PgvectorKeywordRetriever

Retrieve documents from the PgvectorDocumentStore, based on keywords.

Basic Information

• Type: haystack_integrations.pgvector.src.haystack_integrations.components.retrievers.pgvector.keyword_retriever.PgvectorKeywordRetriever

Inputs

Parameter	Type	Default	Description
query	str		String to search in Documents' content.
filters	Optional[Dict[str, Any]]	None	Filters applied to the retrieved Documents. The way runtime filters are applied depends on the filter_policy chosen at retriever initialization. See init method docstring for more details.
top_k	Optional[int]	None	Maximum number of Documents to return.

Outputs

Parameter	Type	Default	Description
documents	List[Document]		A dictionary with the following keys: - documents: List of Documents that match the query.

Overview

Work in Progress

Bear with us while we're working on adding pipeline examples and most common components connections.

Retrieve documents from the PgvectorDocumentStore, based on keywords.

To rank the documents, the ts_rank_cd function of PostgreSQL is used. It considers how often the query terms appear in the document, how close together the terms are in the document, and how important is the part of the document where they occur. For more details, see Postgres documentation.

Usage example:

from haystack.document_stores import DuplicatePolicy
from haystack import Document

from haystack_integrations.document_stores.pgvector import PgvectorDocumentStore
from haystack_integrations.components.retrievers.pgvector import PgvectorKeywordRetriever

# Set an environment variable `PG_CONN_STR` with the connection string to your PostgreSQL database.
# e.g., "postgresql://USER:PASSWORD@HOST:PORT/DB_NAME"

document_store = PgvectorDocumentStore(language="english", recreate_table=True)

documents = [Document(content="There are over 7,000 languages spoken around the world today."),
    Document(content="Elephants have been observed to behave in a way that indicates..."),
    Document(content="In certain places, you can witness the phenomenon of bioluminescent waves.")]

document_store.write_documents(documents_with_embeddings.get("documents"), policy=DuplicatePolicy.OVERWRITE)

retriever = PgvectorKeywordRetriever(document_store=document_store)

result = retriever.run(query="languages")

assert res['retriever']['documents'][0].content == "There are over 7,000 languages spoken around the world today."


## Usage Example

```yaml
components:
  PgvectorKeywordRetriever:
    type: pgvector.src.haystack_integrations.components.retrievers.pgvector.keyword_retriever.PgvectorKeywordRetriever
    init_parameters:

Parameters

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	Filters applied to the retrieved Documents.
top_k	int	10	Maximum number of Documents to return.
filter_policy	Union[str, FilterPolicy]	FilterPolicy.REPLACE	Policy to determine how filters are applied.

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		String to search in Documents' content.
filters	Optional[Dict[str, Any]]	None	Filters applied to the retrieved Documents. The way runtime filters are applied depends on the filter_policy chosen at retriever initialization. See init method docstring for more details.
top_k	Optional[int]	None	Maximum number of Documents to return.