PgvectorKeywordRetriever

Retrieve documents from a PgvectorDocumentStore using keyword-based full-text search.

Key Features

• Full-text search using PostgreSQL's ts_rank_cd ranking function.
• Ranks documents based on query term frequency, proximity, and position.
• 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 PgvectorKeywordRetriever 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 PgvectorDocumentStore with your PostgreSQL connection string.
   • Set top_k to control the maximum number of documents to retrieve.
4. Go to the Advanced tab to configure filter_policy.

Connections

PgvectorKeywordRetriever receives the user query as a text string, typically from the Input component. It sends retrieved documents to downstream components such as PromptBuilder or a ranker.

Source Code

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

Usage Examples

Basic Configuration

  PgvectorKeywordRetriever:
    type: haystack_integrations.components.retrievers.pgvector.keyword_retriever.PgvectorKeywordRetriever
    init_parameters:
      top_k: 10
      document_store:
        type: haystack_integrations.document_stores.pgvector.document_store.PgvectorDocumentStore
        init_parameters:
          connection_string:
            type: env_var
            env_vars:
            - PG_CONN_STR
            strict: false
          table_name: haystack_documents
          language: english

Using the Component in a Pipeline

# haystack-pipeline
components:
  PgvectorKeywordRetriever:
    type: haystack_integrations.components.retrievers.pgvector.keyword_retriever.PgvectorKeywordRetriever
    init_parameters:
      top_k: 10
      document_store:
        type: haystack_integrations.document_stores.pgvector.document_store.PgvectorDocumentStore
        init_parameters:
          connection_string:
            type: env_var
            env_vars:
            - PG_CONN_STR
            strict: false
          table_name: haystack_documents

connections: []

max_runs_per_component: 100

metadata: {}

inputs:
  query:
  - PgvectorKeywordRetriever.query

outputs:
  documents: PgvectorKeywordRetriever.documents

Parameters

Inputs

Parameter	Type	Description
query	str	The text query to search for.
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	PgvectorDocumentStore		An instance of PgvectorDocumentStore.
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	str		The text query to search for.
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