MistralAIEmbeddings integration

This will help you get started with MistralAIEmbeddings embedding models using LangChain. For detailed documentation on MistralAIEmbeddings features and configuration options, please refer to the API reference.

Overview

Integration details

Class	Package	Local	Py support	Downloads	Version
`MistralAIEmbeddings`	`@langchain/mistralai`	❌	✅

Setup

To access MistralAI embedding models you’ll need to create a MistralAI account, get an API key, and install the @langchain/mistralai integration package.

Credentials

Head to console.mistral.ai to sign up to MistralAI and generate an API key. Once you’ve done this set the MISTRAL_API_KEY environment variable:

export MISTRAL_API_KEY="your-api-key"

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

Installation

The LangChain MistralAIEmbeddings integration lives in the @langchain/mistralai package:

npm install @langchain/mistralai @langchain/core

yarn add @langchain/mistralai @langchain/core

pnpm add @langchain/mistralai @langchain/core

Instantiation

Now we can instantiate our model object and generate chat completions:

import { MistralAIEmbeddings } from "@langchain/mistralai";

const embeddings = new MistralAIEmbeddings({
  model: "mistral-embed", // Default value
});

Indexing and retrieval

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 Learn tab. 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 using the demo MemoryVectorStore.

// Create a vector store with a sample text
import { MemoryVectorStore } from "@langchain/classic/vectorstores/memory";

const text = "LangChain is the framework for building context-aware reasoning applications";

const vectorstore = await MemoryVectorStore.fromDocuments(
  [{ pageContent: text, metadata: {} }],
  embeddings,
);

// Use the vector store as a retriever that returns a single document
const retriever = vectorstore.asRetriever(1);

// Retrieve the most similar text
const retrievedDocuments = await retriever.invoke("What is LangChain?");

retrievedDocuments[0].pageContent;

LangChain is the framework for building context-aware reasoning applications

Direct usage

Under the hood, the vectorstore and retriever implementations are calling embeddings.embedDocument(...) and embeddings.embedQuery(...) to create embeddings for the text(s) used in fromDocuments and the retriever’s invoke operations, respectively. You can directly call these methods to get embeddings for your own use cases.

Embed single texts

You can embed queries for search with embedQuery. This generates a vector representation specific to the query:

const singleVector = await embeddings.embedQuery(text);

console.log(singleVector.slice(0, 100));

[
          -0.04443359375,         0.01885986328125,      0.018035888671875,
    -0.00864410400390625,        0.049652099609375,     -0.001190185546875,
       0.028900146484375,       -0.035675048828125,   -0.00702667236328125,
  0.00016105175018310547,          -0.027587890625,      0.029388427734375,
      -0.053253173828125,   -0.0003020763397216797,     -0.046112060546875,
      0.0258026123046875,   -0.0010776519775390625,       0.02703857421875,
       0.040985107421875,       -0.004547119140625,     -0.020172119140625,
       -0.02606201171875,     -0.01457977294921875,          0.01220703125,
     -0.0078582763671875,         -0.0084228515625,      -0.02056884765625,
         -0.071044921875,         -0.0404052734375,    0.00923919677734375,
     0.01407623291015625,      -0.0210113525390625,  0.0006284713745117188,
    -0.01465606689453125,       0.0186309814453125,     -0.015838623046875,
   0.0007920265197753906,        -0.04437255859375,      0.008758544921875,
        -0.0172119140625,         0.01312255859375,   -0.01358795166015625,
     -0.0212860107421875, -0.000035822391510009766,    -0.0226898193359375,
    -0.01390838623046875,       -0.007659912109375,     -0.016021728515625,
       0.025909423828125,       -0.034515380859375,       -0.0372314453125,
       0.020355224609375,        -0.02606201171875,    -0.0158843994140625,
      -0.037994384765625,      0.00450897216796875,        0.0142822265625,
      -0.012725830078125,         -0.0770263671875,       0.02630615234375,
      -0.048614501953125,        0.006072998046875,    0.00417327880859375,
   -0.005138397216796875,         0.02557373046875,        0.0311279296875,
       0.026519775390625,      -0.0103607177734375,    -0.0108489990234375,
      -0.029510498046875,        0.022186279296875,     0.0256500244140625,
     -0.0186309814453125,          0.0443115234375,    -0.0304107666015625,
       -0.03131103515625,     0.007427215576171875,     0.0234527587890625,
      0.0224761962890625,      0.00463104248046875, -0.0037021636962890625,
      0.0302581787109375,          0.0733642578125,    -0.0121612548828125,
     -0.0172576904296875,        0.019317626953125,         0.029052734375,
     -0.0024871826171875,       0.0174713134765625,      0.026092529296875,
        0.04425048828125,   -0.0004563331604003906,     0.0146026611328125,
    -0.00748443603515625,         0.06146240234375,          0.02294921875,
         -0.016845703125,   -0.0014057159423828125,   -0.01435089111328125,
        0.06097412109375
]

Embed multiple texts

You can embed multiple texts for indexing with embedDocuments. The internals used for this method may (but do not have to) differ from embedding queries:

const text2 = "LangGraph is a library for building stateful, multi-actor applications with LLMs";

const vectors = await embeddings.embedDocuments([text, text2]);

console.log(vectors[0].slice(0, 100));
console.log(vectors[1].slice(0, 100));

[
          -0.04443359375,         0.01885986328125,    0.0180511474609375,
     -0.0086517333984375,        0.049652099609375,  -0.00121307373046875,
      0.0289154052734375,        -0.03570556640625, -0.007015228271484375,
   0.0001499652862548828,      -0.0276641845703125,    0.0294036865234375,
          -0.05322265625,   -0.0002808570861816406,     -0.04608154296875,
        0.02581787109375,   -0.0011072158813476562,        0.027099609375,
       0.040985107421875,       -0.004547119140625,   -0.0201873779296875,
     -0.0260772705078125,      -0.0146026611328125,    0.0121917724609375,
      -0.007843017578125,      -0.0084381103515625,   -0.0205535888671875,
       -0.07110595703125,        -0.04046630859375,   0.00931549072265625,
        0.01409912109375,           -0.02099609375, 0.0006232261657714844,
      -0.014678955078125,       0.0186614990234375,   -0.0158233642578125,
    0.000812530517578125,        -0.04437255859375,   0.00873565673828125,
        -0.0172119140625,        0.013092041015625,      -0.0135498046875,
     -0.0212860107421875, -0.000006735324859619141,   -0.0226898193359375,
    -0.01389312744140625,      -0.0076751708984375,   -0.0160064697265625,
      0.0259246826171875,         -0.0345458984375,    -0.037200927734375,
       0.020355224609375,         -0.0260009765625,   -0.0159149169921875,
       -0.03802490234375,     0.004489898681640625,    0.0143280029296875,
    -0.01274871826171875,        -0.07708740234375,    0.0263214111328125,
       -0.04864501953125,      0.00608062744140625,  0.004192352294921875,
   -0.005115509033203125,       0.0255889892578125,       0.0311279296875,
      0.0265045166015625,      -0.0103607177734375,  -0.01084136962890625,
     -0.0294952392578125,        0.022186279296875,    0.0256500244140625,
        -0.0186767578125,        0.044342041015625,    -0.030426025390625,
       -0.03131103515625,     0.007396697998046875,    0.0234527587890625,
            0.0224609375,     0.004634857177734375, -0.003643035888671875,
      0.0302886962890625,         0.07342529296875,  -0.01221466064453125,
      -0.017303466796875,       0.0193023681640625,        0.029052734375,
  -0.0024890899658203125,       0.0174407958984375,        0.026123046875,
       0.044219970703125,   -0.0004944801330566406,   0.01462554931640625,
   -0.007450103759765625,         0.06146240234375,     0.022979736328125,
         -0.016845703125,    -0.001445770263671875,   -0.0143890380859375,
        0.06097412109375
]
[
       -0.02032470703125,       0.02606201171875,      0.051605224609375,
        -0.0281982421875,      0.055755615234375,   0.001987457275390625,
          0.031982421875,    -0.0131378173828125,       -0.0252685546875,
    0.001010894775390625,     -0.024017333984375,      0.053375244140625,
      -0.042816162109375,      0.005584716796875,      -0.04132080078125,
        0.03021240234375,       0.01324462890625,      0.016876220703125,
       0.041961669921875,  -0.004299163818359375,    -0.0273895263671875,
      -0.039642333984375,     -0.021575927734375,     0.0309295654296875,
     -0.0099945068359375,    -0.0163726806640625,   -0.00968170166015625,
       -0.07733154296875,     -0.030364990234375,  -0.003864288330078125,
       0.016387939453125,       -0.0389404296875,    -0.0026702880859375,
     -0.0176544189453125,     0.0264434814453125,      -0.01226806640625,
  -0.0022220611572265625,     -0.039703369140625,   -0.00907135009765625,
     -0.0260467529296875,       0.03155517578125, -0.0004324913024902344,
      -0.019500732421875,    -0.0120697021484375,        -0.008544921875,
       -0.01654052734375,       0.00067138671875,    -0.0134735107421875,
        0.01080322265625,     -0.034759521484375,         -0.06201171875,
       0.012359619140625,  -0.006237030029296875,    -0.0168914794921875,
     -0.0183563232421875,     0.0236053466796875, -0.0021419525146484375,
     -0.0164947509765625,     -0.052581787109375,      0.022125244140625,
      -0.045745849609375, -0.0009088516235351562,     0.0097808837890625,
  -0.0009326934814453125,      0.041656494140625,        0.0269775390625,
          0.016845703125, -0.0022335052490234375,    -0.0182342529296875,
     -0.0245208740234375,  0.0036602020263671875,    -0.0188751220703125,
     -0.0023956298828125,     0.0238800048828125,     -0.034942626953125,
      -0.033782958984375,     0.0046234130859375,        0.0318603515625,
      0.0251007080078125, -0.0023288726806640625,    -0.0225677490234375,
   0.0004394054412841797,         0.064208984375,    -0.0254669189453125,
     -0.0234222412109375,  0.0009264945983886719,    0.01464080810546875,
    0.006626129150390625,  -0.007450103759765625,       0.02642822265625,
         0.0260009765625,    0.00536346435546875,    0.01479339599609375,
  -0.0032253265380859375,           0.0498046875,      0.048248291015625,
    -0.01519012451171875,    0.00605010986328125,      0.019744873046875,
      0.0296478271484375
]

Hooks

Mistral AI supports custom hooks for three events: beforeRequest, requestError, and response. Examples of the function signature for each hook type can be seen below:

const beforeRequestHook = (req: Request): Request | void | Promise<Request | void> => {
    // Code to run before a request is processed by Mistral
};

const requestErrorHook = (err: unknown, req: Request): void | Promise<void> => {
    // Code to run when an error occurs as Mistral is processing a request
};

const responseHook = (res: Response, req: Request): void | Promise<void> => {
    // Code to run before Mistral sends a successful response
};

To add these hooks to the embeddings model, either pass them as arguments and they are automatically added:

import { MistralAIEmbeddings } from "@langchain/mistralai"

const modelWithHooks = new MistralAIEmbeddings({
    model: "mistral-embed",
    beforeRequestHooks: [ beforeRequestHook ],
    requestErrorHooks: [ requestErrorHook ],
    responseHooks: [ responseHook ],
    // other params...
});

Or assign and add them manually after instantiation:

import { MistralAIEmbeddings } from "@langchain/mistralai"

const model = new MistralAIEmbeddings({
    model: "mistral-embed",
    // other params...
});

model.beforeRequestHooks = [ ...model.beforeRequestHooks, beforeRequestHook ];
model.requestErrorHooks = [ ...model.requestErrorHooks, requestErrorHook ];
model.responseHooks = [ ...model.responseHooks, responseHook ];

model.addAllHooksToHttpClient();

The method addAllHooksToHttpClient clears all currently added hooks before assigning the entire updated hook lists to avoid hook duplication. Hooks can be removed one at a time, or all hooks can be cleared from the model at once.

model.removeHookFromHttpClient(beforeRequestHook);

model.removeAllHooksFromHttpClient();

API reference

For detailed documentation of all MistralAIEmbeddings features and configurations head to the API reference.

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General integrations

RAG integrations

MistralAIEmbeddings integration

Overview

Integration details

Setup

Credentials

Installation

Instantiation

Indexing and retrieval

Direct usage

Embed single texts

Embed multiple texts

Hooks

API reference

​Overview

​Integration details

​Setup

​Credentials

​Installation

​Instantiation

​Indexing and retrieval

​Direct usage

​Embed single texts

​Embed multiple texts

​Hooks

​API reference

Overview

Integration details

Setup

Credentials

Installation

Instantiation

Indexing and retrieval

Direct usage

Embed single texts

Embed multiple texts

Hooks

API reference