Amazon Bedrock Provider

The Amazon Bedrock provider for the AI SDK contains language model support for the Amazon Bedrock APIs.

Setup

The Bedrock provider is available in the @ai-sdk/amazon-bedrock module. You can install it with

pnpm add @ai-sdk/amazon-bedrock

Prerequisites

Access to Amazon Bedrock foundation models isn't granted by default. In order to gain access to a foundation model, an IAM user with sufficient permissions needs to request access to it through the console. Once access is provided to a model, it is available for all users in the account.

See the Model Access Docs for more information.

Authentication

Step 1: Creating AWS Access Key and Secret Key

To get started, you'll need to create an AWS access key and secret key. Here's how:

Login to AWS Management Console

• Go to the AWS Management Console and log in with your AWS account credentials.

Create an IAM User

• Navigate to the IAM dashboard and click on "Users" in the left-hand navigation menu.
• Click on "Create user" and fill in the required details to create a new IAM user.
• Make sure to select "Programmatic access" as the access type.
• The user account needs the AmazonBedrockFullAccess policy attached to it.

Create Access Key

• Click on the "Security credentials" tab and then click on "Create access key".
• Click "Create access key" to generate a new access key pair.
• Download the .csv file containing the access key ID and secret access key.

Step 2: Configuring the Access Key and Secret Key

Within your project add a .env file if you don't already have one. This file will be used to set the access key and secret key as environment variables. Add the following lines to the .env file:

AWS_ACCESS_KEY_ID=YOUR_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY=YOUR_SECRET_ACCESS_KEY
AWS_REGION=YOUR_REGION

Remember to replace YOUR_ACCESS_KEY_ID, YOUR_SECRET_ACCESS_KEY, and YOUR_REGION with the actual values from your AWS account.

Provider Instance

You can import the default provider instance bedrock from @ai-sdk/amazon-bedrock:

import { bedrock } from '@ai-sdk/amazon-bedrock';

If you need a customized setup, you can import createAmazonBedrock from @ai-sdk/amazon-bedrock and create a provider instance with your settings:

import { createAmazonBedrock } from '@ai-sdk/amazon-bedrock';

const bedrock = createAmazonBedrock({
  region: 'us-east-1',
  accessKeyId: 'xxxxxxxxx',
  secretAccessKey: 'xxxxxxxxx',
  sessionToken: 'xxxxxxxxx',
});

// or with bedrockOptions
const bedrock = createAmazonBedrock({
  bedrockOptions: {
    region: 'us-east-1',
    credentials: {
      // ...
    },
  },
});

You can use the following optional settings to customize the Amazon Bedrock provider instance:

• region string

  The AWS region that you want to use for the API calls. It uses the AWS_REGION environment variable by default.

• accessKeyId string

  The AWS access key ID that you want to use for the API calls. It uses the AWS_ACCESS_KEY_ID environment variable by default.

• secretAccessKey string

  The AWS secret access key that you want to use for the API calls. It uses the AWS_SECRET_ACCESS_KEY environment variable by default.

• sessionToken string

  Optional. The AWS session token that you want to use for the API calls. It uses the AWS_SESSION_TOKEN environment variable by default.

• bedrockOptions object

  Optional. The configuration options used by the Amazon Bedrock Library (BedrockRuntimeClientConfig), including:

  • region string The AWS region that you want to use for the API calls.

  • credentials object The AWS credentials that you want to use for the API calls.

  When bedrockOptions are provided, the region, accessKeyId, and secretAccessKey settings are ignored.

Language Models

You can create models that call the Bedrock API using the provider instance. The first argument is the model id, e.g. meta.llama3-70b-instruct-v1:0.

const model = bedrock('meta.llama3-70b-instruct-v1:0');

Amazon Bedrock models also support some model specific settings that are not part of the standard call settings. You can pass them as an options argument:

const model = bedrock('anthropic.claude-3-sonnet-20240229-v1:0', {
  additionalModelRequestFields: { top_k: 350 },
});

Documentation for additional settings based on the selected model can be found within the Amazon Bedrock Inference Parameter Documentation.

You can use Amazon Bedrock language models to generate text with the generateText function:

import { bedrock } from '@ai-sdk/amazon-bedrock';
import { generateText } from 'ai';

const { text } = await generateText({
  model: bedrock('meta.llama3-70b-instruct-v1:0'),
  prompt: 'Write a vegetarian lasagna recipe for 4 people.',
});

Amazon Bedrock language models can also be used in the streamText function (see AI SDK Core).

File Inputs

The Amazon Bedrock provider supports file inputs, e.g. PDF files.

import { bedrock } from '@ai-sdk/amazon-bedrock';
import { generateText } from 'ai';

const result = await generateText({
  model: bedrock('anthropic.claude-3-haiku-20240307-v1:0'),
  messages: [
    {
      role: 'user',
      content: [
        { type: 'text', text: 'Describe the pdf in detail.' },
        {
          type: 'file',
          data: fs.readFileSync('./data/ai.pdf'),
          mimeType: 'application/pdf',
        },
      ],
    },
  ],
});

Guardrails

You can use the bedrock provider metadata to utilize Amazon Bedrock Guardrails:

const result = await generateText({
  bedrock('anthropic.claude-3-sonnet-20240229-v1:0'),
  experimental_providerMetadata: {
    bedrock: {
      guardrailConfig: {
        guardrailIdentifier: '1abcd2ef34gh',
        guardrailVersion: '1',
        trace: 'enabled' as const,
        streamProcessingMode: 'async',
      },
    },
  },
});

Tracing information will be returned in the provider metadata if you have tracing enabled.

if (result.experimental_providerMetadata?.bedrock.trace) {
  // ...
}

See the Amazon Bedrock Guardrails documentation for more information.

Model Capabilities

Embedding Models

You can create models that call the Bedrock API Bedrock API using the .embedding() factory method.

const model = bedrock.embedding('amazon.titan-embed-text-v1');

Bedrock Titan embedding model amazon.titan-embed-text-v2:0 supports several aditional settings. You can pass them as an options argument:

const model = bedrock.embedding('amazon.titan-embed-text-v2:0', {
  dimensions: 512 // optional, number of dimensions for the embedding
  normalize: true // optional  normalize the output embeddings
})

The following optional settings are available for Bedrock Titan embedding models:

• dimensions: number

  The number of dimensions the output embeddings should have. The following values are accepted: 1024 (default), 512, 256.

• normalize boolean

  Flag indicating whether or not to normalize the output embeddings. Defaults to true.

Model Capabilities