docs[patch]: fix bullet points (#14684)

- docs fixes - escape - bullets
10 months ago · ab94119a53
parent 7e4dbb26a8
commit ab94119a53
23 changed files with 41 additions and 5 deletions
--- a/docs/docs/expression_language/how_to/functions.ipynb
+++ b/docs/docs/expression_language/how_to/functions.ipynb
@ -1,7 +1,8 @@
 {
 "cells": [
  {
-   "cell_type": "markdown",
+   "cell_type": "raw",
+   "id": "ce0e08fd",
   "metadata": {},
   "source": [
    "---\n",
--- a/docs/docs/expression_language/how_to/message_history.ipynb
+++ b/docs/docs/expression_language/how_to/message_history.ipynb
@ -10,11 +10,13 @@
    "The `RunnableWithMessageHistory` let's us add message history to certain types of chains.\n",
    "\n",
    "Specifically, it can be used for any Runnable that takes as input one of\n",
+    "\n",
    "* a sequence of `BaseMessage`\n",
    "* a dict with a key that takes a sequence of `BaseMessage`\n",
    "* a dict with a key that takes the latest message(s) as a string or sequence of `BaseMessage`, and a separate key that takes historical messages\n",
    "\n",
    "And returns as output one of\n",
+    "\n",
    "* a string that can be treated as the contents of an `AIMessage`\n",
    "* a sequence of `BaseMessage`\n",
    "* a dict with a key that contains a sequence of `BaseMessage`\n",
--- a/docs/docs/guides/evaluation/comparison/pairwise_string.ipynb
+++ b/docs/docs/guides/evaluation/comparison/pairwise_string.ipynb
@ -89,6 +89,7 @@
    "- reference (str) – (Only for the labeled_pairwise_string variant) The reference response.\n",
    "\n",
    "They return a dictionary with the following values:\n",
+    "\n",
    "- value: 'A' or 'B', indicating whether `prediction` or `prediction_b` is preferred, respectively\n",
    "- score: Integer 0 or 1 mapped from the 'value', where a score of 1 would mean that the first `prediction` is preferred, and a score of 0 would mean `prediction_b` is preferred.\n",
    "- reasoning: String \"chain of thought reasoning\" from the LLM generated prior to creating the score"
@ -159,6 +160,7 @@
    "## Defining the Criteria\n",
    "\n",
    "By default, the LLM is instructed to select the 'preferred' response based on helpfulness, relevance, correctness, and depth of thought. You can customize the criteria by passing in a `criteria` argument, where the criteria could take any of the following forms:\n",
+    "\n",
    "- [`Criteria`](https://api.python.langchain.com/en/latest/evaluation/langchain.evaluation.criteria.eval_chain.Criteria.html#langchain.evaluation.criteria.eval_chain.Criteria) enum or its string value - to use one of the default criteria and their descriptions\n",
    "- [Constitutional principal](https://api.python.langchain.com/en/latest/chains/langchain.chains.constitutional_ai.models.ConstitutionalPrinciple.html#langchain.chains.constitutional_ai.models.ConstitutionalPrinciple) - use one any of the constitutional principles defined in langchain\n",
    "- Dictionary: a list of custom criteria, where the key is the name of the criteria, and the value is the description.\n",
--- a/docs/docs/guides/local_llms.ipynb
+++ b/docs/docs/guides/local_llms.ipynb
@ -249,14 +249,17 @@
    "* Meaning: Only one layer of the model will be loaded into GPU memory (1 is often sufficient).\n",
    "\n",
    "`n_batch`: number of tokens the model should process in parallel \n",
+    "\n",
    "* Value: n_batch\n",
    "* Meaning: It's recommended to choose a value between 1 and n_ctx (which in this case is set to 2048)\n",
    "\n",
-    "`n_ctx`: Token context window .\n",
+    "`n_ctx`: Token context window\n",
+    "\n",
    "* Value: 2048\n",
    "* Meaning: The model will consider a window of 2048 tokens at a time\n",
    "\n",
    "`f16_kv`: whether the model should use half-precision for the key/value cache\n",
+    "\n",
    "* Value: True\n",
    "* Meaning: The model will use half-precision, which can be more memory efficient; Metal only supports True."
   ]
--- a/docs/docs/integrations/callbacks/sagemaker_tracking.ipynb
+++ b/docs/docs/integrations/callbacks/sagemaker_tracking.ipynb
@ -12,6 +12,7 @@
    ">[Amazon SageMaker Experiments](https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html) is a capability of `Amazon SageMaker` that lets you organize, track, compare and evaluate ML experiments and model versions.\n",
    "\n",
    "This notebook shows how LangChain Callback can be used to log and track prompts and other LLM hyperparameters into `SageMaker Experiments`. Here, we use different scenarios to showcase the capability:\n",
+    "\n",
    "* **Scenario 1**: *Single LLM* - A case where a single LLM model is used to generate output based on a given prompt.\n",
    "* **Scenario 2**: *Sequential Chain* - A case where a sequential chain of two LLM models is used.\n",
    "* **Scenario 3**: *Agent with Tools (Chain of Thought)* - A case where multiple tools (search and math) are used in addition to an LLM.\n",
@ -50,6 +51,7 @@
   },
   "source": [
    "First, setup the required API keys\n",
+    "\n",
    "* OpenAI: https://platform.openai.com/account/api-keys (For OpenAI LLM model)\n",
    "* Google SERP API: https://serpapi.com/manage-api-key (For Google Search Tool)"
   ]
--- a/docs/docs/integrations/chat/ollama.ipynb
+++ b/docs/docs/integrations/chat/ollama.ipynb
@ -43,11 +43,13 @@
    "You can easily access models in a few ways:\n",
    "\n",
    "1/ if the app is running:\n",
+    "\n",
    "* All of your local models are automatically served on `localhost:11434`\n",
    "* Select your model when setting `llm = Ollama(..., model=\"<model family>:<version>\")`\n",
    "* If you set `llm = Ollama(..., model=\"<model family\")` withoout a version it will simply look for `latest`\n",
    "\n",
    "2/ if building from source or just running the binary: \n",
+    "\n",
    "* Then you must run `ollama serve`\n",
    "* All of your local models are automatically served on `localhost:11434`\n",
    "* Then, select as shown above\n",
--- a/docs/docs/integrations/document_loaders/airbyte_cdk.ipynb
+++ b/docs/docs/integrations/document_loaders/airbyte_cdk.ipynb
@ -86,6 +86,7 @@
   "metadata": {},
   "source": [
    "Now you can create an `AirbyteCDKLoader` based on the imported source. It takes a `config` object that's passed to the connector. You also have to pick the stream you want to retrieve records from by name (`stream_name`). Check the connectors documentation page and spec definition for more information on the config object and available streams. For the Github connectors these are:\n",
+    "\n",
    "* [https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-github/source_github/spec.json](https://github.com/airbytehq/airbyte/blob/master/airbyte-integrations/connectors/source-github/source_github/spec.json).\n",
    "* [https://docs.airbyte.com/integrations/sources/github/](https://docs.airbyte.com/integrations/sources/github/)"
   ]
--- a/docs/docs/integrations/document_loaders/etherscan.ipynb
+++ b/docs/docs/integrations/document_loaders/etherscan.ipynb
@ -18,6 +18,7 @@
    "You will need a `Etherscan api key` to proceed. The free api key has 5 calls per seconds quota.\n",
    "\n",
    "The loader supports the following six functionalities:\n",
+    "\n",
    "* Retrieve normal transactions under specific account on Ethereum Mainet\n",
    "* Retrieve internal transactions under specific account on Ethereum Mainet\n",
    "* Retrieve erc20 transactions under specific account on Ethereum Mainet\n",
@ -29,6 +30,7 @@
    "If the account does not have corresponding transactions, the loader will a list with one document. The content of document is ''.\n",
    "\n",
    "You can pass different filters to loader to access different functionalities we mentioned above:\n",
+    "\n",
    "* \"normal_transaction\"\n",
    "* \"internal_transaction\"\n",
    "* \"erc20_transaction\"\n",
@ -40,6 +42,7 @@
    "If you have any questions, you can access [Etherscan API Doc](https://etherscan.io/tx/0x0ffa32c787b1398f44303f731cb06678e086e4f82ce07cebf75e99bb7c079c77) or contact me via i@inevitable.tech.\n",
    "\n",
    "All functions related to transactions histories are restricted 1000 histories maximum because of Etherscan limit. You can use the following parameters to find the transaction histories you need:\n",
+    "\n",
    "* offset: default to 20. Shows 20 transactions for one time\n",
    "* page: default to 1. This controls pagination.\n",
    "* start_block: Default to 0. The transaction histories starts from 0 block.\n",
--- a/docs/docs/integrations/document_loaders/google_drive.ipynb
+++ b/docs/docs/integrations/document_loaders/google_drive.ipynb
@ -24,6 +24,7 @@
    "The first time you use GoogleDriveLoader, you will be displayed with the consent screen in your browser. If this doesn't happen and you get a `RefreshError`, do not use `credentials_path` in your `GoogleDriveLoader` constructor call. Instead, put that path in a `GOOGLE_APPLICATION_CREDENTIALS` environmental variable.\n",
    "\n",
    "`GoogleDriveLoader` can load from a list of Google Docs document ids or a folder id. You can obtain your folder and document id from the URL:\n",
+    "\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\"`"
   ]
--- a/docs/docs/integrations/document_transformers/doctran_extract_properties.ipynb
+++ b/docs/docs/integrations/document_transformers/doctran_extract_properties.ipynb
@ -9,6 +9,7 @@
    "We can extract useful features of documents using the [Doctran](https://github.com/psychic-api/doctran) library, which uses OpenAI's function calling feature to extract specific metadata.\n",
    "\n",
    "Extracting metadata from documents is helpful for a variety of tasks, including:\n",
+    "\n",
    "* **Classification:** classifying documents into different categories\n",
    "* **Data mining:** Extract structured data that can be used for data analysis\n",
    "* **Style transfer:** Change the way text is written to more closely match expected user input, improving vector search results"
--- a/docs/docs/integrations/llms/databricks.ipynb
+++ b/docs/docs/integrations/llms/databricks.ipynb
@ -19,6 +19,7 @@
    "\n",
    "This example notebook shows how to wrap Databricks endpoints as LLMs in LangChain.\n",
    "It supports two endpoint types:\n",
+    "\n",
    "* Serving endpoint, recommended for production and development,\n",
    "* Cluster driver proxy app, recommended for interactive development."
   ]
@ -48,9 +49,7 @@
   "source": [
    "## Wrapping a serving endpoint: External model\n",
    "\n",
-    "Prerequisite:\n",
-    "\n",
-    "- Register an OpenAI API key as a secret:\n",
+    "Prerequisite: Register an OpenAI API key as a secret:\n",
    "\n",
    "  ```bash\n",
    "  databricks secrets create-scope <scope>\n",
@ -159,10 +158,12 @@
    "## Wrapping a serving endpoint: Custom model\n",
    "\n",
    "Prerequisites:\n",
+    "\n",
    "* An LLM was registered and deployed to [a Databricks serving endpoint](https://docs.databricks.com/machine-learning/model-serving/index.html).\n",
    "* You have [\"Can Query\" permission](https://docs.databricks.com/security/auth-authz/access-control/serving-endpoint-acl.html) to the endpoint.\n",
    "\n",
    "The expected MLflow model signature is:\n",
+    "\n",
    "  * inputs: `[{\"name\": \"prompt\", \"type\": \"string\"}, {\"name\": \"stop\", \"type\": \"list[string]\"}]`\n",
    "  * outputs: `[{\"type\": \"string\"}]`\n",
    "\n",
@ -381,12 +382,14 @@
    "## Wrapping a cluster driver proxy app\n",
    "\n",
    "Prerequisites:\n",
+    "\n",
    "* An LLM loaded on a Databricks interactive cluster in \"single user\" or \"no isolation shared\" mode.\n",
    "* A local HTTP server running on the driver node to serve the model at `\"/\"` using HTTP POST with JSON input/output.\n",
    "* It uses a port number between `[3000, 8000]` and listens to the driver IP address or simply `0.0.0.0` instead of localhost only.\n",
    "* You have \"Can Attach To\" permission to the cluster.\n",
    "\n",
    "The expected server schema (using JSON schema) is:\n",
+    "\n",
    "* inputs:\n",
    "  ```json\n",
    "  {\"type\": \"object\",\n",
--- a/docs/docs/integrations/llms/ollama.ipynb
+++ b/docs/docs/integrations/llms/ollama.ipynb
@ -34,11 +34,13 @@
    "You can easily access models in a few ways:\n",
    "\n",
    "1/ if the app is running:\n",
+    "\n",
    "* All of your local models are automatically served on `localhost:11434`\n",
    "* Select your model when setting `llm = Ollama(..., model=\"<model family>:<version>\")`\n",
    "* If you set `llm = Ollama(..., model=\"<model family\")` withoout a version it will simply look for `latest`\n",
    "\n",
    "2/ if building from source or just running the binary: \n",
+    "\n",
    "* Then you must run `ollama serve`\n",
    "* All of your local models are automatically served on `localhost:11434`\n",
    "* Then, select as shown above\n",
--- a/docs/docs/integrations/llms/vllm.ipynb
+++ b/docs/docs/integrations/llms/vllm.ipynb
@ -8,6 +8,7 @@
    "# vLLM\n",
    "\n",
    "[vLLM](https://vllm.readthedocs.io/en/latest/index.html) is a fast and easy-to-use library for LLM inference and serving, offering:\n",
+    "\n",
    "* State-of-the-art serving throughput \n",
    "* Efficient management of attention key and value memory with PagedAttention\n",
    "* Continuous batching of incoming requests\n",
--- a/docs/docs/integrations/providers/whylabs_profiling.ipynb
+++ b/docs/docs/integrations/providers/whylabs_profiling.ipynb
@ -39,6 +39,7 @@
   "metadata": {},
   "source": [
    "Make sure to set the required API keys and config required to send telemetry to WhyLabs:\n",
+    "\n",
    "* WhyLabs API Key: https://whylabs.ai/whylabs-free-sign-up\n",
    "* Org and Dataset [https://docs.whylabs.ai/docs/whylabs-onboarding](https://docs.whylabs.ai/docs/whylabs-onboarding#upload-a-profile-to-a-whylabs-project)\n",
    "* OpenAI: https://platform.openai.com/account/api-keys\n",
--- a/docs/docs/integrations/retrievers/google_drive.ipynb
+++ b/docs/docs/integrations/retrievers/google_drive.ipynb
@ -32,6 +32,7 @@
   "metadata": {},
   "source": [
    "You can obtain your folder and document id from the URL:\n",
+    "\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",
--- a/docs/docs/integrations/toolkits/github.ipynb
+++ b/docs/docs/integrations/toolkits/github.ipynb
@ -10,6 +10,7 @@
    "The tool is a wrapper for the [PyGitHub](https://github.com/PyGithub/PyGithub) library. \n",
    "\n",
    "## Quickstart\n",
+    "\n",
    "1. Install the pygithub library\n",
    "2. Create a Github app\n",
    "3. Set your environmental variables\n",
@ -69,6 +70,7 @@
    "### 2. Create a Github App\n",
    "\n",
    "[Follow the instructions here](https://docs.github.com/en/apps/creating-github-apps/registering-a-github-app/registering-a-github-app) to create and register a Github app. Make sure your app has the following [repository permissions:](https://docs.github.com/en/rest/overview/permissions-required-for-github-apps?apiVersion=2022-11-28)\n",
+    "\n",
    "* Commit statuses (read only)\n",
    "* Contents (read and write)\n",
    "* Issues (read and write)\n",
--- a/docs/docs/integrations/toolkits/gitlab.ipynb
+++ b/docs/docs/integrations/toolkits/gitlab.ipynb
@ -69,6 +69,7 @@
    "### 2. Create a Gitlab personal access token\n",
    "\n",
    "[Follow the instructions here](https://docs.gitlab.com/ee/user/profile/personal_access_tokens.html) to create a Gitlab personal access token. Make sure your app has the following repository permissions:\n",
+    "\n",
    "* read_api\n",
    "* read_repository\n",
    "* write_repository\n",
--- a/docs/docs/integrations/toolkits/google_drive.ipynb
+++ b/docs/docs/integrations/toolkits/google_drive.ipynb
@ -38,6 +38,7 @@
   "metadata": {},
   "source": [
    "You can obtain your folder and document id from the URL:\n",
+    "\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",
--- a/docs/docs/integrations/tools/google_drive.ipynb
+++ b/docs/docs/integrations/tools/google_drive.ipynb
@ -38,6 +38,7 @@
   "metadata": {},
   "source": [
    "You can obtain your folder and document id from the URL:\n",
+    "\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",
--- a/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
+++ b/docs/docs/integrations/vectorstores/mongodb_atlas.ipynb
@ -23,6 +23,7 @@
   "metadata": {},
   "source": [
    "> Note: \n",
+    ">\n",
    ">* This feature is Generally Available and ready for production deployments.\n",
    ">* The langchain version 0.0.305 ([release notes](https://github.com/langchain-ai/langchain/releases/tag/v0.0.305)) introduces the support for $vectorSearch MQL stage, which is available with MongoDB Atlas 6.0.11 and 7.0.2. Users utilizing earlier versions of MongoDB Atlas need to pin their LangChain version to <=0.0.304\n",
    "> \n",
--- a/docs/docs/integrations/vectorstores/vectara.ipynb
+++ b/docs/docs/integrations/vectorstores/vectara.ipynb
@ -68,6 +68,7 @@
    "In this example, we assume that you've created an account and a corpus, and added your VECTARA_CUSTOMER_ID, VECTARA_CORPUS_ID and VECTARA_API_KEY (created with permissions for both indexing and query) as environment variables.\n",
    "\n",
    "The corpus has 3 fields defined as metadata for filtering:\n",
+    "\n",
    "* url: a string field containing the source URL of the document (where relevant)\n",
    "* speech: a string field containing the name of the speech\n",
    "* author: the name of the author\n",
@ -136,6 +137,7 @@
    "To use this, we added the add_files() method (as well as from_files()). \n",
    "\n",
    "Let's see this in action. We pick two PDF documents to upload: \n",
+    "\n",
    "1. The \"I have a dream\" speech by Dr. King\n",
    "2. Churchill's \"We Shall Fight on the Beaches\" speech"
   ]
--- a/docs/docs/modules/chains/index.ipynb
+++ b/docs/docs/modules/chains/index.ipynb
@ -30,6 +30,7 @@
    "## LCEL\n",
    "\n",
    "The most visible part of LCEL is that it provides an intuitive and readable syntax for composition. But more importantly, it also provides first-class support for:\n",
+    "\n",
    "* [streaming](/docs/expression_language/interface#stream),\n",
    "* [async calls](/docs/expression_language/interface#async-stream),\n",
    "* [batching](/docs/expression_language/interface#batch),\n",
--- a/docs/docs/use_cases/chatbots.ipynb
+++ b/docs/docs/use_cases/chatbots.ipynb
@ -203,6 +203,7 @@
    "## Memory \n",
    "\n",
    "As we mentioned above, the core component of chatbots is the memory system. One of the simplest and most commonly used forms of memory is `ConversationBufferMemory`:\n",
+    "\n",
    "* This memory allows for storing of messages in a `buffer`\n",
    "* When called in a chain, it returns all of the messages it has stored\n",
    "\n",