AmazonBedrockRanker

Rank documents based on their similarity to the query using models hosted on Amazon Bedrock. Documents are returned in descending order of relevance.

Amazon Bedrock is a fully managed service that makes state-of-the-art language models available for use through a unified API. To learn more, see Amazon Bedrock documentation.

Key Features

• Ranks documents by semantic relevance to the user query.
• Supports Cohere and Amazon ranking models.
• Configurable number of top results to return.
• Allows embedding document metadata along with document content for richer ranking.
• Works with any component that outputs or accepts a list of documents.

Configuration

Authentication

To use this component, connect Haystack Platform with Amazon Bedrock first. You'll need the region name, access key ID, and secret access key.

For detailed explanation, see Use Amazon Bedrock and SageMaker Models.

1. Drag the AmazonBedrockRanker component onto the canvas from the Component Library.
2. Click the component to open the configuration panel.
3. On the General tab:
   1. Select the ranking model from the list.
4. Go to the Advanced tab to configure the AWS credentials, number of top results, maximum chunks per document, metadata fields to embed, and metadata separator.

Connections

AmazonBedrockRanker accepts a query string, a list of documents to rank, and an optional top_k value as inputs. It outputs a list of documents sorted by relevance to the query.

Connect a retriever's documents output to the documents input. Connect the pipeline's query input to the query input. Connect the documents output to PromptBuilder or another component that processes ranked documents.

Usage Example

Using the Component in a Pipeline

This is an example of a document search pipeline that uses AmazonBedrockRanker with the cohere ranking model:

components:
  bm25_retriever: # Selects the most similar documents from the document store
    type: haystack_integrations.components.retrievers.opensearch.bm25_retriever.OpenSearchBM25Retriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: Standard-Index-English
          max_chunk_bytes: 104857600
          embedding_dim: 768
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:
      top_k: 20 # The number of results to return
      fuzziness: 0

  query_embedder:
    type: deepset_cloud_custom_nodes.embedders.nvidia.text_embedder.DeepsetNvidiaTextEmbedder
    init_parameters:
      normalize_embeddings: true
      model: intfloat/e5-base-v2

  embedding_retriever: # Selects the most similar documents from the document store
    type: haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          hosts:
          index: Standard-Index-English
          max_chunk_bytes: 104857600
          embedding_dim: 768
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          http_auth:
          use_ssl:
          verify_certs:
          timeout:
      top_k: 20 # The number of results to return

  document_joiner:
    type: haystack.components.joiners.document_joiner.DocumentJoiner
    init_parameters:
      join_mode: concatenate

  AmazonBedrockRanker:
    type: haystack_integrations.components.rankers.amazon_bedrock.ranker.AmazonBedrockRanker
    init_parameters:
      model: cohere.rerank-v3-5:0
      top_k: 10
      aws_access_key_id:
        type: env_var
        env_vars:
        - AWS_ACCESS_KEY_ID
        strict: false
      aws_secret_access_key:
        type: env_var
        env_vars:
        - AWS_SECRET_ACCESS_KEY
        strict: false
      aws_session_token:
        type: env_var
        env_vars:
        - AWS_SESSION_TOKEN
        strict: false
      aws_region_name:
        type: env_var
        env_vars:
        - AWS_DEFAULT_REGION
        strict: false
      aws_profile_name:
        type: env_var
        env_vars:
        - AWS_PROFILE
        strict: false
      max_chunks_per_doc:
      meta_fields_to_embed:
      meta_data_separator: \n

connections:  # Defines how the components are connected
- sender: bm25_retriever.documents
  receiver: document_joiner.documents
- sender: query_embedder.embedding
  receiver: embedding_retriever.query_embedding
- sender: embedding_retriever.documents
  receiver: document_joiner.documents
- sender: document_joiner.documents
  receiver: AmazonBedrockRanker.documents

inputs:  # Define the inputs for your pipeline
  query:  # These components will receive the query as input
  - "bm25_retriever.query"
  - "query_embedder.text"
  - "AmazonBedrockRanker.query"
  filters:  # These components will receive a potential query filter as input
  - "bm25_retriever.filters"
  - "embedding_retriever.filters"

outputs:  # Defines the output of your pipeline
  documents: "AmazonBedrockRanker.documents" # The output of the pipeline is the retrieved documents

max_runs_per_component: 100

metadata: {}

Parameters

Inputs

Parameter	Type	Default	Description
query	str		The query used for ranking documents by their similarity to the query.
documents	List[Document]		The documents to be ranked.
top_k	Optional[int]	None	The maximum number of documents you want the Ranker to return.

Outputs

Parameter	Type	Default	Description
documents	List[Document]		Documents most similar to the query in descending order of similarity.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	str	cohere.rerank-v3-5:0	The ranking model to use.
top_k	int	10	The maximum number of documents to return.
aws_access_key_id	Optional[Secret]	Secret.from_env_var(["AWS_ACCESS_KEY_ID"], strict=False)	AWS access key ID.
aws_secret_access_key	Optional[Secret]	Secret.from_env_var(["AWS_SECRET_ACCESS_KEY"], strict=False)	AWS secret access key.
aws_session_token	Optional[Secret]	Secret.from_env_var([AWS_SESSION_TOKEN], strict=False)	AWS session token.
aws_region_name	Optional[Secret]	Secret.from_env_var(["AWS_DEFAULT_REGION"], strict=False)	AWS region name.
aws_profile_name	Optional[Secret]	Secret.from_env_var(["AWS_PROFILE"], strict=False)	AWS profile name.
max_chunks_per_doc	Optional[int]	None	If your document exceeds 512 tokens, this setting determines the maximum number of chunks a document can be split into. If set to None, uses the default of 10 chunks. This parameter is not used currently but it's included for future compatibility.
meta_fields_to_embed	Optional[List[str]]	None	A list of metadata fields to embed in the document content.
meta_data_separator	str	\n	The separator used to concatenate the metadata fields to the document content.

Run Method Parameters

These are the parameters you can configure for the component's run() method. This means you can pass these parameters at query time through the API, in Playground, or when running a job. For details, see Modify Pipeline Parameters at Query Time.

Parameter	Type	Default	Description
query	str		The user query for ranking the documents.
documents	List[Document]		The documents to rank.
top_k	Optional[int]	None	The maximum number of documents you want the Ranker to return.