langchain.js
    Preparing search index...

    Module @langchain/mistralai - v0.2.1

    @langchain/mistralai

    This package contains the LangChain.js integrations for Mistral through their SDK.

    npm install @langchain/mistralai @langchain/core
    

    This package, along with the main LangChain package, depends on @langchain/core. If you are using this package with other LangChain packages, you should make sure that all of the packages depend on the same instance of @langchain/core. You can do so by adding appropriate field to your project's package.json like this:

    {
    "name": "your-project",
    "version": "0.0.0",
    "dependencies": {
    "@langchain/core": "^0.3.0",
    "@langchain/mistralai": "^0.0.0"
    },
    "resolutions": {
    "@langchain/core": "^0.3.0"
    },
    "overrides": {
    "@langchain/core": "^0.3.0"
    },
    "pnpm": {
    "overrides": {
    "@langchain/core": "^0.3.0"
    }
    }
    }

    The field you need depends on the package manager you're using, but we recommend adding a field for the common yarn, npm, and pnpm to maximize compatibility.

    This package contains the ChatMistralAI class, which is the recommended way to interface with the Mistral series of models.

    To use, install the requirements, and configure your environment.

    export MISTRAL_API_KEY=your-api-key
    

    Then initialize

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

    const model = new ChatMistralAI({
    apiKey: process.env.MISTRAL_API_KEY,
    modelName: "mistral-small",
    });
    const response = await model.invoke(new HumanMessage("Hello world!"));
    import { ChatMistralAI } from "@langchain/mistralai";

    const model = new ChatMistralAI({
    apiKey: process.env.MISTRAL_API_KEY,
    modelName: "mistral-small",
    });
    const response = await model.stream(new HumanMessage("Hello world!"));

    This package also adds support for Mistral's embeddings model.

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

    const embeddings = new MistralAIEmbeddings({
    apiKey: process.env.MISTRAL_API_KEY,
    });
    const res = await embeddings.embedQuery("Hello world");

    To develop the Mistral package, you'll need to follow these instructions:

    pnpm install
    
    pnpm build
    

    Or from the repo root:

    pnpm build --filter @langchain/mistralai
    

    Test files should live within a tests/ file in the src/ folder. Unit tests should end in .test.ts and integration tests should end in .int.test.ts:

    $ pnpm test
    $ pnpm test:int

    Run the linter & formatter to ensure your code is up to standard:

    pnpm lint && pnpm format
    

    If you add a new file to be exported, either import & re-export from src/index.ts, or add it to the exports field in the package.json file and run pnpm build to generate the new entrypoint.

    Classes

    ChatMistralAI
    MistralAI
    MistralAIEmbeddings

    Interfaces

    ChatMistralAICallOptions
    ChatMistralAIInput
    MistralAICallOptions
    MistralAIEmbeddingsParams
    MistralAIInput

    Functions

    convertMessagesToMistralMessages