AWS Bedrock

Portkey provides a robust and secure gateway to facilitate the integration of various Large Language Models (LLMs) into your applications, including models hosted on AWS Bedrock.

With Portkey, you can take advantage of features like fast AI gateway access, observability, prompt management, and more, all while ensuring the secure management of your LLM API keys through a virtual key system.

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Portkey SDK Integration with AWS Bedrock

Portkey provides a consistent API to interact with models from various providers. To integrate Bedrock with Portkey:

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1. Install the Portkey SDK

Add the Portkey SDK to your application to interact with Anthropic’s API through Portkey’s gateway.

• NodeJS
• Python

npm install --save portkey-ai

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2. Initialize Portkey with the Virtual Key

To integrate AWS Bedrock with Portkey, you need the AWS Secret Access Key, AWS Access Key Id, and AWS Region with which you can create your Virtual key on Portkey.

Here’s a guide on where to find your AWS credentials on AWS.

• NodeJS SDK
• Python SDK

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
    virtualKey: "VIRTUAL_KEY" // Your Bedrock Virtual Key
})

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Using Virtual Key with AWS STS

If you’re using AWS Security Token Service, you can pass your aws_session_token along with the Virtual key:

• NodeJS
• Python

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
    virtualKey: "VIRTUAL_KEY" // Your Bedrock Virtual Key,
    aws_session_token: ""
})

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Not using Virtual Keys?

Check out this example on how you can directly use your AWS details to make a Bedrock request through Portkey.

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3. Invoke Chat Completions with AWS bedrock

Use the Portkey instance to send requests to Anthropic. You can also override the virtual key directly in the API call if needed.

• NodeJS SDK
• Python SDK

const chatCompletion = await portkey.chat.completions.create({
    messages: [{ role: 'user', content: 'Say this is a test' }],
    model: 'anthropic.claude-v2:1',
    max_tokens: 250 // Required field for Anthropic
});

console.log(chatCompletion.choices);

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Using Vision Models

Portkey’s multimodal Gateway fully supports Bedrock’s vision models anthropic.claude-3-sonnet, anthropic.claude-3-haiku, and anthropic.claude-3-opus

For more info, check out this guide:

Vision

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Managing AWS Bedrock Prompts

You can manage all prompts to AWS bedrock in the Prompt Library. All the current models of Anthropic are supported and you can easily start testing different prompts.

Once you’re ready with your prompt, you can use the portkey.prompts.completions.create interface to use the prompt in your application.

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Making Requests without Virtual Keys

If you do not want to add your AWS details to Portkey vault, you can also directly pass them while instantiating the Portkey client.

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Mapping the Bedrock Details

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Example

• NodeJS
• Python
• REST API

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY",
    provider: "bedrock",
    awsAccessKeyId: "AWS_ACCESS_KEY_ID",
    awsSecretAccessKey: "AWS_SECRET_ACCESS_KEY",
    awsRegion: "us-east-1",
    awsSessionToken: "AWS_SESSION_TOKEN"
})

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Supported Models

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How to Find Your AWS Credentials

Navigate here in the AWS Management Console to obtain your AWS Access Key ID and AWS Secret Access Key.

• In the console, you’ll find the ’Access keys’ section. Click on ’Create access key‘.
• Copy the Secret Access Key once it is generated, and you can view the Access Key ID along with it.

• On the same page under the ’Access keys’ section, where you created your Secret Access key, you will also find your Access Key ID.

• And lastly, get Your **AWS Region** from the Home Page of AWS Bedrock as shown in the image below.

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Next Steps

The complete list of features supported in the SDK are available on the link below.

You’ll find more information in the relevant sections:

1. Add metadata to your requests
2. Add gateway configs to your Bedrock requests
3. Tracing Bedrock requests
4. Setup a fallback from OpenAI to Beckrock APIs