
Most common position in a pipeline	Before an embedding Retriever in a query/RAG pipeline
Mandatory init variables	"model": The embedding 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	“text”: A string
Output variables	“embedding”: A list of float numbers (vector)
API reference	Amazon Bedrock
GitHub link	https://github.com/deepset-ai/haystack-core-integrations/tree/main/integrations/amazon_bedrock

Overview

Amazon Bedrock is a fully managed service that makes language models from leading AI startups and Amazon available for your use through a unified API.

Supported models are amazon.titan-embed-text-v1, cohere.embed-english-v3 and cohere.embed-multilingual-v3.

Use AmazonBedrockTextEmbedder to embed a simple string (such as a query) into a vector. Use the AmazonBedrockDocumentEmbedder to enrich the documents with the computed embedding, also known as vector.

Authentication

AmazonBedrockTextEmbedder uses AWS for authentication. You can either provide credentials as parameters directly to the component or use the AWS CLI and authenticate through your IAM. For more information on how to set up an IAM identity-based policy, see the official documentation.
To initialize AmazonBedrockTextEmbedder and authenticate by providing credentials, provide the model name, as well as aws_access_key_id, aws_secret_access_key, and aws_region_name. Other parameters are optional, you can check them out in our API reference.

Model-specific parameters

Even if Haystack provides a unified interface, each model offered by Bedrock can accept specific parameters. You can pass these parameters at initialization.

For example, the Cohere models support input_type and truncate, as seen in Bedrock documentation.

from haystack_integrations.components.embedders.amazon_bedrock import AmazonBedrockTextEmbedder

embedder = AmazonBedrockTextEmbedder(model="cohere.embed-english-v3",
                                     input_type="search_query",
                                     truncate="LEFT")

Usage

Installation

You need to install amazon-bedrock-haystack package to use the AmazonBedrockTextEmbedder:

pip install amazon-bedrock-haystack

On its own

Basic usage:

import os
from haystack_integrations.components.embedders.amazon_bedrock import AmazonBedrockTextEmbedder

os.environ["AWS_ACCESS_KEY_ID"] = "..."
os.environ["AWS_SECRET_ACCESS_KEY"] = "..."
os.environ["AWS_DEFAULT_REGION"] = "us-east-1" # just an example

text_to_embed = "I love pizza!"

text_embedder = AmazonBedrockTextEmbedder(model="cohere.embed-english-v3",
																					input_type="search_query"

print(text_embedder.run(text_to_embed))
# {'embedding': [-0.453125, 1.2236328, 2.0058594, 0.67871094...]}

In a pipeline

In a RAG pipeline:

from haystack import Document
from haystack import Pipeline
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")]

document_embedder = AmazonBedrockDocumentEmbedder(model="cohere.embed-english-v3")
documents_with_embeddings = document_embedder.run(documents)['documents']
document_store.write_documents(documents_with_embeddings)

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')

Additional References

🧑‍🍳 Cookbook: PDF-Based Question Answering with Amazon Bedrock and Haystack