---
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
`aws_access_key_id`: AWS access key ID. Can be set with `AWS_ACCESS_KEY_ID` env var.
`aws_secret_access_key`: AWS secret access key. Can be set with `AWS_SECRET_ACCESS_KEY` env var.
`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).
:::info[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)