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https://github.com/hwchase17/langchain
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cli/docs: embedding template standardization (#24849)
This commit is contained in:
Erick Friis
GitHub
parent
1181c10c65
commit
3999e9035c
@ -28,7 +28,7 @@
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"- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
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"- TODO: Make sure API reference links are correct.\n",
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"\n",
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"| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
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"| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/llms/__package_name_short_snake__) | Package downloads | Package latest |\n",
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"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
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"| [__ModuleName__LLM](https://api.python.langchain.com/en/latest/llms/__module_name__.llms.__ModuleName__LLM.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ |  |  |\n",
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"\n",
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@ -173,7 +173,7 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_core.prompts import ChatPromptTemplate\n",
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"from langchain_core.prompts import PromptTemplate\n",
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"\n",
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"prompt = PromptTemplate(\n",
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" \"How to say {input} in {output_language}:\\n\"\n",
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@ -12,14 +12,217 @@
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},
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{
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"cell_type": "markdown",
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"id": "e49f1e0d",
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"id": "9a3d6f34",
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"metadata": {},
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"source": [
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"# __ModuleName__Embeddings\n",
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"\n",
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"This notebook covers how to get started with __ModuleName__ embedding models.\n",
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"- [ ] TODO: Make sure API reference link is correct\n",
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"\n",
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"## Installation"
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"This will help you get started with __ModuleName__ embedding models using LangChain. For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/__module_name__.embeddings.__ModuleName__Embeddings.html).\n",
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"\n",
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"## Overview\n",
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"### Integration details\n",
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"\n",
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"- TODO: Fill in table features.\n",
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"- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
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"- TODO: Make sure API reference links are correct.\n",
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"\n",
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"| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/text_embedding/__package_name_short_snake__) | Package downloads | Package latest |\n",
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"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
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"| [__ModuleName__Embeddings](https://api.python.langchain.com/en/latest/embeddings/__module_name__.embeddings.__ModuleName__Embeddings.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ |  |  |\n",
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"\n",
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"## Setup\n",
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"\n",
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"- [ ] TODO: Update with relevant info.\n",
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"\n",
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"To access __ModuleName__ embedding models you'll need to create a/an __ModuleName__ account, get an API key, and install the `__package_name__` integration package.\n",
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"\n",
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"### Credentials\n",
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"\n",
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"- TODO: Update with relevant info.\n",
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"\n",
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"Head to (TODO: link) to sign up to __ModuleName__ and generate an API key. Once you've done this set the __MODULE_NAME___API_KEY environment variable:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "36521c2a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import getpass\n",
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"import os\n",
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"\n",
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"if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
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" os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "c84fb993",
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"metadata": {},
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"source": [
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"If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "39a4953b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
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"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d9664366",
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"metadata": {},
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"source": [
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"### Installation\n",
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"\n",
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"The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "64853226",
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"metadata": {},
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"outputs": [],
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"source": [
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"%pip install -qU __package_name__"
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]
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},
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{
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"cell_type": "markdown",
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"id": "45dd1724",
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"metadata": {},
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"source": [
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"## Instantiation\n",
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"\n",
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"Now we can instantiate our model object and generate chat completions:\n",
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"\n",
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"- TODO: Update model instantiation with relevant params."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9ea7a09b",
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"metadata": {},
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"outputs": [],
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"source": [
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"from __module_name__ import __ModuleName__Embeddings\n",
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"\n",
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"embeddings = __ModuleName__Embeddings(\n",
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" model=\"model-name\",\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"id": "77d271b6",
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"metadata": {},
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"source": [
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"## Indexing and Retrieval\n",
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"\n",
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"Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the [working with external knowledge tutorials](/docs/tutorials/#working-with-external-knowledge).\n",
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"\n",
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"Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d817716b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create a vector store with a sample text\n",
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"from langchain_core.vectorstores import InMemoryVectorStore\n",
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"\n",
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"text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
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"\n",
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"vectorstore = InMemoryVectorStore.from_texts(\n",
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" [text],\n",
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" embedding=embeddings,\n",
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")\n",
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"\n",
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"# Use the vectorstore as a retriever\n",
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"retriever = vectorstore.as_retriever()\n",
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"\n",
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"# Retrieve the most similar text\n",
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"retrieved_document = retriever.invoke(\"What is LangChain?\")\n",
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"\n",
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"# show the retrieved document's content\n",
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"retrieved_document.page_content"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e02b9855",
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"metadata": {},
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"source": [
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"## Direct Usage\n",
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"\n",
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"Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.\n",
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"\n",
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"You can directly call these methods to get embeddings for your own use cases.\n",
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"\n",
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"### Embed single texts\n",
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"\n",
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"You can embed single texts or documents with `embed_query`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0d2befcd",
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"metadata": {},
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"outputs": [],
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"source": [
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"single_vector = embeddings.embed_query(text)\n",
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"print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1b5a7d03",
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"metadata": {},
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"source": [
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"### Embed multiple texts\n",
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"\n",
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"You can embed multiple texts with `embed_documents`:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2f4d6e97",
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"metadata": {},
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"outputs": [],
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"source": [
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"text2 = (\n",
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" \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
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")\n",
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"two_vectors = embeddings.embed_queries([text, text2])\n",
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"for vector in two_vectors:\n",
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" print(str(vector)[:100]) # Show the first 100 characters of the vector"
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]
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},
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{
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"cell_type": "markdown",
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"id": "98785c12",
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"metadata": {},
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"source": [
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"### Async Usage\n",
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"\n",
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"You can also use `aembed_query` and `aembed_documents` for producing embeddings asynchronously:\n"
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]
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},
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{
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@ -29,83 +232,22 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# install package\n",
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"!pip install -U __package_name__"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2b4f3e15",
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"metadata": {},
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"source": [
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"## Environment Setup\n",
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"import asyncio\n",
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"\n",
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"Make sure to set the following environment variables:\n",
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"async def async_example():\n",
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" single_vector = await embeddings.embed_query(text)\n",
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" print(str(single_vector)[:100]) # Show the first 100 characters of the vector\n",
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"\n",
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"- TODO: fill out relevant environment variables or secrets\n",
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"\n",
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"## Usage"
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"asyncio.run(async_example())"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "62e0dbc3",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from __module_name__.embeddings import __ModuleName__Embeddings\n",
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"\n",
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"embeddings = __ModuleName__Embeddings()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "12fcfb4b",
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"id": "f1bd4396",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings.embed_query(\"My query to look up\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1f2e6104",
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"metadata": {},
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"outputs": [],
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"source": [
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"embeddings.embed_documents(\n",
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" [\"This is a content of the document\", \"This is another document\"]\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "46739f68",
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"metadata": {},
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"outputs": [],
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"source": [
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"# async embed query\n",
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"await embeddings.aembed_query(\"My query to look up\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e48632ea",
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"metadata": {},
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"outputs": [],
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"source": [
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"# async embed documents\n",
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"await embeddings.aembed_documents(\n",
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" [\"This is a content of the document\", \"This is another document\"]\n",
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")"
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]
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"source": []
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}
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],
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"metadata": {
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