ChromaEmbeddingRetriever

Retrieve documents from a ChromaDocumentStore using vector embeddings.

Key Features

• Dense vector-based retrieval from a Chroma vector database.
• Configurable number of results with top_k.
• Supports metadata filtering to narrow down the search space.
• Configurable filter policy (replace or merge) for runtime filters.

Configuration

1. Drag the ChromaEmbeddingRetriever component onto the canvas from the Component Library.
2. Click on the component to open the configuration panel.
3. On the General tab:
   • Configure the ChromaDocumentStore with your Chroma instance details.
   • Set top_k to control the maximum number of documents to retrieve.
4. Go to the Advanced tab to configure filter_policy.

Connections

ChromaEmbeddingRetriever receives query embeddings from a text embedder. It sends retrieved documents to downstream components such as PromptBuilder or a ranker.

Source Code

To check this component's source code, open retriever.py in the Haystack Core Integrations repository.

Usage Examples

Basic Configuration

  ChromaEmbeddingRetriever:
    type: haystack_integrations.components.retrievers.chroma.retriever.ChromaEmbeddingRetriever
    init_parameters:
      top_k: 10
      document_store:
        type: haystack_integrations.document_stores.chroma.document_store.ChromaDocumentStore
        init_parameters:
          collection_name: documents
          host: localhost
          port: 8000

Using the Component in a Pipeline

# haystack-pipeline
components:
  ChromaEmbeddingRetriever:
    type: haystack_integrations.components.retrievers.chroma.retriever.ChromaEmbeddingRetriever
    init_parameters:
      top_k: 10
      filter_policy: replace
      document_store:
        type: haystack_integrations.document_stores.chroma.document_store.ChromaDocumentStore
        init_parameters:
          collection_name: documents
          host: localhost
          port: 8000

connections: []

max_runs_per_component: 100

metadata: {}

inputs:
  query_embedding:
  - ChromaEmbeddingRetriever.query_embedding

outputs:
  documents: ChromaEmbeddingRetriever.documents

Parameters

Inputs

Parameter	Type	Description
query_embedding	List[float]	The embedding of the query.
filters	Optional[Dict[str, Any]]	Filters to apply to the search results.
top_k	Optional[int]	The maximum number of documents to return.

Outputs

Parameter	Type	Description
documents	List[Document]	The retrieved documents.

Init Parameters

These are the parameters you can configure in Pipeline Builder:

Parameter	Type	Default	Description
document_store	ChromaDocumentStore		An instance of ChromaDocumentStore.
filters	Optional[Dict[str, Any]]	None	Default filters applied when running the retriever.
top_k	int	10	The maximum number of documents to retrieve.
filter_policy	Union[str, FilterPolicy]	FilterPolicy.REPLACE	Policy for how runtime filters are applied relative to init-time filters.

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_embedding	List[float]		The embedding of the query.
filters	Optional[Dict[str, Any]]	None	Filters to apply at query time.
top_k	Optional[int]	None	Override the init-time top_k setting.

Related Information

• Pipeline Components