deepset-ai--haystack/docs-website/versioned_docs/version-2.18/pipeline-components/generators/amazonbedrockchatgenerator.mdx

---
title: "AmazonBedrockChatGenerator"
id: amazonbedrockchatgenerator
slug: "/amazonbedrockchatgenerator"
description: "This component enables chat completion using models through Amazon Bedrock service."
---

# AmazonBedrockChatGenerator

This component enables chat completion using models through Amazon Bedrock service.

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [ChatPromptBuilder](../builders/chatpromptbuilder.mdx)  |
| **Mandatory init variables** | "model": The model to use  <br /> <br />"aws_access_key_id": AWS access key ID. Can be set with `AWS_ACCESS_KEY_ID` env var.  <br /> <br />"aws_secret_access_key": AWS secret access key. Can be set with `AWS_SECRET_ACCESS_KEY` env var.  <br /> <br />"aws_region_name": AWS region name. Can be set with `AWS_DEFAULT_REGION` env var. |
| **Mandatory run variables** | “messages”: A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  instances |
| **Output variables** | "replies": A list of [`ChatMessage`](../../concepts/data-classes/chatmessage.mdx)  objects  <br /> <br />”meta”: A list of dictionaries with the metadata associated with each reply, such as token count, finish reason, and so on |
| **API reference** | [Amazon Bedrock](/reference/integrations-amazon-bedrock) |
| **GitHub link** | https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/amazon_bedrock |

[Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html) 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](https://docs.aws.amazon.com/bedrock/latest/userguide/security_iam_id-based-policy-examples.html).

:::note
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](https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html). This way, you won't need to provide detailed authentication parameters when initializing Amazon Bedrock Generator in Haystack.
:::

To use this component for text generation, initialize an AmazonBedrockGenerator with the model name, the AWS credentials (`AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_DEFAULT_REGION`) should be set as environment variables, be configured as described above or passed as [Secret](../../concepts/secret-management.mdx) arguments. Note, make sure the region you set supports Amazon Bedrock.

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

```shell
pip install amazon-bedrock-haystack
```

### Streaming

This Generator supports [streaming](guides-to-generators/choosing-the-right-generator.mdx#streaming-support) 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:

```python
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:

```python
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)
```