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docs: update integration docs for mistral ai embedding model (#25253)
Related issue: https://github.com/langchain-ai/langchain/issues/24856 ```json [ { "provider": "mistralai", "js": true, "local": false, "serializable": false, "native_async": true } ] ``` --------- Co-authored-by: Isaac Francisco <78627776+isahers1@users.noreply.github.com> Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
This commit is contained in:
Eugene Yurtsev
GitHub
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b14a24db",
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"cell_type": "raw",
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"id": "afaf8039",
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"metadata": {},
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"source": [
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"# MistralAI\n",
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"---\n",
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"sidebar_label: MistralAI\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": "9a3d6f34",
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"metadata": {},
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"source": [
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"# MistralAIEmbeddings\n",
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"\n",
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"This notebook explains how to use MistralAIEmbeddings, which is included in the langchain_mistralai package, to embed texts in langchain."
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"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",
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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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"import { ItemTable } from \"@theme/FeatureTables\";\n",
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"\n",
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"<ItemTable category=\"text_embedding\" item=\"MistralAI\" />\n",
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"\n",
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"## Setup\n",
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"\n",
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"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",
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"\n",
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"### Credentials\n",
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"\n",
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"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:"
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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": 1,
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"id": "0ab948fc",
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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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"# pip install -U langchain-mistralai"
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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(\"MISTRALAI_API_KEY\"):\n",
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" os.environ[\"MISTRALAI_API_KEY\"] = getpass.getpass(\"Enter your MistralAI 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": "67c637ca",
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"id": "c84fb993",
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"metadata": {},
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"source": [
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"## import the library"
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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": 2,
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"id": "5709b030",
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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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"from langchain_mistralai import MistralAIEmbeddings"
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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": 3,
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"id": "1756b1ba",
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"metadata": {},
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"outputs": [],
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"source": [
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"embedding = MistralAIEmbeddings(api_key=\"your-api-key\")"
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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": "4a2a098d",
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"id": "d9664366",
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"metadata": {},
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"source": [
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"# Using the Embedding Model\n",
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"With `MistralAIEmbeddings`, you can directly use the default model 'mistral-embed', or set a different one if available."
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"### Installation\n",
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"\n",
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"The LangChain MistralAI integration lives in the `langchain-mistralai` 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 langchain-mistralai"
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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:"
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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": 4,
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"id": "584b9af5",
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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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"embedding.model = \"mistral-embed\" # or your preferred model if available"
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"from langchain_mistralai import MistralAIEmbeddings\n",
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"\n",
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"embeddings = MistralAIEmbeddings(\n",
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" model=\"mistral-embed\",\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": 5,
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"id": "be18b873",
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"id": "d817716b",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'LangChain is the framework for building context-aware reasoning applications'"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"res_query = embedding.embed_query(\"The test information\")\n",
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"res_document = embedding.embed_documents([\"test1\", \"another test\"])"
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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_documents = 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_documents[0].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": 6,
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"id": "0d2befcd",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[-0.04443359375, 0.01885986328125, 0.018035888671875, -0.00864410400390625, 0.049652099609375, -0.00\n"
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]
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}
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],
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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": 7,
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"id": "2f4d6e97",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"[-0.04443359375, 0.01885986328125, 0.0180511474609375, -0.0086517333984375, 0.049652099609375, -0.00\n",
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"[-0.02032470703125, 0.02606201171875, 0.051605224609375, -0.0281982421875, 0.055755615234375, 0.0019\n"
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]
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}
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],
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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_documents([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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"## API Reference\n",
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"\n",
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"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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]
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}
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],
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