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docs: update integration docs for mistral ai embedding model (#25253)
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Related issue: #24856

```json
[
   {
      "provider": "mistralai",
      "js":  true,
      "local": false,
     "serializable": false,
    "native_async": true
   }
]
```

---------

Co-authored-by: Isaac Francisco <[email protected]>
Co-authored-by: isaac hershenson <[email protected]>
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@eyurtsev @isahers1
3 people authored Aug 14, 2024
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205 changes: 184 additions & 21 deletions docs/docs/integrations/text_embedding/mistralai.ipynb
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{
"cells": [
{
"cell_type": "raw",
"id": "afaf8039",
"metadata": {},
"source": [
"---\n",
"sidebar_label: MistralAI\n",
"---"
]
},
{
"cell_type": "markdown",
"id": "b14a24db",
"id": "9a3d6f34",
"metadata": {},
"source": [
"# MistralAI\n",
"# MistralAIEmbeddings\n",
"\n",
"This will help you get started with MistralAI embedding models using LangChain. For detailed documentation on `MistralAIEmbeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html).\n",
"\n",
"## Overview\n",
"### Integration details\n",
"\n",
"import { ItemTable } from \"@theme/FeatureTables\";\n",
"\n",
"<ItemTable category=\"text_embedding\" item=\"MistralAI\" />\n",
"\n",
"This notebook explains how to use MistralAIEmbeddings, which is included in the langchain_mistralai package, to embed texts in langchain."
"## Setup\n",
"\n",
"To access MistralAI embedding models you'll need to create a/an MistralAI account, get an API key, and install the `langchain-mistralai` integration package.\n",
"\n",
"### Credentials\n",
"\n",
"Head to [https://console.mistral.ai/](https://console.mistral.ai/) to sign up to MistralAI and generate an API key. Once you've done this set the MISTRALAI_API_KEY environment variable:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "0ab948fc",
"id": "36521c2a",
"metadata": {},
"outputs": [],
"source": [
"# pip install -U langchain-mistralai"
"import getpass\n",
"import os\n",
"\n",
"if not os.getenv(\"MISTRALAI_API_KEY\"):\n",
" os.environ[\"MISTRALAI_API_KEY\"] = getpass.getpass(\"Enter your MistralAI API key: \")"
]
},
{
"cell_type": "markdown",
"id": "67c637ca",
"id": "c84fb993",
"metadata": {},
"source": [
"## import the library"
"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": 2,
"id": "5709b030",
"id": "39a4953b",
"metadata": {},
"outputs": [],
"source": [
"from langchain_mistralai import MistralAIEmbeddings"
"# 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",
"The LangChain MistralAI integration lives in the `langchain-mistralai` package:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1756b1ba",
"execution_count": null,
"id": "64853226",
"metadata": {},
"outputs": [],
"source": [
"embedding = MistralAIEmbeddings(api_key=\"your-api-key\")"
"%pip install -qU langchain-mistralai"
]
},
{
"cell_type": "markdown",
"id": "4a2a098d",
"id": "45dd1724",
"metadata": {},
"source": [
"# Using the Embedding Model\n",
"With `MistralAIEmbeddings`, you can directly use the default model 'mistral-embed', or set a different one if available."
"## Instantiation\n",
"\n",
"Now we can instantiate our model object and generate chat completions:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "584b9af5",
"id": "9ea7a09b",
"metadata": {},
"outputs": [],
"source": [
"embedding.model = \"mistral-embed\" # or your preferred model if available"
"from langchain_mistralai import MistralAIEmbeddings\n",
"\n",
"embeddings = MistralAIEmbeddings(\n",
" model=\"mistral-embed\",\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",
"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": 5,
"id": "be18b873",
"id": "d817716b",
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/plain": [
"'LangChain is the framework for building context-aware reasoning applications'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# 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_documents = retriever.invoke(\"What is LangChain?\")\n",
"\n",
"# show the retrieved document's content\n",
"retrieved_documents[0].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": 6,
"id": "0d2befcd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-0.04443359375, 0.01885986328125, 0.018035888671875, -0.00864410400390625, 0.049652099609375, -0.00\n"
]
}
],
"source": [
"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": 7,
"id": "2f4d6e97",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-0.04443359375, 0.01885986328125, 0.0180511474609375, -0.0086517333984375, 0.049652099609375, -0.00\n",
"[-0.02032470703125, 0.02606201171875, 0.051605224609375, -0.0281982421875, 0.055755615234375, 0.0019\n"
]
}
],
"source": [
"res_query = embedding.embed_query(\"The test information\")\n",
"res_document = embedding.embed_documents([\"test1\", \"another test\"])"
"text2 = (\n",
" \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
")\n",
"two_vectors = embeddings.embed_documents([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": [
"## API Reference\n",
"\n",
"For detailed documentation on `MistralAIEmbeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/langchain_mistralai.embeddings.MistralAIEmbeddings.html).\n"
]
}
],
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