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cli/docs: embedding template standardization (langchain-ai#24849)
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efriis authored Jul 31, 2024
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4 changes: 2 additions & 2 deletions libs/cli/langchain_cli/integration_template/docs/llms.ipynb
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"- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
"- TODO: Make sure API reference links are correct.\n",
"\n",
"| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/chat/__package_name_short_snake__) | Package downloads | Package latest |\n",
"| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/llms/__package_name_short_snake__) | Package downloads | Package latest |\n",
"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
"| [__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/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
"\n",
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"metadata": {},
"outputs": [],
"source": [
"from langchain_core.prompts import ChatPromptTemplate\n",
"from langchain_core.prompts import PromptTemplate\n",
"\n",
"prompt = PromptTemplate(\n",
" \"How to say {input} in {output_language}:\\n\"\n",
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204 changes: 173 additions & 31 deletions libs/cli/langchain_cli/integration_template/docs/text_embedding.ipynb
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},
{
"cell_type": "markdown",
"id": "e49f1e0d",
"id": "9a3d6f34",
"metadata": {},
"source": [
"# __ModuleName__Embeddings\n",
"\n",
"This notebook covers how to get started with __ModuleName__ embedding models.\n",
"- [ ] TODO: Make sure API reference link is correct\n",
"\n",
"## Installation"
"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",
"\n",
"## Overview\n",
"### Integration details\n",
"\n",
"- TODO: Fill in table features.\n",
"- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
"- TODO: Make sure API reference links are correct.\n",
"\n",
"| 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",
"| :--- | :--- | :---: | :---: | :---: | :---: | :---: |\n",
"| [__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/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
"\n",
"## Setup\n",
"\n",
"- [ ] TODO: Update with relevant info.\n",
"\n",
"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",
"\n",
"### Credentials\n",
"\n",
"- TODO: Update with relevant info.\n",
"\n",
"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:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c3bef91",
"id": "36521c2a",
"metadata": {},
"outputs": [],
"source": [
"# install package\n",
"!pip install -U __package_name__"
"import getpass\n",
"import os\n",
"\n",
"if not os.getenv(\"__MODULE_NAME___API_KEY\"):\n",
" os.environ[\"__MODULE_NAME___API_KEY\"] = getpass.getpass(\"Enter your __ModuleName__ API key: \")"
]
},
{
"cell_type": "markdown",
"id": "2b4f3e15",
"id": "c84fb993",
"metadata": {},
"source": [
"## Environment Setup\n",
"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:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "39a4953b",
"metadata": {},
"outputs": [],
"source": [
"# os.environ[\"LANGCHAIN_TRACING_V2\"] = \"true\"\n",
"# os.environ[\"LANGCHAIN_API_KEY\"] = getpass.getpass(\"Enter your LangSmith API key: \")"
]
},
{
"cell_type": "markdown",
"id": "d9664366",
"metadata": {},
"source": [
"### Installation\n",
"\n",
"Make sure to set the following environment variables:\n",
"The LangChain __ModuleName__ integration lives in the `__package_name__` package:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "64853226",
"metadata": {},
"outputs": [],
"source": [
"%pip install -qU __package_name__"
]
},
{
"cell_type": "markdown",
"id": "45dd1724",
"metadata": {},
"source": [
"## Instantiation\n",
"\n",
"- TODO: fill out relevant environment variables or secrets\n",
"Now we can instantiate our model object and generate chat completions:\n",
"\n",
"## Usage"
"- TODO: Update model instantiation with relevant params."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "62e0dbc3",
"metadata": {
"tags": []
},
"id": "9ea7a09b",
"metadata": {},
"outputs": [],
"source": [
"from __module_name__.embeddings import __ModuleName__Embeddings\n",
"from __module_name__ import __ModuleName__Embeddings\n",
"\n",
"embeddings = __ModuleName__Embeddings(\n",
" model=\"model-name\",\n",
")"
]
},
{
"cell_type": "markdown",
"id": "77d271b6",
"metadata": {},
"source": [
"## Indexing and Retrieval\n",
"\n",
"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",
"\n",
"embeddings = __ModuleName__Embeddings()"
"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`."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "12fcfb4b",
"id": "d817716b",
"metadata": {},
"outputs": [],
"source": [
"embeddings.embed_query(\"My query to look up\")"
"# Create a vector store with a sample text\n",
"from langchain_core.vectorstores import InMemoryVectorStore\n",
"\n",
"text = \"LangChain is the framework for building context-aware reasoning applications\"\n",
"\n",
"vectorstore = InMemoryVectorStore.from_texts(\n",
" [text],\n",
" embedding=embeddings,\n",
")\n",
"\n",
"# Use the vectorstore as a retriever\n",
"retriever = vectorstore.as_retriever()\n",
"\n",
"# Retrieve the most similar text\n",
"retrieved_document = retriever.invoke(\"What is LangChain?\")\n",
"\n",
"# show the retrieved document's content\n",
"retrieved_document.page_content"
]
},
{
"cell_type": "markdown",
"id": "e02b9855",
"metadata": {},
"source": [
"## Direct Usage\n",
"\n",
"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",
"\n",
"You can directly call these methods to get embeddings for your own use cases.\n",
"\n",
"### Embed single texts\n",
"\n",
"You can embed single texts or documents with `embed_query`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "1f2e6104",
"id": "0d2befcd",
"metadata": {},
"outputs": [],
"source": [
"embeddings.embed_documents(\n",
" [\"This is a content of the document\", \"This is another document\"]\n",
")"
"single_vector = embeddings.embed_query(text)\n",
"print(str(single_vector)[:100]) # Show the first 100 characters of the vector"
]
},
{
"cell_type": "markdown",
"id": "1b5a7d03",
"metadata": {},
"source": [
"### Embed multiple texts\n",
"\n",
"You can embed multiple texts with `embed_documents`:"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "46739f68",
"id": "2f4d6e97",
"metadata": {},
"outputs": [],
"source": [
"# async embed query\n",
"await embeddings.aembed_query(\"My query to look up\")"
"text2 = (\n",
" \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
")\n",
"two_vectors = embeddings.embed_queries([text, text2])\n",
"for vector in two_vectors:\n",
" print(str(vector)[:100]) # Show the first 100 characters of the vector"
]
},
{
"cell_type": "markdown",
"id": "98785c12",
"metadata": {},
"source": [
"### Async Usage\n",
"\n",
"You can also use `aembed_query` and `aembed_documents` for producing embeddings asynchronously:\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e48632ea",
"id": "4c3bef91",
"metadata": {},
"outputs": [],
"source": [
"# async embed documents\n",
"await embeddings.aembed_documents(\n",
" [\"This is a content of the document\", \"This is another document\"]\n",
")"
"import asyncio\n",
"\n",
"async def async_example():\n",
" single_vector = await embeddings.embed_query(text)\n",
" print(str(single_vector)[:100]) # Show the first 100 characters of the vector\n",
"\n",
"asyncio.run(async_example())"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f1bd4396",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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