslator, DeepLake: DeepLakeTranslator, ElasticsearchStore: ElasticsearchTranslator, + Milvus: MilvusTranslator, } if vectorstore_cls not in BUILTIN_TRANSLATORS: raise ValueError( diff --git a/libs/langchain/langchain/retrievers/self_query/chroma.py b/libs/langchain/langchain/retrievers/self_query/chroma.py index 9f9900358a..8c9a79b12d 100644 --- a/libs/langchain/langchain/retrievers/self_query/chroma.py +++ b/libs/langchain/langchain/retrievers/self_query/chroma.py @@ -17,6 +17,7 @@ class ChromaTranslator(Visitor): """Subset of allowed logical operators.""" allowed_comparators = [ Comparator.EQ, + Comparator.NE, Comparator.GT, Comparator.GTE, Comparator.LT, diff --git a/libs/langchain/langchain/retrievers/self_query/dashvector.py b/libs/langchain/langchain/retrievers/self_query/dashvector.py new file mode 100644 index 0000000000..24ae50239a --- /dev/null +++ b/libs/langchain/langchain/retrievers/self_query/dashvector.py @@ -0,0 +1,64 @@ +"""Logic for converting internal query language to a valid DashVector query.""" +from typing import Tuple, Union + +from langchain.chains.query_constructor.ir import ( + Comparator, + Comparison, + Operation, + Operator, + StructuredQuery, + Visitor, +) + + +class DashvectorTranslator(Visitor): + """Logic for converting internal query language elements to valid filters.""" + + allowed_operators = [Operator.AND, Operator.OR] + allowed_comparators = [ + Comparator.EQ, + Comparator.GT, + Comparator.GTE, + Comparator.LT, + Comparator.LTE, + Comparator.LIKE, + ] + + map_dict = { + Operator.AND: " AND ", + Operator.OR: " OR ", + Comparator.EQ: " = ", + Comparator.GT: " > ", + Comparator.GTE: " >= ", + Comparator.LT: " < ", + Comparator.LTE: " <= ", + Comparator.LIKE: " LIKE ", + } + + def _format_func(self, func: Union[Operator, Comparator]) -> str: + self._validate_func(func) + return self.map_dict[func] + + def visit_operation(self, operation: Operation) -> str: + args = [arg.accept(self) for arg in operation.arguments] + return self._format_func(operation.operator).join(args) + + def visit_comparison(self, comparison: Comparison) -> str: + value = comparison.value + if isinstance(value, str): + if comparison.comparator == Comparator.LIKE: + value = f"'%{value}%'" + else: + value = f"'{value}'" + return ( + f"{comparison.attribute}{self._format_func(comparison.comparator)}{value}" + ) + + def visit_structured_query( + self, structured_query: StructuredQuery + ) -> Tuple[str, dict]: + if structured_query.filter is None: + kwargs = {} + else: + kwargs = {"filter": structured_query.filter.accept(self)} + return structured_query.query, kwargs diff --git a/libs/langchain/langchain/retrievers/self_query/deeplake.py b/libs/langchain/langchain/retrievers/self_query/deeplake.py index 1e65eb39a1..030933b32e 100644 --- a/libs/langchain/langchain/retrievers/self_query/deeplake.py +++ b/libs/langchain/langchain/retrievers/self_query/deeplake.py @@ -22,6 +22,7 @@ COMPARATOR_TO_TQL = { OPERATOR_TO_TQL = { Operator.AND: "and", Operator.OR: "or", + Operator.NOT: "NOT", } @@ -37,7 +38,7 @@ def can_cast_to_float(string: str) -> bool: class DeepLakeTranslator(Visitor): """Translate `DeepLake` internal query language elements to valid filters.""" - allowed_operators = [Operator.AND, Operator.OR] + allowed_operators = [Operator.AND, Operator.OR, Operator.NOT] """Subset of allowed logical operators.""" allowed_comparators = [ Comparator.EQ, diff --git a/libs/langchain/langchain/retrievers/self_query/milvus.py b/libs/langchain/langchain/retrievers/self_query/milvus.py new file mode 100644 index 0000000000..2b4af4500f --- /dev/null +++ b/libs/langchain/langchain/retrievers/self_query/milvus.py @@ -0,0 +1,83 @@ +"""Logic for converting internal query language to a valid Milvus query.""" +from typing import Tuple, Union + +from langchain.chains.query_constructor.ir import ( + Comparator, + Comparison, + Operation, + Operator, + StructuredQuery, + Visitor, +) + +COMPARATOR_TO_BER = { + Comparator.EQ: "==", + Comparator.GT: ">", + Comparator.GTE: ">=", + Comparator.LT: "<", + Comparator.LTE: "<=", +} + +UNARY_OPERATORS = [Operator.NOT] + + +def process_value(value: Union[int, float, str]) -> str: + # required for comparators involving strings + if isinstance(value, str): + # If the value is already a string, add double quotes + return f'"{value}"' + else: + # If the valueis not a string, convert it to a string without double quotes + return str(value) + + +class MilvusTranslator(Visitor): + """Translate Milvus internal query language elements to valid filters.""" + + """Subset of allowed logical operators.""" + allowed_operators = [Operator.AND, Operator.NOT, Operator.OR] + + """Subset of allowed logical comparators.""" + allowed_comparators = [ + Comparator.EQ, + Comparator.GT, + Comparator.GTE, + Comparator.LT, + Comparator.LTE, + ] + + def _format_func(self, func: Union[Operator, Comparator]) -> str: + self._validate_func(func) + value = func.value + if isinstance(func, Comparator): + value = COMPARATOR_TO_BER[func] + return f"{value}" + + def visit_operation(self, operation: Operation) -> str: + if operation.operator in UNARY_OPERATORS and len(operation.arguments) == 1: + operator = self._format_func(operation.operator) + return operator + "(" + operation.arguments[0].accept(self) + ")" + elif operation.operator in UNARY_OPERATORS: + raise ValueError( + f'"{operation.operator.value}" can have only one argument in Milvus' + ) + else: + args = [arg.accept(self) for arg in operation.arguments] + operator = self._format_func(operation.operator) + return "(" + (" " + operator + " ").join(args) + ")" + + def visit_comparison(self, comparison: Comparison) -> str: + comparator = self._format_func(comparison.comparator) + processed_value = process_value(comparison.value) + attribute = comparison.attribute + + return "( " + attribute + " " + comparator + " " + processed_value + " )" + + def visit_structured_query( + self, structured_query: StructuredQuery + ) -> Tuple[str, dict]: + if structured_query.filter is None: + kwargs = {} + else: + kwargs = {"expr": structured_query.filter.accept(self)} + return structured_query.query, kwargs diff --git a/libs/langchain/langchain/retrievers/self_query/pinecone.py b/libs/langchain/langchain/retrievers/self_query/pinecone.py index 003514953c..a39fc310bf 100644 --- a/libs/langchain/langchain/retrievers/self_query/pinecone.py +++ b/libs/langchain/langchain/retrievers/self_query/pinecone.py @@ -15,6 +15,7 @@ class PineconeTranslator(Visitor): allowed_comparators = ( Comparator.EQ, + Comparator.NE, Comparator.LT, Comparator.LTE, Comparator.GT, diff --git a/libs/langchain/langchain/retrievers/time_weighted_retriever.py b/libs/langchain/langchain/retrievers/time_weighted_retriever.py index 44d7c6ac6a..75b49a8c37 100644 --- a/libs/langchain/langchain/retrievers/time_weighted_retriever.py +++ b/libs/langchain/langchain/retrievers/time_weighted_retriever.py @@ -47,6 +47,14 @@ class TimeWeightedVectorStoreRetriever(BaseRetriever): arbitrary_types_allowed = True + def _document_get_date(self, field: str, document: Document) -> datetime.datetime: + """Return the value of the date field of a document.""" + if field in document.metadata: + if type(document.metadata[field]) == float: + return datetime.datetime.fromtimestamp(document.metadata[field]) + return document.metadata[field] + return datetime.datetime.now() + def _get_combined_score( self, document: Document, @@ -56,7 +64,7 @@ class TimeWeightedVectorStoreRetriever(BaseRetriever): """Return the combined score for a document.""" hours_passed = _get_hours_passed( current_time, - document.metadata["last_accessed_at"], + self._document_get_date("last_accessed_at", document), ) score = (1.0 - self.decay_rate) ** hours_passed for key in self.other_score_keys: diff --git a/libs/langchain/langchain/schema/language_model.py b/libs/langchain/langchain/schema/language_model.py index 6a46165e43..8623233807 100644 --- a/libs/langchain/langchain/schema/language_model.py +++ b/libs/langchain/langchain/schema/language_model.py @@ -1,6 +1,7 @@ from __future__ import annotations from abc import ABC, abstractmethod +from functools import lru_cache from typing import ( TYPE_CHECKING, Any, @@ -23,10 +24,8 @@ if TYPE_CHECKING: from langchain.callbacks.manager import Callbacks -def _get_token_ids_default_method(text: str) -> List[int]: - """Encode the text into token IDs.""" - # TODO: this method may not be exact. - # TODO: this method may differ based on model (eg codex). +@lru_cache(maxsize=None) # Cache the tokenizer +def get_tokenizer() -> Any: try: from transformers import GPT2TokenizerFast except ImportError: @@ -36,7 +35,13 @@ def _get_token_ids_default_method(text: str) -> List[int]: "Please install it with `pip install transformers`." ) # create a GPT-2 tokenizer instance - tokenizer = GPT2TokenizerFast.from_pretrained("gpt2") + return GPT2TokenizerFast.from_pretrained("gpt2") + + +def _get_token_ids_default_method(text: str) -> List[int]: + """Encode the text into token IDs.""" + # get the cached tokenizer + tokenizer = get_tokenizer() # tokenize the text using the GPT-2 tokenizer return tokenizer.encode(text) diff --git a/libs/langchain/langchain/schema/runnable/base.py b/libs/langchain/langchain/schema/runnable/base.py index bdbd7fc699..9b23e50438 100644 --- a/libs/langchain/langchain/schema/runnable/base.py +++ b/libs/langchain/langchain/schema/runnable/base.py @@ -42,6 +42,7 @@ from langchain.schema.runnable.config import ( ensure_config, get_async_callback_manager_for_config, get_callback_manager_for_config, + get_config_list, get_executor_for_config, patch_config, ) @@ -104,34 +105,58 @@ class Runnable(Generic[Input, Output], ABC): self, inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, + *, + return_exceptions: bool = False, **kwargs: Optional[Any], ) -> List[Output]: """ Default implementation of batch, which calls invoke N times. Subclasses should override this method if they can batch more efficiently. """ - configs = self._get_config_list(config, len(inputs)) + configs = get_config_list(config, len(inputs)) + + def invoke(input: Input, config: RunnableConfig) -> Union[Output, Exception]: + if return_exceptions: + try: + return self.invoke(input, config, **kwargs) + except Exception as e: + return e + else: + return self.invoke(input, config, **kwargs) # If there's only one input, don't bother with the executor if len(inputs) == 1: - return [self.invoke(inputs[0], configs[0], **kwargs)] + return cast(List[Output], [invoke(inputs[0], configs[0])]) with get_executor_for_config(configs[0]) as executor: - return list(executor.map(partial(self.invoke, **kwargs), inputs, configs)) + return cast(List[Output], list(executor.map(invoke, inputs, configs))) async def abatch( self, inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, + *, + return_exceptions: bool = False, **kwargs: Optional[Any], ) -> List[Output]: """ Default implementation of abatch, which calls ainvoke N times. Subclasses should override this method if they can batch more efficiently. """ - configs = self._get_config_list(config, len(inputs)) - coros = map(partial(self.ainvoke, **kwargs), inputs, configs) + configs = get_config_list(config, len(inputs)) + async def ainvoke( + input: Input, config: RunnableConfig + ) -> Union[Output, Exception]: + if return_exceptions: + try: + return await self.ainvoke(input, config, **kwargs) + except Exception as e: + return e + else: + return await self.ainvoke(input, config, **kwargs) + + coros = map(ainvoke, inputs, configs) return await gather_with_concurrency(configs[0].get("max_concurrency"), *coros) def stream( @@ -210,7 +235,38 @@ class Runnable(Generic[Input, Output], ABC): """ Bind arguments to a Runnable, returning a new Runnable. """ - return RunnableBinding(bound=self, kwargs=kwargs) + return RunnableBinding(bound=self, kwargs=kwargs, config={}) + + def with_config( + self, + config: Optional[RunnableConfig] = None, + # Sadly Unpack is not well supported by mypy so this will have to be untyped + **kwargs: Any, + ) -> Runnable[Input, Output]: + """ + Bind config to a Runnable, returning a new Runnable. + """ + return RunnableBinding( + bound=self, config={**(config or {}), **kwargs}, kwargs={} + ) + + def with_retry( + self, + *, + retry_if_exception_type: Tuple[Type[BaseException]] = (Exception,), + wait_exponential_jitter: bool = True, + stop_after_attempt: int = 3, + ) -> Runnable[Input, Output]: + from langchain.schema.runnable.retry import RunnableRetry + + return RunnableRetry( + bound=self, + kwargs={}, + config={}, + retry_exception_types=retry_if_exception_type, + wait_exponential_jitter=wait_exponential_jitter, + max_attempt_number=stop_after_attempt, + ) def map(self) -> Runnable[List[Input], List[Output]]: """ @@ -233,27 +289,6 @@ class Runnable(Generic[Input, Output], ABC): """ --- Helper methods for Subclasses --- """ - def _get_config_list( - self, config: Optional[Union[RunnableConfig, List[RunnableConfig]]], length: int - ) -> List[RunnableConfig]: - """ - Helper method to get a list of configs from a single config or a list of - configs, useful for subclasses overriding batch() or abatch(). - """ - if length < 1: - raise ValueError(f"length must be >= 1, but got {length}") - if isinstance(config, list) and len(config) != length: - raise ValueError( - f"config must be a list of the same length as inputs, " - f"but got {len(config)} configs for {length} inputs" - ) - - return ( - list(map(ensure_config, config)) - if isinstance(config, list) - else [patch_config(config, deep_copy_locals=True) for _ in range(length)] - ) - def _call_with_config( self, func: Union[ @@ -273,6 +308,7 @@ class Runnable(Generic[Input, Output], ABC): dumpd(self), input, run_type=run_type, + name=config.get("run_name"), ) try: if accepts_run_manager_and_config(func): @@ -314,6 +350,7 @@ class Runnable(Generic[Input, Output], ABC): dumpd(self), input, run_type=run_type, + name=config.get("run_name"), ) try: if accepts_run_manager_and_config(func): @@ -336,6 +373,145 @@ class Runnable(Generic[Input, Output], ABC): await run_manager.on_chain_end(dumpd(output)) return output + def _batch_with_config( + self, + func: Union[ + Callable[[List[Input]], List[Union[Exception, Output]]], + Callable[ + [List[Input], List[CallbackManagerForChainRun]], + List[Union[Exception, Output]], + ], + Callable[ + [List[Input], List[CallbackManagerForChainRun], List[RunnableConfig]], + List[Union[Exception, Output]], + ], + ], + input: List[Input], + config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, + *, + return_exceptions: bool = False, + run_type: Optional[str] = None, + ) -> List[Output]: + """Helper method to transform an Input value to an Output value, + with callbacks. Use this method to implement invoke() in subclasses.""" + configs = get_config_list(config, len(input)) + callback_managers = [get_callback_manager_for_config(c) for c in configs] + run_managers = [ + callback_manager.on_chain_start( + dumpd(self), + input, + run_type=run_type, + name=config.get("run_name"), + ) + for callback_manager, input, config in zip( + callback_managers, input, configs + ) + ] + try: + if accepts_run_manager_and_config(func): + output = func( + input, + run_manager=run_managers, + config=configs, + ) # type: ignore[call-arg] + elif accepts_run_manager(func): + output = func(input, run_manager=run_managers) # type: ignore[call-arg] + else: + output = func(input) # type: ignore[call-arg] + except Exception as e: + for run_manager in run_managers: + run_manager.on_chain_error(e) + if return_exceptions: + return cast(List[Output], [e for _ in input]) + else: + raise + else: + first_exception: Optional[Exception] = None + for run_manager, out in zip(run_managers, output): + if isinstance(out, Exception): + first_exception = first_exception or out + run_manager.on_chain_error(out) + else: + run_manager.on_chain_end(dumpd(out)) + if return_exceptions or first_exception is None: + return cast(List[Output], output) + else: + raise first_exception + + async def _abatch_with_config( + self, + func: Union[ + Callable[[List[Input]], Awaitable[List[Union[Exception, Output]]]], + Callable[ + [List[Input], List[AsyncCallbackManagerForChainRun]], + Awaitable[List[Union[Exception, Output]]], + ], + Callable[ + [ + List[Input], + List[AsyncCallbackManagerForChainRun], + List[RunnableConfig], + ], + Awaitable[List[Union[Exception, Output]]], + ], + ], + input: List[Input], + config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, + *, + return_exceptions: bool = False, + run_type: Optional[str] = None, + ) -> List[Output]: + """Helper method to transform an Input value to an Output value, + with callbacks. Use this method to implement invoke() in subclasses.""" + configs = get_config_list(config, len(input)) + callback_managers = [get_async_callback_manager_for_config(c) for c in configs] + run_managers: List[AsyncCallbackManagerForChainRun] = await asyncio.gather( + *( + callback_manager.on_chain_start( + dumpd(self), + input, + run_type=run_type, + name=config.get("run_name"), + ) + for callback_manager, input, config in zip( + callback_managers, input, configs + ) + ) + ) + try: + if accepts_run_manager_and_config(func): + output = await func( + input, + run_manager=run_managers, + config=configs, + ) # type: ignore[call-arg] + elif accepts_run_manager(func): + output = await func(input, run_manager=run_managers) # type: ignore + else: + output = await func(input) # type: ignore[call-arg] + except Exception as e: + await asyncio.gather( + *(run_manager.on_chain_error(e) for run_manager in run_managers) + ) + if return_exceptions: + return cast(List[Output], [e for _ in input]) + else: + raise + else: + first_exception: Optional[Exception] = None + coros: List[Awaitable[None]] = [] + for run_manager, out in zip(run_managers, output): + if isinstance(out, Exception): + first_exception = first_exception or out + coros.append(run_manager.on_chain_error(out)) + else: + coros.append(run_manager.on_chain_end(dumpd(out))) + await asyncio.gather(*coros) + if return_exceptions or first_exception is None: + return cast(List[Output], output) + else: + raise first_exception + def _transform_stream_with_config( self, input: Iterator[Input], @@ -371,6 +547,7 @@ class Runnable(Generic[Input, Output], ABC): dumpd(self), {"input": ""}, run_type=run_type, + name=config.get("run_name"), ) try: if accepts_run_manager_and_config(transformer): @@ -451,6 +628,7 @@ class Runnable(Generic[Input, Output], ABC): dumpd(self), {"input": ""}, run_type=run_type, + name=config.get("run_name"), ) try: # mypy can't quite work out thew type guard here, but this is safe, @@ -526,7 +704,9 @@ class RunnableWithFallbacks(Serializable, Runnable[Input, Output]): config = ensure_config(config) callback_manager = get_callback_manager_for_config(config) # start the root run - run_manager = callback_manager.on_chain_start(dumpd(self), input) + run_manager = callback_manager.on_chain_start( + dumpd(self), input, name=config.get("run_name") + ) first_error = None for runnable in self.runnables: try: @@ -558,7 +738,9 @@ class RunnableWithFallbacks(Serializable, Runnable[Input, Output]): config = ensure_config(config) callback_manager = get_async_callback_manager_for_config(config) # start the root run - run_manager = await callback_manager.on_chain_start(dumpd(self), input) + run_manager = await callback_manager.on_chain_start( + dumpd(self), input, name=config.get("run_name") + ) first_error = None for runnable in self.runnables: @@ -585,12 +767,17 @@ class RunnableWithFallbacks(Serializable, Runnable[Input, Output]): self, inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, + *, + return_exceptions: bool = False, **kwargs: Optional[Any], ) -> List[Output]: from langchain.callbacks.manager import CallbackManager + if return_exceptions: + raise NotImplementedError() + # setup callbacks - configs = self._get_config_list(config, len(inputs)) + configs = get_config_list(config, len(inputs)) callback_managers = [ CallbackManager.configure( inheritable_callbacks=config.get("callbacks"), @@ -606,9 +793,11 @@ class RunnableWithFallbacks(Serializable, Runnable[Input, Output]): # start the root runs, one per input run_managers = [ cm.on_chain_start( - dumpd(self), input if isinstance(input, dict) else {"input": input} + dumpd(self), + input if isinstance(input, dict) else {"input": input}, + name=config.get("run_name"), ) - for cm, input in zip(callback_managers, inputs) + for cm, input, config in zip(callback_managers, inputs, configs) ] first_error = None @@ -643,12 +832,17 @@ class RunnableWithFallbacks(Serializable, Runnable[Input, Output]): self, inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, + *, + return_exceptions: bool = False, **kwargs: Optional[Any], ) -> List[Output]: from langchain.callbacks.manager import AsyncCallbackManager + if return_exceptions: + raise NotImplementedError() + # setup callbacks - configs = self._get_config_list(config, len(inputs)) + configs = get_config_list(config, len(inputs)) callback_managers = [ AsyncCallbackManager.configure( inheritable_callbacks=config.get("callbacks"), @@ -664,8 +858,12 @@ class RunnableWithFallbacks(Serializable, Runnable[Input, Output]): # start the root runs, one per input run_managers: List[AsyncCallbackManagerForChainRun] = await asyncio.gather( *( - cm.on_chain_start(dumpd(self), input) - for cm, input in zip(callback_managers, inputs) + cm.on_chain_start( + dumpd(self), + input, + name=config.get("run_name"), + ) + for cm, input, config in zip(callback_managers, inputs, configs) ) ) @@ -770,15 +968,19 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): config = ensure_config(config) callback_manager = get_callback_manager_for_config(config) # start the root run - run_manager = callback_manager.on_chain_start(dumpd(self), input) + run_manager = callback_manager.on_chain_start( + dumpd(self), input, name=config.get("run_name") + ) # invoke all steps in sequence try: - for step in self.steps: + for i, step in enumerate(self.steps): input = step.invoke( input, # mark each step as a child run - patch_config(config, callbacks=run_manager.get_child()), + patch_config( + config, callbacks=run_manager.get_child(f"seq:step:{i+1}") + ), ) # finish the root run except (KeyboardInterrupt, Exception) as e: @@ -798,15 +1000,19 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): config = ensure_config(config) callback_manager = get_async_callback_manager_for_config(config) # start the root run - run_manager = await callback_manager.on_chain_start(dumpd(self), input) + run_manager = await callback_manager.on_chain_start( + dumpd(self), input, name=config.get("run_name") + ) # invoke all steps in sequence try: - for step in self.steps: + for i, step in enumerate(self.steps): input = await step.ainvoke( input, # mark each step as a child run - patch_config(config, callbacks=run_manager.get_child()), + patch_config( + config, callbacks=run_manager.get_child(f"seq:step:{i+1}") + ), ) # finish the root run except (KeyboardInterrupt, Exception) as e: @@ -820,12 +1026,14 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): self, inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, + *, + return_exceptions: bool = False, **kwargs: Optional[Any], ) -> List[Output]: from langchain.callbacks.manager import CallbackManager # setup callbacks - configs = self._get_config_list(config, len(inputs)) + configs = get_config_list(config, len(inputs)) callback_managers = [ CallbackManager.configure( inheritable_callbacks=config.get("callbacks"), @@ -840,35 +1048,102 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): ] # start the root runs, one per input run_managers = [ - cm.on_chain_start(dumpd(self), input) - for cm, input in zip(callback_managers, inputs) + cm.on_chain_start( + dumpd(self), + input, + name=config.get("run_name"), + ) + for cm, input, config in zip(callback_managers, inputs, configs) ] # invoke try: - for step in self.steps: - inputs = step.batch( - inputs, - [ - # each step a child run of the corresponding root run - patch_config(config, callbacks=rm.get_child()) - for rm, config in zip(run_managers, configs) - ], - ) + if return_exceptions: + # Track which inputs (by index) failed so far + # If an input has failed it will be present in this map, + # and the value will be the exception that was raised. + failed_inputs_map: Dict[int, Exception] = {} + for stepidx, step in enumerate(self.steps): + # Assemble the original indexes of the remaining inputs + # (i.e. the ones that haven't failed yet) + remaining_idxs = [ + i for i in range(len(configs)) if i not in failed_inputs_map + ] + # Invoke the step on the remaining inputs + inputs = step.batch( + [ + inp + for i, inp in zip(remaining_idxs, inputs) + if i not in failed_inputs_map + ], + [ + # each step a child run of the corresponding root run + patch_config( + config, callbacks=rm.get_child(f"seq:step:{stepidx+1}") + ) + for i, (rm, config) in enumerate(zip(run_managers, configs)) + if i not in failed_inputs_map + ], + return_exceptions=return_exceptions, + **kwargs, + ) + # If an input failed, add it to the map + for i, inp in zip(remaining_idxs, inputs): + if isinstance(inp, Exception): + failed_inputs_map[i] = inp + inputs = [inp for inp in inputs if not isinstance(inp, Exception)] + # If all inputs have failed, stop processing + if len(failed_inputs_map) == len(configs): + break + + # Reassemble the outputs, inserting Exceptions for failed inputs + inputs_copy = inputs.copy() + inputs = [] + for i in range(len(configs)): + if i in failed_inputs_map: + inputs.append(cast(Input, failed_inputs_map[i])) + else: + inputs.append(inputs_copy.pop(0)) + else: + for i, step in enumerate(self.steps): + inputs = step.batch( + inputs, + [ + # each step a child run of the corresponding root run + patch_config( + config, callbacks=rm.get_child(f"seq:step:{i+1}") + ) + for rm, config in zip(run_managers, configs) + ], + ) + # finish the root runs except (KeyboardInterrupt, Exception) as e: for rm in run_managers: rm.on_chain_error(e) - raise + if return_exceptions: + return cast(List[Output], [e for _ in inputs]) + else: + raise else: - for rm, input in zip(run_managers, inputs): - rm.on_chain_end(input) - return cast(List[Output], inputs) + first_exception: Optional[Exception] = None + for run_manager, out in zip(run_managers, inputs): + if isinstance(out, Exception): + first_exception = first_exception or out + run_manager.on_chain_error(out) + else: + run_manager.on_chain_end(dumpd(out)) + if return_exceptions or first_exception is None: + return cast(List[Output], inputs) + else: + raise first_exception async def abatch( self, inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, + *, + return_exceptions: bool = False, **kwargs: Optional[Any], ) -> List[Output]: from langchain.callbacks.manager import ( @@ -876,7 +1151,7 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): ) # setup callbacks - configs = self._get_config_list(config, len(inputs)) + configs = get_config_list(config, len(inputs)) callback_managers = [ AsyncCallbackManager.configure( inheritable_callbacks=config.get("callbacks"), @@ -892,32 +1167,97 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): # start the root runs, one per input run_managers: List[AsyncCallbackManagerForChainRun] = await asyncio.gather( *( - cm.on_chain_start(dumpd(self), input) - for cm, input in zip(callback_managers, inputs) + cm.on_chain_start( + dumpd(self), + input, + name=config.get("run_name"), + ) + for cm, input, config in zip(callback_managers, inputs, configs) ) ) # invoke .batch() on each step # this uses batching optimizations in Runnable subclasses, like LLM try: - for step in self.steps: - inputs = await step.abatch( - inputs, - [ - # each step a child run of the corresponding root run - patch_config(config, callbacks=rm.get_child()) - for rm, config in zip(run_managers, configs) - ], - ) + if return_exceptions: + # Track which inputs (by index) failed so far + # If an input has failed it will be present in this map, + # and the value will be the exception that was raised. + failed_inputs_map: Dict[int, Exception] = {} + for stepidx, step in enumerate(self.steps): + # Assemble the original indexes of the remaining inputs + # (i.e. the ones that haven't failed yet) + remaining_idxs = [ + i for i in range(len(configs)) if i not in failed_inputs_map + ] + # Invoke the step on the remaining inputs + inputs = await step.abatch( + [ + inp + for i, inp in zip(remaining_idxs, inputs) + if i not in failed_inputs_map + ], + [ + # each step a child run of the corresponding root run + patch_config( + config, callbacks=rm.get_child(f"seq:step:{stepidx+1}") + ) + for i, (rm, config) in enumerate(zip(run_managers, configs)) + if i not in failed_inputs_map + ], + return_exceptions=return_exceptions, + **kwargs, + ) + # If an input failed, add it to the map + for i, inp in zip(remaining_idxs, inputs): + if isinstance(inp, Exception): + failed_inputs_map[i] = inp + inputs = [inp for inp in inputs if not isinstance(inp, Exception)] + # If all inputs have failed, stop processing + if len(failed_inputs_map) == len(configs): + break + + # Reassemble the outputs, inserting Exceptions for failed inputs + inputs_copy = inputs.copy() + inputs = [] + for i in range(len(configs)): + if i in failed_inputs_map: + inputs.append(cast(Input, failed_inputs_map[i])) + else: + inputs.append(inputs_copy.pop(0)) + else: + for i, step in enumerate(self.steps): + inputs = await step.abatch( + inputs, + [ + # each step a child run of the corresponding root run + patch_config( + config, callbacks=rm.get_child(f"seq:step:{i+1}") + ) + for rm, config in zip(run_managers, configs) + ], + ) # finish the root runs except (KeyboardInterrupt, Exception) as e: await asyncio.gather(*(rm.on_chain_error(e) for rm in run_managers)) - raise + if return_exceptions: + return cast(List[Output], [e for _ in inputs]) + else: + raise else: - await asyncio.gather( - *(rm.on_chain_end(input) for rm, input in zip(run_managers, inputs)) - ) - return cast(List[Output], inputs) + first_exception: Optional[Exception] = None + coros: List[Awaitable[None]] = [] + for run_manager, out in zip(run_managers, inputs): + if isinstance(out, Exception): + first_exception = first_exception or out + coros.append(run_manager.on_chain_error(out)) + else: + coros.append(run_manager.on_chain_end(dumpd(out))) + await asyncio.gather(*coros) + if return_exceptions or first_exception is None: + return cast(List[Output], inputs) + else: + raise first_exception def stream( self, @@ -929,7 +1269,9 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): config = ensure_config(config) callback_manager = get_callback_manager_for_config(config) # start the root run - run_manager = callback_manager.on_chain_start(dumpd(self), input) + run_manager = callback_manager.on_chain_start( + dumpd(self), input, name=config.get("run_name") + ) steps = [self.first] + self.middle + [self.last] streaming_start_index = 0 @@ -946,7 +1288,12 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): input = step.invoke( input, # mark each step as a child run - patch_config(config, callbacks=run_manager.get_child()), + patch_config( + config, + callbacks=run_manager.get_child( + f"seq:step:{steps.index(step)+1}" + ), + ), ) except (KeyboardInterrupt, Exception) as e: run_manager.on_chain_error(e) @@ -958,13 +1305,24 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): try: # stream the first of the last steps with non-streaming input final_pipeline = steps[streaming_start_index].stream( - input, patch_config(config, callbacks=run_manager.get_child()) + input, + patch_config( + config, + callbacks=run_manager.get_child( + f"seq:step:{streaming_start_index+1}" + ), + ), ) # stream the rest of the last steps with streaming input for step in steps[streaming_start_index + 1 :]: final_pipeline = step.transform( final_pipeline, - patch_config(config, callbacks=run_manager.get_child()), + patch_config( + config, + callbacks=run_manager.get_child( + f"seq:step:{steps.index(step)+1}" + ), + ), ) for output in final_pipeline: yield output @@ -996,7 +1354,9 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): config = ensure_config(config) callback_manager = get_async_callback_manager_for_config(config) # start the root run - run_manager = await callback_manager.on_chain_start(dumpd(self), input) + run_manager = await callback_manager.on_chain_start( + dumpd(self), input, name=config.get("run_name") + ) steps = [self.first] + self.middle + [self.last] streaming_start_index = len(steps) - 1 @@ -1013,7 +1373,12 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): input = await step.ainvoke( input, # mark each step as a child run - patch_config(config, callbacks=run_manager.get_child()), + patch_config( + config, + callbacks=run_manager.get_child( + f"seq:step:{steps.index(step)+1}" + ), + ), ) except (KeyboardInterrupt, Exception) as e: await run_manager.on_chain_error(e) @@ -1025,13 +1390,24 @@ class RunnableSequence(Serializable, Runnable[Input, Output]): try: # stream the first of the last steps with non-streaming input final_pipeline = steps[streaming_start_index].astream( - input, patch_config(config, callbacks=run_manager.get_child()) + input, + patch_config( + config, + callbacks=run_manager.get_child( + f"seq:step:{streaming_start_index+1}" + ), + ), ) # stream the rest of the last steps with streaming input for step in steps[streaming_start_index + 1 :]: final_pipeline = step.atransform( final_pipeline, - patch_config(config, callbacks=run_manager.get_child()), + patch_config( + config, + callbacks=run_manager.get_child( + f"seq:step:{steps.index(step)+1}" + ), + ), ) async for output in final_pipeline: yield output @@ -1127,7 +1503,9 @@ class RunnableMap(Serializable, Runnable[Input, Dict[str, Any]]): local_metadata=None, ) # start the root run - run_manager = callback_manager.on_chain_start(dumpd(self), input) + run_manager = callback_manager.on_chain_start( + dumpd(self), input, name=config.get("run_name") + ) # gather results from all steps try: @@ -1142,10 +1520,10 @@ class RunnableMap(Serializable, Runnable[Input, Dict[str, Any]]): patch_config( config, deep_copy_locals=True, - callbacks=run_manager.get_child(), + callbacks=run_manager.get_child(f"map:key:{key}"), ), ) - for step in steps.values() + for key, step in steps.items() ] output = {key: future.result() for key, future in zip(steps, futures)} # finish the root run @@ -1166,7 +1544,9 @@ class RunnableMap(Serializable, Runnable[Input, Dict[str, Any]]): config = ensure_config(config) callback_manager = get_async_callback_manager_for_config(config) # start the root run - run_manager = await callback_manager.on_chain_start(dumpd(self), input) + run_manager = await callback_manager.on_chain_start( + dumpd(self), input, name=config.get("run_name") + ) # gather results from all steps try: @@ -1177,9 +1557,11 @@ class RunnableMap(Serializable, Runnable[Input, Dict[str, Any]]): step.ainvoke( input, # mark each step as a child run - patch_config(config, callbacks=run_manager.get_child()), + patch_config( + config, callbacks=run_manager.get_child(f"map:key:{key}") + ), ) - for step in steps.values() + for key, step in steps.items() ) ) output = {key: value for key, value in zip(steps, results)} @@ -1209,7 +1591,9 @@ class RunnableMap(Serializable, Runnable[Input, Dict[str, Any]]): name, step.transform( input_copies.pop(), - patch_config(config, callbacks=run_manager.get_child()), + patch_config( + config, callbacks=run_manager.get_child(f"map:key:{name}") + ), ), ) for name, step in steps.items() @@ -1271,7 +1655,9 @@ class RunnableMap(Serializable, Runnable[Input, Dict[str, Any]]): name, step.atransform( input_copies.pop(), - patch_config(config, callbacks=run_manager.get_child()), + patch_config( + config, callbacks=run_manager.get_child(f"map:key:{name}") + ), ), ) for name, step in steps.items() @@ -1361,6 +1747,9 @@ class RunnableLambda(Runnable[Input, Output]): else: return False + def __repr__(self) -> str: + return "RunnableLambda(...)" + def _invoke( self, input: Input, @@ -1459,15 +1848,35 @@ class RunnableEach(Serializable, Runnable[List[Input], List[Output]]): def bind(self, **kwargs: Any) -> RunnableEach[Input, Output]: return RunnableEach(bound=self.bound.bind(**kwargs)) + def _invoke( + self, + inputs: List[Input], + run_manager: CallbackManagerForChainRun, + config: RunnableConfig, + ) -> List[Output]: + return self.bound.batch( + inputs, patch_config(config, callbacks=run_manager.get_child()) + ) + def invoke( self, input: List[Input], config: Optional[RunnableConfig] = None ) -> List[Output]: - return self.bound.batch(input, config) + return self._call_with_config(self._invoke, input, config) + + async def _ainvoke( + self, + inputs: List[Input], + run_manager: AsyncCallbackManagerForChainRun, + config: RunnableConfig, + ) -> List[Output]: + return await self.bound.abatch( + inputs, patch_config(config, callbacks=run_manager.get_child()) + ) async def ainvoke( self, input: List[Input], config: Optional[RunnableConfig] = None, **kwargs: Any ) -> List[Output]: - return await self.bound.abatch(input, config, **kwargs) + return await self._acall_with_config(self._ainvoke, input, config) class RunnableBinding(Serializable, Runnable[Input, Output]): @@ -1479,6 +1888,8 @@ class RunnableBinding(Serializable, Runnable[Input, Output]): kwargs: Mapping[str, Any] + config: Mapping[str, Any] = Field(default_factory=dict) + class Config: arbitrary_types_allowed = True @@ -1490,8 +1901,38 @@ class RunnableBinding(Serializable, Runnable[Input, Output]): def lc_namespace(self) -> List[str]: return self.__class__.__module__.split(".")[:-1] + def _merge_config(self, config: Optional[RunnableConfig]) -> RunnableConfig: + copy = cast(RunnableConfig, dict(self.config)) + if config: + for key in config: + # Even though the keys aren't literals this is correct + # because both dicts are same type + copy[key] = config[key] or copy.get(key) # type: ignore + return copy + def bind(self, **kwargs: Any) -> Runnable[Input, Output]: - return self.__class__(bound=self.bound, kwargs={**self.kwargs, **kwargs}) + return self.__class__( + bound=self.bound, config=self.config, kwargs={**self.kwargs, **kwargs} + ) + + def with_config( + self, + config: Optional[RunnableConfig] = None, + # Sadly Unpack is not well supported by mypy so this will have to be untyped + **kwargs: Any, + ) -> Runnable[Input, Output]: + return self.__class__( + bound=self.bound, + kwargs=self.kwargs, + config={**self.config, **(config or {}), **kwargs}, + ) + + def with_retry(self, **kwargs: Any) -> Runnable[Input, Output]: + return self.__class__( + bound=self.bound.with_retry(**kwargs), + kwargs=self.kwargs, + config=self.config, + ) def invoke( self, @@ -1499,7 +1940,11 @@ class RunnableBinding(Serializable, Runnable[Input, Output]): config: Optional[RunnableConfig] = None, **kwargs: Optional[Any], ) -> Output: - return self.bound.invoke(input, config, **{**self.kwargs, **kwargs}) + return self.bound.invoke( + input, + self._merge_config(config), + **{**self.kwargs, **kwargs}, + ) async def ainvoke( self, @@ -1507,23 +1952,59 @@ class RunnableBinding(Serializable, Runnable[Input, Output]): config: Optional[RunnableConfig] = None, **kwargs: Optional[Any], ) -> Output: - return await self.bound.ainvoke(input, config, **{**self.kwargs, **kwargs}) + return await self.bound.ainvoke( + input, + self._merge_config(config), + **{**self.kwargs, **kwargs}, + ) def batch( self, inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, + *, + return_exceptions: bool = False, **kwargs: Optional[Any], ) -> List[Output]: - return self.bound.batch(inputs, config, **{**self.kwargs, **kwargs}) + if isinstance(config, list): + configs = cast( + List[RunnableConfig], [self._merge_config(conf) for conf in config] + ) + else: + configs = [ + patch_config(self._merge_config(config), deep_copy_locals=True) + for _ in range(len(inputs)) + ] + return self.bound.batch( + inputs, + configs, + return_exceptions=return_exceptions, + **{**self.kwargs, **kwargs}, + ) async def abatch( self, inputs: List[Input], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, + *, + return_exceptions: bool = False, **kwargs: Optional[Any], ) -> List[Output]: - return await self.bound.abatch(inputs, config, **{**self.kwargs, **kwargs}) + if isinstance(config, list): + configs = cast( + List[RunnableConfig], [self._merge_config(conf) for conf in config] + ) + else: + configs = [ + patch_config(self._merge_config(config), deep_copy_locals=True) + for _ in range(len(inputs)) + ] + return await self.bound.abatch( + inputs, + configs, + return_exceptions=return_exceptions, + **{**self.kwargs, **kwargs}, + ) def stream( self, @@ -1531,7 +2012,11 @@ class RunnableBinding(Serializable, Runnable[Input, Output]): config: Optional[RunnableConfig] = None, **kwargs: Optional[Any], ) -> Iterator[Output]: - yield from self.bound.stream(input, config, **{**self.kwargs, **kwargs}) + yield from self.bound.stream( + input, + self._merge_config(config), + **{**self.kwargs, **kwargs}, + ) async def astream( self, @@ -1540,7 +2025,9 @@ class RunnableBinding(Serializable, Runnable[Input, Output]): **kwargs: Optional[Any], ) -> AsyncIterator[Output]: async for item in self.bound.astream( - input, config, **{**self.kwargs, **kwargs} + input, + self._merge_config(config), + **{**self.kwargs, **kwargs}, ): yield item @@ -1550,7 +2037,11 @@ class RunnableBinding(Serializable, Runnable[Input, Output]): config: Optional[RunnableConfig] = None, **kwargs: Any, ) -> Iterator[Output]: - yield from self.bound.transform(input, config, **{**self.kwargs, **kwargs}) + yield from self.bound.transform( + input, + self._merge_config(config), + **{**self.kwargs, **kwargs}, + ) async def atransform( self, @@ -1559,11 +2050,16 @@ class RunnableBinding(Serializable, Runnable[Input, Output]): **kwargs: Any, ) -> AsyncIterator[Output]: async for item in self.bound.atransform( - input, config, **{**self.kwargs, **kwargs} + input, + self._merge_config(config), + **{**self.kwargs, **kwargs}, ): yield item +RunnableBinding.update_forward_refs(RunnableConfig=RunnableConfig) + + def coerce_to_runnable( thing: Union[ Runnable[Input, Output], diff --git a/libs/langchain/langchain/schema/runnable/config.py b/libs/langchain/langchain/schema/runnable/config.py index b97d904414..3f87f04403 100644 --- a/libs/langchain/langchain/schema/runnable/config.py +++ b/libs/langchain/langchain/schema/runnable/config.py @@ -3,7 +3,9 @@ from __future__ import annotations from concurrent.futures import Executor, ThreadPoolExecutor from contextlib import contextmanager from copy import deepcopy -from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, TypedDict +from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Union + +from typing_extensions import TypedDict if TYPE_CHECKING: from langchain.callbacks.base import BaseCallbackManager, Callbacks @@ -31,6 +33,11 @@ class RunnableConfig(TypedDict, total=False): Tags are passed to all callbacks, metadata is passed to handle*Start callbacks. """ + run_name: str + """ + Name for the tracer run for this call. Defaults to the name of the class. + """ + _locals: Dict[str, Any] """ Local variables @@ -48,7 +55,7 @@ class RunnableConfig(TypedDict, total=False): """ -def ensure_config(config: Optional[RunnableConfig]) -> RunnableConfig: +def ensure_config(config: Optional[RunnableConfig] = None) -> RunnableConfig: empty = RunnableConfig( tags=[], metadata={}, @@ -61,20 +68,52 @@ def ensure_config(config: Optional[RunnableConfig]) -> RunnableConfig: return empty +def get_config_list( + config: Optional[Union[RunnableConfig, List[RunnableConfig]]], length: int +) -> List[RunnableConfig]: + """ + Helper method to get a list of configs from a single config or a list of + configs, useful for subclasses overriding batch() or abatch(). + """ + if length < 1: + raise ValueError(f"length must be >= 1, but got {length}") + if isinstance(config, list) and len(config) != length: + raise ValueError( + f"config must be a list of the same length as inputs, " + f"but got {len(config)} configs for {length} inputs" + ) + + return ( + list(map(ensure_config, config)) + if isinstance(config, list) + else [patch_config(config, deep_copy_locals=True) for _ in range(length)] + ) + + def patch_config( config: Optional[RunnableConfig], *, deep_copy_locals: bool = False, callbacks: Optional[BaseCallbackManager] = None, recursion_limit: Optional[int] = None, + max_concurrency: Optional[int] = None, + run_name: Optional[str] = None, ) -> RunnableConfig: config = ensure_config(config) if deep_copy_locals: config["_locals"] = deepcopy(config["_locals"]) if callbacks is not None: + # If we're replacing callbacks we need to unset run_name + # As that should apply only to the same run as the original callbacks config["callbacks"] = callbacks + if "run_name" in config: + del config["run_name"] if recursion_limit is not None: config["recursion_limit"] = recursion_limit + if max_concurrency is not None: + config["max_concurrency"] = max_concurrency + if run_name is not None: + config["run_name"] = run_name return config diff --git a/libs/langchain/langchain/schema/runnable/retry.py b/libs/langchain/langchain/schema/runnable/retry.py new file mode 100644 index 0000000000..37de03f600 --- /dev/null +++ b/libs/langchain/langchain/schema/runnable/retry.py @@ -0,0 +1,245 @@ +from typing import Any, Dict, List, Optional, Tuple, Type, TypeVar, Union, cast + +from tenacity import ( + AsyncRetrying, + RetryCallState, + RetryError, + Retrying, + retry_if_exception_type, + stop_after_attempt, + wait_exponential_jitter, +) + +from langchain.callbacks.manager import ( + AsyncCallbackManagerForChainRun, + CallbackManagerForChainRun, +) +from langchain.schema.runnable.base import Input, Output, RunnableBinding +from langchain.schema.runnable.config import RunnableConfig, patch_config + +T = TypeVar("T", CallbackManagerForChainRun, AsyncCallbackManagerForChainRun) +U = TypeVar("U") + + +class RunnableRetry(RunnableBinding[Input, Output]): + """Retry a Runnable if it fails.""" + + retry_exception_types: Tuple[Type[BaseException]] = (Exception,) + + wait_exponential_jitter: bool = True + + max_attempt_number: int = 3 + + @property + def _kwargs_retrying(self) -> Dict[str, Any]: + kwargs: Dict[str, Any] = dict() + + if self.max_attempt_number: + kwargs["stop"] = stop_after_attempt(self.max_attempt_number) + + if self.wait_exponential_jitter: + kwargs["wait"] = wait_exponential_jitter() + + if self.retry_exception_types: + kwargs["retry"] = retry_if_exception_type(self.retry_exception_types) + + return kwargs + + def _sync_retrying(self, **kwargs: Any) -> Retrying: + return Retrying(**self._kwargs_retrying, **kwargs) + + def _async_retrying(self, **kwargs: Any) -> AsyncRetrying: + return AsyncRetrying(**self._kwargs_retrying, **kwargs) + + def _patch_config( + self, + config: RunnableConfig, + run_manager: T, + retry_state: RetryCallState, + ) -> RunnableConfig: + attempt = retry_state.attempt_number + tag = "retry:attempt:{}".format(attempt) if attempt > 1 else None + return patch_config(config, callbacks=run_manager.get_child(tag)) + + def _patch_config_list( + self, + config: List[RunnableConfig], + run_manager: List[T], + retry_state: RetryCallState, + ) -> List[RunnableConfig]: + return [ + self._patch_config(c, rm, retry_state) for c, rm in zip(config, run_manager) + ] + + def _invoke( + self, + input: Input, + run_manager: CallbackManagerForChainRun, + config: RunnableConfig, + ) -> Output: + for attempt in self._sync_retrying(reraise=True): + with attempt: + result = super().invoke( + input, + self._patch_config(config, run_manager, attempt.retry_state), + ) + if attempt.retry_state.outcome and not attempt.retry_state.outcome.failed: + attempt.retry_state.set_result(result) + return result + + def invoke( + self, input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> Output: + return self._call_with_config(self._invoke, input, config, **kwargs) + + async def _ainvoke( + self, + input: Input, + run_manager: AsyncCallbackManagerForChainRun, + config: RunnableConfig, + ) -> Output: + async for attempt in self._async_retrying(reraise=True): + with attempt: + result = await super().ainvoke( + input, + self._patch_config(config, run_manager, attempt.retry_state), + ) + if attempt.retry_state.outcome and not attempt.retry_state.outcome.failed: + attempt.retry_state.set_result(result) + return result + + async def ainvoke( + self, input: Input, config: Optional[RunnableConfig] = None, **kwargs: Any + ) -> Output: + return await self._acall_with_config(self._ainvoke, input, config, **kwargs) + + def _batch( + self, + inputs: List[Input], + run_manager: List[CallbackManagerForChainRun], + config: List[RunnableConfig], + ) -> List[Union[Output, Exception]]: + results_map: Dict[int, Output] = {} + + def pending(iterable: List[U]) -> List[U]: + return [item for idx, item in enumerate(iterable) if idx not in results_map] + + try: + for attempt in self._sync_retrying(): + with attempt: + # Get the results of the inputs that have not succeeded yet. + result = super().batch( + pending(inputs), + self._patch_config_list( + pending(config), pending(run_manager), attempt.retry_state + ), + return_exceptions=True, + ) + # Register the results of the inputs that have succeeded. + first_exception = None + for i, r in enumerate(result): + if isinstance(r, Exception): + if not first_exception: + first_exception = r + continue + results_map[i] = r + # If any exception occurred, raise it, to retry the failed ones + if first_exception: + raise first_exception + if ( + attempt.retry_state.outcome + and not attempt.retry_state.outcome.failed + ): + attempt.retry_state.set_result(result) + except RetryError as e: + try: + result + except UnboundLocalError: + result = cast(List[Output], [e] * len(inputs)) + + outputs: List[Union[Output, Exception]] = [] + for idx, _ in enumerate(inputs): + if idx in results_map: + outputs.append(results_map[idx]) + else: + outputs.append(result.pop(0)) + return outputs + + def batch( + self, + inputs: List[Input], + config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, + *, + return_exceptions: bool = False, + **kwargs: Any + ) -> List[Output]: + return self._batch_with_config( + self._batch, inputs, config, return_exceptions=return_exceptions, **kwargs + ) + + async def _abatch( + self, + inputs: List[Input], + run_manager: List[AsyncCallbackManagerForChainRun], + config: List[RunnableConfig], + ) -> List[Union[Output, Exception]]: + results_map: Dict[int, Output] = {} + + def pending(iterable: List[U]) -> List[U]: + return [item for idx, item in enumerate(iterable) if idx not in results_map] + + try: + async for attempt in self._async_retrying(): + with attempt: + # Get the results of the inputs that have not succeeded yet. + result = await super().abatch( + pending(inputs), + self._patch_config_list( + pending(config), pending(run_manager), attempt.retry_state + ), + return_exceptions=True, + ) + # Register the results of the inputs that have succeeded. + first_exception = None + for i, r in enumerate(result): + if isinstance(r, Exception): + if not first_exception: + first_exception = r + continue + results_map[i] = r + # If any exception occurred, raise it, to retry the failed ones + if first_exception: + raise first_exception + if ( + attempt.retry_state.outcome + and not attempt.retry_state.outcome.failed + ): + attempt.retry_state.set_result(result) + except RetryError as e: + try: + result + except UnboundLocalError: + result = cast(List[Output], [e] * len(inputs)) + + outputs: List[Union[Output, Exception]] = [] + for idx, _ in enumerate(inputs): + if idx in results_map: + outputs.append(results_map[idx]) + else: + outputs.append(result.pop(0)) + return outputs + + async def abatch( + self, + inputs: List[Input], + config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, + *, + return_exceptions: bool = False, + **kwargs: Any + ) -> List[Output]: + return await self._abatch_with_config( + self._abatch, inputs, config, return_exceptions=return_exceptions, **kwargs + ) + + # stream() and transform() are not retried because retrying a stream + # is not very intuitive. diff --git a/libs/langchain/langchain/schema/runnable/router.py b/libs/langchain/langchain/schema/runnable/router.py index 68989bfa7d..a51d0907ca 100644 --- a/libs/langchain/langchain/schema/runnable/router.py +++ b/libs/langchain/langchain/schema/runnable/router.py @@ -1,6 +1,5 @@ from __future__ import annotations -from concurrent.futures import ThreadPoolExecutor from typing import ( Any, AsyncIterator, @@ -12,6 +11,7 @@ from typing import ( Optional, TypedDict, Union, + cast, ) from langchain.load.serializable import Serializable @@ -23,7 +23,11 @@ from langchain.schema.runnable.base import ( RunnableSequence, coerce_to_runnable, ) -from langchain.schema.runnable.config import RunnableConfig +from langchain.schema.runnable.config import ( + RunnableConfig, + get_config_list, + get_executor_for_config, +) from langchain.schema.runnable.utils import gather_with_concurrency @@ -122,7 +126,7 @@ class RouterRunnable( inputs: List[RouterInput], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, - max_concurrency: Optional[int] = None, + return_exceptions: bool = False, **kwargs: Optional[Any], ) -> List[Output]: keys = [input["key"] for input in inputs] @@ -130,16 +134,23 @@ class RouterRunnable( if any(key not in self.runnables for key in keys): raise ValueError("One or more keys do not have a corresponding runnable") + def invoke( + runnable: Runnable, input: Input, config: RunnableConfig + ) -> Union[Output, Exception]: + if return_exceptions: + try: + return runnable.invoke(input, config, **kwargs) + except Exception as e: + return e + else: + return runnable.invoke(input, config, **kwargs) + runnables = [self.runnables[key] for key in keys] - configs = self._get_config_list(config, len(inputs)) - with ThreadPoolExecutor(max_workers=max_concurrency) as executor: - return list( - executor.map( - lambda runnable, input, config: runnable.invoke(input, config), - runnables, - actual_inputs, - configs, - ) + configs = get_config_list(config, len(inputs)) + with get_executor_for_config(configs[0]) as executor: + return cast( + List[Output], + list(executor.map(invoke, runnables, actual_inputs, configs)), ) async def abatch( @@ -147,7 +158,7 @@ class RouterRunnable( inputs: List[RouterInput], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, - max_concurrency: Optional[int] = None, + return_exceptions: bool = False, **kwargs: Optional[Any], ) -> List[Output]: keys = [input["key"] for input in inputs] @@ -155,12 +166,23 @@ class RouterRunnable( if any(key not in self.runnables for key in keys): raise ValueError("One or more keys do not have a corresponding runnable") + async def ainvoke( + runnable: Runnable, input: Input, config: RunnableConfig + ) -> Union[Output, Exception]: + if return_exceptions: + try: + return await runnable.ainvoke(input, config, **kwargs) + except Exception as e: + return e + else: + return await runnable.ainvoke(input, config, **kwargs) + runnables = [self.runnables[key] for key in keys] - configs = self._get_config_list(config, len(inputs)) + configs = get_config_list(config, len(inputs)) return await gather_with_concurrency( - max_concurrency, + configs[0].get("max_concurrency"), *( - runnable.ainvoke(input, config) + ainvoke(runnable, input, config) for runnable, input, config in zip(runnables, actual_inputs, configs) ), ) diff --git a/libs/langchain/langchain/tools/__init__.py b/libs/langchain/langchain/tools/__init__.py index 14e34d7320..56958d90b2 100644 --- a/libs/langchain/langchain/tools/__init__.py +++ b/libs/langchain/langchain/tools/__init__.py @@ -34,6 +34,16 @@ from langchain.tools.bing_search.tool import BingSearchResults, BingSearchRun from langchain.tools.brave_search.tool import BraveSearch from langchain.tools.convert_to_openai import format_tool_to_openai_function from langchain.tools.ddg_search.tool import DuckDuckGoSearchResults, DuckDuckGoSearchRun +from langchain.tools.edenai import ( + EdenAiExplicitImageTool, + EdenAiObjectDetectionTool, + EdenAiParsingIDTool, + EdenAiParsingInvoiceTool, + EdenAiSpeechToTextTool, + EdenAiTextModerationTool, + EdenAiTextToSpeechTool, + EdenaiTool, +) from langchain.tools.file_management import ( CopyFileTool, DeleteFileTool, @@ -149,6 +159,14 @@ __all__ = [ "DeleteFileTool", "DuckDuckGoSearchResults", "DuckDuckGoSearchRun", + "EdenAiExplicitImageTool", + "EdenAiObjectDetectionTool", + "EdenAiParsingIDTool", + "EdenAiParsingInvoiceTool", + "EdenAiTextToSpeechTool", + "EdenAiSpeechToTextTool", + "EdenAiTextModerationTool", + "EdenaiTool", "ExtractHyperlinksTool", "ExtractTextTool", "FileSearchTool", diff --git a/libs/langchain/langchain/tools/edenai/__init__.py b/libs/langchain/langchain/tools/edenai/__init__.py new file mode 100644 index 0000000000..b1c254ba5f --- /dev/null +++ b/libs/langchain/langchain/tools/edenai/__init__.py @@ -0,0 +1,34 @@ +"""Edenai Tools.""" +from langchain.tools.edenai.audio_speech_to_text import ( + EdenAiSpeechToTextTool, +) +from langchain.tools.edenai.audio_text_to_speech import ( + EdenAiTextToSpeechTool, +) +from langchain.tools.edenai.edenai_base_tool import EdenaiTool +from langchain.tools.edenai.image_explicitcontent import ( + EdenAiExplicitImageTool, +) +from langchain.tools.edenai.image_objectdetection import ( + EdenAiObjectDetectionTool, +) +from langchain.tools.edenai.ocr_identityparser import ( + EdenAiParsingIDTool, +) +from langchain.tools.edenai.ocr_invoiceparser import ( + EdenAiParsingInvoiceTool, +) +from langchain.tools.edenai.text_moderation import ( + EdenAiTextModerationTool, +) + +__all__ = [ + "EdenAiExplicitImageTool", + "EdenAiObjectDetectionTool", + "EdenAiParsingIDTool", + "EdenAiParsingInvoiceTool", + "EdenAiTextToSpeechTool", + "EdenAiSpeechToTextTool", + "EdenAiTextModerationTool", + "EdenaiTool", +] diff --git a/libs/langchain/langchain/tools/edenai/audio_speech_to_text.py b/libs/langchain/langchain/tools/edenai/audio_speech_to_text.py new file mode 100644 index 0000000000..28dbce47d0 --- /dev/null +++ b/libs/langchain/langchain/tools/edenai/audio_speech_to_text.py @@ -0,0 +1,103 @@ +from __future__ import annotations + +import json +import logging +import time +from typing import List, Optional + +import requests + +from langchain.callbacks.manager import CallbackManagerForToolRun +from langchain.pydantic_v1 import validator +from langchain.tools.edenai.edenai_base_tool import EdenaiTool + +logger = logging.getLogger(__name__) + + +class EdenAiSpeechToTextTool(EdenaiTool): + """Tool that queries the Eden AI Speech To Text API. + + for api reference check edenai documentation: + https://app.edenai.run/bricks/speech/asynchronous-speech-to-text. + + To use, you should have + the environment variable ``EDENAI_API_KEY`` set with your API token. + You can find your token here: https://app.edenai.run/admin/account/settings + + """ + + edenai_api_key: Optional[str] = None + + name = "edenai_speech_to_text" + description = ( + "A wrapper around edenai Services speech to text " + "Useful for when you have to convert audio to text." + "Input should be a url to an audio file." + ) + is_async = True + + language: Optional[str] = "en" + speakers: Optional[int] + profanity_filter: bool = False + custom_vocabulary: Optional[List[str]] + + feature: str = "audio" + subfeature: str = "speech_to_text_async" + base_url = "https://api.edenai.run/v2/audio/speech_to_text_async/" + + @validator("providers") + def check_only_one_provider_selected(cls, v: List[str]) -> List[str]: + """ + This tool has no feature to combine providers results. + Therefore we only allow one provider + """ + if len(v) > 1: + raise ValueError( + "Please select only one provider. " + "The feature to combine providers results is not available " + "for this tool." + ) + return v + + def _wait_processing(self, url: str) -> requests.Response: + for _ in range(10): + time.sleep(1) + audio_analysis_result = self._get_edenai(url) + temp = audio_analysis_result.json() + if temp["status"] == "finished": + if temp["results"][self.providers[0]]["error"] is not None: + raise Exception( + f"""EdenAI returned an unexpected response + {temp['results'][self.providers[0]]['error']}""" + ) + else: + return audio_analysis_result + + raise Exception("Edenai speech to text job id processing Timed out") + + def _parse_response(self, response: dict) -> str: + return response["public_id"] + + def _run( + self, + query: str, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the tool.""" + all_params = { + "file_url": query, + "language": self.language, + "speakers": self.speakers, + "profanity_filter": self.profanity_filter, + "custom_vocabulary": self.custom_vocabulary, + } + + # filter so we don't send val to api when val is `None + query_params = {k: v for k, v in all_params.items() if v is not None} + + job_id = self._call_eden_ai(query_params) + url = self.base_url + job_id + audio_analysis_result = self._wait_processing(url) + result = audio_analysis_result.text + formatted_text = json.loads(result) + return formatted_text["results"][self.providers[0]]["text"] diff --git a/libs/langchain/langchain/tools/edenai/audio_text_to_speech.py b/libs/langchain/langchain/tools/edenai/audio_text_to_speech.py new file mode 100644 index 0000000000..968d03c1a2 --- /dev/null +++ b/libs/langchain/langchain/tools/edenai/audio_text_to_speech.py @@ -0,0 +1,116 @@ +from __future__ import annotations + +import logging +from typing import Dict, List, Literal, Optional + +import requests + +from langchain.callbacks.manager import CallbackManagerForToolRun +from langchain.pydantic_v1 import Field, root_validator, validator +from langchain.tools.edenai.edenai_base_tool import EdenaiTool + +logger = logging.getLogger(__name__) + + +class EdenAiTextToSpeechTool(EdenaiTool): + """Tool that queries the Eden AI Text to speech API. + for api reference check edenai documentation: + https://docs.edenai.co/reference/audio_text_to_speech_create. + + To use, you should have + the environment variable ``EDENAI_API_KEY`` set with your API token. + You can find your token here: https://app.edenai.run/admin/account/settings + + """ + + name = "edenai_text_to_speech" + description = ( + "A wrapper around edenai Services text to speech." + "Useful for when you need to convert text to speech." + """the output is a string representing the URL of the audio file, + or the path to the downloaded wav file """ + ) + + language: Optional[str] = "en" + """ + language of the text passed to the model. + """ + + # optional params see api documentation for more info + return_type: Literal["url", "wav"] = "url" + rate: Optional[int] + pitch: Optional[int] + volume: Optional[int] + audio_format: Optional[str] + sampling_rate: Optional[int] + voice_models: Dict[str, str] = Field(default_factory=dict) + + voice: Literal["MALE", "FEMALE"] + """voice option : 'MALE' or 'FEMALE' """ + + feature: str = "audio" + subfeature: str = "text_to_speech" + + @validator("providers") + def check_only_one_provider_selected(cls, v: List[str]) -> List[str]: + """ + This tool has no feature to combine providers results. + Therefore we only allow one provider + """ + if len(v) > 1: + raise ValueError( + "Please select only one provider. " + "The feature to combine providers results is not available " + "for this tool." + ) + return v + + @root_validator + def check_voice_models_key_is_provider_name(cls, values: dict) -> dict: + for key in values.get("voice_models", {}).keys(): + if key not in values.get("providers", []): + raise ValueError( + "voice_model should be formatted like this " + "{: }" + ) + return values + + def _download_wav(self, url: str, save_path: str) -> None: + response = requests.get(url) + if response.status_code == 200: + with open(save_path, "wb") as f: + f.write(response.content) + else: + raise ValueError("Error while downloading wav file") + + def _parse_response(self, response: list) -> str: + result = response[0] + if self.return_type == "url": + return result["audio_resource_url"] + else: + self._download_wav(result["audio_resource_url"], "audio.wav") + return "audio.wav" + + def _run( + self, + query: str, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the tool.""" + all_params = { + "text": query, + "language": self.language, + "option": self.voice, + "return_type": self.return_type, + "rate": self.rate, + "pitch": self.pitch, + "volume": self.volume, + "audio_format": self.audio_format, + "sampling_rate": self.sampling_rate, + "settings": self.voice_models, + } + + # filter so we don't send val to api when val is `None + query_params = {k: v for k, v in all_params.items() if v is not None} + + return self._call_eden_ai(query_params) diff --git a/libs/langchain/langchain/tools/edenai/edenai_base_tool.py b/libs/langchain/langchain/tools/edenai/edenai_base_tool.py new file mode 100644 index 0000000000..e0ebc543a8 --- /dev/null +++ b/libs/langchain/langchain/tools/edenai/edenai_base_tool.py @@ -0,0 +1,160 @@ +from __future__ import annotations + +import logging +from abc import abstractmethod +from typing import Any, Dict, List, Optional + +import requests + +from langchain.callbacks.manager import CallbackManagerForToolRun +from langchain.pydantic_v1 import root_validator +from langchain.tools.base import BaseTool +from langchain.utils import get_from_dict_or_env + +logger = logging.getLogger(__name__) + + +class EdenaiTool(BaseTool): + + """ + the base tool for all the EdenAI Tools . + you should have + the environment variable ``EDENAI_API_KEY`` set with your API token. + You can find your token here: https://app.edenai.run/admin/account/settings + """ + + feature: str + subfeature: str + edenai_api_key: Optional[str] = None + is_async: bool = False + + providers: List[str] + """provider to use for the API call.""" + + @root_validator(allow_reuse=True) + def validate_environment(cls, values: Dict) -> Dict: + """Validate that api key exists in environment.""" + values["edenai_api_key"] = get_from_dict_or_env( + values, "edenai_api_key", "EDENAI_API_KEY" + ) + return values + + @staticmethod + def get_user_agent() -> str: + from langchain import __version__ + + return f"langchain/{__version__}" + + def _call_eden_ai(self, query_params: Dict[str, Any]) -> str: + """ + Make an API call to the EdenAI service with the specified query parameters. + + Args: + query_params (dict): The parameters to include in the API call. + + Returns: + requests.Response: The response from the EdenAI API call. + + """ + + # faire l'API call + + headers = { + "Authorization": f"Bearer {self.edenai_api_key}", + "User-Agent": self.get_user_agent(), + } + + url = f"https://api.edenai.run/v2/{self.feature}/{self.subfeature}" + + payload = { + "providers": str(self.providers), + "response_as_dict": False, + "attributes_as_list": True, + "show_original_response": False, + } + + payload.update(query_params) + + response = requests.post(url, json=payload, headers=headers) + + self._raise_on_error(response) + + try: + return self._parse_response(response.json()) + except Exception as e: + raise RuntimeError(f"An error occurred while running tool: {e}") + + def _raise_on_error(self, response: requests.Response) -> None: + if response.status_code >= 500: + raise Exception(f"EdenAI Server: Error {response.status_code}") + elif response.status_code >= 400: + raise ValueError(f"EdenAI received an invalid payload: {response.text}") + elif response.status_code != 200: + raise Exception( + f"EdenAI returned an unexpected response with status " + f"{response.status_code}: {response.text}" + ) + + # case where edenai call succeeded but provider returned an error + # (eg: rate limit, server error, etc.) + if self.is_async is False: + # async call are different and only return a job_id, + # not the provider response directly + provider_response = response.json()[0] + if provider_response.get("status") == "fail": + err_msg = provider_response["error"]["message"] + raise ValueError(err_msg) + + @abstractmethod + def _run( + self, query: str, run_manager: Optional[CallbackManagerForToolRun] = None + ) -> str: + pass + + @abstractmethod + def _parse_response(self, response: Any) -> str: + """Take a dict response and condense it's data in a human readable string""" + pass + + def _get_edenai(self, url: str) -> requests.Response: + headers = { + "accept": "application/json", + "authorization": f"Bearer {self.edenai_api_key}", + "User-Agent": self.get_user_agent(), + } + + response = requests.get(url, headers=headers) + + self._raise_on_error(response) + + return response + + def _parse_json_multilevel( + self, extracted_data: dict, formatted_list: list, level: int = 0 + ) -> None: + for section, subsections in extracted_data.items(): + indentation = " " * level + if isinstance(subsections, str): + subsections = subsections.replace("\n", ",") + formatted_list.append(f"{indentation}{section} : {subsections}") + + elif isinstance(subsections, list): + formatted_list.append(f"{indentation}{section} : ") + self._list_handling(subsections, formatted_list, level + 1) + + elif isinstance(subsections, dict): + formatted_list.append(f"{indentation}{section} : ") + self._parse_json_multilevel(subsections, formatted_list, level + 1) + + def _list_handling( + self, subsection_list: list, formatted_list: list, level: int + ) -> None: + for list_item in subsection_list: + if isinstance(list_item, dict): + self._parse_json_multilevel(list_item, formatted_list, level) + + elif isinstance(list_item, list): + self._list_handling(list_item, formatted_list, level + 1) + + else: + formatted_list.append(f"{' ' * level}{list_item}") diff --git a/libs/langchain/langchain/tools/edenai/image_explicitcontent.py b/libs/langchain/langchain/tools/edenai/image_explicitcontent.py new file mode 100644 index 0000000000..e1367a8567 --- /dev/null +++ b/libs/langchain/langchain/tools/edenai/image_explicitcontent.py @@ -0,0 +1,67 @@ +from __future__ import annotations + +import logging +from typing import Optional + +from langchain.callbacks.manager import CallbackManagerForToolRun +from langchain.tools.edenai.edenai_base_tool import EdenaiTool + +logger = logging.getLogger(__name__) + + +class EdenAiExplicitImageTool(EdenaiTool): + + """Tool that queries the Eden AI Explicit image detection. + + for api reference check edenai documentation: + https://docs.edenai.co/reference/image_explicit_content_create. + + To use, you should have + the environment variable ``EDENAI_API_KEY`` set with your API token. + You can find your token here: https://app.edenai.run/admin/account/settings + + """ + + name = "edenai_image_explicit_content_detection" + + description = ( + "A wrapper around edenai Services Explicit image detection. " + """Useful for when you have to extract Explicit Content from images. + it detects adult only content in images, + that is generally inappropriate for people under + the age of 18 and includes nudity, sexual activity, + pornography, violence, gore content, etc.""" + "Input should be the string url of the image ." + ) + + combine_available = True + feature = "image" + subfeature = "explicit_content" + + def _parse_json(self, json_data: dict) -> str: + result_str = f"nsfw_likelihood: {json_data['nsfw_likelihood']}\n" + for idx, found_obj in enumerate(json_data["items"]): + label = found_obj["label"].lower() + likelihood = found_obj["likelihood"] + result_str += f"{idx}: {label} likelihood {likelihood},\n" + + return result_str[:-2] + + def _parse_response(self, json_data: list) -> str: + if len(json_data) == 1: + result = self._parse_json(json_data[0]) + else: + for entry in json_data: + if entry.get("provider") == "eden-ai": + result = self._parse_json(entry) + + return result + + def _run( + self, + query: str, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the tool.""" + query_params = {"file_url": query, "attributes_as_list": False} + return self._call_eden_ai(query_params) diff --git a/libs/langchain/langchain/tools/edenai/image_objectdetection.py b/libs/langchain/langchain/tools/edenai/image_objectdetection.py new file mode 100644 index 0000000000..764d976851 --- /dev/null +++ b/libs/langchain/langchain/tools/edenai/image_objectdetection.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +import logging +from typing import Optional + +from langchain.callbacks.manager import CallbackManagerForToolRun +from langchain.tools.edenai.edenai_base_tool import EdenaiTool + +logger = logging.getLogger(__name__) + + +class EdenAiObjectDetectionTool(EdenaiTool): + """Tool that queries the Eden AI Object detection API. + + for api reference check edenai documentation: + https://docs.edenai.co/reference/image_object_detection_create. + + To use, you should have + the environment variable ``EDENAI_API_KEY`` set with your API token. + You can find your token here: https://app.edenai.run/admin/account/settings + + """ + + name = "edenai_object_detection" + + description = ( + "A wrapper around edenai Services Object Detection . " + """Useful for when you have to do an to identify and locate + (with bounding boxes) objects in an image """ + "Input should be the string url of the image to identify." + ) + + show_positions: bool = False + + feature = "image" + subfeature = "object_detection" + + def _parse_json(self, json_data: dict) -> str: + result = [] + label_info = [] + + for found_obj in json_data["items"]: + label_str = f"{found_obj['label']} - Confidence {found_obj['confidence']}" + x_min = found_obj.get("x_min") + x_max = found_obj.get("x_max") + y_min = found_obj.get("y_min") + y_max = found_obj.get("y_max") + if self.show_positions and all( + [x_min, x_max, y_min, y_max] + ): # some providers don't return positions + label_str += f""",at the position x_min: {x_min}, x_max: {x_max}, + y_min: {y_min}, y_max: {y_max}""" + label_info.append(label_str) + + result.append("\n".join(label_info)) + return "\n\n".join(result) + + def _parse_response(self, response: list) -> str: + if len(response) == 1: + result = self._parse_json(response[0]) + else: + for entry in response: + if entry.get("provider") == "eden-ai": + result = self._parse_json(entry) + + return result + + def _run( + self, + query: str, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the tool.""" + query_params = {"file_url": query, "attributes_as_list": False} + return self._call_eden_ai(query_params) diff --git a/libs/langchain/langchain/tools/edenai/ocr_identityparser.py b/libs/langchain/langchain/tools/edenai/ocr_identityparser.py new file mode 100644 index 0000000000..55ccb477e2 --- /dev/null +++ b/libs/langchain/langchain/tools/edenai/ocr_identityparser.py @@ -0,0 +1,68 @@ +from __future__ import annotations + +import logging +from typing import Optional + +from langchain.callbacks.manager import CallbackManagerForToolRun +from langchain.tools.edenai.edenai_base_tool import EdenaiTool + +logger = logging.getLogger(__name__) + + +class EdenAiParsingIDTool(EdenaiTool): + """Tool that queries the Eden AI Identity parsing API. + + for api reference check edenai documentation: + https://docs.edenai.co/reference/ocr_identity_parser_create. + + To use, you should have + the environment variable ``EDENAI_API_KEY`` set with your API token. + You can find your token here: https://app.edenai.run/admin/account/settings + + """ + + name = "edenai_identity_parsing" + + description = ( + "A wrapper around edenai Services Identity parsing. " + "Useful for when you have to extract information from an ID Document " + "Input should be the string url of the document to parse." + ) + + feature = "ocr" + subfeature = "identity_parser" + + language: Optional[str] = None + """ + language of the text passed to the model. + """ + + def _parse_response(self, response: list) -> str: + formatted_list: list = [] + + if len(response) == 1: + self._parse_json_multilevel( + response[0]["extracted_data"][0], formatted_list + ) + else: + for entry in response: + if entry.get("provider") == "eden-ai": + self._parse_json_multilevel( + entry["extracted_data"][0], formatted_list + ) + + return "\n".join(formatted_list) + + def _run( + self, + query: str, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the tool.""" + query_params = { + "file_url": query, + "language": self.language, + "attributes_as_list": False, + } + + return self._call_eden_ai(query_params) diff --git a/libs/langchain/langchain/tools/edenai/ocr_invoiceparser.py b/libs/langchain/langchain/tools/edenai/ocr_invoiceparser.py new file mode 100644 index 0000000000..4bbf9ee923 --- /dev/null +++ b/libs/langchain/langchain/tools/edenai/ocr_invoiceparser.py @@ -0,0 +1,72 @@ +from __future__ import annotations + +import logging +from typing import Optional + +from langchain.callbacks.manager import CallbackManagerForToolRun +from langchain.tools.edenai.edenai_base_tool import EdenaiTool + +logger = logging.getLogger(__name__) + + +class EdenAiParsingInvoiceTool(EdenaiTool): + """Tool that queries the Eden AI Invoice parsing API. + + for api reference check edenai documentation: + https://docs.edenai.co/reference/ocr_invoice_parser_create. + + To use, you should have + the environment variable ``EDENAI_API_KEY`` set with your API token. + You can find your token here: https://app.edenai.run/admin/account/settings + + """ + + name = "edenai_invoice_parsing" + + description = ( + "A wrapper around edenai Services invoice parsing. " + """Useful for when you have to extract information from + an image it enables to take invoices + in a variety of formats and returns the data in contains + (items, prices, addresses, vendor name, etc.) + in a structured format to automate the invoice processing """ + "Input should be the string url of the document to parse." + ) + + language: Optional[str] = None + """ + language of the image passed to the model. + """ + + feature = "ocr" + subfeature = "invoice_parser" + + def _parse_response(self, response: list) -> str: + formatted_list: list = [] + + if len(response) == 1: + self._parse_json_multilevel( + response[0]["extracted_data"][0], formatted_list + ) + else: + for entry in response: + if entry.get("provider") == "eden-ai": + self._parse_json_multilevel( + entry["extracted_data"][0], formatted_list + ) + + return "\n".join(formatted_list) + + def _run( + self, + query: str, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the tool.""" + query_params = { + "file_url": query, + "language": self.language, + "attributes_as_list": False, + } + + return self._call_eden_ai(query_params) diff --git a/libs/langchain/langchain/tools/edenai/text_moderation.py b/libs/langchain/langchain/tools/edenai/text_moderation.py new file mode 100644 index 0000000000..f03f1d58ae --- /dev/null +++ b/libs/langchain/langchain/tools/edenai/text_moderation.py @@ -0,0 +1,72 @@ +from __future__ import annotations + +import logging +from typing import Optional + +from langchain.callbacks.manager import CallbackManagerForToolRun +from langchain.tools.edenai.edenai_base_tool import EdenaiTool + +logger = logging.getLogger(__name__) + + +class EdenAiTextModerationTool(EdenaiTool): + """Tool that queries the Eden AI Explicit text detection. + + for api reference check edenai documentation: + https://docs.edenai.co/reference/image_explicit_content_create. + + To use, you should have + the environment variable ``EDENAI_API_KEY`` set with your API token. + You can find your token here: https://app.edenai.run/admin/account/settings + + """ + + name = "edenai_explicit_content_detection_text" + + description = ( + "A wrapper around edenai Services explicit content detection for text. " + """Useful for when you have to scan text for offensive, + sexually explicit or suggestive content, + it checks also if there is any content of self-harm, + violence, racist or hate speech.""" + """the structure of the output is : + 'the type of the explicit content : the likelihood of it being explicit' + the likelihood is a number + between 1 and 5, 1 being the lowest and 5 the highest. + something is explicit if the likelihood is equal or higher than 3. + for example : + nsfw_likelihood: 1 + this is not explicit. + for example : + nsfw_likelihood: 3 + this is explicit. + """ + "Input should be a string." + ) + + language: str + + feature: str = "text" + subfeature: str = "moderation" + + def _parse_response(self, response: list) -> str: + formatted_result = [] + for result in response: + if "nsfw_likelihood" in result.keys(): + formatted_result.append( + "nsfw_likelihood: " + str(result["nsfw_likelihood"]) + ) + + for label, likelihood in zip(result["label"], result["likelihood"]): + formatted_result.append(f'"{label}": {str(likelihood)}') + + return "\n".join(formatted_result) + + def _run( + self, + query: str, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the tool.""" + query_params = {"text": query, "language": self.language} + return self._call_eden_ai(query_params) diff --git a/libs/langchain/langchain/tools/gmail/get_message.py b/libs/langchain/langchain/tools/gmail/get_message.py index e1cca7b13e..a5f35ba71e 100644 --- a/libs/langchain/langchain/tools/gmail/get_message.py +++ b/libs/langchain/langchain/tools/gmail/get_message.py @@ -46,7 +46,16 @@ class GmailGetMessage(GmailBaseTool): subject = email_msg["Subject"] sender = email_msg["From"] - message_body = email_msg.get_payload() + message_body = "" + if email_msg.is_multipart(): + for part in email_msg.walk(): + ctype = part.get_content_type() + cdispo = str(part.get("Content-Disposition")) + if ctype == "text/plain" and "attachment" not in cdispo: + message_body = part.get_payload(decode=True).decode("utf-8") + break + else: + message_body = email_msg.get_payload(decode=True).decode("utf-8") body = clean_email_body(message_body) diff --git a/libs/langchain/langchain/tools/gmail/search.py b/libs/langchain/langchain/tools/gmail/search.py index 045834abc1..f4de000f51 100644 --- a/libs/langchain/langchain/tools/gmail/search.py +++ b/libs/langchain/langchain/tools/gmail/search.py @@ -91,7 +91,16 @@ class GmailSearch(GmailBaseTool): subject = email_msg["Subject"] sender = email_msg["From"] - message_body = email_msg.get_payload() + message_body = "" + if email_msg.is_multipart(): + for part in email_msg.walk(): + ctype = part.get_content_type() + cdispo = str(part.get("Content-Disposition")) + if ctype == "text/plain" and "attachment" not in cdispo: + message_body = part.get_payload(decode=True).decode("utf-8") + break + else: + message_body = email_msg.get_payload(decode=True).decode("utf-8") body = clean_email_body(message_body) diff --git a/libs/langchain/langchain/tools/yahoo_finance_news.py b/libs/langchain/langchain/tools/yahoo_finance_news.py new file mode 100644 index 0000000000..d1aa55dd60 --- /dev/null +++ b/libs/langchain/langchain/tools/yahoo_finance_news.py @@ -0,0 +1,67 @@ +from typing import Iterable, Optional + +from requests.exceptions import HTTPError, ReadTimeout +from urllib3.exceptions import ConnectionError + +from langchain.callbacks.manager import CallbackManagerForToolRun +from langchain.document_loaders.web_base import WebBaseLoader +from langchain.schema import Document +from langchain.tools.base import BaseTool + + +class YahooFinanceNewsTool(BaseTool): + """Tool that searches financial news on Yahoo Finance.""" + + name: str = "yahoo_finance_news" + description: str = ( + "Useful for when you need to find financial news " + "about a public company. " + "Input should be a company ticker. " + "For example, AAPL for Apple, MSFT for Microsoft." + ) + top_k: int = 10 + """The number of results to return.""" + + def _run( + self, + query: str, + run_manager: Optional[CallbackManagerForToolRun] = None, + ) -> str: + """Use the Yahoo Finance News tool.""" + try: + import yfinance + except ImportError: + raise ImportError( + "Could not import yfinance python package. " + "Please install it with `pip install yfinance`." + ) + company = yfinance.Ticker(query) + try: + if company.isin is None: + return f"Company ticker {query} not found." + except (HTTPError, ReadTimeout, ConnectionError): + return f"Company ticker {query} not found." + + links = [] + try: + links = [n["link"] for n in company.news if n["type"] == "STORY"] + except (HTTPError, ReadTimeout, ConnectionError): + if not links: + return f"No news found for company that searched with {query} ticker." + if not links: + return f"No news found for company that searched with {query} ticker." + loader = WebBaseLoader(links) + docs = loader.load() + result = self._format_results(docs, query) + if not result: + return f"No news found for company that searched with {query} ticker." + return result + + @staticmethod + def _format_results(docs: Iterable[Document], query: str) -> str: + doc_strings = [ + "\n".join([doc.metadata["title"], doc.metadata["description"]]) + for doc in docs + if query in doc.metadata["description"] or query in doc.metadata["title"] + ] + return "\n\n".join(doc_strings) diff --git a/libs/langchain/langchain/utilities/redis.py b/libs/langchain/langchain/utilities/redis.py index e6c5cb1388..a45391c8bc 100644 --- a/libs/langchain/langchain/utilities/redis.py +++ b/libs/langchain/langchain/utilities/redis.py @@ -108,7 +108,7 @@ def get_client(redis_url: str, **kwargs: Any) -> RedisType: try: import redis except ImportError: - raise ValueError( + raise ImportError( "Could not import redis python package. " "Please install it with `pip install redis>=4.1.0`." ) diff --git a/libs/langchain/langchain/utilities/serpapi.py b/libs/langchain/langchain/utilities/serpapi.py index 4d5af09977..337e89a119 100644 --- a/libs/langchain/langchain/utilities/serpapi.py +++ b/libs/langchain/langchain/utilities/serpapi.py @@ -129,42 +129,92 @@ class SerpAPIWrapper(BaseModel): """Process response from SerpAPI.""" if "error" in res.keys(): raise ValueError(f"Got error from SerpAPI: {res['error']}") - if "answer_box" in res.keys() and isinstance(res["answer_box"], list): - res["answer_box"] = res["answer_box"][0] - if "answer_box" in res.keys() and "answer" in res["answer_box"].keys(): - toret = res["answer_box"]["answer"] - elif "answer_box" in res.keys() and "snippet" in res["answer_box"].keys(): - toret = res["answer_box"]["snippet"] - elif ( - "answer_box" in res.keys() - and "snippet_highlighted_words" in res["answer_box"].keys() - ): - toret = res["answer_box"]["snippet_highlighted_words"][0] - elif ( - "sports_results" in res.keys() - and "game_spotlight" in res["sports_results"].keys() - ): - toret = res["sports_results"]["game_spotlight"] + if "answer_box_list" in res.keys(): + res["answer_box"] = res["answer_box_list"] + if "answer_box" in res.keys(): + answer_box = res["answer_box"] + if isinstance(answer_box, list): + answer_box = answer_box[0] + if "result" in answer_box.keys(): + return answer_box["result"] + elif "answer" in answer_box.keys(): + return answer_box["answer"] + elif "snippet" in answer_box.keys(): + return answer_box["snippet"] + elif "snippet_highlighted_words" in answer_box.keys(): + return answer_box["snippet_highlighted_words"] + else: + answer = {} + for key, value in answer_box.items(): + if not isinstance(value, (list, dict)) and not ( + type(value) == str and value.startswith("http") + ): + answer[key] = value + return str(answer) + elif "events_results" in res.keys(): + return res["events_results"][:10] + elif "sports_results" in res.keys(): + return res["sports_results"] + elif "top_stories" in res.keys(): + return res["top_stories"] + elif "news_results" in res.keys(): + return res["news_results"] + elif "jobs_results" in res.keys() and "jobs" in res["jobs_results"].keys(): + return res["jobs_results"]["jobs"] elif ( "shopping_results" in res.keys() and "title" in res["shopping_results"][0].keys() ): - toret = res["shopping_results"][:3] + return res["shopping_results"][:3] + elif "questions_and_answers" in res.keys(): + return res["questions_and_answers"] elif ( - "knowledge_graph" in res.keys() - and "description" in res["knowledge_graph"].keys() + "popular_destinations" in res.keys() + and "destinations" in res["popular_destinations"].keys() ): - toret = res["knowledge_graph"]["description"] - elif "snippet" in res["organic_results"][0].keys(): - toret = res["organic_results"][0]["snippet"] - elif "link" in res["organic_results"][0].keys(): - toret = res["organic_results"][0]["link"] + return res["popular_destinations"]["destinations"] + elif "top_sights" in res.keys() and "sights" in res["top_sights"].keys(): + return res["top_sights"]["sights"] elif ( "images_results" in res.keys() and "thumbnail" in res["images_results"][0].keys() ): - thumbnails = [item["thumbnail"] for item in res["images_results"][:10]] - toret = thumbnails + return str([item["thumbnail"] for item in res["images_results"][:10]]) + + snippets = [] + if "knowledge_graph" in res.keys(): + knowledge_graph = res["knowledge_graph"] + title = knowledge_graph["title"] if "title" in knowledge_graph else "" + if "description" in knowledge_graph.keys(): + snippets.append(knowledge_graph["description"]) + for key, value in knowledge_graph.items(): + if ( + type(key) == str + and type(value) == str + and key not in ["title", "description"] + and not key.endswith("_stick") + and not key.endswith("_link") + and not value.startswith("http") + ): + snippets.append(f"{title} {key}: {value}.") + if "organic_results" in res.keys(): + first_organic_result = res["organic_results"][0] + if "snippet" in first_organic_result.keys(): + snippets.append(first_organic_result["snippet"]) + elif "snippet_highlighted_words" in first_organic_result.keys(): + snippets.append(first_organic_result["snippet_highlighted_words"]) + elif "rich_snippet" in first_organic_result.keys(): + snippets.append(first_organic_result["rich_snippet"]) + elif "rich_snippet_table" in first_organic_result.keys(): + snippets.append(first_organic_result["rich_snippet_table"]) + elif "link" in first_organic_result.keys(): + snippets.append(first_organic_result["link"]) + if "buying_guide" in res.keys(): + snippets.append(res["buying_guide"]) + if "local_results" in res.keys() and "places" in res["local_results"].keys(): + snippets.append(res["local_results"]["places"]) + + if len(snippets) > 0: + return str(snippets) else: - toret = "No good search result found" - return toret + return "No good search result found" diff --git a/libs/langchain/langchain/vectorstores/__init__.py b/libs/langchain/langchain/vectorstores/__init__.py index 94bbc64a90..a130472b93 100644 --- a/libs/langchain/langchain/vectorstores/__init__.py +++ b/libs/langchain/langchain/vectorstores/__init__.py @@ -63,6 +63,7 @@ from langchain.vectorstores.rocksetdb import Rockset from langchain.vectorstores.scann import ScaNN from langchain.vectorstores.singlestoredb import SingleStoreDB from langchain.vectorstores.sklearn import SKLearnVectorStore +from langchain.vectorstores.sqlitevss import SQLiteVSS from langchain.vectorstores.starrocks import StarRocks from langchain.vectorstores.supabase import SupabaseVectorStore from langchain.vectorstores.tair import Tair @@ -125,6 +126,7 @@ __all__ = [ "ScaNN", "SingleStoreDB", "SingleStoreDB", + "SQLiteVSS", "StarRocks", "SupabaseVectorStore", "Tair", diff --git a/libs/langchain/langchain/vectorstores/myscale.py b/libs/langchain/langchain/vectorstores/myscale.py index 3c4361fcdc..81812550c7 100644 --- a/libs/langchain/langchain/vectorstores/myscale.py +++ b/libs/langchain/langchain/vectorstores/myscale.py @@ -147,7 +147,12 @@ class MyScale(VectorStore): ) for k in ["id", "vector", "text", "metadata"]: assert k in self.config.column_map - assert self.config.metric in ["ip", "cosine", "l2"] + assert self.config.metric.upper() in ["IP", "COSINE", "L2"] + if self.config.metric in ["ip", "cosine", "l2"]: + logger.warning( + "Lower case metric types will be deprecated " + "the future. Please use one of ('IP', 'Cosine', 'L2')" + ) # initialize the schema dim = len(embedding.embed_query("try this out")) @@ -174,7 +179,9 @@ class MyScale(VectorStore): self.BS = "\\" self.must_escape = ("\\", "'") self._embeddings = embedding - self.dist_order = "ASC" if self.config.metric in ["cosine", "l2"] else "DESC" + self.dist_order = ( + "ASC" if self.config.metric.upper() in ["COSINE", "L2"] else "DESC" + ) # Create a connection to myscale self.client = get_client( diff --git a/libs/langchain/langchain/vectorstores/pinecone.py b/libs/langchain/langchain/vectorstores/pinecone.py index c6793b4de8..ec9fff790a 100644 --- a/libs/langchain/langchain/vectorstores/pinecone.py +++ b/libs/langchain/langchain/vectorstores/pinecone.py @@ -3,15 +3,19 @@ from __future__ import annotations import logging import uuid import warnings -from typing import Any, Callable, Iterable, List, Optional, Tuple, Union +from typing import TYPE_CHECKING, Any, Callable, Iterable, List, Optional, Tuple, Union import numpy as np from langchain.docstore.document import Document from langchain.embeddings.base import Embeddings +from langchain.utils.iter import batch_iterate from langchain.vectorstores.base import VectorStore from langchain.vectorstores.utils import DistanceStrategy, maximal_marginal_relevance +if TYPE_CHECKING: + from pinecone import Index + logger = logging.getLogger(__name__) @@ -51,16 +55,16 @@ class Pinecone(VectorStore): "Could not import pinecone python package. " "Please install it with `pip install pinecone-client`." ) - if not isinstance(index, pinecone.index.Index): - raise ValueError( - f"client should be an instance of pinecone.index.Index, " - f"got {type(index)}" - ) if not isinstance(embedding, Embeddings): warnings.warn( "Passing in `embedding` as a Callable is deprecated. Please pass in an" " Embeddings object instead." ) + if not isinstance(index, pinecone.index.Index): + raise ValueError( + f"client should be an instance of pinecone.index.Index, " + f"got {type(index)}" + ) self._index = index self._embedding = embedding self._text_key = text_key @@ -93,15 +97,22 @@ class Pinecone(VectorStore): ids: Optional[List[str]] = None, namespace: Optional[str] = None, batch_size: int = 32, + embedding_chunk_size: int = 1000, **kwargs: Any, ) -> List[str]: """Run more texts through the embeddings and add to the vectorstore. + Upsert optimization is done by chunking the embeddings and upserting them. + This is done to avoid memory issues and optimize using HTTP based embeddings. + For OpenAI embeddings, use pool_threads>4 when constructing the pinecone.Index, + embedding_chunk_size>1000 and batch_size~64 for best performance. Args: texts: Iterable of strings to add to the vectorstore. metadatas: Optional list of metadatas associated with the texts. ids: Optional list of ids to associate with the texts. namespace: Optional pinecone namespace to add the texts to. + batch_size: Batch size to use when adding the texts to the vectorstore. + embedding_chunk_size: Chunk size to use when embedding the texts. Returns: List of ids from adding the texts into the vectorstore. @@ -109,18 +120,34 @@ class Pinecone(VectorStore): """ if namespace is None: namespace = self._namespace - # Embed and create the documents - docs = [] + + texts = list(texts) ids = ids or [str(uuid.uuid4()) for _ in texts] - embeddings = self._embed_documents(texts) - for i, (text, embedding) in enumerate(zip(texts, embeddings)): - metadata = metadatas[i] if metadatas else {} + metadatas = metadatas or [{} for _ in texts] + for metadata, text in zip(metadatas, texts): metadata[self._text_key] = text - docs.append((ids[i], embedding, metadata)) - # upsert to Pinecone - self._index.upsert( - vectors=docs, namespace=namespace, batch_size=batch_size, **kwargs - ) + + # For loops to avoid memory issues and optimize when using HTTP based embeddings + # The first loop runs the embeddings, it benefits when using OpenAI embeddings + # The second loops runs the pinecone upsert asynchoronously. + for i in range(0, len(texts), embedding_chunk_size): + chunk_texts = texts[i : i + embedding_chunk_size] + chunk_ids = ids[i : i + embedding_chunk_size] + chunk_metadatas = metadatas[i : i + embedding_chunk_size] + embeddings = self._embed_documents(chunk_texts) + async_res = [ + self._index.upsert( + vectors=batch, + namespace=namespace, + async_req=True, + **kwargs, + ) + for batch in batch_iterate( + batch_size, zip(chunk_ids, embeddings, chunk_metadatas) + ) + ] + [res.get() for res in async_res] + return ids def similarity_search_with_score( @@ -302,6 +329,45 @@ class Pinecone(VectorStore): embedding, k, fetch_k, lambda_mult, filter, namespace ) + @classmethod + def get_pinecone_index( + cls, + index_name: Optional[str], + pool_threads: int = 4, + ) -> Index: + """Return a Pinecone Index instance. + + Args: + index_name: Name of the index to use. + pool_threads: Number of threads to use for index upsert. + Returns: + Pinecone Index instance.""" + + try: + import pinecone + except ImportError: + raise ValueError( + "Could not import pinecone python package. " + "Please install it with `pip install pinecone-client`." + ) + + indexes = pinecone.list_indexes() # checks if provided index exists + + if index_name in indexes: + index = pinecone.Index(index_name, pool_threads=pool_threads) + elif len(indexes) == 0: + raise ValueError( + "No active indexes found in your Pinecone project, " + "are you sure you're using the right Pinecone API key and Environment? " + "Please double check your Pinecone dashboard." + ) + else: + raise ValueError( + f"Index '{index_name}' not found in your Pinecone project. " + f"Did you mean one of the following indexes: {', '.join(indexes)}" + ) + return index + @classmethod def from_texts( cls, @@ -311,9 +377,11 @@ class Pinecone(VectorStore): ids: Optional[List[str]] = None, batch_size: int = 32, text_key: str = "text", - index_name: Optional[str] = None, namespace: Optional[str] = None, + index_name: Optional[str] = None, upsert_kwargs: Optional[dict] = None, + pool_threads: int = 4, + embeddings_chunk_size: int = 1000, **kwargs: Any, ) -> Pinecone: """Construct Pinecone wrapper from raw documents. @@ -324,6 +392,7 @@ class Pinecone(VectorStore): This is intended to be a quick way to get started. + The `pool_threads` affects the speed of the upsert operations. Example: .. code-block:: python @@ -341,54 +410,19 @@ class Pinecone(VectorStore): index_name="langchain-demo" ) """ - try: - import pinecone - except ImportError: - raise ValueError( - "Could not import pinecone python package. " - "Please install it with `pip install pinecone-client`." - ) + pinecone_index = cls.get_pinecone_index(index_name, pool_threads) + pinecone = cls(pinecone_index, embedding, text_key, namespace, **kwargs) - indexes = pinecone.list_indexes() # checks if provided index exists - - if index_name in indexes: - index = pinecone.Index(index_name) - elif len(indexes) == 0: - raise ValueError( - "No active indexes found in your Pinecone project, " - "are you sure you're using the right API key and environment?" - ) - else: - raise ValueError( - f"Index '{index_name}' not found in your Pinecone project. " - f"Did you mean one of the following indexes: {', '.join(indexes)}" - ) - - for i in range(0, len(texts), batch_size): - # set end position of batch - i_end = min(i + batch_size, len(texts)) - # get batch of texts and ids - lines_batch = texts[i:i_end] - # create ids if not provided - if ids: - ids_batch = ids[i:i_end] - else: - ids_batch = [str(uuid.uuid4()) for n in range(i, i_end)] - # create embeddings - embeds = embedding.embed_documents(lines_batch) - # prep metadata and upsert batch - if metadatas: - metadata = metadatas[i:i_end] - else: - metadata = [{} for _ in range(i, i_end)] - for j, line in enumerate(lines_batch): - metadata[j][text_key] = line - to_upsert = zip(ids_batch, embeds, metadata) - - # upsert to Pinecone - _upsert_kwargs = upsert_kwargs or {} - index.upsert(vectors=list(to_upsert), namespace=namespace, **_upsert_kwargs) - return cls(index, embedding, text_key, namespace, **kwargs) + pinecone.add_texts( + texts, + metadatas=metadatas, + ids=ids, + namespace=namespace, + batch_size=batch_size, + embedding_chunk_size=embeddings_chunk_size, + **(upsert_kwargs or {}), + ) + return pinecone @classmethod def from_existing_index( @@ -397,17 +431,11 @@ class Pinecone(VectorStore): embedding: Embeddings, text_key: str = "text", namespace: Optional[str] = None, + pool_threads: int = 4, ) -> Pinecone: """Load pinecone vectorstore from index name.""" - try: - import pinecone - except ImportError: - raise ValueError( - "Could not import pinecone python package. " - "Please install it with `pip install pinecone-client`." - ) - - return cls(pinecone.Index(index_name), embedding, text_key, namespace) + pinecone_index = cls.get_pinecone_index(index_name, pool_threads) + return cls(pinecone_index, embedding, text_key, namespace) def delete( self, diff --git a/libs/langchain/langchain/vectorstores/sqlitevss.py b/libs/langchain/langchain/vectorstores/sqlitevss.py new file mode 100644 index 0000000000..93b1d67649 --- /dev/null +++ b/libs/langchain/langchain/vectorstores/sqlitevss.py @@ -0,0 +1,227 @@ +from __future__ import annotations + +import json +import logging +import warnings +from typing import ( + TYPE_CHECKING, + Any, + Iterable, + List, + Optional, + Tuple, + Type, +) + +from langchain.docstore.document import Document +from langchain.embeddings.base import Embeddings +from langchain.vectorstores.base import VectorStore + +if TYPE_CHECKING: + import sqlite3 + +logger = logging.getLogger(__name__) + + +class SQLiteVSS(VectorStore): + """Wrapper around SQLite with vss extension as a vector database. + To use, you should have the ``sqlite-vss`` python package installed. + Example: + .. code-block:: python + from langchain.vectorstores import SQLiteVSS + from langchain.embeddings.openai import OpenAIEmbeddings + ... + """ + + def __init__( + self, + table: str, + connection: Optional[sqlite3.Connection], + embedding: Embeddings, + db_file: str = "vss.db", + ): + """Initialize with sqlite client with vss extension.""" + try: + import sqlite_vss # noqa # pylint: disable=unused-import + except ImportError: + raise ImportError( + "Could not import sqlite-vss python package. " + "Please install it with `pip install sqlite-vss`." + ) + + if not connection: + connection = self.create_connection(db_file) + + if not isinstance(embedding, Embeddings): + warnings.warn("embeddings input must be Embeddings object.") + + self._connection = connection + self._table = table + self._embedding = embedding + + self.create_table_if_not_exists() + + def create_table_if_not_exists(self) -> None: + self._connection.execute( + f""" + CREATE TABLE IF NOT EXISTS {self._table} + ( + rowid INTEGER PRIMARY KEY AUTOINCREMENT, + text TEXT, + metadata BLOB, + text_embedding BLOB + ) + ; + """ + ) + self._connection.execute( + f""" + CREATE VIRTUAL TABLE IF NOT EXISTS vss_{self._table} USING vss0( + text_embedding({self.get_dimensionality()}) + ); + """ + ) + self._connection.execute( + f""" + CREATE TRIGGER IF NOT EXISTS embed_text + AFTER INSERT ON {self._table} + BEGIN + INSERT INTO vss_{self._table}(rowid, text_embedding) + VALUES (new.rowid, new.text_embedding) + ; + END; + """ + ) + self._connection.commit() + + def add_texts( + self, + texts: Iterable[str], + metadatas: Optional[List[dict]] = None, + **kwargs: Any, + ) -> List[str]: + """Add more texts to the vectorstore index. + Args: + texts: Iterable of strings to add to the vectorstore. + metadatas: Optional list of metadatas associated with the texts. + kwargs: vectorstore specific parameters + """ + max_id = self._connection.execute( + f"SELECT max(rowid) as rowid FROM {self._table}" + ).fetchone()["rowid"] + if max_id is None: # no text added yet + max_id = 0 + + embeds = self._embedding.embed_documents(list(texts)) + if not metadatas: + metadatas = [{} for _ in texts] + data_input = [ + (text, json.dumps(metadata), json.dumps(embed)) + for text, metadata, embed in zip(texts, metadatas, embeds) + ] + self._connection.executemany( + f"INSERT INTO {self._table}(text, metadata, text_embedding) " + f"VALUES (?,?,?)", + data_input, + ) + self._connection.commit() + # pulling every ids we just inserted + results = self._connection.execute( + f"SELECT rowid FROM {self._table} WHERE rowid > {max_id}" + ) + return [row["rowid"] for row in results] + + def similarity_search_with_score_by_vector( + self, embedding: List[float], k: int = 4, **kwargs: Any + ) -> List[Tuple[Document, float]]: + sql_query = f""" + SELECT + text, + metadata, + distance + FROM {self._table} e + INNER JOIN vss_{self._table} v on v.rowid = e.rowid + WHERE vss_search( + v.text_embedding, + vss_search_params('{json.dumps(embedding)}', {k}) + ) + """ + cursor = self._connection.cursor() + cursor.execute(sql_query) + results = cursor.fetchall() + + documents = [] + for row in results: + metadata = json.loads(row["metadata"]) or {} + doc = Document(page_content=row["text"], metadata=metadata) + documents.append((doc, row["distance"])) + + return documents + + def similarity_search( + self, query: str, k: int = 4, **kwargs: Any + ) -> List[Document]: + """Return docs most similar to query.""" + embedding = self._embedding.embed_query(query) + documents = self.similarity_search_with_score_by_vector( + embedding=embedding, k=k + ) + return [doc for doc, _ in documents] + + def similarity_search_with_score( + self, query: str, k: int = 4, **kwargs: Any + ) -> List[Tuple[Document, float]]: + """Return docs most similar to query.""" + embedding = self._embedding.embed_query(query) + documents = self.similarity_search_with_score_by_vector( + embedding=embedding, k=k + ) + return documents + + def similarity_search_by_vector( + self, embedding: List[float], k: int = 4, **kwargs: Any + ) -> List[Document]: + documents = self.similarity_search_with_score_by_vector( + embedding=embedding, k=k + ) + return [doc for doc, _ in documents] + + @classmethod + def from_texts( + cls: Type[SQLiteVSS], + texts: List[str], + embedding: Embeddings, + metadatas: Optional[List[dict]] = None, + table: str = "langchain", + db_file: str = "vss.db", + **kwargs: Any, + ) -> SQLiteVSS: + """Return VectorStore initialized from texts and embeddings.""" + connection = cls.create_connection(db_file) + vss = cls( + table=table, connection=connection, db_file=db_file, embedding=embedding + ) + vss.add_texts(texts=texts, metadatas=metadatas) + return vss + + @staticmethod + def create_connection(db_file: str) -> sqlite3.Connection: + import sqlite3 + + import sqlite_vss + + connection = sqlite3.connect(db_file) + connection.row_factory = sqlite3.Row + connection.enable_load_extension(True) + sqlite_vss.load(connection) + connection.enable_load_extension(False) + return connection + + def get_dimensionality(self) -> int: + """ + Function that does a dummy embedding to figure out how many dimensions + this embedding function returns. Needed for the virtual table DDL. + """ + dummy_text = "This is a dummy text" + dummy_embedding = self._embedding.embed_query(dummy_text) + return len(dummy_embedding) diff --git a/libs/langchain/langchain/vectorstores/weaviate.py b/libs/langchain/langchain/vectorstores/weaviate.py index 5f09a785d4..3f96521224 100644 --- a/libs/langchain/langchain/vectorstores/weaviate.py +++ b/libs/langchain/langchain/vectorstores/weaviate.py @@ -99,7 +99,7 @@ class Weaviate(VectorStore): try: import weaviate except ImportError: - raise ValueError( + raise ImportError( "Could not import weaviate python package. " "Please install it with `pip install weaviate-client`." ) @@ -343,6 +343,8 @@ class Weaviate(VectorStore): if kwargs.get("search_distance"): content["certainty"] = kwargs.get("search_distance") query_obj = self._client.query.get(self._index_name, self._query_attrs) + if kwargs.get("where_filter"): + query_obj = query_obj.with_where(kwargs.get("where_filter")) embedded_query = self._embedding.embed_query(query) if not self._by_text: diff --git a/libs/langchain/poetry.lock b/libs/langchain/poetry.lock index c003e52f28..0f67f9379a 100644 --- a/libs/langchain/poetry.lock +++ b/libs/langchain/poetry.lock @@ -1,9 +1,10 @@ -# This file is automatically @generated by Poetry 1.5.1 and should not be changed by hand. +# This file is automatically @generated by Poetry and should not be changed by hand. [[package]] name = "absl-py" version = "1.4.0" description = "Abseil Python Common Libraries, see https://github.com/abseil/abseil-py." +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -15,6 +16,7 @@ files = [ name = "aioboto3" version = "11.3.0" description = "Async boto3 wrapper" +category = "main" optional = true python-versions = ">=3.7,<4.0" files = [ @@ -33,6 +35,7 @@ s3cse = ["cryptography (>=2.3.1)"] name = "aiobotocore" version = "2.6.0" description = "Async client for aws services using botocore and aiohttp" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -55,6 +58,7 @@ boto3 = ["boto3 (>=1.28.17,<1.28.18)"] name = "aiodns" version = "3.0.0" description = "Simple DNS resolver for asyncio" +category = "main" optional = true python-versions = "*" files = [ @@ -69,6 +73,7 @@ pycares = ">=4.0.0" name = "aiofiles" version = "23.2.1" description = "File support for asyncio." +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -80,6 +85,7 @@ files = [ name = "aiohttp" version = "3.8.5" description = "Async http client/server framework (asyncio)" +category = "main" optional = false python-versions = ">=3.6" files = [ @@ -188,6 +194,7 @@ speedups = ["Brotli", "aiodns", "cchardet"] name = "aiohttp-retry" version = "2.8.3" description = "Simple retry client for aiohttp" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -202,6 +209,7 @@ aiohttp = "*" name = "aioitertools" version = "0.11.0" description = "itertools and builtins for AsyncIO and mixed iterables" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -216,6 +224,7 @@ typing_extensions = {version = ">=4.0", markers = "python_version < \"3.10\""} name = "aiosignal" version = "1.3.1" description = "aiosignal: a list of registered asynchronous callbacks" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -230,6 +239,7 @@ frozenlist = ">=1.1.0" name = "aleph-alpha-client" version = "2.17.0" description = "python client to interact with Aleph Alpha api endpoints" +category = "main" optional = true python-versions = "*" files = [ @@ -257,6 +267,7 @@ types = ["mypy", "types-Pillow", "types-requests"] name = "altair" version = "4.2.2" description = "Altair: A declarative statistical visualization library for Python." +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -279,6 +290,7 @@ dev = ["black", "docutils", "flake8", "ipython", "m2r", "mistune (<2.0.0)", "pyt name = "amadeus" version = "8.1.0" description = "Python module for the Amadeus travel APIs" +category = "main" optional = true python-versions = ">=3.4.8" files = [ @@ -289,6 +301,7 @@ files = [ name = "amazon-textract-caller" version = "0.0.29" description = "Amazon Textract Caller tools" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -308,6 +321,7 @@ testing = ["amazon-textract-response-parser", "pytest"] name = "amazon-textract-response-parser" version = "1.0.0" description = "Easily parse JSON returned by Amazon Textract." +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -323,6 +337,7 @@ marshmallow = ">=3.14,<4" name = "anyio" version = "3.7.1" description = "High level compatibility layer for multiple asynchronous event loop implementations" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -344,6 +359,7 @@ trio = ["trio (<0.22)"] name = "appnope" version = "0.1.3" description = "Disable App Nap on macOS >= 10.9" +category = "dev" optional = false python-versions = "*" files = [ @@ -355,6 +371,7 @@ files = [ name = "argon2-cffi" version = "23.1.0" description = "Argon2 for Python" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -375,6 +392,7 @@ typing = ["mypy"] name = "argon2-cffi-bindings" version = "21.2.0" description = "Low-level CFFI bindings for Argon2" +category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -412,6 +430,7 @@ tests = ["pytest"] name = "arrow" version = "1.2.3" description = "Better dates & times for Python" +category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -426,6 +445,7 @@ python-dateutil = ">=2.7.0" name = "arxiv" version = "1.4.8" description = "Python wrapper for the arXiv API: http://arxiv.org/help/api/" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -440,6 +460,7 @@ feedparser = "*" name = "assemblyai" version = "0.17.0" description = "AssemblyAI Python SDK" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -460,6 +481,7 @@ extras = ["pyaudio (>=0.2.13)"] name = "asttokens" version = "2.2.1" description = "Annotate AST trees with source code positions" +category = "dev" optional = false python-versions = "*" files = [ @@ -477,6 +499,7 @@ test = ["astroid", "pytest"] name = "astunparse" version = "1.6.3" description = "An AST unparser for Python" +category = "main" optional = true python-versions = "*" files = [ @@ -492,6 +515,7 @@ wheel = ">=0.23.0,<1.0" name = "async-lru" version = "2.0.4" description = "Simple LRU cache for asyncio" +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -506,6 +530,7 @@ typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.11\""} name = "async-timeout" version = "4.0.3" description = "Timeout context manager for asyncio programs" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -517,6 +542,7 @@ files = [ name = "atlassian-python-api" version = "3.41.0" description = "Python Atlassian REST API Wrapper" +category = "main" optional = true python-versions = "*" files = [ @@ -538,6 +564,7 @@ kerberos = ["requests-kerberos"] name = "attr" version = "0.3.2" description = "Simple decorator to set attributes of target function or class in a DRY way." +category = "main" optional = true python-versions = "*" files = [ @@ -549,6 +576,7 @@ files = [ name = "attrs" version = "23.1.0" description = "Classes Without Boilerplate" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -567,6 +595,7 @@ tests-no-zope = ["cloudpickle", "hypothesis", "mypy (>=1.1.1)", "pympler", "pyte name = "audioread" version = "3.0.0" description = "multi-library, cross-platform audio decoding" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -577,6 +606,7 @@ files = [ name = "authlib" version = "1.2.1" description = "The ultimate Python library in building OAuth and OpenID Connect servers and clients." +category = "main" optional = true python-versions = "*" files = [ @@ -591,6 +621,7 @@ cryptography = ">=3.2" name = "awadb" version = "0.3.10" description = "AI Native database for embedding vectors" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -617,6 +648,7 @@ test = ["pytest (>=6.0)"] name = "azure-ai-formrecognizer" version = "3.3.0" description = "Microsoft Azure Form Recognizer Client Library for Python" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -634,6 +666,7 @@ typing-extensions = ">=4.0.1" name = "azure-ai-vision" version = "0.11.1b1" description = "Microsoft Azure AI Vision SDK for Python" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -645,6 +678,7 @@ files = [ name = "azure-cognitiveservices-speech" version = "1.31.0" description = "Microsoft Cognitive Services Speech SDK for Python" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -660,6 +694,7 @@ files = [ name = "azure-common" version = "1.1.28" description = "Microsoft Azure Client Library for Python (Common)" +category = "main" optional = true python-versions = "*" files = [ @@ -671,6 +706,7 @@ files = [ name = "azure-core" version = "1.29.1" description = "Microsoft Azure Core Library for Python" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -690,6 +726,7 @@ aio = ["aiohttp (>=3.0)"] name = "azure-cosmos" version = "4.5.0" description = "Microsoft Azure Cosmos Client Library for Python" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -704,6 +741,7 @@ azure-core = ">=1.23.0,<2.0.0" name = "azure-identity" version = "1.14.0" description = "Microsoft Azure Identity Library for Python" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -721,6 +759,7 @@ msal-extensions = ">=0.3.0,<2.0.0" name = "azure-search-documents" version = "11.4.0b8" description = "Microsoft Azure Cognitive Search Client Library for Python" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -737,6 +776,7 @@ isodate = ">=0.6.0" name = "babel" version = "2.12.1" description = "Internationalization utilities" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -751,6 +791,7 @@ pytz = {version = ">=2015.7", markers = "python_version < \"3.9\""} name = "backcall" version = "0.2.0" description = "Specifications for callback functions passed in to an API" +category = "dev" optional = false python-versions = "*" files = [ @@ -762,6 +803,7 @@ files = [ name = "backoff" version = "2.2.1" description = "Function decoration for backoff and retry" +category = "main" optional = true python-versions = ">=3.7,<4.0" files = [ @@ -773,6 +815,7 @@ files = [ name = "backports-zoneinfo" version = "0.2.1" description = "Backport of the standard library zoneinfo module" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -801,6 +844,7 @@ tzdata = ["tzdata"] name = "beautifulsoup4" version = "4.12.2" description = "Screen-scraping library" +category = "main" optional = false python-versions = ">=3.6.0" files = [ @@ -819,6 +863,7 @@ lxml = ["lxml"] name = "bibtexparser" version = "1.4.0" description = "Bibtex parser for python 3" +category = "main" optional = true python-versions = "*" files = [ @@ -832,6 +877,7 @@ pyparsing = ">=2.0.3" name = "black" version = "23.7.0" description = "The uncompromising code formatter." +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -878,6 +924,7 @@ uvloop = ["uvloop (>=0.15.2)"] name = "bleach" version = "6.0.0" description = "An easy safelist-based HTML-sanitizing tool." +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -896,6 +943,7 @@ css = ["tinycss2 (>=1.1.0,<1.2)"] name = "blinker" version = "1.6.2" description = "Fast, simple object-to-object and broadcast signaling" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -907,6 +955,7 @@ files = [ name = "boto3" version = "1.28.17" description = "The AWS SDK for Python" +category = "main" optional = true python-versions = ">= 3.7" files = [ @@ -926,6 +975,7 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"] name = "botocore" version = "1.31.17" description = "Low-level, data-driven core of boto 3." +category = "main" optional = true python-versions = ">= 3.7" files = [ @@ -945,6 +995,7 @@ crt = ["awscrt (==0.16.26)"] name = "brotli" version = "1.0.9" description = "Python bindings for the Brotli compression library" +category = "main" optional = true python-versions = "*" files = [ @@ -1036,6 +1087,7 @@ files = [ name = "brotlicffi" version = "1.0.9.2" description = "Python CFFI bindings to the Brotli library" +category = "main" optional = true python-versions = "*" files = [ @@ -1078,6 +1130,7 @@ cffi = ">=1.0.0" name = "build" version = "0.10.0" description = "A simple, correct Python build frontend" +category = "main" optional = true python-versions = ">= 3.7" files = [ @@ -1101,6 +1154,7 @@ virtualenv = ["virtualenv (>=20.0.35)"] name = "cachetools" version = "5.3.1" description = "Extensible memoizing collections and decorators" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -1112,6 +1166,7 @@ files = [ name = "cassandra-driver" version = "3.28.0" description = "DataStax Driver for Apache Cassandra" +category = "main" optional = false python-versions = "*" files = [ @@ -1161,13 +1216,14 @@ graph = ["gremlinpython (==3.4.6)"] [[package]] name = "cassio" -version = "0.0.7" -description = "A framework-agnostic Python library to seamlessly integrate Apache Cassandra with ML/LLM/genAI workloads." +version = "0.1.0" +description = "A framework-agnostic Python library to seamlessly integrate Apache Cassandra(R) with ML/LLM/genAI workloads." +category = "main" optional = false python-versions = ">=3.8" files = [ - {file = "cassio-0.0.7-py3-none-any.whl", hash = "sha256:394d8ca18076f051543917e46c06a61d43679c22c0ddf2717308259fcb3d7452"}, - {file = "cassio-0.0.7.tar.gz", hash = "sha256:68b3b036f6de64e728f91538ba81a3f0eb4cf217320ab4bf50bb7135bac9a903"}, + {file = "cassio-0.1.0-py3-none-any.whl", hash = "sha256:32839d07b7d67c2a48b2efe951af4b330a8a3f0dfd9dfb53cd09ee75dbd03b5a"}, + {file = "cassio-0.1.0.tar.gz", hash = "sha256:1e66d440bad82e647d7600567603c68a8566b01c1a41a753b61562ed0735e4b8"}, ] [package.dependencies] @@ -1178,6 +1234,7 @@ numpy = ">=1.0" name = "certifi" version = "2023.7.22" description = "Python package for providing Mozilla's CA Bundle." +category = "main" optional = false python-versions = ">=3.6" files = [ @@ -1189,6 +1246,7 @@ files = [ name = "cffi" version = "1.15.1" description = "Foreign Function Interface for Python calling C code." +category = "main" optional = false python-versions = "*" files = [ @@ -1265,6 +1323,7 @@ pycparser = "*" name = "chardet" version = "5.2.0" description = "Universal encoding detector for Python 3" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -1276,6 +1335,7 @@ files = [ name = "charset-normalizer" version = "3.2.0" description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." +category = "main" optional = false python-versions = ">=3.7.0" files = [ @@ -1360,6 +1420,7 @@ files = [ name = "clarifai" version = "9.7.1" description = "Clarifai Python Utilities" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -1376,6 +1437,7 @@ tritonclient = "2.34.0" name = "clarifai-grpc" version = "9.7.3" description = "Clarifai gRPC API Client" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -1393,6 +1455,7 @@ requests = ">=2.25.1" name = "click" version = "8.1.7" description = "Composable command line interface toolkit" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -1407,6 +1470,7 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""} name = "click-plugins" version = "1.1.1" description = "An extension module for click to enable registering CLI commands via setuptools entry-points." +category = "main" optional = true python-versions = "*" files = [ @@ -1424,6 +1488,7 @@ dev = ["coveralls", "pytest (>=3.6)", "pytest-cov", "wheel"] name = "clickhouse-connect" version = "0.5.25" description = "ClickHouse core driver, SqlAlchemy, and Superset libraries" +category = "main" optional = true python-versions = "~=3.7" files = [ @@ -1513,6 +1578,7 @@ superset = ["apache-superset (>=1.4.1)"] name = "cligj" version = "0.7.2" description = "Click params for commmand line interfaces to GeoJSON" +category = "main" optional = true python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, <4" files = [ @@ -1530,6 +1596,7 @@ test = ["pytest-cov"] name = "codespell" version = "2.2.5" description = "Codespell" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1547,6 +1614,7 @@ types = ["chardet (>=5.1.0)", "mypy", "pytest", "pytest-cov", "pytest-dependency name = "cohere" version = "4.21" description = "" +category = "main" optional = true python-versions = ">=3.7,<4.0" files = [ @@ -1566,6 +1634,7 @@ urllib3 = ">=1.26,<3" name = "colorama" version = "0.4.6" description = "Cross-platform colored terminal text." +category = "main" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7" files = [ @@ -1577,6 +1646,7 @@ files = [ name = "colored" version = "1.4.4" description = "Simple library for color and formatting to terminal" +category = "dev" optional = false python-versions = "*" files = [ @@ -1587,6 +1657,7 @@ files = [ name = "comm" version = "0.1.4" description = "Jupyter Python Comm implementation, for usage in ipykernel, xeus-python etc." +category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -1606,6 +1677,7 @@ typing = ["mypy (>=0.990)"] name = "coverage" version = "7.3.0" description = "Code coverage measurement for Python" +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -1673,6 +1745,7 @@ toml = ["tomli"] name = "cryptography" version = "41.0.3" description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers." +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -1718,6 +1791,7 @@ test-randomorder = ["pytest-randomly"] name = "cssselect" version = "1.2.0" description = "cssselect parses CSS3 Selectors and translates them to XPath 1.0" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -1725,10 +1799,31 @@ files = [ {file = "cssselect-1.2.0.tar.gz", hash = "sha256:666b19839cfaddb9ce9d36bfe4c969132c647b92fc9088c4e23f786b30f1b3dc"}, ] +[[package]] +name = "dashvector" +version = "1.0.1" +description = "DashVector Client Python Sdk Library" +category = "main" +optional = true +python-versions = ">=3.7.0" +files = [ + {file = "dashvector-1.0.1-py3-none-any.whl", hash = "sha256:e2fc362c65979d55cf605fb90deca4a292c69e1c2101df22430c80db744591ad"}, +] + +[package.dependencies] +aiohttp = ">=3.1.0" +grpcio = [ + {version = ">=1.22.0", markers = "python_version < \"3.11\""}, + {version = ">=1.49.1", markers = "python_version >= \"3.11\""}, +] +numpy = "*" +protobuf = ">=3.8.0,<4.0.0" + [[package]] name = "dataclasses-json" version = "0.5.9" description = "Easily serialize dataclasses to and from JSON" +category = "main" optional = false python-versions = ">=3.6" files = [ @@ -1748,6 +1843,7 @@ dev = ["flake8", "hypothesis", "ipython", "mypy (>=0.710)", "portray", "pytest ( name = "debugpy" version = "1.6.7.post1" description = "An implementation of the Debug Adapter Protocol for Python" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -1775,6 +1871,7 @@ files = [ name = "decorator" version = "5.1.1" description = "Decorators for Humans" +category = "main" optional = false python-versions = ">=3.5" files = [ @@ -1786,6 +1883,7 @@ files = [ name = "deeplake" version = "3.6.19" description = "Activeloop Deep Lake" +category = "main" optional = true python-versions = "*" files = [ @@ -1823,6 +1921,7 @@ visualizer = ["IPython", "flask"] name = "defusedxml" version = "0.7.1" description = "XML bomb protection for Python stdlib modules" +category = "dev" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" files = [ @@ -1834,6 +1933,7 @@ files = [ name = "deprecated" version = "1.2.14" description = "Python @deprecated decorator to deprecate old python classes, functions or methods." +category = "main" optional = true python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" files = [ @@ -1851,6 +1951,7 @@ dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "sphinx (<2)", "tox"] name = "deprecation" version = "2.1.0" description = "A library to handle automated deprecations" +category = "main" optional = true python-versions = "*" files = [ @@ -1865,6 +1966,7 @@ packaging = "*" name = "dill" version = "0.3.7" description = "serialize all of Python" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -1879,6 +1981,7 @@ graph = ["objgraph (>=1.7.2)"] name = "dnspython" version = "2.4.2" description = "DNS toolkit" +category = "main" optional = true python-versions = ">=3.8,<4.0" files = [ @@ -1898,6 +2001,7 @@ wmi = ["wmi (>=1.5.1,<2.0.0)"] name = "docarray" version = "0.32.1" description = "The data structure for multimodal data" +category = "main" optional = true python-versions = ">=3.7,<4.0" files = [ @@ -1936,6 +2040,7 @@ web = ["fastapi (>=0.87.0)"] name = "docker" version = "6.1.3" description = "A Python library for the Docker Engine API." +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -1957,6 +2062,7 @@ ssh = ["paramiko (>=2.4.3)"] name = "docopt" version = "0.6.2" description = "Pythonic argument parser, that will make you smile" +category = "main" optional = true python-versions = "*" files = [ @@ -1967,6 +2073,7 @@ files = [ name = "duckdb" version = "0.8.1" description = "DuckDB embedded database" +category = "dev" optional = false python-versions = "*" files = [ @@ -2028,6 +2135,7 @@ files = [ name = "duckdb-engine" version = "0.7.3" description = "SQLAlchemy driver for duckdb" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -2044,6 +2152,7 @@ sqlalchemy = ">=1.3.22" name = "duckduckgo-search" version = "3.8.5" description = "Search for words, documents, images, news, maps and text translation using the DuckDuckGo.com search engine." +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -2061,6 +2170,7 @@ lxml = ">=4.9.2" name = "elastic-transport" version = "8.4.0" description = "Transport classes and utilities shared among Python Elastic client libraries" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -2079,6 +2189,7 @@ develop = ["aiohttp", "mock", "pytest", "pytest-asyncio", "pytest-cov", "pytest- name = "elasticsearch" version = "8.9.0" description = "Python client for Elasticsearch" +category = "main" optional = true python-versions = ">=3.6, <4" files = [ @@ -2097,6 +2208,7 @@ requests = ["requests (>=2.4.0,<3.0.0)"] name = "entrypoints" version = "0.4" description = "Discover and load entry points from installed packages." +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -2108,6 +2220,7 @@ files = [ name = "esprima" version = "4.0.1" description = "ECMAScript parsing infrastructure for multipurpose analysis in Python" +category = "main" optional = true python-versions = "*" files = [ @@ -2118,6 +2231,7 @@ files = [ name = "exceptiongroup" version = "1.1.3" description = "Backport of PEP 654 (exception groups)" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -2132,6 +2246,7 @@ test = ["pytest (>=6)"] name = "executing" version = "1.2.0" description = "Get the currently executing AST node of a frame, and other information" +category = "dev" optional = false python-versions = "*" files = [ @@ -2146,6 +2261,7 @@ tests = ["asttokens", "littleutils", "pytest", "rich"] name = "faiss-cpu" version = "1.7.4" description = "A library for efficient similarity search and clustering of dense vectors." +category = "main" optional = true python-versions = "*" files = [ @@ -2180,6 +2296,7 @@ files = [ name = "fastavro" version = "1.8.2" description = "Fast read/write of AVRO files" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -2220,6 +2337,7 @@ zstandard = ["zstandard"] name = "fastjsonschema" version = "2.18.0" description = "Fastest Python implementation of JSON schema" +category = "dev" optional = false python-versions = "*" files = [ @@ -2234,6 +2352,7 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc name = "feedfinder2" version = "0.0.4" description = "Find the feed URLs for a website." +category = "main" optional = true python-versions = "*" files = [ @@ -2249,6 +2368,7 @@ six = "*" name = "feedparser" version = "6.0.10" description = "Universal feed parser, handles RSS 0.9x, RSS 1.0, RSS 2.0, CDF, Atom 0.3, and Atom 1.0 feeds" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -2263,6 +2383,7 @@ sgmllib3k = "*" name = "filelock" version = "3.12.2" description = "A platform independent file lock." +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -2278,6 +2399,7 @@ testing = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "diff-cover (>=7.5)", "p name = "fiona" version = "1.9.4.post1" description = "Fiona reads and writes spatial data files" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -2322,6 +2444,7 @@ test = ["Fiona[s3]", "pytest (>=7)", "pytest-cov", "pytz"] name = "flatbuffers" version = "23.5.26" description = "The FlatBuffers serialization format for Python" +category = "main" optional = true python-versions = "*" files = [ @@ -2333,6 +2456,7 @@ files = [ name = "fqdn" version = "1.5.1" description = "Validates fully-qualified domain names against RFC 1123, so that they are acceptable to modern bowsers" +category = "dev" optional = false python-versions = ">=2.7, !=3.0, !=3.1, !=3.2, !=3.3, !=3.4, <4" files = [ @@ -2344,6 +2468,7 @@ files = [ name = "freezegun" version = "1.2.2" description = "Let your Python tests travel through time" +category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -2358,6 +2483,7 @@ python-dateutil = ">=2.7" name = "frozenlist" version = "1.4.0" description = "A list-like structure which implements collections.abc.MutableSequence" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -2428,6 +2554,7 @@ files = [ name = "fsspec" version = "2023.6.0" description = "File-system specification" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -2463,6 +2590,7 @@ tqdm = ["tqdm"] name = "future" version = "0.18.3" description = "Clean single-source support for Python 3 and 2" +category = "main" optional = true python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*" files = [ @@ -2473,6 +2601,7 @@ files = [ name = "gast" version = "0.4.0" description = "Python AST that abstracts the underlying Python version" +category = "main" optional = true python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" files = [ @@ -2484,6 +2613,7 @@ files = [ name = "geojson" version = "2.5.0" description = "Python bindings and utilities for GeoJSON" +category = "main" optional = true python-versions = "*" files = [ @@ -2495,6 +2625,7 @@ files = [ name = "geomet" version = "0.2.1.post1" description = "GeoJSON <-> WKT/WKB conversion utilities" +category = "main" optional = false python-versions = ">2.6, !=3.3.*, <4" files = [ @@ -2510,6 +2641,7 @@ six = "*" name = "geopandas" version = "0.13.2" description = "Geographic pandas extensions" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -2528,6 +2660,7 @@ shapely = ">=1.7.1" name = "gitdb" version = "4.0.10" description = "Git Object Database" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -2542,6 +2675,7 @@ smmap = ">=3.0.1,<6" name = "gitpython" version = "3.1.32" description = "GitPython is a Python library used to interact with Git repositories" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -2556,6 +2690,7 @@ gitdb = ">=4.0.1,<5" name = "google-api-core" version = "2.11.1" description = "Google API client core library" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -2578,6 +2713,7 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"] name = "google-api-python-client" version = "2.70.0" description = "Google API Client Library for Python" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -2586,7 +2722,7 @@ files = [ ] [package.dependencies] -google-api-core = ">=1.31.5,<2.0.dev0 || >2.3.0,<3.0.0dev" +google-api-core = ">=1.31.5,<2.0.0 || >2.3.0,<3.0.0dev" google-auth = ">=1.19.0,<3.0.0dev" google-auth-httplib2 = ">=0.1.0" httplib2 = ">=0.15.0,<1dev" @@ -2596,6 +2732,7 @@ uritemplate = ">=3.0.1,<5" name = "google-auth" version = "2.22.0" description = "Google Authentication Library" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -2621,6 +2758,7 @@ requests = ["requests (>=2.20.0,<3.0.0.dev0)"] name = "google-auth-httplib2" version = "0.1.0" description = "Google Authentication Library: httplib2 transport" +category = "main" optional = true python-versions = "*" files = [ @@ -2637,6 +2775,7 @@ six = "*" name = "google-auth-oauthlib" version = "1.0.0" description = "Google Authentication Library" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -2655,6 +2794,7 @@ tool = ["click (>=6.0.0)"] name = "google-pasta" version = "0.2.0" description = "pasta is an AST-based Python refactoring library" +category = "main" optional = true python-versions = "*" files = [ @@ -2670,6 +2810,7 @@ six = "*" name = "google-search-results" version = "2.4.2" description = "Scrape and search localized results from Google, Bing, Baidu, Yahoo, Yandex, Ebay, Homedepot, youtube at scale using SerpApi.com" +category = "main" optional = true python-versions = ">=3.5" files = [ @@ -2683,6 +2824,7 @@ requests = "*" name = "googleapis-common-protos" version = "1.60.0" description = "Common protobufs used in Google APIs" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -2700,6 +2842,7 @@ grpc = ["grpcio (>=1.44.0,<2.0.0.dev0)"] name = "gptcache" version = "0.1.39.1" description = "GPTCache, a powerful caching library that can be used to speed up and lower the cost of chat applications that rely on the LLM service. GPTCache works as a memcache for AIGC applications, similar to how Redis works for traditional applications." +category = "main" optional = true python-versions = ">=3.8.1" files = [ @@ -2716,6 +2859,7 @@ requests = "*" name = "gql" version = "3.4.1" description = "GraphQL client for Python" +category = "main" optional = true python-versions = "*" files = [ @@ -2742,6 +2886,7 @@ websockets = ["websockets (>=10,<11)", "websockets (>=9,<10)"] name = "graphql-core" version = "3.2.3" description = "GraphQL implementation for Python, a port of GraphQL.js, the JavaScript reference implementation for GraphQL." +category = "main" optional = true python-versions = ">=3.6,<4" files = [ @@ -2753,6 +2898,7 @@ files = [ name = "greenlet" version = "2.0.2" description = "Lightweight in-process concurrent programming" +category = "main" optional = false python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*" files = [ @@ -2826,6 +2972,7 @@ test = ["objgraph", "psutil"] name = "grpcio" version = "1.57.0" description = "HTTP/2-based RPC framework" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -2883,6 +3030,7 @@ protobuf = ["grpcio-tools (>=1.57.0)"] name = "grpcio-tools" version = "1.48.2" description = "Protobuf code generator for gRPC" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -2943,6 +3091,7 @@ setuptools = "*" name = "h11" version = "0.14.0" description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -2954,6 +3103,7 @@ files = [ name = "h2" version = "4.1.0" description = "HTTP/2 State-Machine based protocol implementation" +category = "main" optional = true python-versions = ">=3.6.1" files = [ @@ -2969,6 +3119,7 @@ hyperframe = ">=6.0,<7" name = "h5py" version = "3.9.0" description = "Read and write HDF5 files from Python" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -3002,6 +3153,7 @@ numpy = ">=1.17.3" name = "hnswlib" version = "0.7.0" description = "hnswlib" +category = "main" optional = true python-versions = "*" files = [ @@ -3015,6 +3167,7 @@ numpy = "*" name = "hpack" version = "4.0.0" description = "Pure-Python HPACK header compression" +category = "main" optional = true python-versions = ">=3.6.1" files = [ @@ -3026,6 +3179,7 @@ files = [ name = "html2text" version = "2020.1.16" description = "Turn HTML into equivalent Markdown-structured text." +category = "main" optional = true python-versions = ">=3.5" files = [ @@ -3037,6 +3191,7 @@ files = [ name = "httpcore" version = "0.17.3" description = "A minimal low-level HTTP client." +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -3048,16 +3203,17 @@ files = [ anyio = ">=3.0,<5.0" certifi = "*" h11 = ">=0.13,<0.15" -sniffio = "==1.*" +sniffio = ">=1.0.0,<2.0.0" [package.extras] http2 = ["h2 (>=3,<5)"] -socks = ["socksio (==1.*)"] +socks = ["socksio (>=1.0.0,<2.0.0)"] [[package]] name = "httplib2" version = "0.22.0" description = "A comprehensive HTTP client library." +category = "main" optional = true python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" files = [ @@ -3072,6 +3228,7 @@ pyparsing = {version = ">=2.4.2,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.0.2 || >3.0 name = "httpx" version = "0.24.1" description = "The next generation HTTP client." +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -3087,18 +3244,19 @@ h2 = {version = ">=3,<5", optional = true, markers = "extra == \"http2\""} httpcore = ">=0.15.0,<0.18.0" idna = "*" sniffio = "*" -socksio = {version = "==1.*", optional = true, markers = "extra == \"socks\""} +socksio = {version = ">=1.0.0,<2.0.0", optional = true, markers = "extra == \"socks\""} [package.extras] brotli = ["brotli", "brotlicffi"] -cli = ["click (==8.*)", "pygments (==2.*)", "rich (>=10,<14)"] +cli = ["click (>=8.0.0,<9.0.0)", "pygments (>=2.0.0,<3.0.0)", "rich (>=10,<14)"] http2 = ["h2 (>=3,<5)"] -socks = ["socksio (==1.*)"] +socks = ["socksio (>=1.0.0,<2.0.0)"] [[package]] name = "huggingface-hub" version = "0.16.4" description = "Client library to download and publish models, datasets and other repos on the huggingface.co hub" +category = "main" optional = true python-versions = ">=3.7.0" files = [ @@ -3131,6 +3289,7 @@ typing = ["pydantic", "types-PyYAML", "types-requests", "types-simplejson", "typ name = "humbug" version = "0.3.2" description = "Humbug: Do you build developer tools? Humbug helps you know your users." +category = "main" optional = true python-versions = "*" files = [ @@ -3150,6 +3309,7 @@ profile = ["GPUtil", "psutil", "types-psutil"] name = "hyperframe" version = "6.0.1" description = "HTTP/2 framing layer for Python" +category = "main" optional = true python-versions = ">=3.6.1" files = [ @@ -3161,6 +3321,7 @@ files = [ name = "idna" version = "3.4" description = "Internationalized Domain Names in Applications (IDNA)" +category = "main" optional = false python-versions = ">=3.5" files = [ @@ -3172,6 +3333,7 @@ files = [ name = "importlib-metadata" version = "6.8.0" description = "Read metadata from Python packages" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -3191,6 +3353,7 @@ testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs name = "importlib-resources" version = "6.0.1" description = "Read resources from Python packages" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -3209,6 +3372,7 @@ testing = ["pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", name = "iniconfig" version = "2.0.0" description = "brain-dead simple config-ini parsing" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -3220,6 +3384,7 @@ files = [ name = "ipykernel" version = "6.25.1" description = "IPython Kernel for Jupyter" +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -3233,7 +3398,7 @@ comm = ">=0.1.1" debugpy = ">=1.6.5" ipython = ">=7.23.1" jupyter-client = ">=6.1.12" -jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" +jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" matplotlib-inline = ">=0.1" nest-asyncio = "*" packaging = "*" @@ -3253,6 +3418,7 @@ test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio" name = "ipython" version = "8.12.2" description = "IPython: Productive Interactive Computing" +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -3292,6 +3458,7 @@ test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pa name = "ipython-genutils" version = "0.2.0" description = "Vestigial utilities from IPython" +category = "dev" optional = false python-versions = "*" files = [ @@ -3303,6 +3470,7 @@ files = [ name = "ipywidgets" version = "8.1.0" description = "Jupyter interactive widgets" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -3324,6 +3492,7 @@ test = ["ipykernel", "jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"] name = "isodate" version = "0.6.1" description = "An ISO 8601 date/time/duration parser and formatter" +category = "main" optional = true python-versions = "*" files = [ @@ -3338,6 +3507,7 @@ six = "*" name = "isoduration" version = "20.11.0" description = "Operations with ISO 8601 durations" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -3352,6 +3522,7 @@ arrow = ">=0.15.0" name = "jaraco-context" version = "4.3.0" description = "Context managers by jaraco" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -3367,6 +3538,7 @@ testing = ["flake8 (<5)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-chec name = "jedi" version = "0.19.0" description = "An autocompletion tool for Python that can be used for text editors." +category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -3386,6 +3558,7 @@ testing = ["Django (<3.1)", "attrs", "colorama", "docopt", "pytest (<7.0.0)"] name = "jieba3k" version = "0.35.1" description = "Chinese Words Segementation Utilities" +category = "main" optional = true python-versions = "*" files = [ @@ -3396,6 +3569,7 @@ files = [ name = "jinja2" version = "3.1.2" description = "A very fast and expressive template engine." +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -3413,6 +3587,7 @@ i18n = ["Babel (>=2.7)"] name = "jmespath" version = "1.0.1" description = "JSON Matching Expressions" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -3424,6 +3599,7 @@ files = [ name = "joblib" version = "1.3.2" description = "Lightweight pipelining with Python functions" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -3435,6 +3611,7 @@ files = [ name = "jq" version = "1.4.1" description = "jq is a lightweight and flexible JSON processor." +category = "main" optional = true python-versions = ">=3.5" files = [ @@ -3499,6 +3676,7 @@ files = [ name = "json5" version = "0.9.14" description = "A Python implementation of the JSON5 data format." +category = "dev" optional = false python-versions = "*" files = [ @@ -3513,6 +3691,7 @@ dev = ["hypothesis"] name = "jsonable" version = "0.3.1" description = "An abstract class that supports jsonserialization/deserialization." +category = "main" optional = true python-versions = "*" files = [ @@ -3524,6 +3703,7 @@ files = [ name = "jsonlines" version = "3.1.0" description = "Library with helpers for the jsonlines file format" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -3538,6 +3718,7 @@ attrs = ">=19.2.0" name = "jsonpointer" version = "2.4" description = "Identify specific nodes in a JSON document (RFC 6901)" +category = "dev" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*" files = [ @@ -3549,6 +3730,7 @@ files = [ name = "jsonschema" version = "4.19.0" description = "An implementation of JSON Schema validation for Python" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -3580,6 +3762,7 @@ format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339- name = "jsonschema-specifications" version = "2023.7.1" description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -3595,6 +3778,7 @@ referencing = ">=0.28.0" name = "jupyter" version = "1.0.0" description = "Jupyter metapackage. Install all the Jupyter components in one go." +category = "dev" optional = false python-versions = "*" files = [ @@ -3615,6 +3799,7 @@ qtconsole = "*" name = "jupyter-client" version = "8.3.0" description = "Jupyter protocol implementation and client libraries" +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -3624,7 +3809,7 @@ files = [ [package.dependencies] importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""} -jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" +jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" python-dateutil = ">=2.8.2" pyzmq = ">=23.0" tornado = ">=6.2" @@ -3638,6 +3823,7 @@ test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pyt name = "jupyter-console" version = "6.6.3" description = "Jupyter terminal console" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -3649,7 +3835,7 @@ files = [ ipykernel = ">=6.14" ipython = "*" jupyter-client = ">=7.0.0" -jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" +jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" prompt-toolkit = ">=3.0.30" pygments = "*" pyzmq = ">=17" @@ -3662,6 +3848,7 @@ test = ["flaky", "pexpect", "pytest"] name = "jupyter-core" version = "5.3.1" description = "Jupyter core package. A base package on which Jupyter projects rely." +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -3682,6 +3869,7 @@ test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"] name = "jupyter-events" version = "0.7.0" description = "Jupyter Event System library" +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -3707,6 +3895,7 @@ test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "p name = "jupyter-lsp" version = "2.2.0" description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server" +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -3722,6 +3911,7 @@ jupyter-server = ">=1.1.2" name = "jupyter-server" version = "2.7.2" description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications." +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -3734,7 +3924,7 @@ anyio = ">=3.1.0" argon2-cffi = "*" jinja2 = "*" jupyter-client = ">=7.4.4" -jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" +jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" jupyter-events = ">=0.6.0" jupyter-server-terminals = "*" nbconvert = ">=6.4.4" @@ -3758,6 +3948,7 @@ test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-sc name = "jupyter-server-terminals" version = "0.4.4" description = "A Jupyter Server Extension Providing Terminals." +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -3777,6 +3968,7 @@ test = ["coverage", "jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-cov", name = "jupyterlab" version = "4.0.5" description = "JupyterLab computational environment" +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -3810,6 +4002,7 @@ test = ["coverage", "pytest (>=7.0)", "pytest-check-links (>=0.7)", "pytest-cons name = "jupyterlab-pygments" version = "0.2.2" description = "Pygments theme using JupyterLab CSS variables" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -3821,6 +4014,7 @@ files = [ name = "jupyterlab-server" version = "2.24.0" description = "A set of server components for JupyterLab and JupyterLab like applications." +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -3847,6 +4041,7 @@ test = ["hatch", "ipykernel", "jupyterlab-server[openapi]", "openapi-spec-valida name = "jupyterlab-widgets" version = "3.0.8" description = "Jupyter interactive widgets for JupyterLab" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -3858,6 +4053,7 @@ files = [ name = "keras" version = "2.13.1" description = "Deep learning for humans." +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -3869,6 +4065,7 @@ files = [ name = "lancedb" version = "0.1.16" description = "lancedb" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -3895,6 +4092,7 @@ tests = ["pandas (>=1.4)", "pytest", "pytest-asyncio", "pytest-mock"] name = "langkit" version = "0.0.15" description = "A collection of text metric udfs for whylogs profiling and monitoring in WhyLabs" +category = "main" optional = true python-versions = ">=3.8,<4.0" files = [ @@ -3914,6 +4112,7 @@ all = ["datasets (>=2.12.0,<3.0.0)", "evaluate (>=0.4.0,<0.5.0)", "nltk (>=3.8.1 name = "langsmith" version = "0.0.25" description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform." +category = "main" optional = false python-versions = ">=3.8.1,<4.0" files = [ @@ -3929,6 +4128,7 @@ requests = ">=2,<3" name = "lark" version = "1.1.7" description = "a modern parsing library" +category = "main" optional = false python-versions = ">=3.6" files = [ @@ -3946,6 +4146,7 @@ regex = ["regex"] name = "lazy-loader" version = "0.3" description = "lazy_loader" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -3961,11 +4162,10 @@ test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"] name = "libclang" version = "16.0.6" description = "Clang Python Bindings, mirrored from the official LLVM repo: https://github.com/llvm/llvm-project/tree/main/clang/bindings/python, to make the installation process easier." +category = "main" optional = true python-versions = "*" files = [ - {file = "libclang-16.0.6-1-py2.py3-none-manylinux2014_aarch64.whl", hash = "sha256:88bc7e7b393c32e41e03ba77ef02fdd647da1f764c2cd028e69e0837080b79f6"}, - {file = "libclang-16.0.6-1-py2.py3-none-manylinux2014_armv7l.whl", hash = "sha256:d80ed5827736ed5ec2bcedf536720476fd9d4fa4c79ef0cb24aea4c59332f361"}, {file = "libclang-16.0.6-py2.py3-none-macosx_10_9_x86_64.whl", hash = "sha256:da9e47ebc3f0a6d90fb169ef25f9fbcd29b4a4ef97a8b0e3e3a17800af1423f4"}, {file = "libclang-16.0.6-py2.py3-none-macosx_11_0_arm64.whl", hash = "sha256:e1a5ad1e895e5443e205568c85c04b4608e4e973dae42f4dfd9cb46c81d1486b"}, {file = "libclang-16.0.6-py2.py3-none-manylinux2010_x86_64.whl", hash = "sha256:9dcdc730939788b8b69ffd6d5d75fe5366e3ee007f1e36a99799ec0b0c001492"}, @@ -3981,6 +4181,7 @@ files = [ name = "libdeeplake" version = "0.0.60" description = "C++ backend for Deep Lake" +category = "main" optional = true python-versions = "*" files = [ @@ -4013,6 +4214,7 @@ numpy = "*" name = "librosa" version = "0.10.1" description = "Python module for audio and music processing" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -4044,6 +4246,7 @@ tests = ["matplotlib (>=3.3.0)", "packaging (>=20.0)", "pytest", "pytest-cov", " name = "llvmlite" version = "0.40.1" description = "lightweight wrapper around basic LLVM functionality" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -4077,6 +4280,7 @@ files = [ name = "loguru" version = "0.7.0" description = "Python logging made (stupidly) simple" +category = "main" optional = true python-versions = ">=3.5" files = [ @@ -4095,6 +4299,7 @@ dev = ["Sphinx (==5.3.0)", "colorama (==0.4.5)", "colorama (==0.4.6)", "freezegu name = "lxml" version = "4.9.3" description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API." +category = "main" optional = true python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, != 3.4.*" files = [ @@ -4202,6 +4407,7 @@ source = ["Cython (>=0.29.35)"] name = "lz4" version = "4.3.2" description = "LZ4 Bindings for Python" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -4251,6 +4457,7 @@ tests = ["psutil", "pytest (!=3.3.0)", "pytest-cov"] name = "manifest-ml" version = "0.0.1" description = "Manifest for Prompt Programming Foundation Models." +category = "main" optional = true python-versions = ">=3.8.0" files = [ @@ -4274,6 +4481,7 @@ dev = ["autopep8 (>=1.6.0)", "black (>=22.3.0)", "docformatter (>=1.4)", "flake8 name = "markdown" version = "3.4.4" description = "Python implementation of John Gruber's Markdown." +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -4289,6 +4497,7 @@ testing = ["coverage", "pyyaml"] name = "markdown-it-py" version = "3.0.0" description = "Python port of markdown-it. Markdown parsing, done right!" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -4313,6 +4522,7 @@ testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"] name = "markdownify" version = "0.11.6" description = "Convert HTML to markdown." +category = "main" optional = true python-versions = "*" files = [ @@ -4328,6 +4538,7 @@ six = ">=1.15,<2" name = "markupsafe" version = "2.1.3" description = "Safely add untrusted strings to HTML/XML markup." +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -4387,6 +4598,7 @@ files = [ name = "marqo" version = "1.2.4" description = "Tensor search for humans" +category = "main" optional = true python-versions = ">=3" files = [ @@ -4405,6 +4617,7 @@ urllib3 = "*" name = "marshmallow" version = "3.20.1" description = "A lightweight library for converting complex datatypes to and from native Python datatypes." +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -4425,6 +4638,7 @@ tests = ["pytest", "pytz", "simplejson"] name = "marshmallow-enum" version = "1.5.1" description = "Enum field for Marshmallow" +category = "main" optional = false python-versions = "*" files = [ @@ -4439,6 +4653,7 @@ marshmallow = ">=2.0.0" name = "matplotlib-inline" version = "0.1.6" description = "Inline Matplotlib backend for Jupyter" +category = "dev" optional = false python-versions = ">=3.5" files = [ @@ -4453,6 +4668,7 @@ traitlets = "*" name = "mdurl" version = "0.1.2" description = "Markdown URL utilities" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -4464,6 +4680,7 @@ files = [ name = "mistune" version = "3.0.1" description = "A sane and fast Markdown parser with useful plugins and renderers" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -4475,6 +4692,7 @@ files = [ name = "mmh3" version = "3.1.0" description = "Python wrapper for MurmurHash (MurmurHash3), a set of fast and robust hash functions." +category = "main" optional = true python-versions = "*" files = [ @@ -4519,6 +4737,7 @@ files = [ name = "momento" version = "1.7.1" description = "SDK for Momento" +category = "main" optional = true python-versions = ">=3.7,<4.0" files = [ @@ -4535,6 +4754,7 @@ pyjwt = ">=2.4.0,<3.0.0" name = "momento-wire-types" version = "0.67.0" description = "Momento Client Proto Generated Files" +category = "main" optional = true python-versions = ">=3.7,<4.0" files = [ @@ -4550,6 +4770,7 @@ protobuf = ">=3,<5" name = "more-itertools" version = "10.1.0" description = "More routines for operating on iterables, beyond itertools" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -4561,6 +4782,7 @@ files = [ name = "mpmath" version = "1.3.0" description = "Python library for arbitrary-precision floating-point arithmetic" +category = "main" optional = true python-versions = "*" files = [ @@ -4578,6 +4800,7 @@ tests = ["pytest (>=4.6)"] name = "msal" version = "1.23.0" description = "The Microsoft Authentication Library (MSAL) for Python library enables your app to access the Microsoft Cloud by supporting authentication of users with Microsoft Azure Active Directory accounts (AAD) and Microsoft Accounts (MSA) using industry standard OAuth2 and OpenID Connect." +category = "main" optional = true python-versions = "*" files = [ @@ -4597,6 +4820,7 @@ broker = ["pymsalruntime (>=0.13.2,<0.14)"] name = "msal-extensions" version = "1.0.0" description = "Microsoft Authentication Library extensions (MSAL EX) provides a persistence API that can save your data on disk, encrypted on Windows, macOS and Linux. Concurrent data access will be coordinated by a file lock mechanism." +category = "main" optional = true python-versions = "*" files = [ @@ -4615,6 +4839,7 @@ portalocker = [ name = "msgpack" version = "1.0.5" description = "MessagePack serializer" +category = "main" optional = true python-versions = "*" files = [ @@ -4687,6 +4912,7 @@ files = [ name = "msrest" version = "0.7.1" description = "AutoRest swagger generator Python client runtime." +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -4708,6 +4934,7 @@ async = ["aiodns", "aiohttp (>=3.0)"] name = "multidict" version = "6.0.4" description = "multidict implementation" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -4791,6 +5018,7 @@ files = [ name = "multiprocess" version = "0.70.15" description = "better multiprocessing and multithreading in Python" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -4819,6 +5047,7 @@ dill = ">=0.3.7" name = "mwcli" version = "0.0.3" description = "Utilities for processing MediaWiki on the command line." +category = "main" optional = true python-versions = "*" files = [ @@ -4835,6 +5064,7 @@ para = "*" name = "mwparserfromhell" version = "0.6.4" description = "MWParserFromHell is a parser for MediaWiki wikicode." +category = "main" optional = true python-versions = ">= 3.6" files = [ @@ -4872,6 +5102,7 @@ files = [ name = "mwtypes" version = "0.3.2" description = "A set of types for processing MediaWiki data." +category = "main" optional = true python-versions = "*" files = [ @@ -4886,6 +5117,7 @@ jsonable = ">=0.3.0" name = "mwxml" version = "0.3.3" description = "A set of utilities for processing MediaWiki XML dump data." +category = "main" optional = true python-versions = "*" files = [ @@ -4903,6 +5135,7 @@ para = ">=0.0.1" name = "mypy" version = "0.991" description = "Optional static typing for Python" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -4953,6 +5186,7 @@ reports = ["lxml"] name = "mypy-extensions" version = "1.0.0" description = "Type system extensions for programs checked with the mypy type checker." +category = "main" optional = false python-versions = ">=3.5" files = [ @@ -4964,6 +5198,7 @@ files = [ name = "mypy-protobuf" version = "3.3.0" description = "Generate mypy stub files from protobuf specs" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -4979,6 +5214,7 @@ types-protobuf = ">=3.19.12" name = "nbclient" version = "0.8.0" description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor." +category = "dev" optional = false python-versions = ">=3.8.0" files = [ @@ -4988,7 +5224,7 @@ files = [ [package.dependencies] jupyter-client = ">=6.1.12" -jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" +jupyter-core = ">=4.12,<5.0.0 || >=5.1.0" nbformat = ">=5.1" traitlets = ">=5.4" @@ -5001,6 +5237,7 @@ test = ["flaky", "ipykernel (>=6.19.3)", "ipython", "ipywidgets", "nbconvert (>= name = "nbconvert" version = "7.7.4" description = "Converting Jupyter Notebooks" +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -5039,6 +5276,7 @@ webpdf = ["playwright"] name = "nbformat" version = "5.9.2" description = "The Jupyter Notebook format" +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -5060,6 +5298,7 @@ test = ["pep440", "pre-commit", "pytest", "testpath"] name = "nebula3-python" version = "3.4.0" description = "Python client for NebulaGraph V3.4" +category = "main" optional = true python-versions = "*" files = [ @@ -5077,6 +5316,7 @@ six = ">=1.16.0" name = "neo4j" version = "5.11.0" description = "Neo4j Bolt driver for Python" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -5094,6 +5334,7 @@ pandas = ["numpy (>=1.7.0,<2.0.0)", "pandas (>=1.1.0,<3.0.0)"] name = "nest-asyncio" version = "1.5.7" description = "Patch asyncio to allow nested event loops" +category = "main" optional = false python-versions = ">=3.5" files = [ @@ -5105,6 +5346,7 @@ files = [ name = "networkx" version = "2.8.8" description = "Python package for creating and manipulating graphs and networks" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -5123,6 +5365,7 @@ test = ["codecov (>=2.1)", "pytest (>=7.2)", "pytest-cov (>=4.0)"] name = "newspaper3k" version = "0.2.8" description = "Simplified python article discovery & extraction." +category = "main" optional = true python-versions = "*" files = [ @@ -5149,6 +5392,7 @@ tldextract = ">=2.0.1" name = "nlpcloud" version = "1.1.44" description = "Python client for the NLP Cloud API" +category = "main" optional = true python-versions = "*" files = [ @@ -5163,6 +5407,7 @@ requests = "*" name = "nltk" version = "3.8.1" description = "Natural Language Toolkit" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -5188,6 +5433,7 @@ twitter = ["twython"] name = "nomic" version = "1.1.14" description = "The offical Nomic python client." +category = "main" optional = true python-versions = "*" files = [ @@ -5215,6 +5461,7 @@ gpt4all = ["peft (==0.3.0.dev0)", "sentencepiece", "torch", "transformers (==4.2 name = "notebook" version = "7.0.2" description = "Jupyter Notebook - A web-based notebook environment for interactive computing" +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -5239,6 +5486,7 @@ test = ["ipykernel", "jupyter-server[test] (>=2.4.0,<3)", "jupyterlab-server[tes name = "notebook-shim" version = "0.2.3" description = "A shim layer for notebook traits and config" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -5256,6 +5504,7 @@ test = ["pytest", "pytest-console-scripts", "pytest-jupyter", "pytest-tornasync" name = "numba" version = "0.57.1" description = "compiling Python code using LLVM" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -5287,13 +5536,14 @@ files = [ [package.dependencies] importlib-metadata = {version = "*", markers = "python_version < \"3.9\""} -llvmlite = "==0.40.*" +llvmlite = ">=0.40.0dev0,<0.41" numpy = ">=1.21,<1.25" [[package]] name = "numcodecs" version = "0.11.0" description = "A Python package providing buffer compression and transformation codecs for use" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -5326,6 +5576,7 @@ zfpy = ["zfpy (>=1.0.0)"] name = "numexpr" version = "2.8.5" description = "Fast numerical expression evaluator for NumPy" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -5368,6 +5619,7 @@ numpy = ">=1.13.3" name = "numpy" version = "1.24.3" description = "Fundamental package for array computing in Python" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -5405,6 +5657,7 @@ files = [ name = "nvidia-cublas-cu11" version = "11.10.3.66" description = "CUBLAS native runtime libraries" +category = "main" optional = true python-versions = ">=3" files = [ @@ -5420,6 +5673,7 @@ wheel = "*" name = "nvidia-cuda-nvrtc-cu11" version = "11.7.99" description = "NVRTC native runtime libraries" +category = "main" optional = true python-versions = ">=3" files = [ @@ -5436,6 +5690,7 @@ wheel = "*" name = "nvidia-cuda-runtime-cu11" version = "11.7.99" description = "CUDA Runtime native Libraries" +category = "main" optional = true python-versions = ">=3" files = [ @@ -5451,6 +5706,7 @@ wheel = "*" name = "nvidia-cudnn-cu11" version = "8.5.0.96" description = "cuDNN runtime libraries" +category = "main" optional = true python-versions = ">=3" files = [ @@ -5466,6 +5722,7 @@ wheel = "*" name = "o365" version = "2.0.27" description = "Microsoft Graph and Office 365 API made easy" +category = "main" optional = true python-versions = ">=3.4" files = [ @@ -5486,6 +5743,7 @@ tzlocal = ">=4.0,<5.0" name = "oauthlib" version = "3.2.2" description = "A generic, spec-compliant, thorough implementation of the OAuth request-signing logic" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -5502,6 +5760,7 @@ signedtoken = ["cryptography (>=3.0.0)", "pyjwt (>=2.0.0,<3)"] name = "openai" version = "0.27.8" description = "Python client library for the OpenAI API" +category = "main" optional = false python-versions = ">=3.7.1" files = [ @@ -5516,7 +5775,7 @@ tqdm = "*" [package.extras] datalib = ["numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"] -dev = ["black (>=21.6b0,<22.0)", "pytest (==6.*)", "pytest-asyncio", "pytest-mock"] +dev = ["black (>=21.6b0,<22.0)", "pytest (>=6.0.0,<7.0.0)", "pytest-asyncio", "pytest-mock"] embeddings = ["matplotlib", "numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)", "plotly", "scikit-learn (>=1.0.2)", "scipy", "tenacity (>=8.0.1)"] wandb = ["numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)", "wandb"] @@ -5524,6 +5783,7 @@ wandb = ["numpy", "openpyxl (>=3.0.7)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1 name = "openapi-schema-pydantic" version = "1.2.4" description = "OpenAPI (v3) specification schema as pydantic class" +category = "main" optional = true python-versions = ">=3.6.1" files = [ @@ -5538,6 +5798,7 @@ pydantic = ">=1.8.2" name = "openlm" version = "0.0.5" description = "Drop-in OpenAI-compatible that can call LLMs from other providers" +category = "main" optional = true python-versions = ">=3.8.1,<4.0" files = [ @@ -5552,6 +5813,7 @@ requests = ">=2,<3" name = "opensearch-py" version = "2.3.1" description = "Python client for OpenSearch" +category = "main" optional = true python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, <4" files = [ @@ -5576,6 +5838,7 @@ kerberos = ["requests-kerberos"] name = "opt-einsum" version = "3.3.0" description = "Optimizing numpys einsum function" +category = "main" optional = true python-versions = ">=3.5" files = [ @@ -5594,6 +5857,7 @@ tests = ["pytest", "pytest-cov", "pytest-pep8"] name = "orjson" version = "3.9.5" description = "Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -5663,6 +5927,7 @@ files = [ name = "overrides" version = "7.4.0" description = "A decorator to automatically detect mismatch when overriding a method." +category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -5674,6 +5939,7 @@ files = [ name = "packaging" version = "23.1" description = "Core utilities for Python packages" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -5685,6 +5951,7 @@ files = [ name = "pandas" version = "2.0.3" description = "Powerful data structures for data analysis, time series, and statistics" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -5752,6 +6019,7 @@ xml = ["lxml (>=4.6.3)"] name = "pandocfilters" version = "1.5.0" description = "Utilities for writing pandoc filters in python" +category = "dev" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" files = [ @@ -5763,6 +6031,7 @@ files = [ name = "para" version = "0.0.8" description = "a set utilities that ake advantage of python's 'multiprocessing' module to distribute CPU-intensive tasks" +category = "main" optional = true python-versions = "*" files = [ @@ -5774,6 +6043,7 @@ files = [ name = "parso" version = "0.8.3" description = "A Python Parser" +category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -5789,6 +6059,7 @@ testing = ["docopt", "pytest (<6.0.0)"] name = "pathos" version = "0.3.1" description = "parallel graph management and execution in heterogeneous computing" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -5806,6 +6077,7 @@ ppft = ">=1.7.6.7" name = "pathspec" version = "0.11.2" description = "Utility library for gitignore style pattern matching of file paths." +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -5817,6 +6089,7 @@ files = [ name = "pdfminer-six" version = "20221105" description = "PDF parser and analyzer" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -5837,6 +6110,7 @@ image = ["Pillow"] name = "pexpect" version = "4.8.0" description = "Pexpect allows easy control of interactive console applications." +category = "main" optional = false python-versions = "*" files = [ @@ -5851,6 +6125,7 @@ ptyprocess = ">=0.5" name = "pgvector" version = "0.1.8" description = "pgvector support for Python" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -5864,6 +6139,7 @@ numpy = "*" name = "pickleshare" version = "0.7.5" description = "Tiny 'shelve'-like database with concurrency support" +category = "dev" optional = false python-versions = "*" files = [ @@ -5875,6 +6151,7 @@ files = [ name = "pillow" version = "10.0.0" description = "Python Imaging Library (Fork)" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -5944,6 +6221,7 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa name = "pinecone-client" version = "2.2.2" description = "Pinecone client and SDK" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -5969,6 +6247,7 @@ grpc = ["googleapis-common-protos (>=1.53.0)", "grpc-gateway-protoc-gen-openapiv name = "pinecone-text" version = "0.4.2" description = "Text utilities library by Pinecone.io" +category = "main" optional = true python-versions = ">=3.8,<4.0" files = [ @@ -5988,6 +6267,7 @@ wget = ">=3.2,<4.0" name = "pkgutil-resolve-name" version = "1.3.10" description = "Resolve a name to an object." +category = "main" optional = false python-versions = ">=3.6" files = [ @@ -5999,6 +6279,7 @@ files = [ name = "platformdirs" version = "3.10.0" description = "A small Python package for determining appropriate platform-specific dirs, e.g. a \"user data dir\"." +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -6014,6 +6295,7 @@ test = ["appdirs (==1.4.4)", "covdefaults (>=2.3)", "pytest (>=7.4)", "pytest-co name = "playwright" version = "1.37.0" description = "A high-level API to automate web browsers" +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -6035,6 +6317,7 @@ typing-extensions = {version = "*", markers = "python_version <= \"3.8\""} name = "pluggy" version = "1.2.0" description = "plugin and hook calling mechanisms for python" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -6050,6 +6333,7 @@ testing = ["pytest", "pytest-benchmark"] name = "pooch" version = "1.7.0" description = "\"Pooch manages your Python library's sample data files: it automatically downloads and stores them in a local directory, with support for versioning and corruption checks.\"" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -6071,6 +6355,7 @@ xxhash = ["xxhash (>=1.4.3)"] name = "portalocker" version = "2.7.0" description = "Wraps the portalocker recipe for easy usage" +category = "main" optional = true python-versions = ">=3.5" files = [ @@ -6090,6 +6375,7 @@ tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "p name = "pox" version = "0.3.3" description = "utilities for filesystem exploration and automated builds" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -6101,6 +6387,7 @@ files = [ name = "ppft" version = "1.7.6.7" description = "distributed and parallel Python" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -6115,6 +6402,7 @@ dill = ["dill (>=0.3.7)"] name = "prometheus-client" version = "0.17.1" description = "Python client for the Prometheus monitoring system." +category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -6129,6 +6417,7 @@ twisted = ["twisted"] name = "prompt-toolkit" version = "3.0.39" description = "Library for building powerful interactive command lines in Python" +category = "dev" optional = false python-versions = ">=3.7.0" files = [ @@ -6143,6 +6432,7 @@ wcwidth = "*" name = "protobuf" version = "3.20.3" description = "Protocol Buffers" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -6174,6 +6464,7 @@ files = [ name = "psutil" version = "5.9.5" description = "Cross-platform lib for process and system monitoring in Python." +category = "dev" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" files = [ @@ -6200,6 +6491,7 @@ test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"] name = "psychicapi" version = "0.8.4" description = "Psychic.dev is an open-source data integration platform for LLMs. This is the Python client for Psychic" +category = "main" optional = true python-versions = "*" files = [ @@ -6214,6 +6506,7 @@ requests = "*" name = "psycopg2-binary" version = "2.9.7" description = "psycopg2 - Python-PostgreSQL Database Adapter" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -6283,6 +6576,7 @@ files = [ name = "ptyprocess" version = "0.7.0" description = "Run a subprocess in a pseudo terminal" +category = "main" optional = false python-versions = "*" files = [ @@ -6294,6 +6588,7 @@ files = [ name = "pure-eval" version = "0.2.2" description = "Safely evaluate AST nodes without side effects" +category = "dev" optional = false python-versions = "*" files = [ @@ -6308,6 +6603,7 @@ tests = ["pytest"] name = "py" version = "1.11.0" description = "library with cross-python path, ini-parsing, io, code, log facilities" +category = "main" optional = true python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" files = [ @@ -6319,6 +6615,7 @@ files = [ name = "py-trello" version = "0.19.0" description = "Python wrapper around the Trello API" +category = "main" optional = true python-versions = "*" files = [ @@ -6335,6 +6632,7 @@ requests-oauthlib = ">=0.4.1" name = "py4j" version = "0.10.9.7" description = "Enables Python programs to dynamically access arbitrary Java objects" +category = "main" optional = true python-versions = "*" files = [ @@ -6346,6 +6644,7 @@ files = [ name = "pyaes" version = "1.6.1" description = "Pure-Python Implementation of the AES block-cipher and common modes of operation" +category = "main" optional = true python-versions = "*" files = [ @@ -6356,6 +6655,7 @@ files = [ name = "pyarrow" version = "12.0.1" description = "Python library for Apache Arrow" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -6393,6 +6693,7 @@ numpy = ">=1.16.6" name = "pyasn1" version = "0.5.0" description = "Pure-Python implementation of ASN.1 types and DER/BER/CER codecs (X.208)" +category = "main" optional = true python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7" files = [ @@ -6404,6 +6705,7 @@ files = [ name = "pyasn1-modules" version = "0.3.0" description = "A collection of ASN.1-based protocols modules" +category = "main" optional = true python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7" files = [ @@ -6418,6 +6720,7 @@ pyasn1 = ">=0.4.6,<0.6.0" name = "pycares" version = "4.3.0" description = "Python interface for c-ares" +category = "main" optional = true python-versions = "*" files = [ @@ -6485,6 +6788,7 @@ idna = ["idna (>=2.1)"] name = "pycparser" version = "2.21" description = "C parser in Python" +category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" files = [ @@ -6496,6 +6800,7 @@ files = [ name = "pydantic" version = "1.10.12" description = "Data validation and settings management using python type hints" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -6548,6 +6853,7 @@ email = ["email-validator (>=1.0.3)"] name = "pydeck" version = "0.8.0" description = "Widget for deck.gl maps" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -6567,6 +6873,7 @@ jupyter = ["ipykernel (>=5.1.2)", "ipython (>=5.8.0)", "ipywidgets (>=7,<8)", "t name = "pyee" version = "9.0.4" description = "A port of node.js's EventEmitter to python." +category = "dev" optional = false python-versions = "*" files = [ @@ -6581,6 +6888,7 @@ typing-extensions = "*" name = "pygments" version = "2.16.1" description = "Pygments is a syntax highlighting package written in Python." +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -6595,6 +6903,7 @@ plugins = ["importlib-metadata"] name = "pyjwt" version = "2.8.0" description = "JSON Web Token implementation in Python" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -6615,6 +6924,7 @@ tests = ["coverage[toml] (==5.0.4)", "pytest (>=6.0.0,<7.0.0)"] name = "pylance" version = "0.5.10" description = "python wrapper for lance-rs" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -6636,6 +6946,7 @@ tests = ["duckdb", "ml_dtypes", "pandas (>=1.4)", "polars[pandas,pyarrow]", "pyt name = "pymongo" version = "4.5.0" description = "Python driver for MongoDB " +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -6737,6 +7048,7 @@ zstd = ["zstandard"] name = "pympler" version = "1.0.1" description = "A development tool to measure, monitor and analyze the memory behavior of Python objects." +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -6748,6 +7060,7 @@ files = [ name = "pymupdf" version = "1.22.5" description = "Python bindings for the PDF toolkit and renderer MuPDF" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -6787,6 +7100,7 @@ files = [ name = "pyowm" version = "3.3.0" description = "A Python wrapper around OpenWeatherMap web APIs" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -6806,6 +7120,7 @@ requests = [ name = "pyparsing" version = "3.1.1" description = "pyparsing module - Classes and methods to define and execute parsing grammars" +category = "main" optional = true python-versions = ">=3.6.8" files = [ @@ -6820,6 +7135,7 @@ diagrams = ["jinja2", "railroad-diagrams"] name = "pypdf" version = "3.15.2" description = "A pure-python PDF library capable of splitting, merging, cropping, and transforming PDF files" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -6841,6 +7157,7 @@ image = ["Pillow (>=8.0.0)"] name = "pypdfium2" version = "4.18.0" description = "Python bindings to PDFium" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -6862,6 +7179,7 @@ files = [ name = "pyphen" version = "0.14.0" description = "Pure Python module to hyphenate text" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -6877,6 +7195,7 @@ test = ["flake8", "isort", "pytest"] name = "pyproj" version = "3.5.0" description = "Python interface to PROJ (cartographic projections and coordinate transformations library)" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -6924,6 +7243,7 @@ certifi = "*" name = "pyproject-hooks" version = "1.0.0" description = "Wrappers to call pyproject.toml-based build backend hooks." +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -6938,6 +7258,7 @@ tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""} name = "pysocks" version = "1.7.1" description = "A Python SOCKS client module. See https://github.com/Anorov/PySocks for more information." +category = "main" optional = true python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" files = [ @@ -6950,6 +7271,7 @@ files = [ name = "pyspark" version = "3.4.1" description = "Apache Spark Python API" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -6970,6 +7292,7 @@ sql = ["numpy (>=1.15)", "pandas (>=1.0.5)", "pyarrow (>=1.0.0)"] name = "pytesseract" version = "0.3.10" description = "Python-tesseract is a python wrapper for Google's Tesseract-OCR" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -6985,6 +7308,7 @@ Pillow = ">=8.0.0" name = "pytest" version = "7.4.0" description = "pytest: simple powerful testing with Python" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -7007,6 +7331,7 @@ testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "no name = "pytest-asyncio" version = "0.20.3" description = "Pytest support for asyncio" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -7025,6 +7350,7 @@ testing = ["coverage (>=6.2)", "flaky (>=3.5.0)", "hypothesis (>=5.7.1)", "mypy name = "pytest-cov" version = "4.1.0" description = "Pytest plugin for measuring coverage." +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -7043,6 +7369,7 @@ testing = ["fields", "hunter", "process-tests", "pytest-xdist", "six", "virtuale name = "pytest-dotenv" version = "0.5.2" description = "A py.test plugin that parses environment files before running tests" +category = "dev" optional = false python-versions = "*" files = [ @@ -7058,6 +7385,7 @@ python-dotenv = ">=0.9.1" name = "pytest-mock" version = "3.11.1" description = "Thin-wrapper around the mock package for easier use with pytest" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -7075,6 +7403,7 @@ dev = ["pre-commit", "pytest-asyncio", "tox"] name = "pytest-socket" version = "0.6.0" description = "Pytest Plugin to disable socket calls during tests" +category = "dev" optional = false python-versions = ">=3.7,<4.0" files = [ @@ -7089,6 +7418,7 @@ pytest = ">=3.6.3" name = "pytest-vcr" version = "1.0.2" description = "Plugin for managing VCR.py cassettes" +category = "dev" optional = false python-versions = "*" files = [ @@ -7104,6 +7434,7 @@ vcrpy = "*" name = "pytest-watcher" version = "0.2.6" description = "Continiously runs pytest on changes in *.py files" +category = "dev" optional = false python-versions = ">=3.7.0,<4.0.0" files = [ @@ -7118,6 +7449,7 @@ watchdog = ">=2.0.0" name = "python-arango" version = "7.6.0" description = "Python Driver for ArangoDB" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -7141,6 +7473,7 @@ dev = ["black (>=22.3.0)", "flake8 (>=4.0.1)", "isort (>=5.10.1)", "mock", "mypy name = "python-dateutil" version = "2.8.2" description = "Extensions to the standard Python datetime module" +category = "main" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" files = [ @@ -7155,6 +7488,7 @@ six = ">=1.5" name = "python-dotenv" version = "1.0.0" description = "Read key-value pairs from a .env file and set them as environment variables" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -7169,6 +7503,7 @@ cli = ["click (>=5.0)"] name = "python-json-logger" version = "2.0.7" description = "A python library adding a json log formatter" +category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -7180,6 +7515,7 @@ files = [ name = "python-rapidjson" version = "1.10" description = "Python wrapper around rapidjson" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -7245,6 +7581,7 @@ files = [ name = "pytz" version = "2023.3" description = "World timezone definitions, modern and historical" +category = "main" optional = false python-versions = "*" files = [ @@ -7256,6 +7593,7 @@ files = [ name = "pytz-deprecation-shim" version = "0.1.0.post0" description = "Shims to make deprecation of pytz easier" +category = "main" optional = true python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,>=2.7" files = [ @@ -7271,6 +7609,7 @@ tzdata = {version = "*", markers = "python_version >= \"3.6\""} name = "pyvespa" version = "0.33.0" description = "Python API for vespa.ai" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -7295,6 +7634,7 @@ ml = ["keras-tuner", "tensorflow", "tensorflow-ranking", "torch (<1.13)", "trans name = "pywin32" version = "306" description = "Python for Window Extensions" +category = "main" optional = false python-versions = "*" files = [ @@ -7318,6 +7658,7 @@ files = [ name = "pywinpty" version = "2.0.11" description = "Pseudo terminal support for Windows from Python." +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -7332,6 +7673,7 @@ files = [ name = "pyyaml" version = "6.0.1" description = "YAML parser and emitter for Python" +category = "main" optional = false python-versions = ">=3.6" files = [ @@ -7381,6 +7723,7 @@ files = [ name = "pyzmq" version = "25.1.1" description = "Python bindings for 0MQ" +category = "dev" optional = false python-versions = ">=3.6" files = [ @@ -7486,6 +7829,7 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""} name = "qdrant-client" version = "1.4.0" description = "Client library for the Qdrant vector search engine" +category = "main" optional = true python-versions = ">=3.7,<3.12" files = [ @@ -7506,6 +7850,7 @@ urllib3 = ">=1.26.14,<2.0.0" name = "qtconsole" version = "5.4.3" description = "Jupyter Qt console" +category = "dev" optional = false python-versions = ">= 3.7" files = [ @@ -7532,6 +7877,7 @@ test = ["flaky", "pytest", "pytest-qt"] name = "qtpy" version = "2.3.1" description = "Provides an abstraction layer on top of the various Qt bindings (PyQt5/6 and PySide2/6)." +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -7549,6 +7895,7 @@ test = ["pytest (>=6,!=7.0.0,!=7.0.1)", "pytest-cov (>=3.0.0)", "pytest-qt"] name = "rank-bm25" version = "0.2.2" description = "Various BM25 algorithms for document ranking" +category = "main" optional = true python-versions = "*" files = [ @@ -7566,6 +7913,7 @@ dev = ["pytest"] name = "rapidfuzz" version = "3.2.0" description = "rapid fuzzy string matching" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -7670,6 +8018,7 @@ full = ["numpy"] name = "ratelimiter" version = "1.2.0.post0" description = "Simple python rate limiting object" +category = "main" optional = true python-versions = "*" files = [ @@ -7684,6 +8033,7 @@ test = ["pytest (>=3.0)", "pytest-asyncio"] name = "rdflib" version = "6.3.2" description = "RDFLib is a Python library for working with RDF, a simple yet powerful language for representing information." +category = "main" optional = true python-versions = ">=3.7,<4.0" files = [ @@ -7705,6 +8055,7 @@ networkx = ["networkx (>=2.0.0,<3.0.0)"] name = "redis" version = "4.6.0" description = "Python client for Redis database and key-value store" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -7723,6 +8074,7 @@ ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==20.0.1)", "requests (>=2.26.0)" name = "referencing" version = "0.30.2" description = "JSON Referencing + Python" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -7738,6 +8090,7 @@ rpds-py = ">=0.7.0" name = "regex" version = "2023.8.8" description = "Alternative regular expression module, to replace re." +category = "main" optional = false python-versions = ">=3.6" files = [ @@ -7835,6 +8188,7 @@ files = [ name = "requests" version = "2.31.0" description = "Python HTTP for Humans." +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -7857,6 +8211,7 @@ use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"] name = "requests-file" version = "1.5.1" description = "File transport adapter for Requests" +category = "main" optional = true python-versions = "*" files = [ @@ -7872,6 +8227,7 @@ six = "*" name = "requests-oauthlib" version = "1.3.1" description = "OAuthlib authentication support for Requests." +category = "main" optional = true python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" files = [ @@ -7890,6 +8246,7 @@ rsa = ["oauthlib[signedtoken] (>=3.0.0)"] name = "requests-toolbelt" version = "1.0.0" description = "A utility belt for advanced users of python-requests" +category = "main" optional = true python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*" files = [ @@ -7904,6 +8261,7 @@ requests = ">=2.0.1,<3.0.0" name = "responses" version = "0.22.0" description = "A utility library for mocking out the `requests` Python library." +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -7924,6 +8282,7 @@ tests = ["coverage (>=6.0.0)", "flake8", "mypy", "pytest (>=7.0.0)", "pytest-asy name = "retry" version = "0.9.2" description = "Easy to use retry decorator." +category = "main" optional = true python-versions = "*" files = [ @@ -7939,6 +8298,7 @@ py = ">=1.4.26,<2.0.0" name = "rfc3339-validator" version = "0.1.4" description = "A pure python RFC3339 validator" +category = "dev" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" files = [ @@ -7953,6 +8313,7 @@ six = "*" name = "rfc3986-validator" version = "0.1.1" description = "Pure python rfc3986 validator" +category = "dev" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" files = [ @@ -7964,6 +8325,7 @@ files = [ name = "rich" version = "13.5.2" description = "Render rich text, tables, progress bars, syntax highlighting, markdown and more to the terminal" +category = "main" optional = true python-versions = ">=3.7.0" files = [ @@ -7983,6 +8345,7 @@ jupyter = ["ipywidgets (>=7.5.1,<9)"] name = "rpds-py" version = "0.9.2" description = "Python bindings to Rust's persistent data structures (rpds)" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -8089,6 +8452,7 @@ files = [ name = "rsa" version = "4.9" description = "Pure-Python RSA implementation" +category = "main" optional = true python-versions = ">=3.6,<4" files = [ @@ -8103,6 +8467,7 @@ pyasn1 = ">=0.1.3" name = "ruff" version = "0.0.249" description = "An extremely fast Python linter, written in Rust." +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -8129,6 +8494,7 @@ files = [ name = "s3transfer" version = "0.6.2" description = "An Amazon S3 Transfer Manager" +category = "main" optional = true python-versions = ">= 3.7" files = [ @@ -8146,6 +8512,7 @@ crt = ["botocore[crt] (>=1.20.29,<2.0a.0)"] name = "safetensors" version = "0.3.2" description = "Fast and Safe Tensor serialization" +category = "main" optional = true python-versions = "*" files = [ @@ -8219,6 +8586,7 @@ torch = ["torch (>=1.10)"] name = "scikit-learn" version = "1.3.0" description = "A set of python modules for machine learning and data mining" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -8261,6 +8629,7 @@ tests = ["black (>=23.3.0)", "matplotlib (>=3.1.3)", "mypy (>=1.3)", "numpydoc ( name = "scipy" version = "1.9.3" description = "Fundamental algorithms for scientific computing in Python" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -8299,6 +8668,7 @@ test = ["asv", "gmpy2", "mpmath", "pytest", "pytest-cov", "pytest-xdist", "sciki name = "semver" version = "3.0.1" description = "Python helper for Semantic Versioning (https://semver.org)" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -8310,6 +8680,7 @@ files = [ name = "send2trash" version = "1.8.2" description = "Send file to trash natively under Mac OS X, Windows and Linux" +category = "dev" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7" files = [ @@ -8326,6 +8697,7 @@ win32 = ["pywin32"] name = "sentence-transformers" version = "2.2.2" description = "Multilingual text embeddings" +category = "main" optional = true python-versions = ">=3.6.0" files = [ @@ -8348,6 +8720,7 @@ transformers = ">=4.6.0,<5.0.0" name = "sentencepiece" version = "0.1.99" description = "SentencePiece python wrapper" +category = "main" optional = true python-versions = "*" files = [ @@ -8402,6 +8775,7 @@ files = [ name = "setuptools" version = "67.8.0" description = "Easily download, build, install, upgrade, and uninstall Python packages" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -8418,6 +8792,7 @@ testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs ( name = "sgmllib3k" version = "1.0.0" description = "Py3k port of sgmllib." +category = "main" optional = true python-versions = "*" files = [ @@ -8428,6 +8803,7 @@ files = [ name = "shapely" version = "2.0.1" description = "Manipulation and analysis of geometric objects" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -8475,13 +8851,14 @@ files = [ numpy = ">=1.14" [package.extras] -docs = ["matplotlib", "numpydoc (==1.1.*)", "sphinx", "sphinx-book-theme", "sphinx-remove-toctrees"] +docs = ["matplotlib", "numpydoc (>=1.1.0,<1.2.0)", "sphinx", "sphinx-book-theme", "sphinx-remove-toctrees"] test = ["pytest", "pytest-cov"] [[package]] name = "singlestoredb" version = "0.7.1" description = "Interface to the SingleStore database and cluster management APIs" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -8514,6 +8891,7 @@ sqlalchemy = ["sqlalchemy-singlestoredb"] name = "six" version = "1.16.0" description = "Python 2 and 3 compatibility utilities" +category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*" files = [ @@ -8525,6 +8903,7 @@ files = [ name = "smmap" version = "5.0.0" description = "A pure Python implementation of a sliding window memory map manager" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -8536,6 +8915,7 @@ files = [ name = "sniffio" version = "1.3.0" description = "Sniff out which async library your code is running under" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -8547,6 +8927,7 @@ files = [ name = "socksio" version = "1.0.0" description = "Sans-I/O implementation of SOCKS4, SOCKS4A, and SOCKS5." +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -8558,6 +8939,7 @@ files = [ name = "soundfile" version = "0.12.1" description = "An audio library based on libsndfile, CFFI and NumPy" +category = "main" optional = true python-versions = "*" files = [ @@ -8581,6 +8963,7 @@ numpy = ["numpy"] name = "soupsieve" version = "2.4.1" description = "A modern CSS selector implementation for Beautiful Soup." +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -8592,6 +8975,7 @@ files = [ name = "soxr" version = "0.3.6" description = "High quality, one-dimensional sample-rate conversion library" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -8633,6 +9017,7 @@ test = ["pytest"] name = "sqlalchemy" version = "2.0.20" description = "Database Abstraction Library" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -8680,7 +9065,7 @@ files = [ ] [package.dependencies] -greenlet = {version = "!=0.4.17", markers = "platform_machine == \"win32\" or platform_machine == \"WIN32\" or platform_machine == \"AMD64\" or platform_machine == \"amd64\" or platform_machine == \"x86_64\" or platform_machine == \"ppc64le\" or platform_machine == \"aarch64\""} +greenlet = {version = "!=0.4.17", markers = "platform_machine == \"aarch64\" or platform_machine == \"ppc64le\" or platform_machine == \"x86_64\" or platform_machine == \"amd64\" or platform_machine == \"AMD64\" or platform_machine == \"win32\" or platform_machine == \"WIN32\""} typing-extensions = ">=4.2.0" [package.extras] @@ -8707,10 +9092,27 @@ postgresql-psycopgbinary = ["psycopg[binary] (>=3.0.7)"] pymysql = ["pymysql"] sqlcipher = ["sqlcipher3-binary"] +[[package]] +name = "sqlite-vss" +version = "0.1.2" +description = "" +category = "main" +optional = true +python-versions = ">=3.7" +files = [ + {file = "sqlite_vss-0.1.2-py3-none-macosx_10_6_x86_64.whl", hash = "sha256:9eefa4207f8b522e32b2747fce44422c773e36710bf807613795218c7ba125f0"}, + {file = "sqlite_vss-0.1.2-py3-none-macosx_11_0_arm64.whl", hash = "sha256:84994eaf7fe700218b258422358c4536a6aca39b96026c308b28630967f954c4"}, + {file = "sqlite_vss-0.1.2-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux1_x86_64.whl", hash = "sha256:e44f03bc4cb214bb77b206519abfb623e3e4795967a569218e288927a7715806"}, +] + +[package.extras] +test = ["pytest"] + [[package]] name = "sqlitedict" version = "2.1.0" description = "Persistent dict in Python, backed up by sqlite3 and pickle, multithread-safe." +category = "main" optional = true python-versions = "*" files = [ @@ -8721,6 +9123,7 @@ files = [ name = "sqlparams" version = "5.1.0" description = "Convert between various DB API 2.0 parameter styles." +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -8732,6 +9135,7 @@ files = [ name = "stack-data" version = "0.6.2" description = "Extract data from python stack frames and tracebacks for informative displays" +category = "dev" optional = false python-versions = "*" files = [ @@ -8751,6 +9155,7 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"] name = "streamlit" version = "1.22.0" description = "A faster way to build and share data apps" +category = "main" optional = true python-versions = ">=3.7, !=3.9.7" files = [ @@ -8791,6 +9196,7 @@ snowflake = ["snowflake-snowpark-python"] name = "stringcase" version = "1.2.0" description = "String case converter." +category = "main" optional = true python-versions = "*" files = [ @@ -8801,6 +9207,7 @@ files = [ name = "sympy" version = "1.12" description = "Computer algebra system (CAS) in Python" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -8815,6 +9222,7 @@ mpmath = ">=0.19" name = "syrupy" version = "4.2.1" description = "Pytest Snapshot Test Utility" +category = "dev" optional = false python-versions = ">=3.8.1,<4" files = [ @@ -8830,6 +9238,7 @@ pytest = ">=7.0.0,<8.0.0" name = "telethon" version = "1.29.3" description = "Full-featured Telegram client library for Python 3" +category = "main" optional = true python-versions = ">=3.5" files = [ @@ -8847,6 +9256,7 @@ cryptg = ["cryptg"] name = "tenacity" version = "8.2.3" description = "Retry code until it succeeds" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -8861,6 +9271,7 @@ doc = ["reno", "sphinx", "tornado (>=4.5)"] name = "tensorboard" version = "2.13.0" description = "TensorBoard lets you watch Tensors Flow" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -8885,6 +9296,7 @@ wheel = ">=0.26" name = "tensorboard-data-server" version = "0.7.1" description = "Fast data loading for TensorBoard" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -8897,6 +9309,7 @@ files = [ name = "tensorflow" version = "2.13.0" description = "TensorFlow is an open source machine learning framework for everyone." +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -8949,6 +9362,7 @@ wrapt = ">=1.11.0" name = "tensorflow-estimator" version = "2.13.0" description = "TensorFlow Estimator." +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -8959,6 +9373,7 @@ files = [ name = "tensorflow-hub" version = "0.14.0" description = "TensorFlow Hub is a library to foster the publication, discovery, and consumption of reusable parts of machine learning models." +category = "main" optional = true python-versions = "*" files = [ @@ -8973,6 +9388,7 @@ protobuf = ">=3.19.6" name = "tensorflow-io-gcs-filesystem" version = "0.33.0" description = "TensorFlow IO" +category = "main" optional = true python-versions = ">=3.7, <3.12" files = [ @@ -9003,6 +9419,7 @@ tensorflow-rocm = ["tensorflow-rocm (>=2.13.0,<2.14.0)"] name = "tensorflow-macos" version = "2.13.0" description = "TensorFlow is an open source machine learning framework for everyone." +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -9038,6 +9455,7 @@ wrapt = ">=1.11.0" name = "tensorflow-text" version = "2.13.0" description = "TF.Text is a TensorFlow library of text related ops, modules, and subgraphs." +category = "main" optional = true python-versions = "*" files = [ @@ -9062,6 +9480,7 @@ tests = ["absl-py", "pytest", "tensorflow-datasets (>=3.2.0)"] name = "termcolor" version = "2.3.0" description = "ANSI color formatting for output in terminal" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -9076,6 +9495,7 @@ tests = ["pytest", "pytest-cov"] name = "terminado" version = "0.17.1" description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library." +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -9096,6 +9516,7 @@ test = ["pre-commit", "pytest (>=7.0)", "pytest-timeout"] name = "textstat" version = "0.7.3" description = "Calculate statistical features from text" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -9110,6 +9531,7 @@ pyphen = "*" name = "threadpoolctl" version = "3.2.0" description = "threadpoolctl" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -9121,6 +9543,7 @@ files = [ name = "tigrisdb" version = "1.0.0b6" description = "Python SDK for Tigris " +category = "main" optional = true python-versions = ">=3.8,<4.0" files = [ @@ -9136,6 +9559,7 @@ protobuf = ">=3.19.6" name = "tiktoken" version = "0.3.3" description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -9181,6 +9605,7 @@ blobfile = ["blobfile (>=2)"] name = "tinycss2" version = "1.2.1" description = "A tiny CSS parser" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -9199,6 +9624,7 @@ test = ["flake8", "isort", "pytest"] name = "tinysegmenter" version = "0.3" description = "Very compact Japanese tokenizer" +category = "main" optional = true python-versions = "*" files = [ @@ -9209,6 +9635,7 @@ files = [ name = "tldextract" version = "3.4.4" description = "Accurately separates a URL's subdomain, domain, and public suffix, using the Public Suffix List (PSL). By default, this includes the public ICANN TLDs and their exceptions. You can optionally support the Public Suffix List's private domains as well." +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -9226,6 +9653,7 @@ requests-file = ">=1.4" name = "tokenizers" version = "0.13.3" description = "Fast and Customizable Tokenizers" +category = "main" optional = true python-versions = "*" files = [ @@ -9280,6 +9708,7 @@ testing = ["black (==22.3)", "datasets", "numpy", "pytest", "requests"] name = "toml" version = "0.10.2" description = "Python Library for Tom's Obvious, Minimal Language" +category = "main" optional = false python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*" files = [ @@ -9291,6 +9720,7 @@ files = [ name = "tomli" version = "2.0.1" description = "A lil' TOML parser" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -9302,6 +9732,7 @@ files = [ name = "toolz" version = "0.12.0" description = "List processing tools and functional utilities" +category = "main" optional = true python-versions = ">=3.5" files = [ @@ -9313,6 +9744,7 @@ files = [ name = "torch" version = "1.13.1" description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration" +category = "main" optional = true python-versions = ">=3.7.0" files = [ @@ -9353,6 +9785,7 @@ opt-einsum = ["opt-einsum (>=3.3)"] name = "torchvision" version = "0.14.1" description = "image and video datasets and models for torch deep learning" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -9379,7 +9812,7 @@ files = [ [package.dependencies] numpy = "*" -pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0" +pillow = ">=5.3.0,<8.3.0 || >=8.4.0" requests = "*" torch = "1.13.1" typing-extensions = "*" @@ -9391,6 +9824,7 @@ scipy = ["scipy"] name = "tornado" version = "6.3.3" description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed." +category = "main" optional = false python-versions = ">= 3.8" files = [ @@ -9411,6 +9845,7 @@ files = [ name = "tqdm" version = "4.66.1" description = "Fast, Extensible Progress Meter" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -9431,6 +9866,7 @@ telegram = ["requests"] name = "traitlets" version = "5.9.0" description = "Traitlets Python configuration system" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -9446,6 +9882,7 @@ test = ["argcomplete (>=2.0)", "pre-commit", "pytest", "pytest-mock"] name = "transformers" version = "4.32.0" description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow" +category = "main" optional = true python-versions = ">=3.8.0" files = [ @@ -9515,6 +9952,7 @@ vision = ["Pillow (<10.0.0)"] name = "tritonclient" version = "2.34.0" description = "Python client library and utilities for communicating with Triton Inference Server" +category = "main" optional = true python-versions = "*" files = [ @@ -9536,6 +9974,7 @@ http = ["aiohttp (>=3.8.1,<4.0.0)", "geventhttpclient (>=1.4.4,<=2.0.2)", "numpy name = "types-chardet" version = "5.0.4.6" description = "Typing stubs for chardet" +category = "dev" optional = false python-versions = "*" files = [ @@ -9547,6 +9986,7 @@ files = [ name = "types-protobuf" version = "4.24.0.1" description = "Typing stubs for protobuf" +category = "dev" optional = false python-versions = "*" files = [ @@ -9558,6 +9998,7 @@ files = [ name = "types-pyopenssl" version = "23.2.0.2" description = "Typing stubs for pyOpenSSL" +category = "dev" optional = false python-versions = "*" files = [ @@ -9572,6 +10013,7 @@ cryptography = ">=35.0.0" name = "types-pytz" version = "2023.3.0.1" description = "Typing stubs for pytz" +category = "dev" optional = false python-versions = "*" files = [ @@ -9583,6 +10025,7 @@ files = [ name = "types-pyyaml" version = "6.0.12.11" description = "Typing stubs for PyYAML" +category = "dev" optional = false python-versions = "*" files = [ @@ -9594,6 +10037,7 @@ files = [ name = "types-redis" version = "4.6.0.5" description = "Typing stubs for redis" +category = "dev" optional = false python-versions = "*" files = [ @@ -9609,6 +10053,7 @@ types-pyOpenSSL = "*" name = "types-requests" version = "2.31.0.2" description = "Typing stubs for requests" +category = "main" optional = false python-versions = "*" files = [ @@ -9623,6 +10068,7 @@ types-urllib3 = "*" name = "types-toml" version = "0.10.8.7" description = "Typing stubs for toml" +category = "dev" optional = false python-versions = "*" files = [ @@ -9634,6 +10080,7 @@ files = [ name = "types-urllib3" version = "1.26.25.14" description = "Typing stubs for urllib3" +category = "main" optional = false python-versions = "*" files = [ @@ -9645,6 +10092,7 @@ files = [ name = "typing-extensions" version = "4.5.0" description = "Backported and Experimental Type Hints for Python 3.7+" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -9656,6 +10104,7 @@ files = [ name = "typing-inspect" version = "0.9.0" description = "Runtime inspection utilities for typing module." +category = "main" optional = false python-versions = "*" files = [ @@ -9671,6 +10120,7 @@ typing-extensions = ">=3.7.4" name = "tzdata" version = "2023.3" description = "Provider of IANA time zone data" +category = "main" optional = false python-versions = ">=2" files = [ @@ -9682,6 +10132,7 @@ files = [ name = "tzlocal" version = "4.3.1" description = "tzinfo object for the local timezone" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -9701,6 +10152,7 @@ devenv = ["black", "check-manifest", "flake8", "pyroma", "pytest (>=4.3)", "pyte name = "uri-template" version = "1.3.0" description = "RFC 6570 URI Template Processor" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -9715,6 +10167,7 @@ dev = ["flake8", "flake8-annotations", "flake8-bandit", "flake8-bugbear", "flake name = "uritemplate" version = "4.1.1" description = "Implementation of RFC 6570 URI Templates" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -9726,6 +10179,7 @@ files = [ name = "urllib3" version = "1.26.16" description = "HTTP library with thread-safe connection pooling, file post, and more." +category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*" files = [ @@ -9742,6 +10196,7 @@ socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"] name = "validators" version = "0.21.0" description = "Python Data Validation for Humans™" +category = "main" optional = true python-versions = ">=3.8,<4.0" files = [ @@ -9753,6 +10208,7 @@ files = [ name = "vcrpy" version = "5.1.0" description = "Automatically mock your HTTP interactions to simplify and speed up testing" +category = "dev" optional = false python-versions = ">=3.8" files = [ @@ -9797,6 +10253,7 @@ numpy = "*" name = "watchdog" version = "3.0.0" description = "Filesystem events monitoring" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -9836,6 +10293,7 @@ watchmedo = ["PyYAML (>=3.10)"] name = "wcwidth" version = "0.2.6" description = "Measures the displayed width of unicode strings in a terminal" +category = "dev" optional = false python-versions = "*" files = [ @@ -9847,6 +10305,7 @@ files = [ name = "weaviate-client" version = "3.23.0" description = "A python native Weaviate client" +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -9867,6 +10326,7 @@ grpc = ["grpcio", "grpcio-tools"] name = "webcolors" version = "1.13" description = "A library for working with the color formats defined by HTML and CSS." +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -9882,6 +10342,7 @@ tests = ["pytest", "pytest-cov"] name = "webencodings" version = "0.5.1" description = "Character encoding aliases for legacy web content" +category = "dev" optional = false python-versions = "*" files = [ @@ -9893,6 +10354,7 @@ files = [ name = "websocket-client" version = "1.6.2" description = "WebSocket client for Python with low level API options" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -9909,6 +10371,7 @@ test = ["websockets"] name = "websockets" version = "11.0.3" description = "An implementation of the WebSocket Protocol (RFC 6455 & 7692)" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -9988,6 +10451,7 @@ files = [ name = "werkzeug" version = "2.3.7" description = "The comprehensive WSGI web application library." +category = "main" optional = true python-versions = ">=3.8" files = [ @@ -10005,6 +10469,7 @@ watchdog = ["watchdog (>=2.3)"] name = "wget" version = "3.2" description = "pure python download utility" +category = "main" optional = true python-versions = "*" files = [ @@ -10015,6 +10480,7 @@ files = [ name = "wheel" version = "0.41.2" description = "A built-package format for Python" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -10029,6 +10495,7 @@ test = ["pytest (>=6.0.0)", "setuptools (>=65)"] name = "whylabs-client" version = "0.5.4" description = "WhyLabs API client" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -10044,6 +10511,7 @@ urllib3 = ">=1.25.3" name = "whylogs" version = "1.2.6" description = "Profile and monitor your ML data pipeline end-to-end" +category = "main" optional = true python-versions = ">=3.7.1,<4" files = [ @@ -10077,6 +10545,7 @@ viz = ["Pillow (>=9.2.0,<10.0.0)", "ipython", "numpy", "numpy (>=1.23.2)", "pyba name = "whylogs-sketching" version = "3.4.1.dev3" description = "sketching library of whylogs" +category = "main" optional = true python-versions = "*" files = [ @@ -10117,6 +10586,7 @@ files = [ name = "widgetsnbextension" version = "4.0.8" description = "Jupyter interactive widgets for Jupyter Notebook" +category = "dev" optional = false python-versions = ">=3.7" files = [ @@ -10128,6 +10598,7 @@ files = [ name = "wikipedia" version = "1.4.0" description = "Wikipedia API for Python" +category = "main" optional = true python-versions = "*" files = [ @@ -10142,6 +10613,7 @@ requests = ">=2.0.0,<3.0.0" name = "win32-setctime" version = "1.1.0" description = "A small Python utility to set file creation time on Windows" +category = "main" optional = true python-versions = ">=3.5" files = [ @@ -10156,6 +10628,7 @@ dev = ["black (>=19.3b0)", "pytest (>=4.6.2)"] name = "wolframalpha" version = "5.0.0" description = "Wolfram|Alpha 2.0 API client" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -10176,6 +10649,7 @@ testing = ["keyring", "pmxbot", "pytest (>=3.5,!=3.7.3)", "pytest-black (>=0.3.7 name = "wonderwords" version = "2.2.0" description = "A python package for random words and sentences in the english language" +category = "main" optional = true python-versions = ">=3.6" files = [ @@ -10190,6 +10664,7 @@ cli = ["rich (==9.10.0)"] name = "wrapt" version = "1.15.0" description = "Module for decorators, wrappers and monkey patching." +category = "main" optional = false python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7" files = [ @@ -10274,6 +10749,7 @@ files = [ name = "xata" version = "1.0.0b0" description = "Python client for Xata.io" +category = "main" optional = true python-versions = ">=3.8,<4.0" files = [ @@ -10291,6 +10767,7 @@ requests = ">=2.28.1,<3.0.0" name = "xmltodict" version = "0.13.0" description = "Makes working with XML feel like you are working with JSON" +category = "main" optional = true python-versions = ">=3.4" files = [ @@ -10302,6 +10779,7 @@ files = [ name = "yarl" version = "1.9.2" description = "Yet another URL library" +category = "main" optional = false python-versions = ">=3.7" files = [ @@ -10389,6 +10867,7 @@ multidict = ">=4.0" name = "zipp" version = "3.16.2" description = "Backport of pathlib-compatible object wrapper for zip files" +category = "main" optional = false python-versions = ">=3.8" files = [ @@ -10404,6 +10883,7 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p name = "zstandard" version = "0.21.0" description = "Zstandard bindings for Python" +category = "main" optional = true python-versions = ">=3.7" files = [ @@ -10459,15 +10939,15 @@ cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\ cffi = ["cffi (>=1.11)"] [extras] -all = ["O365", "aleph-alpha-client", "amadeus", "arxiv", "atlassian-python-api", "awadb", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-cosmos", "azure-identity", "beautifulsoup4", "clarifai", "clickhouse-connect", "cohere", "deeplake", "docarray", "duckduckgo-search", "elasticsearch", "esprima", "faiss-cpu", "google-api-python-client", "google-auth", "google-search-results", "gptcache", "html2text", "huggingface_hub", "jinja2", "jq", "lancedb", "langkit", "lark", "libdeeplake", "librosa", "lxml", "manifest-ml", "marqo", "momento", "nebula3-python", "neo4j", "networkx", "nlpcloud", "nltk", "nomic", "openai", "openlm", "opensearch-py", "pdfminer-six", "pexpect", "pgvector", "pinecone-client", "pinecone-text", "psycopg2-binary", "pymongo", "pyowm", "pypdf", "pytesseract", "python-arango", "pyvespa", "qdrant-client", "rdflib", "redis", "requests-toolbelt", "sentence-transformers", "singlestoredb", "tensorflow-text", "tigrisdb", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"] -azure = ["azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-core", "azure-cosmos", "azure-identity", "azure-search-documents", "openai"] +all = ["clarifai", "cohere", "openai", "nlpcloud", "huggingface_hub", "manifest-ml", "elasticsearch", "opensearch-py", "google-search-results", "faiss-cpu", "sentence-transformers", "transformers", "nltk", "wikipedia", "beautifulsoup4", "tiktoken", "torch", "jinja2", "pinecone-client", "pinecone-text", "marqo", "pymongo", "weaviate-client", "redis", "google-api-python-client", "google-auth", "wolframalpha", "qdrant-client", "tensorflow-text", "pypdf", "networkx", "nomic", "aleph-alpha-client", "deeplake", "libdeeplake", "pgvector", "psycopg2-binary", "pyowm", "pytesseract", "html2text", "atlassian-python-api", "gptcache", "duckduckgo-search", "arxiv", "azure-identity", "clickhouse-connect", "azure-cosmos", "lancedb", "langkit", "lark", "pexpect", "pyvespa", "O365", "jq", "docarray", "pdfminer-six", "lxml", "requests-toolbelt", "neo4j", "openlm", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "momento", "singlestoredb", "tigrisdb", "nebula3-python", "awadb", "esprima", "rdflib", "amadeus", "librosa", "python-arango"] +azure = ["azure-identity", "azure-cosmos", "openai", "azure-core", "azure-ai-formrecognizer", "azure-ai-vision", "azure-cognitiveservices-speech", "azure-search-documents"] clarifai = ["clarifai"] cohere = ["cohere"] docarray = ["docarray"] embeddings = ["sentence-transformers"] -extended-testing = ["amazon-textract-caller", "assemblyai", "atlassian-python-api", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "esprima", "faiss-cpu", "feedparser", "geopandas", "gitpython", "gql", "html2text", "jinja2", "jq", "lxml", "markdownify", "mwparserfromhell", "mwxml", "newspaper3k", "openai", "openai", "openapi-schema-pydantic", "pandas", "pdfminer-six", "pgvector", "psychicapi", "py-trello", "pymupdf", "pypdf", "pypdfium2", "pyspark", "rank-bm25", "rapidfuzz", "requests-toolbelt", "scikit-learn", "sentence-transformers", "streamlit", "sympy", "telethon", "tqdm", "vowpal-wabbit-next", "xata", "xmltodict"] +extended-testing = ["amazon-textract-caller", "assemblyai", "beautifulsoup4", "bibtexparser", "cassio", "chardet", "esprima", "jq", "pdfminer-six", "pgvector", "pypdf", "pymupdf", "pypdfium2", "tqdm", "lxml", "atlassian-python-api", "mwparserfromhell", "mwxml", "pandas", "telethon", "psychicapi", "gql", "requests-toolbelt", "html2text", "py-trello", "scikit-learn", "streamlit", "pyspark", "openai", "sympy", "rapidfuzz", "openai", "rank-bm25", "geopandas", "jinja2", "gitpython", "newspaper3k", "feedparser", "xata", "xmltodict", "faiss-cpu", "openapi-schema-pydantic", "markdownify", "dashvector", "sqlite-vss"] javascript = ["esprima"] -llms = ["clarifai", "cohere", "huggingface_hub", "manifest-ml", "nlpcloud", "openai", "openlm", "torch", "transformers"] +llms = ["clarifai", "cohere", "openai", "openlm", "nlpcloud", "huggingface_hub", "manifest-ml", "torch", "transformers"] openai = ["openai", "tiktoken"] qdrant = ["qdrant-client"] text-helpers = ["chardet"] @@ -10475,4 +10955,4 @@ text-helpers = ["chardet"] [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "54927d0a4c82ca2038d109f0415f56b0d4b1dd6dbdc35a4eab2a2c6cd0a4c443" +content-hash = "b63078268a80c07577b432114302f4f86d47be25b83a245affb0dbc999fb2c1f" diff --git a/libs/langchain/pyproject.toml b/libs/langchain/pyproject.toml index c00ef4bf0e..00b90087ef 100644 --- a/libs/langchain/pyproject.toml +++ b/libs/langchain/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "langchain" -version = "0.0.278" +version = "0.0.281" description = "Building applications with LLMs through composability" authors = [] license = "MIT" @@ -109,7 +109,7 @@ azure-search-documents = {version = "11.4.0b8", optional = true} esprima = {version = "^4.0.1", optional = true} streamlit = {version = "^1.18.0", optional = true, python = ">=3.8.1,<3.9.7 || >3.9.7,<4.0"} psychicapi = {version = "^0.8.0", optional = true} -cassio = {version = "^0.0.7", optional = true} +cassio = {version = "^0.1.0", optional = true} rdflib = {version = "^6.3.2", optional = true} sympy = {version = "^1.12", optional = true} rapidfuzz = {version = "^3.1.1", optional = true} @@ -128,6 +128,8 @@ xmltodict = {version = "^0.13.0", optional = true} vowpal-wabbit-next = {version = "0.6.0", optional = true} markdownify = {version = "^0.11.6", optional = true} assemblyai = {version = "^0.17.0", optional = true} +dashvector = {version = "^1.0.1", optional = true} +sqlite-vss = {version = "^0.1.2", optional = true} [tool.poetry.group.test.dependencies] @@ -174,7 +176,7 @@ pytest-vcr = "^1.0.2" wrapt = "^1.15.0" openai = "^0.27.4" python-dotenv = "^1.0.0" -cassio = "^0.0.7" +cassio = "^0.1.0" tiktoken = "^0.3.2" [tool.poetry.group.lint.dependencies] @@ -344,6 +346,8 @@ extended_testing = [ "vowpal-wabbit-next", "sentence-transformers", "markdownify", + "dashvector", + "sqlite-vss", ] [tool.ruff] diff --git a/libs/langchain/tests/data.py b/libs/langchain/tests/data.py index 228a9b212e..c3b240bbc5 100644 --- a/libs/langchain/tests/data.py +++ b/libs/langchain/tests/data.py @@ -8,3 +8,4 @@ _EXAMPLES_DIR = _THIS_DIR / "integration_tests" / "examples" # Paths to test PDF files HELLO_PDF = _EXAMPLES_DIR / "hello.pdf" LAYOUT_PARSER_PAPER_PDF = _EXAMPLES_DIR / "layout-parser-paper.pdf" +DUPLICATE_CHARS = _EXAMPLES_DIR / "duplicate-chars.pdf" diff --git a/libs/langchain/tests/integration_tests/cache/test_cassandra.py b/libs/langchain/tests/integration_tests/cache/test_cassandra.py new file mode 100644 index 0000000000..760458f12b --- /dev/null +++ b/libs/langchain/tests/integration_tests/cache/test_cassandra.py @@ -0,0 +1,100 @@ +"""Test Cassandra caches. Requires a running vector-capable Cassandra cluster.""" +import os +import time +from typing import Any, Iterator, Tuple + +import pytest + +import langchain +from langchain.cache import CassandraCache, CassandraSemanticCache +from langchain.schema import Generation, LLMResult +from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings +from tests.unit_tests.llms.fake_llm import FakeLLM + + +@pytest.fixture(scope="module") +def cassandra_connection() -> Iterator[Tuple[Any, str]]: + from cassandra.cluster import Cluster + + keyspace = "langchain_cache_test_keyspace" + # get db connection + if "CASSANDRA_CONTACT_POINTS" in os.environ: + contact_points = os.environ["CONTACT_POINTS"].split(",") + cluster = Cluster(contact_points) + else: + cluster = Cluster() + # + session = cluster.connect() + # ensure keyspace exists + session.execute( + ( + f"CREATE KEYSPACE IF NOT EXISTS {keyspace} " + f"WITH replication = {{'class': 'SimpleStrategy', 'replication_factor': 1}}" + ) + ) + + yield (session, keyspace) + + +def test_cassandra_cache(cassandra_connection: Tuple[Any, str]) -> None: + session, keyspace = cassandra_connection + cache = CassandraCache(session, keyspace) + langchain.llm_cache = cache + llm = FakeLLM() + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + langchain.llm_cache.update("foo", llm_string, [Generation(text="fizz")]) + output = llm.generate(["foo"]) + print(output) + expected_output = LLMResult( + generations=[[Generation(text="fizz")]], + llm_output={}, + ) + print(expected_output) + assert output == expected_output + cache.clear() + + +def test_cassandra_cache_ttl(cassandra_connection: Tuple[Any, str]) -> None: + session, keyspace = cassandra_connection + cache = CassandraCache(session, keyspace, ttl_seconds=2) + langchain.llm_cache = cache + llm = FakeLLM() + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + langchain.llm_cache.update("foo", llm_string, [Generation(text="fizz")]) + expected_output = LLMResult( + generations=[[Generation(text="fizz")]], + llm_output={}, + ) + output = llm.generate(["foo"]) + assert output == expected_output + time.sleep(2.5) + # entry has expired away. + output = llm.generate(["foo"]) + assert output != expected_output + cache.clear() + + +def test_cassandra_semantic_cache(cassandra_connection: Tuple[Any, str]) -> None: + session, keyspace = cassandra_connection + sem_cache = CassandraSemanticCache(session, keyspace, embedding=FakeEmbeddings()) + langchain.llm_cache = sem_cache + llm = FakeLLM() + params = llm.dict() + params["stop"] = None + llm_string = str(sorted([(k, v) for k, v in params.items()])) + langchain.llm_cache.update("foo", llm_string, [Generation(text="fizz")]) + output = llm.generate(["bar"]) # same embedding as 'foo' + expected_output = LLMResult( + generations=[[Generation(text="fizz")]], + llm_output={}, + ) + assert output == expected_output + # clear the cache + sem_cache.clear() + output = llm.generate(["bar"]) # 'fizz' is erased away now + assert output != expected_output + sem_cache.clear() diff --git a/libs/langchain/tests/integration_tests/chat_models/test_anthropic.py b/libs/langchain/tests/integration_tests/chat_models/test_anthropic.py index 0237b055d1..5e3848d382 100644 --- a/libs/langchain/tests/integration_tests/chat_models/test_anthropic.py +++ b/libs/langchain/tests/integration_tests/chat_models/test_anthropic.py @@ -4,11 +4,11 @@ from typing import List import pytest from langchain.callbacks.manager import CallbackManager -from langchain.chat_models.anthropic import ChatAnthropic -from langchain.schema import ( - ChatGeneration, - LLMResult, +from langchain.chat_models.anthropic import ( + ChatAnthropic, + convert_messages_to_prompt_anthropic, ) +from langchain.schema import ChatGeneration, LLMResult from langchain.schema.messages import AIMessage, BaseMessage, HumanMessage from tests.unit_tests.callbacks.fake_callback_handler import FakeCallbackHandler @@ -86,14 +86,12 @@ async def test_anthropic_async_streaming_callback() -> None: def test_formatting() -> None: - chat = ChatAnthropic() - - chat_messages: List[BaseMessage] = [HumanMessage(content="Hello")] - result = chat._convert_messages_to_prompt(chat_messages) + messages: List[BaseMessage] = [HumanMessage(content="Hello")] + result = convert_messages_to_prompt_anthropic(messages) assert result == "\n\nHuman: Hello\n\nAssistant:" - chat_messages = [HumanMessage(content="Hello"), AIMessage(content="Answer:")] - result = chat._convert_messages_to_prompt(chat_messages) + messages = [HumanMessage(content="Hello"), AIMessage(content="Answer:")] + result = convert_messages_to_prompt_anthropic(messages) assert result == "\n\nHuman: Hello\n\nAssistant: Answer:" diff --git a/libs/langchain/tests/integration_tests/document_loaders/parsers/test_pdf_parsers.py b/libs/langchain/tests/integration_tests/document_loaders/parsers/test_pdf_parsers.py index 7b76e0f721..408498c126 100644 --- a/libs/langchain/tests/integration_tests/document_loaders/parsers/test_pdf_parsers.py +++ b/libs/langchain/tests/integration_tests/document_loaders/parsers/test_pdf_parsers.py @@ -19,6 +19,10 @@ LAYOUT_PARSER_PAPER_PDF = ( Path(__file__).parent.parent.parent / "examples" / "layout-parser-paper.pdf" ) +DUPLICATE_CHARS = ( + Path(__file__).parent.parent.parent / "examples" / "duplicate-chars.pdf" +) + def _assert_with_parser(parser: BaseBlobParser, splits_by_page: bool = True) -> None: """Standard tests to verify that the given parser works. @@ -59,6 +63,26 @@ def _assert_with_parser(parser: BaseBlobParser, splits_by_page: bool = True) -> assert metadata["page"] == 0 +def _assert_with_duplicate_parser(parser: BaseBlobParser, dedupe: bool = False) -> None: + """PDFPlumber tests to verify that duplicate characters appear or not + Args: + parser (BaseBlobParser): The parser to test. + splits_by_page (bool): Whether the parser splits by page or not by default. + dedupe: Avoiding the error of duplicate characters if `dedupe=True`. + """ + blob = Blob.from_path(DUPLICATE_CHARS) + doc_generator = parser.lazy_parse(blob) + assert isinstance(doc_generator, Iterator) + docs = list(doc_generator) + + if dedupe: + # use dedupe avoid duplicate characters. + assert "1000 Series" == docs[0].page_content.split("\n")[0] + else: + # duplicate characters will appear in doc if not dedupe + assert "11000000 SSeerriieess" == docs[0].page_content.split("\n")[0] + + def test_pymupdf_loader() -> None: """Test PyMuPDF loader.""" _assert_with_parser(PyMuPDFParser()) @@ -84,3 +108,5 @@ def test_pypdfium2_parser() -> None: def test_pdfplumber_parser() -> None: """Test PDFPlumber parser.""" _assert_with_parser(PDFPlumberParser()) + _assert_with_duplicate_parser(PDFPlumberParser()) + _assert_with_duplicate_parser(PDFPlumberParser(dedupe=True), dedupe=True) diff --git a/libs/langchain/tests/integration_tests/examples/duplicate-chars.pdf b/libs/langchain/tests/integration_tests/examples/duplicate-chars.pdf new file mode 100644 index 0000000000..47467cd035 Binary files /dev/null and b/libs/langchain/tests/integration_tests/examples/duplicate-chars.pdf differ diff --git a/libs/langchain/tests/integration_tests/llms/test_edenai.py b/libs/langchain/tests/integration_tests/llms/test_edenai.py index 11848fd59a..e0766756af 100644 --- a/libs/langchain/tests/integration_tests/llms/test_edenai.py +++ b/libs/langchain/tests/integration_tests/llms/test_edenai.py @@ -13,7 +13,7 @@ from langchain.llms import EdenAI def test_edenai_call() -> None: """Test simple call to edenai.""" - llm = EdenAI(provider="openai", params={"temperature": 0.2, "max_tokens": 250}) + llm = EdenAI(provider="openai", temperature=0.2, max_tokens=250) output = llm("Say foo:") assert llm._llm_type == "edenai" @@ -24,9 +24,23 @@ def test_edenai_call() -> None: async def test_edenai_acall() -> None: """Test simple call to edenai.""" - llm = EdenAI(provider="openai", params={"temperature": 0.2, "max_tokens": 250}) + llm = EdenAI(provider="openai", temperature=0.2, max_tokens=250) output = await llm.agenerate(["Say foo:"]) assert llm._llm_type == "edenai" assert llm.feature == "text" assert llm.subfeature == "generation" assert isinstance(output, str) + + +def test_edenai_call_with_old_params() -> None: + """ + Test simple call to edenai with using `params` + to pass optional parameters to api + """ + llm = EdenAI(provider="openai", params={"temperature": 0.2, "max_tokens": 250}) + output = llm("Say foo:") + + assert llm._llm_type == "edenai" + assert llm.feature == "text" + assert llm.subfeature == "generation" + assert isinstance(output, str) diff --git a/libs/langchain/tests/integration_tests/llms/test_vertexai.py b/libs/langchain/tests/integration_tests/llms/test_vertexai.py index 8ee7836b00..c995147c0e 100644 --- a/libs/langchain/tests/integration_tests/llms/test_vertexai.py +++ b/libs/langchain/tests/integration_tests/llms/test_vertexai.py @@ -7,7 +7,10 @@ pip install google-cloud-aiplatform>=1.25.0 Your end-user credentials would be used to make the calls (make sure you've run `gcloud auth login` first). """ -from langchain.llms import VertexAI +import os + +from langchain.llms import VertexAI, VertexAIModelGarden +from langchain.schema import LLMResult def test_vertex_call() -> None: @@ -16,3 +19,34 @@ def test_vertex_call() -> None: assert isinstance(output, str) assert llm._llm_type == "vertexai" assert llm.model_name == llm.client._model_id + + +def test_model_garden() -> None: + """In order to run this test, you should provide an endpoint name. + + Example: + export ENDPOINT_ID=... + export PROJECT=... + """ + endpoint_id = os.environ["ENDPOINT_ID"] + project = os.environ["PROJECT"] + llm = VertexAIModelGarden(endpoint_id=endpoint_id, project=project) + output = llm("What is the meaning of life?") + print(output) + assert isinstance(output, str) + assert llm._llm_type == "vertexai_model_garden" + + +def test_model_garden_batch() -> None: + """In order to run this test, you should provide an endpoint name. + + Example: + export ENDPOINT_ID=... + export PROJECT=... + """ + endpoint_id = os.environ["ENDPOINT_ID"] + project = os.environ["PROJECT"] + llm = VertexAIModelGarden(endpoint_id=endpoint_id, project=project) + output = llm._generate(["What is the meaning of life?", "How much is 2+2"]) + assert isinstance(output, LLMResult) + assert len(output.generations) == 2 diff --git a/libs/langchain/tests/integration_tests/tools/edenai/__init__.py b/libs/langchain/tests/integration_tests/tools/edenai/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/libs/langchain/tests/integration_tests/tools/edenai/test_audio_speech_to_text.py b/libs/langchain/tests/integration_tests/tools/edenai/test_audio_speech_to_text.py new file mode 100644 index 0000000000..14375cd60a --- /dev/null +++ b/libs/langchain/tests/integration_tests/tools/edenai/test_audio_speech_to_text.py @@ -0,0 +1,25 @@ +"""Test EdenAi's speech to text Tool . + +In order to run this test, you need to have an EdenAI api key. +You can get it by registering for free at https://app.edenai.run/user/register. +A test key can be found at https://app.edenai.run/admin/account/settings by +clicking on the 'sandbox' toggle. +(calls will be free, and will return dummy results) + +You'll then need to set EDENAI_API_KEY environment variable to your api key. +""" +from langchain.tools.edenai import EdenAiSpeechToTextTool + + +def test_edenai_call() -> None: + """Test simple call to edenai's speech to text endpoint.""" + speech2text = EdenAiSpeechToTextTool(providers=["amazon"]) + + output = speech2text( + "https://audio-samples.github.io/samples/mp3/blizzard_unconditional/sample-0.mp3" + ) + + assert speech2text.name == "edenai_speech_to_text" + assert speech2text.feature == "audio" + assert speech2text.subfeature == "speech_to_text_async" + assert isinstance(output, str) diff --git a/libs/langchain/tests/integration_tests/tools/edenai/test_audio_text_to_speech.py b/libs/langchain/tests/integration_tests/tools/edenai/test_audio_text_to_speech.py new file mode 100644 index 0000000000..02fa848dbf --- /dev/null +++ b/libs/langchain/tests/integration_tests/tools/edenai/test_audio_text_to_speech.py @@ -0,0 +1,29 @@ +"""Test EdenAi's text to speech Tool . + +In order to run this test, you need to have an EdenAI api key. +You can get it by registering for free at https://app.edenai.run/user/register. +A test key can be found at https://app.edenai.run/admin/account/settings by +clicking on the 'sandbox' toggle. +(calls will be free, and will return dummy results) + +You'll then need to set EDENAI_API_KEY environment variable to your api key. +""" +from urllib.parse import urlparse + +from langchain.tools.edenai import EdenAiTextToSpeechTool + + +def test_edenai_call() -> None: + """Test simple call to edenai's text to speech endpoint.""" + text2speech = EdenAiTextToSpeechTool( + providers=["amazon"], language="en", voice="MALE" + ) + + output = text2speech("hello") + parsed_url = urlparse(output) + + assert text2speech.name == "edenai_text_to_speech" + assert text2speech.feature == "audio" + assert text2speech.subfeature == "text_to_speech" + assert isinstance(output, str) + assert parsed_url.scheme in ["http", "https"] diff --git a/libs/langchain/tests/integration_tests/tools/edenai/test_image_explicitcontent.py b/libs/langchain/tests/integration_tests/tools/edenai/test_image_explicitcontent.py new file mode 100644 index 0000000000..1fb883c88d --- /dev/null +++ b/libs/langchain/tests/integration_tests/tools/edenai/test_image_explicitcontent.py @@ -0,0 +1,23 @@ +"""Test EdenAi's image moderation Tool . + +In order to run this test, you need to have an EdenAI api key. +You can get it by registering for free at https://app.edenai.run/user/register. +A test key can be found at https://app.edenai.run/admin/account/settings by +clicking on the 'sandbox' toggle. +(calls will be free, and will return dummy results) + +You'll then need to set EDENAI_API_KEY environment variable to your api key. +""" +from langchain.tools.edenai import EdenAiExplicitImageTool + + +def test_edenai_call() -> None: + """Test simple call to edenai's image moderation endpoint.""" + image_moderation = EdenAiExplicitImageTool(providers=["amazon"]) + + output = image_moderation("https://static.javatpoint.com/images/objects.jpg") + + assert image_moderation.name == "edenai_image_explicit_content_detection" + assert image_moderation.feature == "image" + assert image_moderation.subfeature == "explicit_content" + assert isinstance(output, str) diff --git a/libs/langchain/tests/integration_tests/tools/edenai/test_image_objectdetection.py b/libs/langchain/tests/integration_tests/tools/edenai/test_image_objectdetection.py new file mode 100644 index 0000000000..4950543b42 --- /dev/null +++ b/libs/langchain/tests/integration_tests/tools/edenai/test_image_objectdetection.py @@ -0,0 +1,23 @@ +"""Test EdenAi's object detection Tool . + +In order to run this test, you need to have an EdenAI api key. +You can get it by registering for free at https://app.edenai.run/user/register. +A test key can be found at https://app.edenai.run/admin/account/settings by +clicking on the 'sandbox' toggle. +(calls will be free, and will return dummy results) + +You'll then need to set EDENAI_API_KEY environment variable to your api key. +""" +from langchain.tools.edenai import EdenAiObjectDetectionTool + + +def test_edenai_call() -> None: + """Test simple call to edenai's object detection endpoint.""" + object_detection = EdenAiObjectDetectionTool(providers=["google"]) + + output = object_detection("https://static.javatpoint.com/images/objects.jpg") + + assert object_detection.name == "edenai_object_detection" + assert object_detection.feature == "image" + assert object_detection.subfeature == "object_detection" + assert isinstance(output, str) diff --git a/libs/langchain/tests/integration_tests/tools/edenai/test_ocr_identityparser.py b/libs/langchain/tests/integration_tests/tools/edenai/test_ocr_identityparser.py new file mode 100644 index 0000000000..cfd8a1cc7b --- /dev/null +++ b/libs/langchain/tests/integration_tests/tools/edenai/test_ocr_identityparser.py @@ -0,0 +1,25 @@ +"""Test EdenAi's identity parser Tool . + +In order to run this test, you need to have an EdenAI api key. +You can get it by registering for free at https://app.edenai.run/user/register. +A test key can be found at https://app.edenai.run/admin/account/settings by +clicking on the 'sandbox' toggle. +(calls will be free, and will return dummy results) + +You'll then need to set EDENAI_API_KEY environment variable to your api key. +""" +from langchain.tools.edenai import EdenAiParsingIDTool + + +def test_edenai_call() -> None: + """Test simple call to edenai's identity parser endpoint.""" + id_parser = EdenAiParsingIDTool(providers=["amazon"], language="en") + + output = id_parser( + "https://www.citizencard.com/images/citizencard-uk-id-card-2023.jpg" + ) + + assert id_parser.name == "edenai_identity_parsing" + assert id_parser.feature == "ocr" + assert id_parser.subfeature == "identity_parser" + assert isinstance(output, str) diff --git a/libs/langchain/tests/integration_tests/tools/edenai/test_ocr_invoiceparser.py b/libs/langchain/tests/integration_tests/tools/edenai/test_ocr_invoiceparser.py new file mode 100644 index 0000000000..813ad448b4 --- /dev/null +++ b/libs/langchain/tests/integration_tests/tools/edenai/test_ocr_invoiceparser.py @@ -0,0 +1,23 @@ +"""Test EdenAi's invoice parser Tool . + +In order to run this test, you need to have an EdenAI api key. +You can get it by registering for free at https://app.edenai.run/user/register. +A test key can be found at https://app.edenai.run/admin/account/settings by +clicking on the 'sandbox' toggle. +(calls will be free, and will return dummy results) + +You'll then need to set EDENAI_API_KEY environment variable to your api key. +""" +from langchain.tools.edenai import EdenAiParsingInvoiceTool + + +def test_edenai_call() -> None: + """Test simple call to edenai's invoice parser endpoint.""" + invoice_parser = EdenAiParsingInvoiceTool(providers=["amazon"], language="en") + + output = invoice_parser("https://app.edenai.run/assets/img/data_1.72e3bdcc.png") + + assert invoice_parser.name == "edenai_invoice_parsing" + assert invoice_parser.feature == "ocr" + assert invoice_parser.subfeature == "invoice_parser" + assert isinstance(output, str) diff --git a/libs/langchain/tests/integration_tests/tools/edenai/test_text_moderation.py b/libs/langchain/tests/integration_tests/tools/edenai/test_text_moderation.py new file mode 100644 index 0000000000..b713b58231 --- /dev/null +++ b/libs/langchain/tests/integration_tests/tools/edenai/test_text_moderation.py @@ -0,0 +1,24 @@ +"""Test EdenAi's text moderation Tool . + +In order to run this test, you need to have an EdenAI api key. +You can get it by registering for free at https://app.edenai.run/user/register. +A test key can be found at https://app.edenai.run/admin/account/settings by +clicking on the 'sandbox' toggle. +(calls will be free, and will return dummy results) + +You'll then need to set EDENAI_API_KEY environment variable to your api key. +""" +from langchain.tools.edenai.text_moderation import EdenAiTextModerationTool + + +def test_edenai_call() -> None: + """Test simple call to edenai's text moderation endpoint.""" + + text_moderation = EdenAiTextModerationTool(providers=["openai"], language="en") + + output = text_moderation("i hate you") + + assert text_moderation.name == "edenai_explicit_content_detection_text" + assert text_moderation.feature == "text" + assert text_moderation.subfeature == "moderation" + assert isinstance(output, str) diff --git a/libs/langchain/tests/integration_tests/tools/test_yahoo_finance_news.py b/libs/langchain/tests/integration_tests/tools/test_yahoo_finance_news.py new file mode 100644 index 0000000000..45be765dec --- /dev/null +++ b/libs/langchain/tests/integration_tests/tools/test_yahoo_finance_news.py @@ -0,0 +1,31 @@ +import pytest + +from langchain.tools.yahoo_finance_news import YahooFinanceNewsTool + +# skip all tests if yfinance is not installed +yfinance = pytest.importorskip("yfinance") + + +def test_success() -> None: + """Test that the tool runs successfully.""" + tool = YahooFinanceNewsTool() + query = "Microsoft" + result = tool.run(query) + assert result is not None + assert f"Company ticker {query} not found." not in result + + +def test_failure_no_ticker() -> None: + """Test that the tool fails.""" + tool = YahooFinanceNewsTool() + query = "" + result = tool.run(query) + assert f"Company ticker {query} not found." in result + + +def test_failure_wrong_ticker() -> None: + """Test that the tool fails.""" + tool = YahooFinanceNewsTool() + query = "NOT_A_COMPANY" + result = tool.run(query) + assert f"Company ticker {query} not found." in result diff --git a/libs/langchain/tests/integration_tests/vectorstores/test_pinecone.py b/libs/langchain/tests/integration_tests/vectorstores/test_pinecone.py index db6b6cdbd9..66fd3929b8 100644 --- a/libs/langchain/tests/integration_tests/vectorstores/test_pinecone.py +++ b/libs/langchain/tests/integration_tests/vectorstores/test_pinecone.py @@ -231,3 +231,57 @@ class TestPinecone: assert all( (1 >= score or np.isclose(score, 1)) and score >= 0 for _, score in output ) + + @pytest.mark.skipif(reason="slow to run for benchmark") + @pytest.mark.parametrize( + "pool_threads,batch_size,embeddings_chunk_size,data_multiplier", + [ + ( + 1, + 32, + 32, + 1000, + ), # simulate single threaded with embeddings_chunk_size = batch_size = 32 + ( + 1, + 32, + 1000, + 1000, + ), # simulate single threaded with embeddings_chunk_size = 1000 + ( + 4, + 32, + 1000, + 1000, + ), # simulate 4 threaded with embeddings_chunk_size = 1000 + (20, 64, 5000, 1000), + ], # simulate 20 threaded with embeddings_chunk_size = 5000 + ) + def test_from_texts_with_metadatas_benchmark( + self, + pool_threads: int, + batch_size: int, + embeddings_chunk_size: int, + data_multiplier: int, + documents: List[Document], + embedding_openai: OpenAIEmbeddings, + ) -> None: + """Test end to end construction and search.""" + + texts = [document.page_content for document in documents] * data_multiplier + uuids = [uuid.uuid4().hex for _ in range(len(texts))] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = Pinecone.from_texts( + texts, + embedding_openai, + ids=uuids, + metadatas=metadatas, + index_name=index_name, + namespace=namespace_name, + pool_threads=pool_threads, + batch_size=batch_size, + embeddings_chunk_size=embeddings_chunk_size, + ) + + query = "What did the president say about Ketanji Brown Jackson" + _ = docsearch.similarity_search(query, k=1, namespace=namespace_name) diff --git a/libs/langchain/tests/integration_tests/vectorstores/test_sqlitevss.py b/libs/langchain/tests/integration_tests/vectorstores/test_sqlitevss.py new file mode 100644 index 0000000000..0896706d89 --- /dev/null +++ b/libs/langchain/tests/integration_tests/vectorstores/test_sqlitevss.py @@ -0,0 +1,58 @@ +from typing import List, Optional + +import pytest + +from langchain.docstore.document import Document +from langchain.vectorstores import SQLiteVSS +from tests.integration_tests.vectorstores.fake_embeddings import ( + FakeEmbeddings, + fake_texts, +) + + +def _sqlite_vss_from_texts( + metadatas: Optional[List[dict]] = None, drop: bool = True +) -> SQLiteVSS: + return SQLiteVSS.from_texts( + fake_texts, + FakeEmbeddings(), + metadatas=metadatas, + table="test", + db_file=":memory:", + ) + + +@pytest.mark.requires("sqlite-vss") +def test_sqlitevss() -> None: + """Test end to end construction and search.""" + docsearch = _sqlite_vss_from_texts() + output = docsearch.similarity_search("foo", k=1) + assert output == [Document(page_content="foo", metadata={})] + + +@pytest.mark.requires("sqlite-vss") +def test_sqlitevss_with_score() -> None: + """Test end to end construction and search with scores and IDs.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = _sqlite_vss_from_texts(metadatas=metadatas) + output = docsearch.similarity_search_with_score("foo", k=3) + docs = [o[0] for o in output] + distances = [o[1] for o in output] + assert docs == [ + Document(page_content="foo", metadata={"page": 0}), + Document(page_content="bar", metadata={"page": 1}), + Document(page_content="baz", metadata={"page": 2}), + ] + assert distances[0] < distances[1] < distances[2] + + +@pytest.mark.requires("sqlite-vss") +def test_sqlitevss_add_extra() -> None: + """Test end to end construction and MRR search.""" + texts = ["foo", "bar", "baz"] + metadatas = [{"page": i} for i in range(len(texts))] + docsearch = _sqlite_vss_from_texts(metadatas=metadatas) + docsearch.add_texts(texts, metadatas) + output = docsearch.similarity_search("foo", k=10) + assert len(output) == 6 diff --git a/libs/langchain/tests/unit_tests/document_loaders/sample_documents/obsidian/bad_frontmatter.md b/libs/langchain/tests/unit_tests/document_loaders/sample_documents/obsidian/bad_frontmatter.md new file mode 100644 index 0000000000..edc335e195 --- /dev/null +++ b/libs/langchain/tests/unit_tests/document_loaders/sample_documents/obsidian/bad_frontmatter.md @@ -0,0 +1,9 @@ +--- +anArray: + one +- two +- three +tags: 'onetag', 'twotag' ] +--- + +A document with frontmatter that isn't valid. diff --git a/libs/langchain/tests/unit_tests/document_loaders/sample_documents/obsidian/frontmatter.md b/libs/langchain/tests/unit_tests/document_loaders/sample_documents/obsidian/frontmatter.md new file mode 100644 index 0000000000..bb1f5b9f0f --- /dev/null +++ b/libs/langchain/tests/unit_tests/document_loaders/sample_documents/obsidian/frontmatter.md @@ -0,0 +1,5 @@ +--- +tags: journal/entry, obsidian +--- + +No other content than the frontmatter. diff --git a/libs/langchain/tests/unit_tests/document_loaders/sample_documents/obsidian/no_frontmatter.md b/libs/langchain/tests/unit_tests/document_loaders/sample_documents/obsidian/no_frontmatter.md new file mode 100644 index 0000000000..e063126f56 --- /dev/null +++ b/libs/langchain/tests/unit_tests/document_loaders/sample_documents/obsidian/no_frontmatter.md @@ -0,0 +1,14 @@ +### Description +#recipes #dessert #cookies + +A document with HR elements that might trip up a front matter parser: + +--- + +### Ingredients + +- 3/4 cup (170g) **unsalted butter**, slightly softened to room temperature. +- 1 and 1/2 cups (180g) **confectioners’ sugar** + +--- + diff --git a/libs/langchain/tests/unit_tests/document_loaders/sample_documents/obsidian/no_metadata.md b/libs/langchain/tests/unit_tests/document_loaders/sample_documents/obsidian/no_metadata.md new file mode 100644 index 0000000000..70258e5aea --- /dev/null +++ b/libs/langchain/tests/unit_tests/document_loaders/sample_documents/obsidian/no_metadata.md @@ -0,0 +1 @@ +A markdown document with no additional metadata. diff --git a/libs/langchain/tests/unit_tests/document_loaders/sample_documents/obsidian/tags_and_frontmatter.md b/libs/langchain/tests/unit_tests/document_loaders/sample_documents/obsidian/tags_and_frontmatter.md new file mode 100644 index 0000000000..2cefb606d2 --- /dev/null +++ b/libs/langchain/tests/unit_tests/document_loaders/sample_documents/obsidian/tags_and_frontmatter.md @@ -0,0 +1,35 @@ +--- +aFloat: 13.12345 +anInt: 15 +aBool: true +aString: string value +anArray: +- one +- two +- three +aDict: + dictId1: '58417' + dictId2: 1500 +tags: [ 'onetag', 'twotag' ] +--- + +# Tags + + ()#notatag +#12345 + #read +something #tagWithCases +- #tag-with-dash +#tag_with_underscore #tag/with/nesting + +# Dataview + +Here is some data in a [dataview1:: a value] line. +Here is even more data in a (dataview2:: another value) line. +dataview3:: more data +notdataview4: this is not a field +notdataview5: this is not a field + +# Text content + +https://example.com/blog/#not-a-tag diff --git a/libs/langchain/tests/unit_tests/document_loaders/test_json_loader.py b/libs/langchain/tests/unit_tests/document_loaders/test_json_loader.py index 19fb90e0c9..a96facc417 100644 --- a/libs/langchain/tests/unit_tests/document_loaders/test_json_loader.py +++ b/libs/langchain/tests/unit_tests/document_loaders/test_json_loader.py @@ -244,7 +244,7 @@ def test_load_empty_jsonlines(mocker: MockerFixture) -> None: ), ), ) -def test_json_meta( +def test_json_meta_01( patch_func: str, patch_func_value: Any, kwargs: Dict, mocker: MockerFixture ) -> None: mocker.patch("builtins.open", mocker.mock_open()) @@ -270,3 +270,52 @@ def test_json_meta( result = loader.load() assert result == expected_docs + + +@pytest.mark.parametrize( + "patch_func,patch_func_value,kwargs", + ( + # JSON content. + ( + "pathlib.Path.read_text", + '[{"text": "value1"}, {"text": "value2"}]', + {"jq_schema": ".[]", "content_key": "text"}, + ), + # JSON Lines content. + ( + "pathlib.Path.open", + io.StringIO( + """ + {"text": "value1"} + {"text": "value2"} + """ + ), + {"jq_schema": ".", "content_key": "text", "json_lines": True}, + ), + ), +) +def test_json_meta_02( + patch_func: str, patch_func_value: Any, kwargs: Dict, mocker: MockerFixture +) -> None: + mocker.patch("builtins.open", mocker.mock_open()) + mocker.patch(patch_func, return_value=patch_func_value) + + file_path = "/workspaces/langchain/test.json" + expected_docs = [ + Document( + page_content="value1", + metadata={"source": file_path, "seq_num": 1, "x": "value1-meta"}, + ), + Document( + page_content="value2", + metadata={"source": file_path, "seq_num": 2, "x": "value2-meta"}, + ), + ] + + def metadata_func(record: Dict, metadata: Dict) -> Dict: + return {**metadata, "x": f"{record['text']}-meta"} + + loader = JSONLoader(file_path=file_path, metadata_func=metadata_func, **kwargs) + result = loader.load() + + assert result == expected_docs diff --git a/libs/langchain/tests/unit_tests/document_loaders/test_obsidian.py b/libs/langchain/tests/unit_tests/document_loaders/test_obsidian.py new file mode 100644 index 0000000000..50f29d849e --- /dev/null +++ b/libs/langchain/tests/unit_tests/document_loaders/test_obsidian.py @@ -0,0 +1,128 @@ +from pathlib import Path + +from langchain.document_loaders.obsidian import ObsidianLoader + +OBSIDIAN_EXAMPLE_PATH = Path(__file__).parent / "sample_documents" / "obsidian" +STANDARD_METADATA_FIELDS = { + "created", + "path", + "source", + "last_accessed", + "last_modified", +} + +loader = ObsidianLoader(str(OBSIDIAN_EXAMPLE_PATH)) +docs = loader.load() + + +def test_page_content_loaded() -> None: + """Verify that all docs have page_content""" + assert len(docs) == 5 + assert all(doc.page_content for doc in docs) + + +def test_disable_collect_metadata() -> None: + """If collect_metadata is False, no additional metadata should be collected.""" + loader_without_metadata = ObsidianLoader( + str(OBSIDIAN_EXAMPLE_PATH), collect_metadata=False + ) + docs_wo = loader_without_metadata.load() + assert len(docs_wo) == 5 + assert all(doc.page_content for doc in docs_wo) + assert all(set(doc.metadata) == STANDARD_METADATA_FIELDS for doc in docs_wo) + + +def test_metadata_without_frontmatter() -> None: + """Verify docs without frontmatter, still have basic metadata.""" + doc = next(doc for doc in docs if doc.metadata["source"] == "no_metadata.md") + assert set(doc.metadata) == STANDARD_METADATA_FIELDS + + +def test_metadata_with_frontmatter() -> None: + """Verify a standard frontmatter field is loaded.""" + doc = next(doc for doc in docs if doc.metadata["source"] == "frontmatter.md") + assert set(doc.metadata) == STANDARD_METADATA_FIELDS | {"tags"} + assert set(doc.metadata["tags"].split(",")) == {"journal/entry", "obsidian"} + + +def test_metadata_with_bad_frontmatter() -> None: + """Verify a doc with non-yaml frontmatter.""" + doc = next(doc for doc in docs if doc.metadata["source"] == "bad_frontmatter.md") + assert set(doc.metadata) == STANDARD_METADATA_FIELDS + + +def test_metadata_with_tags_and_frontmatter() -> None: + """Verify a doc with frontmatter and tags/dataview tags are all added to + metadata.""" + doc = next( + doc for doc in docs if doc.metadata["source"] == "tags_and_frontmatter.md" + ) + + FRONTMATTER_FIELDS = { + "aBool", + "aFloat", + "anInt", + "anArray", + "aString", + "aDict", + "tags", + } + DATAVIEW_FIELDS = {"dataview1", "dataview2", "dataview3"} + assert ( + set(doc.metadata) + == STANDARD_METADATA_FIELDS | FRONTMATTER_FIELDS | DATAVIEW_FIELDS + ) + + +def test_tags_in_page_content() -> None: + """Verify a doc with tags are included in the metadata""" + doc = next(doc for doc in docs if doc.metadata["source"] == "no_frontmatter.md") + assert set(doc.metadata) == STANDARD_METADATA_FIELDS | {"tags"} + + +def test_boolean_metadata() -> None: + """Verify boolean metadata is loaded correctly""" + doc = next( + doc for doc in docs if doc.metadata["source"] == "tags_and_frontmatter.md" + ) + assert doc.metadata["aBool"] + + +def test_float_metadata() -> None: + """Verify float metadata is loaded correctly""" + doc = next( + doc for doc in docs if doc.metadata["source"] == "tags_and_frontmatter.md" + ) + assert doc.metadata["aFloat"] == 13.12345 + + +def test_int_metadata() -> None: + """Verify int metadata is loaded correctly""" + doc = next( + doc for doc in docs if doc.metadata["source"] == "tags_and_frontmatter.md" + ) + assert doc.metadata["anInt"] == 15 + + +def test_string_metadata() -> None: + """Verify string metadata is loaded correctly""" + doc = next( + doc for doc in docs if doc.metadata["source"] == "tags_and_frontmatter.md" + ) + assert doc.metadata["aString"] == "string value" + + +def test_array_metadata() -> None: + """Verify array metadata is loaded as a string""" + doc = next( + doc for doc in docs if doc.metadata["source"] == "tags_and_frontmatter.md" + ) + assert doc.metadata["anArray"] == "['one', 'two', 'three']" + + +def test_dict_metadata() -> None: + """Verify dict metadata is stored as a string""" + doc = next( + doc for doc in docs if doc.metadata["source"] == "tags_and_frontmatter.md" + ) + assert doc.metadata["aDict"] == "{'dictId1': '58417', 'dictId2': 1500}" diff --git a/libs/langchain/tests/unit_tests/indexes/test_indexing.py b/libs/langchain/tests/unit_tests/indexes/test_indexing.py index 70ebf4fcbc..eee342bf70 100644 --- a/libs/langchain/tests/unit_tests/indexes/test_indexing.py +++ b/libs/langchain/tests/unit_tests/indexes/test_indexing.py @@ -158,7 +158,7 @@ def test_index_simple_delete_full( with patch.object( record_manager, "get_time", return_value=datetime(2021, 1, 1).timestamp() ): - assert index(loader, record_manager, vector_store, delete_mode="full") == { + assert index(loader, record_manager, vector_store, cleanup="full") == { "num_added": 2, "num_deleted": 0, "num_skipped": 0, @@ -168,7 +168,7 @@ def test_index_simple_delete_full( with patch.object( record_manager, "get_time", return_value=datetime(2021, 1, 1).timestamp() ): - assert index(loader, record_manager, vector_store, delete_mode="full") == { + assert index(loader, record_manager, vector_store, cleanup="full") == { "num_added": 0, "num_deleted": 0, "num_skipped": 2, @@ -189,7 +189,7 @@ def test_index_simple_delete_full( with patch.object( record_manager, "get_time", return_value=datetime(2021, 1, 2).timestamp() ): - assert index(loader, record_manager, vector_store, delete_mode="full") == { + assert index(loader, record_manager, vector_store, cleanup="full") == { "num_added": 1, "num_deleted": 1, "num_skipped": 1, @@ -207,7 +207,7 @@ def test_index_simple_delete_full( with patch.object( record_manager, "get_time", return_value=datetime(2021, 1, 2).timestamp() ): - assert index(loader, record_manager, vector_store, delete_mode="full") == { + assert index(loader, record_manager, vector_store, cleanup="full") == { "num_added": 0, "num_deleted": 0, "num_skipped": 2, @@ -238,7 +238,7 @@ def test_incremental_fails_with_bad_source_ids( with pytest.raises(ValueError): # Should raise an error because no source id function was specified - index(loader, record_manager, vector_store, delete_mode="incremental") + index(loader, record_manager, vector_store, cleanup="incremental") with pytest.raises(ValueError): # Should raise an error because no source id function was specified @@ -246,7 +246,7 @@ def test_incremental_fails_with_bad_source_ids( loader, record_manager, vector_store, - delete_mode="incremental", + cleanup="incremental", source_id_key="source", ) @@ -275,7 +275,7 @@ def test_no_delete( loader, record_manager, vector_store, - delete_mode=None, + cleanup=None, source_id_key="source", ) == { "num_added": 2, @@ -292,7 +292,7 @@ def test_no_delete( loader, record_manager, vector_store, - delete_mode=None, + cleanup=None, source_id_key="source", ) == { "num_added": 0, @@ -322,7 +322,7 @@ def test_no_delete( loader, record_manager, vector_store, - delete_mode=None, + cleanup=None, source_id_key="source", ) == { "num_added": 1, @@ -356,7 +356,7 @@ def test_incremental_delete( loader, record_manager, vector_store, - delete_mode="incremental", + cleanup="incremental", source_id_key="source", ) == { "num_added": 2, @@ -380,7 +380,7 @@ def test_incremental_delete( loader, record_manager, vector_store, - delete_mode="incremental", + cleanup="incremental", source_id_key="source", ) == { "num_added": 0, @@ -415,7 +415,7 @@ def test_incremental_delete( loader, record_manager, vector_store, - delete_mode="incremental", + cleanup="incremental", source_id_key="source", ) == { "num_added": 2, @@ -442,7 +442,7 @@ def test_indexing_with_no_docs( """Check edge case when loader returns no new docs.""" loader = ToyLoader(documents=[]) - assert index(loader, record_manager, vector_store, delete_mode="full") == { + assert index(loader, record_manager, vector_store, cleanup="full") == { "num_added": 0, "num_deleted": 0, "num_skipped": 0, @@ -466,7 +466,7 @@ def test_deduplication( ] # Should result in only a single document being added - assert index(docs, record_manager, vector_store, delete_mode="full") == { + assert index(docs, record_manager, vector_store, cleanup="full") == { "num_added": 1, "num_deleted": 0, "num_skipped": 0, diff --git a/libs/langchain/tests/unit_tests/load/__snapshots__/test_dump.ambr b/libs/langchain/tests/unit_tests/load/__snapshots__/test_dump.ambr index ee439181bb..5ab20840ed 100644 --- a/libs/langchain/tests/unit_tests/load/__snapshots__/test_dump.ambr +++ b/libs/langchain/tests/unit_tests/load/__snapshots__/test_dump.ambr @@ -229,7 +229,8 @@ "api_resources", "completion", "Completion" - ] + ], + "repr": "" } } }, diff --git a/libs/langchain/tests/unit_tests/memory/chat_message_histories/test_sql.py b/libs/langchain/tests/unit_tests/memory/chat_message_histories/test_sql.py index 42cff47b04..a01e1b77db 100644 --- a/libs/langchain/tests/unit_tests/memory/chat_message_histories/test_sql.py +++ b/libs/langchain/tests/unit_tests/memory/chat_message_histories/test_sql.py @@ -1,21 +1,26 @@ from pathlib import Path -from typing import Tuple +from typing import Any, Generator, Tuple import pytest +from sqlalchemy import Column, Integer, Text +from sqlalchemy.orm import DeclarativeBase from langchain.memory.chat_message_histories import SQLChatMessageHistory +from langchain.memory.chat_message_histories.sql import DefaultMessageConverter from langchain.schema.messages import AIMessage, HumanMessage -# @pytest.fixture(params=[("SQLite"), ("postgresql")]) -@pytest.fixture(params=[("SQLite")]) -def sql_histories(request, tmp_path: Path): # type: ignore - if request.param == "SQLite": - file_path = tmp_path / "db.sqlite3" - con_str = f"sqlite:///{file_path}" - elif request.param == "postgresql": - con_str = "postgresql://postgres:postgres@localhost/postgres" +@pytest.fixture() +def con_str(tmp_path: Path) -> str: + file_path = tmp_path / "db.sqlite3" + con_str = f"sqlite:///{file_path}" + return con_str + +@pytest.fixture() +def sql_histories( + con_str: str, +) -> Generator[Tuple[SQLChatMessageHistory, SQLChatMessageHistory], None, None]: message_history = SQLChatMessageHistory( session_id="123", connection_string=con_str, table_name="test_table" ) @@ -24,7 +29,7 @@ def sql_histories(request, tmp_path: Path): # type: ignore session_id="456", connection_string=con_str, table_name="test_table" ) - yield (message_history, other_history) + yield message_history, other_history message_history.clear() other_history.clear() @@ -83,3 +88,24 @@ def test_clear_messages( sql_history.clear() assert len(sql_history.messages) == 0 assert len(other_history.messages) == 1 + + +def test_model_no_session_id_field_error(con_str: str) -> None: + class Base(DeclarativeBase): + pass + + class Model(Base): + __tablename__ = "test_table" + id = Column(Integer, primary_key=True) + test_field = Column(Text) + + class CustomMessageConverter(DefaultMessageConverter): + def get_sql_model_class(self) -> Any: + return Model + + with pytest.raises(ValueError): + SQLChatMessageHistory( + "test", + con_str, + custom_message_converter=CustomMessageConverter("test_table"), + ) diff --git a/libs/langchain/tests/unit_tests/output_parsers/test_list_parser.py b/libs/langchain/tests/unit_tests/output_parsers/test_list_parser.py index 84be4db946..a25bbc38bd 100644 --- a/libs/langchain/tests/unit_tests/output_parsers/test_list_parser.py +++ b/libs/langchain/tests/unit_tests/output_parsers/test_list_parser.py @@ -1,4 +1,7 @@ -from langchain.output_parsers.list import CommaSeparatedListOutputParser +from langchain.output_parsers.list import ( + CommaSeparatedListOutputParser, + NumberedListOutputParser, +) def test_single_item() -> None: @@ -11,3 +14,19 @@ def test_multiple_items() -> None: """Test that a string with multiple comma-separated items is parsed to a list.""" parser = CommaSeparatedListOutputParser() assert parser.parse("foo, bar, baz") == ["foo", "bar", "baz"] + + +def test_numbered_list() -> None: + parser = NumberedListOutputParser() + text1 = ( + "Your response should be a numbered list with each item on a new line. " + "For example: \n\n1. foo\n\n2. bar\n\n3. baz" + ) + + text2 = "Items:\n\n1. apple\n\n2. banana\n\n3. cherry" + + text3 = "No items in the list." + + assert parser.parse(text1) == ["foo", "bar", "baz"] + assert parser.parse(text2) == ["apple", "banana", "cherry"] + assert parser.parse(text3) == [] diff --git a/libs/langchain/tests/unit_tests/retrievers/self_query/test_dashvector.py b/libs/langchain/tests/unit_tests/retrievers/self_query/test_dashvector.py new file mode 100644 index 0000000000..137de85fdc --- /dev/null +++ b/libs/langchain/tests/unit_tests/retrievers/self_query/test_dashvector.py @@ -0,0 +1,52 @@ +from typing import Any, Tuple + +import pytest + +from langchain.chains.query_constructor.ir import ( + Comparator, + Comparison, + Operation, + Operator, +) +from langchain.retrievers.self_query.dashvector import DashvectorTranslator + +DEFAULT_TRANSLATOR = DashvectorTranslator() + + +@pytest.mark.parametrize( + "triplet", + [ + (Comparator.EQ, 2, "foo = 2"), + (Comparator.LT, 2, "foo < 2"), + (Comparator.LTE, 2, "foo <= 2"), + (Comparator.GT, 2, "foo > 2"), + (Comparator.GTE, 2, "foo >= 2"), + (Comparator.LIKE, "bar", "foo LIKE '%bar%'"), + ], +) +def test_visit_comparison(triplet: Tuple[Comparator, Any, str]) -> None: + comparator, value, expected = triplet + actual = DEFAULT_TRANSLATOR.visit_comparison( + Comparison(comparator=comparator, attribute="foo", value=value) + ) + assert expected == actual + + +@pytest.mark.parametrize( + "triplet", + [ + (Operator.AND, "foo < 2 AND bar = 'baz'"), + (Operator.OR, "foo < 2 OR bar = 'baz'"), + ], +) +def test_visit_operation(triplet: Tuple[Operator, str]) -> None: + operator, expected = triplet + op = Operation( + operator=operator, + arguments=[ + Comparison(comparator=Comparator.LT, attribute="foo", value=2), + Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"), + ], + ) + actual = DEFAULT_TRANSLATOR.visit_operation(op) + assert expected == actual diff --git a/libs/langchain/tests/unit_tests/retrievers/self_query/test_milvus.py b/libs/langchain/tests/unit_tests/retrievers/self_query/test_milvus.py new file mode 100644 index 0000000000..d444974404 --- /dev/null +++ b/libs/langchain/tests/unit_tests/retrievers/self_query/test_milvus.py @@ -0,0 +1,116 @@ +from typing import Dict, Tuple + +from langchain.chains.query_constructor.ir import ( + Comparator, + Comparison, + Operation, + Operator, + StructuredQuery, +) +from langchain.retrievers.self_query.milvus import MilvusTranslator + +DEFAULT_TRANSLATOR = MilvusTranslator() + + +def test_visit_comparison() -> None: + comp = Comparison(comparator=Comparator.LT, attribute="foo", value=4) + expected = "( foo < 4 )" + actual = DEFAULT_TRANSLATOR.visit_comparison(comp) + + assert expected == actual + + +def test_visit_operation() -> None: + # Non-Unary operator + + op = Operation( + operator=Operator.AND, + arguments=[ + Comparison(comparator=Comparator.LT, attribute="foo", value=2), + Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"), + Comparison(comparator=Comparator.LT, attribute="abc", value="4"), + ], + ) + + expected = '(( foo < 2 ) and ( bar == "baz" ) ' 'and ( abc < "4" ))' + actual = DEFAULT_TRANSLATOR.visit_operation(op) + + assert expected == actual + + # Unary operator: normal execution + op = Operation( + operator=Operator.NOT, + arguments=[ + Comparison(comparator=Comparator.LT, attribute="foo", value=2), + ], + ) + + expected = "not(( foo < 2 ))" + actual = DEFAULT_TRANSLATOR.visit_operation(op) + + assert expected == actual + + # Unary operator: error + op = Operation( + operator=Operator.NOT, + arguments=[ + Comparison(comparator=Comparator.LT, attribute="foo", value=2), + Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"), + Comparison(comparator=Comparator.LT, attribute="abc", value="4"), + ], + ) + + try: + DEFAULT_TRANSLATOR.visit_operation(op) + except ValueError as e: + assert str(e) == '"not" can have only one argument in Milvus' + else: + assert False, "Expected exception not raised" # No exception -> test failed + + +def test_visit_structured_query() -> None: + query = "What is the capital of France?" + structured_query = StructuredQuery( + query=query, + filter=None, + ) + expected: Tuple[str, Dict] = (query, {}) + + actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query) + assert expected == actual + + comp = Comparison(comparator=Comparator.LT, attribute="foo", value=454) + structured_query = StructuredQuery( + query=query, + filter=comp, + ) + + expected = ( + query, + {"expr": "( foo < 454 )"}, + ) + + actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query) + assert expected == actual + + op = Operation( + operator=Operator.AND, + arguments=[ + Comparison(comparator=Comparator.LT, attribute="foo", value=2), + Comparison(comparator=Comparator.EQ, attribute="bar", value="baz"), + Comparison(comparator=Comparator.LT, attribute="abc", value=50), + ], + ) + + structured_query = StructuredQuery( + query=query, + filter=op, + ) + + expected = ( + query, + {"expr": "(( foo < 2 ) " 'and ( bar == "baz" ) ' "and ( abc < 50 ))"}, + ) + + actual = DEFAULT_TRANSLATOR.visit_structured_query(structured_query) + assert expected == actual diff --git a/libs/langchain/tests/unit_tests/retrievers/test_multi_query.py b/libs/langchain/tests/unit_tests/retrievers/test_multi_query.py new file mode 100644 index 0000000000..978950ec58 --- /dev/null +++ b/libs/langchain/tests/unit_tests/retrievers/test_multi_query.py @@ -0,0 +1,40 @@ +from typing import List + +import pytest as pytest + +from langchain.retrievers.multi_query import _unique_documents +from langchain.schema import Document + + +@pytest.mark.parametrize( + "documents,expected", + [ + ([], []), + ([Document(page_content="foo")], [Document(page_content="foo")]), + ([Document(page_content="foo")] * 2, [Document(page_content="foo")]), + ( + [Document(page_content="foo", metadata={"bar": "baz"})] * 2, + [Document(page_content="foo", metadata={"bar": "baz"})], + ), + ( + [Document(page_content="foo", metadata={"bar": [1, 2]})] * 2, + [Document(page_content="foo", metadata={"bar": [1, 2]})], + ), + ( + [Document(page_content="foo", metadata={"bar": {1, 2}})] * 2, + [Document(page_content="foo", metadata={"bar": {1, 2}})], + ), + ( + [ + Document(page_content="foo", metadata={"bar": [1, 2]}), + Document(page_content="foo", metadata={"bar": [2, 1]}), + ], + [ + Document(page_content="foo", metadata={"bar": [1, 2]}), + Document(page_content="foo", metadata={"bar": [2, 1]}), + ], + ), + ], +) +def test__unique_documents(documents: List[Document], expected: List[Document]) -> None: + assert _unique_documents(documents) == expected diff --git a/libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr b/libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr index c48d4edbd4..5ea21b13d6 100644 --- a/libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr +++ b/libs/langchain/tests/unit_tests/schema/runnable/__snapshots__/test_runnable.ambr @@ -95,7 +95,8 @@ "chat_models", "fake", "FakeListChatModel" - ] + ], + "repr": "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo, bar'], sleep=None, i=0)" } ], "last": { @@ -134,7 +135,8 @@ "runnable", "base", "RunnableLambda" - ] + ], + "repr": "RunnableLambda(...)" }, "middle": [ { @@ -220,7 +222,8 @@ "chat_models", "fake", "FakeListChatModel" - ] + ], + "repr": "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['baz, qux'], sleep=None, i=0)" } ], "last": { @@ -334,7 +337,8 @@ "chat_models", "fake", "FakeListChatModel" - ] + ], + "repr": "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo, bar'], sleep=None, i=0)" }, { "lc": 1, @@ -356,7 +360,8 @@ "runnable", "base", "RunnableLambda" - ] + ], + "repr": "RunnableLambda(...)" }, { "lc": 1, @@ -441,7 +446,8 @@ "chat_models", "fake", "FakeListChatModel" - ] + ], + "repr": "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['baz, qux'], sleep=None, i=0)" } ], "last": { @@ -461,7 +467,7 @@ # --- # name: test_combining_sequences.3 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda']}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, example=False)}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': ['foo', 'bar']}, outputs={'question': 'foobar'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'foobar'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nicer assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'foobar', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000006'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['baz, qux'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nicer assistant.\nHuman: foobar']}, outputs={'generations': [[{'text': 'baz, qux', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'baz, qux'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000007'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='baz, qux', additional_kwargs={}, example=False)}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo, bar'], sleep=None, i=0)"}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda'], 'repr': 'RunnableLambda(...)'}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['baz, qux'], sleep=None, i=0)"}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo, bar'], sleep=None, i=0)"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, example=False)}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda'], 'repr': 'RunnableLambda(...)'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': ['foo', 'bar']}, outputs={'question': 'foobar'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:4'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nicer assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'foobar'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nicer assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'foobar', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:5'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000006'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['baz, qux'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['baz, qux'], sleep=None, i=0)"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nicer assistant.\nHuman: foobar']}, outputs={'generations': [[{'text': 'baz, qux', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'baz, qux'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:6'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000007'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='baz, qux', additional_kwargs={}, example=False)}, outputs={'output': ['baz', 'qux']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:7'], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_each @@ -560,7 +566,8 @@ "llms", "fake", "FakeStreamingListLLM" - ] + ], + "repr": "FakeStreamingListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['first item, second item, third item'], sleep=None, i=0)" }, { "lc": 1, @@ -591,7 +598,8 @@ "llms", "fake", "FakeStreamingListLLM" - ] + ], + "repr": "FakeStreamingListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['this', 'is', 'a', 'test'], sleep=None, i=0)" } } } @@ -631,7 +639,8 @@ "runnable", "base", "RunnableLambda" - ] + ], + "repr": "RunnableLambda(...)" }, "input": { "lc": 1, @@ -653,7 +662,8 @@ "runnable", "base", "RunnableLambda" - ] + ], + "repr": "RunnableLambda(...)" } } } @@ -670,7 +680,8 @@ "runnable", "base", "RunnableLambda" - ] + ], + "repr": "RunnableLambda(...)" } } } @@ -708,7 +719,8 @@ "runnable", "base", "RunnableLambda" - ] + ], + "repr": "RunnableLambda(...)" } } } @@ -759,7 +771,8 @@ "llms", "fake", "FakeListLLM" - ] + ], + "repr": "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo'], sleep=None, i=1)" } } }, @@ -800,7 +813,8 @@ "llms", "fake", "FakeListLLM" - ] + ], + "repr": "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['bar'], sleep=None, i=0)" } } } @@ -812,7 +826,8 @@ "id": [ "builtins", "Exception" - ] + ], + "repr": "" } ] } @@ -841,7 +856,8 @@ "llms", "fake", "FakeListLLM" - ] + ], + "repr": "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo'], sleep=None, i=1)" }, "fallbacks": [ { @@ -852,7 +868,8 @@ "llms", "fake", "FakeListLLM" - ] + ], + "repr": "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['bar'], sleep=None, i=0)" } ], "exceptions_to_handle": [ @@ -862,7 +879,8 @@ "id": [ "builtins", "Exception" - ] + ], + "repr": "" } ] } @@ -889,7 +907,8 @@ "llms", "fake", "FakeListLLM" - ] + ], + "repr": "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo'], sleep=None, i=1)" }, "fallbacks": [ { @@ -900,7 +919,8 @@ "llms", "fake", "FakeListLLM" - ] + ], + "repr": "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['baz'], sleep=None, i=1)" }, { "lc": 1, @@ -910,7 +930,8 @@ "llms", "fake", "FakeListLLM" - ] + ], + "repr": "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['bar'], sleep=None, i=0)" } ], "exceptions_to_handle": [ @@ -920,7 +941,8 @@ "id": [ "builtins", "Exception" - ] + ], + "repr": "" } ] } @@ -1022,7 +1044,8 @@ "chat_models", "fake", "FakeListChatModel" - ] + ], + "repr": "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo'], sleep=None, i=0)" } } } @@ -1030,7 +1053,7 @@ # --- # name: test_prompt_with_chat_model.1 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': AIMessage(content='foo', additional_kwargs={}, example=False)}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo'], sleep=None, i=0)"}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': AIMessage(content='foo', additional_kwargs={}, example=False)}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo'], sleep=None, i=0)"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_prompt_with_chat_model_and_parser @@ -1129,7 +1152,8 @@ "chat_models", "fake", "FakeListChatModel" - ] + ], + "repr": "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo, bar'], sleep=None, i=0)" } ], "last": { @@ -1149,7 +1173,7 @@ # --- # name: test_prompt_with_chat_model_and_parser.1 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, example=False)}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo, bar'], sleep=None, i=0)"}], 'last': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListChatModel', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo, bar'], '_type': 'fake-list-chat-model', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'chat_models', 'fake', 'FakeListChatModel'], 'repr': "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo, bar'], sleep=None, i=0)"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo, bar', 'generation_info': None, 'message': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'AIMessage'], 'kwargs': {'content': 'foo, bar'}}}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='CommaSeparatedListOutputParser', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='parser', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'output_parsers', 'list', 'CommaSeparatedListOutputParser'], 'kwargs': {}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': AIMessage(content='foo, bar', additional_kwargs={}, example=False)}, outputs={'output': ['foo', 'bar']}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_prompt_with_llm @@ -1247,7 +1271,8 @@ "llms", "fake", "FakeListLLM" - ] + ], + "repr": "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo', 'bar'], sleep=None, i=0)" } } } @@ -1255,13 +1280,13 @@ # --- # name: test_prompt_with_llm.1 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo', 'bar'], sleep=None, i=0)"}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo', 'bar'], sleep=None, i=0)"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_prompt_with_llm.2 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'bar'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bar', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), - Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your favorite color?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo', 'bar'], sleep=None, i=1)"}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'bar'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo', 'bar'], sleep=None, i=1)"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'bar', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo', 'bar'], sleep=None, i=1)"}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000004'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your favorite color?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your favorite color?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000005'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo', 'bar'], sleep=None, i=1)"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your favorite color?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000003'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_prompt_with_llm_and_async_lambda @@ -1360,7 +1385,8 @@ "llms", "fake", "FakeListLLM" - ] + ], + "repr": "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo', 'bar'], sleep=None, i=0)" } ], "last": { @@ -1372,7 +1398,8 @@ "runnable", "base", "RunnableLambda" - ] + ], + "repr": "RunnableLambda(...)" } } } @@ -1380,7 +1407,7 @@ # --- # name: test_prompt_with_llm_and_async_lambda.1 list([ - Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}], 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda']}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda']}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': 'foo'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=[], execution_order=None, child_execution_order=None, child_runs=[])]), + Run(id=UUID('00000000-0000-4000-8000-000000000000'), name='RunnableSequence', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'runnable', 'RunnableSequence'], 'kwargs': {'first': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, 'middle': [{'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo', 'bar'], sleep=None, i=0)"}], 'last': {'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda'], 'repr': 'RunnableLambda(...)'}}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=None, tags=[], execution_order=None, child_execution_order=None, child_runs=[Run(id=UUID('00000000-0000-4000-8000-000000000001'), name='ChatPromptTemplate', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='prompt', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptTemplate'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'SystemMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': [], 'template': 'You are a nice assistant.', 'template_format': 'f-string', 'partial_variables': {}}}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'HumanMessagePromptTemplate'], 'kwargs': {'prompt': {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'prompt', 'PromptTemplate'], 'kwargs': {'input_variables': ['question'], 'template': '{question}', 'template_format': 'f-string', 'partial_variables': {}}}}}], 'input_variables': ['question']}}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'question': 'What is your name?'}, outputs={'lc': 1, 'type': 'constructor', 'id': ['langchain', 'prompts', 'chat', 'ChatPromptValue'], 'kwargs': {'messages': [{'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'SystemMessage'], 'kwargs': {'content': 'You are a nice assistant.', 'additional_kwargs': {}}}, {'lc': 1, 'type': 'constructor', 'id': ['langchain', 'schema', 'messages', 'HumanMessage'], 'kwargs': {'content': 'What is your name?', 'additional_kwargs': {}}}]}}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:1'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000002'), name='FakeListLLM', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='llm', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={'invocation_params': {'responses': ['foo', 'bar'], '_type': 'fake-list', 'stop': None}, 'options': {'stop': None}}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'llms', 'fake', 'FakeListLLM'], 'repr': "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo', 'bar'], sleep=None, i=0)"}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'prompts': ['System: You are a nice assistant.\nHuman: What is your name?']}, outputs={'generations': [[{'text': 'foo', 'generation_info': None}]], 'llm_output': None, 'run': None}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:2'], execution_order=None, child_execution_order=None, child_runs=[]), Run(id=UUID('00000000-0000-4000-8000-000000000003'), name='RunnableLambda', start_time=FakeDatetime(2023, 1, 1, 0, 0), run_type='chain', end_time=FakeDatetime(2023, 1, 1, 0, 0), extra={}, error=None, serialized={'lc': 1, 'type': 'not_implemented', 'id': ['langchain', 'schema', 'runnable', 'base', 'RunnableLambda'], 'repr': 'RunnableLambda(...)'}, events=[{'name': 'start', 'time': FakeDatetime(2023, 1, 1, 0, 0)}, {'name': 'end', 'time': FakeDatetime(2023, 1, 1, 0, 0)}], inputs={'input': 'foo'}, outputs={'output': 'foo'}, reference_example_id=None, parent_run_id=UUID('00000000-0000-4000-8000-000000000000'), tags=['seq:step:3'], execution_order=None, child_execution_order=None, child_runs=[])]), ]) # --- # name: test_router_runnable @@ -1415,7 +1442,8 @@ "runnable", "base", "RunnableLambda" - ] + ], + "repr": "RunnableLambda(...)" }, "input": { "lc": 1, @@ -1437,7 +1465,8 @@ "runnable", "base", "RunnableLambda" - ] + ], + "repr": "RunnableLambda(...)" } } } @@ -1521,7 +1550,8 @@ "llms", "fake", "FakeListLLM" - ] + ], + "repr": "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['4'], sleep=None, i=0)" } } }, @@ -1590,7 +1620,8 @@ "llms", "fake", "FakeListLLM" - ] + ], + "repr": "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['2'], sleep=None, i=0)" } } } @@ -1654,7 +1685,8 @@ "runnable", "base", "RunnableLambda" - ] + ], + "repr": "RunnableLambda(...)" } } }, @@ -1677,7 +1709,8 @@ "runnable", "base", "RunnableLambda" - ] + ], + "repr": "RunnableLambda(...)" }, "last": { "lc": 1, @@ -1686,7 +1719,8 @@ "runnable", "test_runnable", "FakeRetriever" - ] + ], + "repr": "FakeRetriever(tags=None, metadata=None)" } } }, @@ -1699,7 +1733,8 @@ "runnable", "base", "RunnableLambda" - ] + ], + "repr": "RunnableLambda(...)" } } } @@ -1790,7 +1825,8 @@ "chat_models", "fake", "FakeListChatModel" - ] + ], + "repr": "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=['foo, bar'], sleep=None, i=0)" } ], "last": { @@ -1905,7 +1941,8 @@ "runnable", "base", "RunnableLambda" - ] + ], + "repr": "RunnableLambda(...)" } ], "last": { @@ -1927,7 +1964,8 @@ "chat_models", "fake", "FakeListChatModel" - ] + ], + "repr": "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=[\"i'm a chatbot\"], sleep=None, i=0)" }, "llm": { "lc": 1, @@ -1937,7 +1975,8 @@ "llms", "fake", "FakeListLLM" - ] + ], + "repr": "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=[\"i'm a textbot\"], sleep=None, i=0)" } } } @@ -2043,7 +2082,8 @@ "runnable", "base", "RunnableLambda" - ] + ], + "repr": "RunnableLambda(...)" } ], "last": { @@ -2075,13 +2115,15 @@ "chat_models", "fake", "FakeListChatModel" - ] + ], + "repr": "FakeListChatModel(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=[\"i'm a chatbot\"], sleep=None, i=0)" }, "kwargs": { "stop": [ "Thought:" ] - } + }, + "config": {} } }, "llm": { @@ -2092,7 +2134,8 @@ "llms", "fake", "FakeListLLM" - ] + ], + "repr": "FakeListLLM(cache=None, verbose=False, callbacks=None, callback_manager=None, tags=None, metadata=None, responses=[\"i'm a textbot\"], sleep=None, i=0)" }, "passthrough": { "lc": 1, @@ -2103,7 +2146,8 @@ "runnable", "base", "RunnableLambda" - ] + ], + "repr": "RunnableLambda(...)" } } } diff --git a/libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py b/libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py index f244753310..2e0be35ddc 100644 --- a/libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py +++ b/libs/langchain/tests/unit_tests/schema/runnable/test_runnable.py @@ -11,6 +11,7 @@ from langchain import PromptTemplate from langchain.callbacks.manager import Callbacks from langchain.callbacks.tracers.base import BaseTracer from langchain.callbacks.tracers.schemas import Run +from langchain.callbacks.tracers.stdout import ConsoleCallbackHandler from langchain.chat_models.fake import FakeListChatModel from langchain.llms.fake import FakeListLLM, FakeStreamingListLLM from langchain.load.dump import dumpd, dumps @@ -112,6 +113,124 @@ class FakeRetriever(BaseRetriever): return [Document(page_content="foo"), Document(page_content="bar")] +@pytest.mark.asyncio +async def test_with_config(mocker: MockerFixture) -> None: + fake = FakeRunnable() + spy = mocker.spy(fake, "invoke") + + assert fake.with_config(tags=["a-tag"]).invoke("hello") == 5 + assert spy.call_args_list == [ + mocker.call("hello", dict(tags=["a-tag"])), + ] + spy.reset_mock() + + fake_1: Runnable = RunnablePassthrough() + fake_2: Runnable = RunnablePassthrough() + spy_seq_step = mocker.spy(fake_1.__class__, "invoke") + + sequence = fake_1.with_config(tags=["a-tag"]) | fake_2.with_config( + tags=["b-tag"], max_concurrency=5 + ) + assert sequence.invoke("hello") == "hello" + assert len(spy_seq_step.call_args_list) == 2 + for i, call in enumerate(spy_seq_step.call_args_list): + assert call.args[1] == "hello" + if i == 0: + assert call.args[2].get("tags") == ["a-tag"] + assert call.args[2].get("max_concurrency") is None + else: + assert call.args[2].get("tags") == ["b-tag"] + assert call.args[2].get("max_concurrency") == 5 + mocker.stop(spy_seq_step) + + assert [ + *fake.with_config(tags=["a-tag"]).stream( + "hello", dict(metadata={"key": "value"}) + ) + ] == [5] + assert spy.call_args_list == [ + mocker.call("hello", dict(tags=["a-tag"], metadata={"key": "value"})), + ] + spy.reset_mock() + + assert fake.with_config(recursion_limit=5).batch( + ["hello", "wooorld"], [dict(tags=["a-tag"]), dict(metadata={"key": "value"})] + ) == [5, 7] + + assert len(spy.call_args_list) == 2 + for i, call in enumerate(spy.call_args_list): + assert call.args[0] == ("hello" if i == 0 else "wooorld") + if i == 0: + assert call.args[1].get("recursion_limit") == 5 + assert call.args[1].get("tags") == ["a-tag"] + assert call.args[1].get("metadata") == {} + else: + assert call.args[1].get("recursion_limit") == 5 + assert call.args[1].get("tags") == [] + assert call.args[1].get("metadata") == {"key": "value"} + + spy.reset_mock() + + assert fake.with_config(metadata={"a": "b"}).batch( + ["hello", "wooorld"], dict(tags=["a-tag"]) + ) == [5, 7] + assert len(spy.call_args_list) == 2 + for i, call in enumerate(spy.call_args_list): + assert call.args[0] == ("hello" if i == 0 else "wooorld") + assert call.args[1].get("tags") == ["a-tag"] + assert call.args[1].get("metadata") == {"a": "b"} + spy.reset_mock() + + handler = ConsoleCallbackHandler() + assert ( + await fake.with_config(metadata={"a": "b"}).ainvoke( + "hello", config={"callbacks": [handler]} + ) + == 5 + ) + assert spy.call_args_list == [ + mocker.call("hello", dict(callbacks=[handler], metadata={"a": "b"})), + ] + spy.reset_mock() + + assert [ + part async for part in fake.with_config(metadata={"a": "b"}).astream("hello") + ] == [5] + assert spy.call_args_list == [ + mocker.call("hello", dict(metadata={"a": "b"})), + ] + spy.reset_mock() + + assert await fake.with_config(recursion_limit=5, tags=["c"]).abatch( + ["hello", "wooorld"], dict(metadata={"key": "value"}) + ) == [ + 5, + 7, + ] + assert spy.call_args_list == [ + mocker.call( + "hello", + dict( + metadata={"key": "value"}, + tags=["c"], + callbacks=None, + _locals={}, + recursion_limit=5, + ), + ), + mocker.call( + "wooorld", + dict( + metadata={"key": "value"}, + tags=["c"], + callbacks=None, + _locals={}, + recursion_limit=5, + ), + ), + ] + + @pytest.mark.asyncio async def test_default_method_implementations(mocker: MockerFixture) -> None: fake = FakeRunnable() @@ -1125,6 +1244,14 @@ async def test_map_astream_iterator_input() -> None: assert final_value.get("passthrough") == llm_res +def test_with_config_with_config() -> None: + llm = FakeListLLM(responses=["i'm a textbot"]) + + assert dumpd( + llm.with_config({"metadata": {"a": "b"}}).with_config(tags=["a-tag"]) + ) == dumpd(llm.with_config({"metadata": {"a": "b"}, "tags": ["a-tag"]})) + + def test_bind_bind() -> None: llm = FakeListLLM(responses=["i'm a textbot"]) @@ -1296,3 +1423,365 @@ def test_recursive_lambda() -> None: with pytest.raises(RecursionError): runnable.invoke(0, {"recursion_limit": 9}) + + +def test_retrying(mocker: MockerFixture) -> None: + def _lambda(x: int) -> Union[int, Runnable]: + if x == 1: + raise ValueError("x is 1") + elif x == 2: + raise RuntimeError("x is 2") + else: + return x + + _lambda_mock = mocker.Mock(side_effect=_lambda) + runnable = RunnableLambda(_lambda_mock) + + with pytest.raises(ValueError): + runnable.invoke(1) + + assert _lambda_mock.call_count == 1 + _lambda_mock.reset_mock() + + with pytest.raises(ValueError): + runnable.with_retry( + stop_after_attempt=2, + retry_if_exception_type=(ValueError,), + ).invoke(1) + + assert _lambda_mock.call_count == 2 # retried + _lambda_mock.reset_mock() + + with pytest.raises(RuntimeError): + runnable.with_retry( + stop_after_attempt=2, + retry_if_exception_type=(ValueError,), + ).invoke(2) + + assert _lambda_mock.call_count == 1 # did not retry + _lambda_mock.reset_mock() + + with pytest.raises(ValueError): + runnable.with_retry( + stop_after_attempt=2, + retry_if_exception_type=(ValueError,), + ).batch([1, 2, 0]) + + # 3rd input isn't retried because it succeeded + assert _lambda_mock.call_count == 3 + 2 + _lambda_mock.reset_mock() + + output = runnable.with_retry( + stop_after_attempt=2, + retry_if_exception_type=(ValueError,), + ).batch([1, 2, 0], return_exceptions=True) + + # 3rd input isn't retried because it succeeded + assert _lambda_mock.call_count == 3 + 2 + assert len(output) == 3 + assert isinstance(output[0], ValueError) + assert isinstance(output[1], RuntimeError) + assert output[2] == 0 + _lambda_mock.reset_mock() + + +@pytest.mark.asyncio +async def test_async_retrying(mocker: MockerFixture) -> None: + def _lambda(x: int) -> Union[int, Runnable]: + if x == 1: + raise ValueError("x is 1") + elif x == 2: + raise RuntimeError("x is 2") + else: + return x + + _lambda_mock = mocker.Mock(side_effect=_lambda) + runnable = RunnableLambda(_lambda_mock) + + with pytest.raises(ValueError): + await runnable.ainvoke(1) + + assert _lambda_mock.call_count == 1 + _lambda_mock.reset_mock() + + with pytest.raises(ValueError): + await runnable.with_retry( + stop_after_attempt=2, + retry_if_exception_type=(ValueError,), + ).ainvoke(1) + + assert _lambda_mock.call_count == 2 # retried + _lambda_mock.reset_mock() + + with pytest.raises(RuntimeError): + await runnable.with_retry( + stop_after_attempt=2, + retry_if_exception_type=(ValueError,), + ).ainvoke(2) + + assert _lambda_mock.call_count == 1 # did not retry + _lambda_mock.reset_mock() + + with pytest.raises(ValueError): + await runnable.with_retry( + stop_after_attempt=2, + retry_if_exception_type=(ValueError,), + ).abatch([1, 2, 0]) + + # 3rd input isn't retried because it succeeded + assert _lambda_mock.call_count == 3 + 2 + _lambda_mock.reset_mock() + + output = await runnable.with_retry( + stop_after_attempt=2, + retry_if_exception_type=(ValueError,), + ).abatch([1, 2, 0], return_exceptions=True) + + # 3rd input isn't retried because it succeeded + assert _lambda_mock.call_count == 3 + 2 + assert len(output) == 3 + assert isinstance(output[0], ValueError) + assert isinstance(output[1], RuntimeError) + assert output[2] == 0 + _lambda_mock.reset_mock() + + +@freeze_time("2023-01-01") +def test_seq_batch_return_exceptions(mocker: MockerFixture) -> None: + class ControlledExceptionRunnable(Runnable[str, str]): + def __init__(self, fail_starts_with: str) -> None: + self.fail_starts_with = fail_starts_with + + def invoke(self, input: Any, config: Optional[RunnableConfig] = None) -> Any: + raise NotImplementedError() + + def _batch( + self, + inputs: List[str], + ) -> List: + outputs: List[Any] = [] + for input in inputs: + if input.startswith(self.fail_starts_with): + outputs.append(ValueError()) + else: + outputs.append(input + "a") + return outputs + + def batch( + self, + inputs: List[str], + config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, + *, + return_exceptions: bool = False, + **kwargs: Any, + ) -> List[str]: + return self._batch_with_config( + self._batch, + inputs, + config, + return_exceptions=return_exceptions, + **kwargs, + ) + + chain = ( + ControlledExceptionRunnable("bux") + | ControlledExceptionRunnable("bar") + | ControlledExceptionRunnable("baz") + | ControlledExceptionRunnable("foo") + ) + + assert isinstance(chain, RunnableSequence) + + # Test batch + with pytest.raises(ValueError): + chain.batch(["foo", "bar", "baz", "qux"]) + + spy = mocker.spy(ControlledExceptionRunnable, "batch") + tracer = FakeTracer() + inputs = ["foo", "bar", "baz", "qux"] + outputs = chain.batch(inputs, dict(callbacks=[tracer]), return_exceptions=True) + assert len(outputs) == 4 + assert isinstance(outputs[0], ValueError) + assert isinstance(outputs[1], ValueError) + assert isinstance(outputs[2], ValueError) + assert outputs[3] == "quxaaaa" + assert spy.call_count == 4 + inputs_to_batch = [c[0][1] for c in spy.call_args_list] + assert inputs_to_batch == [ + # inputs to sequence step 0 + # same as inputs to sequence.batch() + ["foo", "bar", "baz", "qux"], + # inputs to sequence step 1 + # == outputs of sequence step 0 as no exceptions were raised + ["fooa", "bara", "baza", "quxa"], + # inputs to sequence step 2 + # 'bar' was dropped as it raised an exception in step 1 + ["fooaa", "bazaa", "quxaa"], + # inputs to sequence step 3 + # 'baz' was dropped as it raised an exception in step 2 + ["fooaaa", "quxaaa"], + ] + parent_runs = sorted( + (r for r in tracer.runs if r.parent_run_id is None), + key=lambda run: inputs.index(run.inputs["input"]), + ) + assert len(parent_runs) == 4 + + parent_run_foo = parent_runs[0] + assert parent_run_foo.inputs["input"] == "foo" + assert parent_run_foo.error == repr(ValueError()) + assert len(parent_run_foo.child_runs) == 4 + assert [r.error for r in parent_run_foo.child_runs] == [ + None, + None, + None, + repr(ValueError()), + ] + + parent_run_bar = parent_runs[1] + assert parent_run_bar.inputs["input"] == "bar" + assert parent_run_bar.error == repr(ValueError()) + assert len(parent_run_bar.child_runs) == 2 + assert [r.error for r in parent_run_bar.child_runs] == [ + None, + repr(ValueError()), + ] + + parent_run_baz = parent_runs[2] + assert parent_run_baz.inputs["input"] == "baz" + assert parent_run_baz.error == repr(ValueError()) + assert len(parent_run_baz.child_runs) == 3 + assert [r.error for r in parent_run_baz.child_runs] == [ + None, + None, + repr(ValueError()), + ] + + parent_run_qux = parent_runs[3] + assert parent_run_qux.inputs["input"] == "qux" + assert parent_run_qux.error is None + assert parent_run_qux.outputs["output"] == "quxaaaa" + assert len(parent_run_qux.child_runs) == 4 + assert [r.error for r in parent_run_qux.child_runs] == [None, None, None, None] + + +@pytest.mark.asyncio +@freeze_time("2023-01-01") +async def test_seq_abatch_return_exceptions(mocker: MockerFixture) -> None: + class ControlledExceptionRunnable(Runnable[str, str]): + def __init__(self, fail_starts_with: str) -> None: + self.fail_starts_with = fail_starts_with + + def invoke(self, input: Any, config: Optional[RunnableConfig] = None) -> Any: + raise NotImplementedError() + + async def _abatch( + self, + inputs: List[str], + ) -> List: + outputs: List[Any] = [] + for input in inputs: + if input.startswith(self.fail_starts_with): + outputs.append(ValueError()) + else: + outputs.append(input + "a") + return outputs + + async def abatch( + self, + inputs: List[str], + config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, + *, + return_exceptions: bool = False, + **kwargs: Any, + ) -> List[str]: + return await self._abatch_with_config( + self._abatch, + inputs, + config, + return_exceptions=return_exceptions, + **kwargs, + ) + + chain = ( + ControlledExceptionRunnable("bux") + | ControlledExceptionRunnable("bar") + | ControlledExceptionRunnable("baz") + | ControlledExceptionRunnable("foo") + ) + + assert isinstance(chain, RunnableSequence) + + # Test abatch + with pytest.raises(ValueError): + await chain.abatch(["foo", "bar", "baz", "qux"]) + + spy = mocker.spy(ControlledExceptionRunnable, "abatch") + tracer = FakeTracer() + inputs = ["foo", "bar", "baz", "qux"] + outputs = await chain.abatch( + inputs, dict(callbacks=[tracer]), return_exceptions=True + ) + assert len(outputs) == 4 + assert isinstance(outputs[0], ValueError) + assert isinstance(outputs[1], ValueError) + assert isinstance(outputs[2], ValueError) + assert outputs[3] == "quxaaaa" + assert spy.call_count == 4 + inputs_to_batch = [c[0][1] for c in spy.call_args_list] + assert inputs_to_batch == [ + # inputs to sequence step 0 + # same as inputs to sequence.batch() + ["foo", "bar", "baz", "qux"], + # inputs to sequence step 1 + # == outputs of sequence step 0 as no exceptions were raised + ["fooa", "bara", "baza", "quxa"], + # inputs to sequence step 2 + # 'bar' was dropped as it raised an exception in step 1 + ["fooaa", "bazaa", "quxaa"], + # inputs to sequence step 3 + # 'baz' was dropped as it raised an exception in step 2 + ["fooaaa", "quxaaa"], + ] + parent_runs = sorted( + (r for r in tracer.runs if r.parent_run_id is None), + key=lambda run: inputs.index(run.inputs["input"]), + ) + assert len(parent_runs) == 4 + + parent_run_foo = parent_runs[0] + assert parent_run_foo.inputs["input"] == "foo" + assert parent_run_foo.error == repr(ValueError()) + assert len(parent_run_foo.child_runs) == 4 + assert [r.error for r in parent_run_foo.child_runs] == [ + None, + None, + None, + repr(ValueError()), + ] + + parent_run_bar = parent_runs[1] + assert parent_run_bar.inputs["input"] == "bar" + assert parent_run_bar.error == repr(ValueError()) + assert len(parent_run_bar.child_runs) == 2 + assert [r.error for r in parent_run_bar.child_runs] == [ + None, + repr(ValueError()), + ] + + parent_run_baz = parent_runs[2] + assert parent_run_baz.inputs["input"] == "baz" + assert parent_run_baz.error == repr(ValueError()) + assert len(parent_run_baz.child_runs) == 3 + assert [r.error for r in parent_run_baz.child_runs] == [ + None, + None, + repr(ValueError()), + ] + + parent_run_qux = parent_runs[3] + assert parent_run_qux.inputs["input"] == "qux" + assert parent_run_qux.error is None + assert parent_run_qux.outputs["output"] == "quxaaaa" + assert len(parent_run_qux.child_runs) == 4 + assert [r.error for r in parent_run_qux.child_runs] == [None, None, None, None] diff --git a/libs/langchain/tests/unit_tests/tools/eden_ai/__init__.py b/libs/langchain/tests/unit_tests/tools/eden_ai/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/libs/langchain/tests/unit_tests/tools/eden_ai/test_tools.py b/libs/langchain/tests/unit_tests/tools/eden_ai/test_tools.py new file mode 100644 index 0000000000..425dbe1b5a --- /dev/null +++ b/libs/langchain/tests/unit_tests/tools/eden_ai/test_tools.py @@ -0,0 +1,103 @@ +from collections.abc import Generator +from unittest.mock import MagicMock, patch + +import pytest + +from langchain.tools.edenai import EdenAiTextModerationTool + +tool = EdenAiTextModerationTool( + providers=["openai"], language="en", edenai_api_key="fake_key" +) + + +@pytest.fixture +def mock_post() -> Generator: + with patch("langchain.tools.edenai.edenai_base_tool.requests.post") as mock: + yield mock + + +def test_provider_not_available(mock_post: MagicMock) -> None: + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = [ + { + "error": { + "message": """Amazon has returned an error: + An error occurred (TextSizeLimitExceededException) + when calling the DetectTargetedSentiment + operation: Input text size exceeds limit. + Max length of request text allowed is 5000 bytes + while in this request the text size is 47380 bytes""", + "type": "ProviderInvalidInputTextLengthError", + }, + "status": "fail", + "provider": "amazon", + "provider_status_code": 400, + "cost": 0.0, + } + ] + mock_post.return_value = mock_response + + with pytest.raises(ValueError): + tool._run("some query") + + +def test_unexpected_response(mock_post: MagicMock) -> None: + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = [ + { + "status": "success", + } + ] + mock_post.return_value = mock_response + with pytest.raises(RuntimeError): + tool._run("some query") + + +def test_incomplete_response(mock_post: MagicMock) -> None: + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = [ + { + "status": "success", + "provider": "microsoft", + "nsfw_likelihood": 5, + "cost": 0.001, + "label": ["sexually explicit", "sexually suggestive", "offensive"], + } + ] + + mock_post.return_value = mock_response + with pytest.raises(RuntimeError): + tool._run("some query") + + +def test_invalid_payload(mock_post: MagicMock) -> None: + mock_response = MagicMock() + mock_response.status_code = 400 + mock_response.json.return_value = {} + mock_post.return_value = mock_response + + with pytest.raises(ValueError): + tool._run("some query") + + +def test_parse_response_format(mock_post: MagicMock) -> None: + mock_response = MagicMock() + mock_response.status_code = 200 + mock_response.json.return_value = [ + { + "status": "success", + "provider": "microsoft", + "nsfw_likelihood": 5, + "cost": 0.001, + "label": ["offensive", "hate_speech"], + "likelihood": [4, 5], + } + ] + mock_post.return_value = mock_response + + result = tool("some query") + + assert result == 'nsfw_likelihood: 5\n"offensive": 4\n"hate_speech": 5' diff --git a/libs/langchain/tests/unit_tests/tools/test_public_api.py b/libs/langchain/tests/unit_tests/tools/test_public_api.py index 6d58dd92ef..e7fd784587 100644 --- a/libs/langchain/tests/unit_tests/tools/test_public_api.py +++ b/libs/langchain/tests/unit_tests/tools/test_public_api.py @@ -28,6 +28,14 @@ _EXPECTED = [ "DeleteFileTool", "DuckDuckGoSearchResults", "DuckDuckGoSearchRun", + "EdenAiExplicitImageTool", + "EdenAiObjectDetectionTool", + "EdenAiParsingIDTool", + "EdenAiParsingInvoiceTool", + "EdenAiSpeechToTextTool", + "EdenAiTextModerationTool", + "EdenAiTextToSpeechTool", + "EdenaiTool", "ExtractHyperlinksTool", "ExtractTextTool", "FileSearchTool", diff --git a/pyproject.toml b/pyproject.toml index dcf5c938a8..c2d3edde5e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -40,4 +40,4 @@ ignore-regex = '.*(Stati Uniti|Tense=Pres).*' # whats is a typo but used frequently in queries so kept as is # aapply - async apply # unsecure - typo but part of API, decided to not bother for now -ignore-words-list = 'momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogyny,unsecure,damon,crate' \ No newline at end of file +ignore-words-list = 'momento,collison,ned,foor,reworkd,parth,whats,aapply,mysogyny,unsecure,damon,crate'