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    Module @langchain/aws - v0.1.15

    @langchain/aws

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

    npm install @langchain/aws
    

    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 fields to your project's package.json like this:

    {
    "name": "your-project",
    "version": "0.0.0",
    "dependencies": {
    "@langchain/aws": "^0.0.1",
    "@langchain/core": "^0.3.0"
    },
    "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 ChatBedrockConverse class, which is the recommended way to interface with the AWS Bedrock Converse series of models.

    To use, install the requirements, and configure your environment following the traditional authentication methods.

    export BEDROCK_AWS_REGION=
    export BEDROCK_AWS_SECRET_ACCESS_KEY=
    export BEDROCK_AWS_ACCESS_KEY_ID=

    Alternatively, set the AWS_BEARER_TOKEN_BEDROCK environment variable locally for API Key authentication. For additional API key details, refer to docs.

    export BEDROCK_AWS_REGION=
    export AWS_BEARER_TOKEN_BEDROCK=

    Then initialize

    import { ChatBedrockConverse } from "@langchain/aws";

    const model = new ChatBedrockConverse({
    region: process.env.BEDROCK_AWS_REGION ?? "us-east-1",
    credentials: {
    secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY,
    accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID,
    },
    });

    const response = await model.invoke(new HumanMessage("Hello world!"));
    import { ChatBedrockConverse } from "@langchain/aws";

    const model = new ChatBedrockConverse({
    region: process.env.BEDROCK_AWS_REGION ?? "us-east-1",
    credentials: {
    secretAccessKey: process.env.BEDROCK_AWS_SECRET_ACCESS_KEY,
    accessKeyId: process.env.BEDROCK_AWS_ACCESS_KEY_ID,
    },
    });

    const response = await model.stream(new HumanMessage("Hello world!"));

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

    pnpm install
    
    pnpm build
    

    Or from the repo root:

    pnpm build --filter @langchain/aws
    

    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.

    After running pnpm build, publish a new version with:

    $ npm publish
    

    Classes

    AmazonKendraRetriever
    AmazonKnowledgeBaseRetriever
    BedrockEmbeddings
    ChatBedrockConverse

    Interfaces

    AmazonKendraRetrieverArgs
    AmazonKnowledgeBaseRetrieverArgs
    BedrockEmbeddingsParams
    ChatBedrockConverseCallOptions
    ChatBedrockConverseInput

    Type Aliases

    BedrockToolChoice
    ChatBedrockConverseToolType
    ConverseCommandParams
    CredentialType
    MessageContentReasoningBlock
    MessageContentReasoningBlockReasoningText
    MessageContentReasoningBlockReasoningTextPartial
    MessageContentReasoningBlockRedacted