Amazon Bedrock is a fully managed service that makes high-performing foundation models from leading AI startups and Amazon available through a unified API. You can choose from various foundation models to find the one best suited for your use case.

AmazonBedrockChatGenerator enables chat completion using chat models from Anthropic, Cohere, Meta Llama 2, and Mistral with a single component.

The models that we currently support are Anthropic's Claude, Meta's Llama 2, and Mistral, but as more chat models are added, their support will be provided through AmazonBedrockChatGenerator.

Overview

This component uses AWS for authentication. You can use the AWS CLI to authenticate through your IAM. For more information on setting up an IAM identity-based policy, see the official documentation.

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Using AWS CLI

Consider using AWS CLI as a more straightforward tool to manage your AWS services. With AWS CLI, you can quickly configure your boto3 credentials. This way, you won't need to provide detailed authentication parameters when initializing Amazon Bedrock Generator in Haystack.

To use this component, initialize it with the model name. The AWS credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION) should be set as environment variables, configured as described above, or passed as Secret arguments. Note, make sure the region you set supports Amazon Bedrock.

Installation

To start using Amazon Bedrock with Haystack, install the amazon-bedrock-haystack package:

pip install amazon-bedrock-haystack

Streaming

This Generator supports streaming the tokens from the LLM directly in output. To do so, pass a function to the streaming_callback init parameter.

Usage

On its own

Basic usage:

from haystack_integrations.components.generators.amazon_bedrock import AmazonBedrockChatGenerator
from haystack.dataclasses import ChatMessage

generator = AmazonBedrockChatGenerator(model="meta.llama2-70b-chat-v1")
messages = [ChatMessage.from_system("You are a helpful assistant that answers question in Spanish only"), ChatMessage.from_user("What's Natural Language Processing? Be brief.")]
    
response = generator.run(messages)
print(response)

In a pipeline

In a RAG pipeline:

from haystack import Pipeline
from haystack.components.builders import ChatPromptBuilder
from haystack.dataclasses import ChatMessage
from haystack_integrations.components.generators.amazon_bedrock import AmazonBedrockChatGenerator

pipe = Pipeline()
pipe.add_component("prompt_builder", ChatPromptBuilder())
pipe.add_component("llm", AmazonBedrockChatGenerator(model="meta.llama2-70b-chat-v1"))
pipe.connect("prompt_builder", "llm")

country = "Germany"
system_message = ChatMessage.from_system("You are an assistant giving out valuable information to language learners.")
messages = [system_message, ChatMessage.from_user("What's the official language of {{ country }}?")]

res = pipe.run(data={"prompt_builder": {"template_variables": {"country": country}, "template": messages}})
print(res)