AmazonBedrockDocumentEmbedder
This component computes embeddings for documents using models through Amazon Bedrock API.
| Most common position in a pipeline | Before a DocumentWriter in an indexing 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 | “documents”: A list of documents to be embedded |
| Output variables | “documents”: A list of documents (enriched with embeddings) |
| 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.
Batch Inference
Note that only Cohere models support batch inference – computing embeddings for more documents with the same request.
This component should be used to embed a list of documents. To embed a string, you should use the AmazonBedrockTextEmbedder.
Authentication
AmazonBedrockDocumentEmbedder 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 AmazonBedrockDocumentEmbedder 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, Cohere models support input_type and truncate, as seen in Bedrock documentation.
from haystack_integrations.components.embedders.amazon_bedrock import AmazonBedrockDocumentEmbedder
embedder = AmazonBedrockDocumentEmbedder(model="cohere.embed-english-v3",
input_type="search_document",
truncate="LEFT")
Embedding Metadata
Text documents often come with a set of metadata. If they are distinctive and semantically meaningful, you can embed them along with the text of the document to improve retrieval.
You can do this easily by using the Document Embedder:
from haystack import Document
from haystack_integrations.components.embedders.amazon_bedrock import AmazonBedrockDocumentEmbedder
doc = Document(content="some text",meta={"title": "relevant title", "page number": 18})
embedder = AmazonBedrockDocumentEmbedder(model="cohere.embed-english-v3",
meta_fields_to_embed=["title"])
docs_w_embeddings = embedder.run(documents=[doc])["documents"]
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 AmazonBedrockDocumentEmbedder
from haystack.dataclasses import DOcument
os.environ["AWS_ACCESS_KEY_ID"] = "..."
os.environ["AWS_SECRET_ACCESS_KEY"] = "..."
os.environ["AWS_DEFAULT_REGION"] = "us-east-1" # just an example
doc = Document(content="I love pizza!")
embedder = AmazonBedrockDocumentEmbedder(model="cohere.embed-english-v3",
input_type="search_document"
result = document_embedder.run([doc])
print(result['documents'][0].embedding)
# [0.017020374536514282, -0.023255806416273117, ...]
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")]
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')
Additional References
🧑🍳 Cookbook: PDF-Based Question Answering with Amazon Bedrock and Haystack
Updated about 1 month ago