CohereRanker

Rank documents based on their similarity to the query using Cohere models.

Basic Information

Type: haystack_integrations.components.rankers.cohere.ranker.CohereRanker
Components it can connect with:
- Retriever: CohereRanker receives documents from Retriever.
- PromptBuilder: CohereRanker can send the ranked documents to PromptBuilder to be used in a prompt.
- Any component that outputs documents or accepts documents as input.

Inputs

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

Outputs

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

Overview

CohereRanker ranks documents based on their semantic similarity to the user query. For a list of supported ranking models, see Cohere documentation.

Documents are ordered from most to least semantically relevant to the query.

Authorization

You need a Cohere API key to use this component. Connect deepset to your Cohere account on the Integrations page.

Add Workspace-Level Integration

Click your profile icon and choose Settings.
Go to Workspace>Integrations.
Find the provider you want to connect and click Connect next to them.
Enter the API key and any other required details.
Click Connect. You can use this integration in pipelines and indexes in the current workspace.

Add Organization-Level Integration

Click your profile icon and choose Settings.
Go to Organization>Integrations.
Find the provider you want to connect and click Connect next to them.
Enter the API key and any other required details.
Click Connect. You can use this integration in pipelines and indexes in all workspaces in the current organization.

Usage Example

Initializing the Component

components:
  CohereRanker:
    type: haystack_integrations.components.rankers.cohere.ranker.CohereRanker
    init_parameters:

Using the Component in a Pipeline

This ia an example of a document search pipeline where CohereRanker receives joined documents from both a keyword and a semantic retriever. It then ranks the documents based on their similarity to the user query and outputs them as the final result.

components:
  bm25_retriever:
    type: haystack_integrations.components.retrievers.opensearch.bm25_retriever.OpenSearchBM25Retriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          use_ssl: true
          verify_certs: false
          hosts:
          - ${OPENSEARCH_HOST}
          http_auth:
          - ${OPENSEARCH_USER}
          - ${OPENSEARCH_PASSWORD}
          embedding_dim: 768
          similarity: cosine
          index: ''
          max_chunk_bytes: 104857600
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          timeout:
      top_k: 20
  embedding_retriever:
    type: haystack_integrations.components.retrievers.opensearch.embedding_retriever.OpenSearchEmbeddingRetriever
    init_parameters:
      document_store:
        type: haystack_integrations.document_stores.opensearch.document_store.OpenSearchDocumentStore
        init_parameters:
          use_ssl: true
          verify_certs: false
          hosts:
          - ${OPENSEARCH_HOST}
          http_auth:
          - ${OPENSEARCH_USER}
          - ${OPENSEARCH_PASSWORD}
          embedding_dim: 768
          similarity: cosine
          index: ''
          max_chunk_bytes: 104857600
          return_embedding: false
          method:
          mappings:
          settings:
          create_index: true
          timeout:
      top_k: 20
  document_joiner:
    type: haystack.components.joiners.document_joiner.DocumentJoiner
    init_parameters:
      join_mode: concatenate
  DeepsetNvidiaTextEmbedder:
    type: deepset_cloud_custom_nodes.embedders.nvidia.text_embedder.DeepsetNvidiaTextEmbedder
    init_parameters:
      model: intfloat/multilingual-e5-base
      prefix: ''
      suffix: ''
      truncate:
      normalize_embeddings: false
      timeout:
      backend_kwargs:
  CohereRanker:
    type: haystack_integrations.components.rankers.cohere.ranker.CohereRanker
    init_parameters:
      model: rerank-v3.5
      top_k: 10
      api_key:
        type: env_var
        env_vars:
        - COHERE_API_KEY
        - CO_API_KEY
        strict: false
      api_base_url: https://api.cohere.com
      meta_fields_to_embed:
      meta_data_separator: \n
      max_tokens_per_doc: 4096

connections:
- sender: bm25_retriever.documents
  receiver: document_joiner.documents
- sender: embedding_retriever.documents
  receiver: document_joiner.documents
- sender: DeepsetNvidiaTextEmbedder.embedding
  receiver: embedding_retriever.query_embedding
- sender: document_joiner.documents
  receiver: CohereRanker.documents

max_runs_per_component: 100

metadata: {}

inputs:
  query:
  - bm25_retriever.query
  - DeepsetNvidiaTextEmbedder.text
  - CohereRanker.query
  filters:
  - bm25_retriever.filters
  - embedding_retriever.filters

outputs:
  documents: CohereRanker.documents

Parameters

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
model	str	rerank-v3.5	Cohere model name. Check the list of supported models in the Cohere documentation.
top_k	int	10	The maximum number of documents to return.
api_key	Secret	Secret.from_env_var(['COHERE_API_KEY', 'CO_API_KEY'])	Cohere API key.
api_base_url	str	https://api.cohere.com	The base URL of the Cohere API.
meta_fields_to_embed	Optional[List[str]]	None	List of meta fields that should be concatenated with the document content for reranking.
meta_data_separator	str	\n	Separator used to concatenate the meta fields to the Document content.
max_tokens_per_doc	int	4096	The maximum number of tokens to embed for each document defaults to 4096.

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		Query string.
documents	List[Document]		List of documents to rank.
top_k	Optional[int]	None	The maximum number of documents you want the Ranker to return.

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Basic Information​

Inputs​

Outputs​

Overview​

Authorization​

Add Workspace-Level Integration​

Add Organization-Level Integration​

Usage Example​

Initializing the Component​

Using the Component in a Pipeline​

Parameters​

Init Parameters​

Run Method Parameters​