---
title: "AmazonBedrockGenerator"
id: amazonbedrockgenerator
slug: "/amazonbedrockgenerator"
description: "This component enables text generation using models through Amazon Bedrock service."
---

# AmazonBedrockGenerator

This component enables text generation using models through Amazon Bedrock service.

|  |  |
| --- | --- |
| **Most common position in a pipeline** | After a [`PromptBuilder`](../builders/promptbuilder.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** | “prompt”: The instructions for the Generator |
| **Output variables** | “replies”: A list of strings with all the replies generated by the model |
| **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.

`AmazonBedrockGenerator` enables text generation using models from AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon with a single component.

The models that we currently support are Anthropic's Claude, AI21 Labs' Jurassic-2, Stability AI's Stable Diffusion, Cohere's Command and Embed, Meta's Llama 2, and the Amazon Titan language and embeddings models.

## 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 (
    AmazonBedrockGenerator,
)

aws_access_key_id = "..."
aws_secret_access_key = "..."
aws_region_name = "eu-central-1"

generator = AmazonBedrockGenerator(model="anthropic.claude-v2")
result = generator.run("Who is the best American actor?")
for reply in result["replies"]:
    print(reply)

## >>> 'There is no definitive "best" American actor, as acting skill and talent a# re subjective. However, some of the most acclaimed and influential American act# ors include Tom Hanks, Daniel Day-Lewis, Denzel Washington, Meryl Streep, Rober# t De Niro, Al Pacino, Marlon Brando, Jack Nicholson, Leonardo DiCaprio and John# ny Depp. Choosing a single "best" actor comes down to personal preference.'
```

### In a pipeline

In a RAG pipeline:

```python
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.builders import PromptBuilder
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack import Pipeline

from haystack_integrations.components.generators.amazon_bedrock import (
    AmazonBedrockGenerator,
)

template = """
Given the following information, answer the question.

Context:
{% for document in documents %}
    {{ document.content }}
{% endfor %}

Question: What's the official language of {{ country }}?
"""

aws_access_key_id = "..."
aws_secret_access_key = "..."
aws_region_name = "eu-central-1"
generator = AmazonBedrockGenerator(model="anthropic.claude-v2")
docstore = InMemoryDocumentStore()

pipe = Pipeline()
pipe.add_component("retriever", InMemoryBM25Retriever(document_store=docstore))
pipe.add_component("prompt_builder", PromptBuilder(template=template))
pipe.add_component("generator", generator)
pipe.connect("retriever", "prompt_builder.documents")
pipe.connect("prompt_builder", "generator")

pipe.run({"retriever": {"query": "France"}, "prompt_builder": {"country": "France"}})

## {'generator': {'replies': ['Based on the context provided, the official language of France is French.']}}
```

## Additional References

🧑‍🍳 Cookbook: [PDF-Based Question Answering with Amazon Bedrock and Haystack](https://haystack.deepset.ai/cookbook/amazon_bedrock_for_documentation_qa)
