AmazonBedrockTextEmbedder

This component computes embeddings for text (such as a query) using models through Amazon Bedrock API.

Most common position in a pipeline	Before an embedding Retriever in a query/RAG pipeline
Mandatory init variables	model: The embedding model to use
Optional init variables	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. If you don't set the access keys, the component uses the boto3 credential chain.
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
Package name	amazon-bedrock-haystack

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.

Amazon Titan and Cohere embedding models are supported, for example amazon.titan-embed-text-v1, amazon.titan-embed-text-v2:0, amazon.titan-embed-image-v1, cohere.embed-english-v3, cohere.embed-multilingual-v3, and cohere.embed-v4:0. To find all supported models, see the Amazon Bedrock documentation, filter for "embedding", and select models from the Amazon Titan and Cohere series.

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.

Running on Amazon EKS

On Amazon EKS, the component can authenticate with the pod's IAM role through IAM roles for service accounts (IRSA) or EKS Pod Identity, so you don't need access keys. When no access keys are set, the component falls back to the boto3 credential chain, which picks up the role that EKS assigns to the pod.

1. Associate an IAM role with the pod's Kubernetes service account and allow the role to call bedrock:InvokeModel.
2. Don't pass aws_access_key_id and aws_secret_access_key to the component, and don't set the AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY environment variables in the pod. Access keys take precedence over the pod's role.
3. Set the region with aws_region_name or the AWS_DEFAULT_REGION environment variable.

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.

python

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:

shell

pip install amazon-bedrock-haystack

On its own

Basic usage:

python

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:

python

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