Haystack
📚 Learn how to turn Haystack pipelines into production-ready REST APIs or MCP tools
Maintained by deepset

Integration: Amazon Bedrock

Use Models from AI21 Labs, Anthropic, Cohere, Meta, and Amazon via Amazon Bedrock with Haystack

Authors
deepset
deepset-ai
GitHub Repo PyPI Package

Table of Contents

Overview

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

Installation

Install the Amazon Bedrock integration:

pip install amazon-bedrock-haystack

Usage

Once installed, you will have access to AmazonBedrockChatGenerator and AmazonBedrockGenerator components that support generative language models on Amazon Bedrock. You will also have access to the AmazonBedrockTextEmbedder and AmazonBedrockDocumentEmbedder, which can be used to compute embeddings.

To use this integration, set the AWS credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION) as environment variables or passed as Secret arguments.

Note: Make sure the region you set supports Amazon Bedrock.

AmazonBedrockChatGenerator

To use this integration for chat models, initialize an AmazonBedrockChatGenerator with the model name:

Some supported models are:

  • AI21 Labs’ Jamba 1.5 Large, Jamba 1.5 Mini
  • Amazon’s Nova Canvas, Nova Lite, Nova Pro
  • Amazon’s Titan Text G1 - Express, Titan Text G1 - Lite
  • Anthropic’s Claude chat models
  • Cohere’s Command, Command Light and other Command models
  • DeepSeek’s DeepSeek-R1
  • Meta’s Llama models
  • Mistral AI’s

Get the complete list of models in Supported foundation models in Amazon Bedrock

from haystack_integrations.components.generators.amazon_bedrock import AmazonBedrockChatGenerator
from haystack.dataclasses import ChatMessage
    
generator = AmazonBedrockChatGenerator(model="amazon.nova-pro-v1:0")
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)

Output:

{'replies': [ChatMessage(_role=<ChatRole.ASSISTANT: 'assistant'>, _content=[TextContent(text='El procesamiento del lenguaje natural (PLN) es una rama de la inteligencia artificial que permite a las computadoras comprender, interpretar y generar lenguaje humano.')], _name=None, _meta={'model': 'amazon.nova-pro-v1:0', 'index': 0, 'finish_reason': 'end_turn', 'usage': {'prompt_tokens': 21, 'completion_tokens': 31, 'total_tokens': 52}})]}

AmazonBedrockGenerator

Most supported models can be used with AmazonBedrockGenerator, but we highly recommend using the AmazonBedrockChatGenerator instead.

from haystack_integrations.components.generators.amazon_bedrock import AmazonBedrockGenerator

generator = AmazonBedrockGenerator(model="mistral.mixtral-8x7b-instruct-v0:1")
result = generator.run("Who is the best American actor?")
print(result)

Output:

{'replies': ['It\'s subjective to determine the "best" American actor as it depends on personal preferences, critical acclaim, and the impact of their work. However, some of the most renowned and influential American actors include:\n\n1. Daniel Day-Lewis - Known for his versatility and commitment to his roles, Day-Lewis is a three-time Academy Award winner for Best Actor.\n2. Meryl Streep - With a record 21 Academy Award nominations and three wins, Streep is widely regarded as one of the greatest actresses in American film history.\n3. Jack Nicholson - A three-time Academy Award winner and 12-time nominee, Nicholson is known for his iconic roles in films like "One Flew Over the Cuckoo\'s Nest," "Terms of Endearment," and "As Good as It Gets."\n4. Robert De Niro - A two-time Academy Award winner, De Niro is known for his collaborations with Martin Scorsese and his memorable roles in films like "Taxi Driver," "Raging Bull," and "The Godfather: Part II."\n5. Leonardo DiCaprio - A four-time Academy Award nominee and one-time winner, DiCaprio has had a successful career in both blockbusters and independent films.\n\nThese are just a few examples of highly acclaimed American actors, and there are many other talented actors who could be considered for this title.'], 'meta': {'RequestId': 'ed9c8566-0b13-4c08-ba72-c88be1aecd02', 'HTTPStatusCode': 200, 'HTTPHeaders': {'date': 'Mon, 28 Apr 2025 11:00:15 GMT', 'content-type': 'application/json', 'content-length': '1322', 'connection': 'keep-alive', 'x-amzn-requestid': 'ed9c8566-0b13-4c08-ba72-c88be1aecd02', 'x-amzn-bedrock-invocation-latency': '7065', 'x-amzn-bedrock-output-token-count': '323', 'x-amzn-bedrock-input-token-count': '16'}, 'RetryAttempts': 0}}

AmazonBedrockTextEmbedder and AmazonBedrockDocumentEmbedder

These two components can compute embeddings for text and Documents, respectively. The supported models are “amazon.titan-embed-text-v1”, “amazon.titan-embed-text-v2:0”, “cohere.embed-english-v3,” and “cohere.embed-multilingual-v3.”

See them in action:

from haystack import Pipeline
from haystack.dataclasses import Document
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.embedders.amazon_bedrock import (
    AmazonBedrockDocumentEmbedder,
    AmazonBedrockTextEmbedder,
)
from haystack.components.retrievers.in_memory import InMemoryEmbeddingRetriever

document_store = InMemoryDocumentStore(embedding_similarity_function="cosine")

documents = [Document(content="My name is Wolfgang and I live in Berlin"),
             Document(content="I saw a black horse running"),
             Document(content="Germany has many big cities")]

indexing_pipeline = Pipeline()
indexing_pipeline.add_component("embedder", AmazonBedrockDocumentEmbedder(model="cohere.embed-english-v3"))
indexing_pipeline.add_component("writer", DocumentWriter(document_store=document_store))
indexing_pipeline.connect("embedder", "writer")

indexing_pipeline.run({"embedder": {"documents": documents}})


query_pipeline = Pipeline()
query_pipeline.add_component("text_embedder", AmazonBedrockTextEmbedder(model="cohere.embed-english-v3"))
query_pipeline.add_component("retriever", InMemoryEmbeddingRetriever(document_store=document_store))
query_pipeline.connect("text_embedder.embedding", "retriever.query_embedding")

query = "Who lives in Berlin?"

result = query_pipeline.run({"text_embedder":{"text": query}})

print(result['retriever']['documents'][0])

# Document(id=..., content: 'My name is Wolfgang and I live in Berlin')