--- 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)