Overview

AmazonBedrockRanker ranks documents based on semantic relevance to a specified query. It uses Amazon Bedrock Rerank API. This list of all supported models can be found in Amazon’s documentation. The default model for this Ranker is cohere.rerank-v3-5:0.

You can also specify the top_k parameter to set the maximum number of documents to return.

Installation

To start using Amazon Bedrock with Haystack, install the amazon-bedrock-haystack package:

pip install amazon-bedrock-haystack

Authentication

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.

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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. This way, you won't need to provide detailed authentication parameters when initializing Amazon Bedrock in Haystack.

To use this component, initialize it with the model name. The AWS credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION) should be set as environment variables, configured as described above, or passed as Secret arguments. Make sure the region you set supports Amazon Bedrock.

Usage

On its own

This example uses AmazonBedrockRanker to rank two simple documents. To run the Ranker, pass a query and provide the documents.

from haystack import Document
from haystack_integrations.components.rankers.amazon_bedrock import AmazonBedrockRanker

docs = [Document(content="Paris"), Document(content="Berlin")]

ranker = AmazonBedrockRanker()

ranker.run(query="City in France", documents=docs, top_k=1)

In a pipeline

Below is an example of a pipeline that retrieves documents from an InMemoryDocumentStore based on keyword search (using InMemoryBM25Retriever). It then uses the AmazonBedrockRanker to rank the retrieved documents according to their similarity to the query. The pipeline uses the default settings of the Ranker.

from haystack import Document, Pipeline
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack_integrations.components.rankers.amazon_bedrock import AmazonBedrockRanker

docs = [
    Document(content="Paris is in France"),
    Document(content="Berlin is in Germany"),
    Document(content="Lyon is in France"),
]
document_store = InMemoryDocumentStore()
document_store.write_documents(docs)

retriever = InMemoryBM25Retriever(document_store=document_store)
ranker = AmazonBedrockRanker()

document_ranker_pipeline = Pipeline()
document_ranker_pipeline.add_component(instance=retriever, name="retriever")
document_ranker_pipeline.add_component(instance=ranker, name="ranker")

document_ranker_pipeline.connect("retriever.documents", "ranker.documents")

query = "Cities in France"
res = document_ranker_pipeline.run(data={"retriever": {"query": query, "top_k": 3}, "ranker": {"query": query, "top_k": 2}})