src/backend/base/langflow/components/vectorstores/pgvector.py

from langchain_community.vectorstores import PGVector

from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store
from langflow.helpers.data import docs_to_data
from langflow.io import DataInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput
from langflow.schema import Data
from langflow.utils.connection_string_parser import transform_connection_string


class PGVectorStoreComponent(LCVectorStoreComponent):
    display_name = "PGVector"
    description = "PGVector Vector Store with search capabilities"
    documentation = "https://python.langchain.com/v0.2/docs/integrations/vectorstores/pgvector/"
    name = "pgvector"
    icon = "cpu"

    inputs = [
        SecretStrInput(name="pg_server_url", display_name="PostgreSQL Server Connection String", required=True),
        StrInput(name="collection_name", display_name="Table", required=True),
        MultilineInput(name="search_query", display_name="Search Query"),
        DataInput(
            name="ingest_data",
            display_name="Ingestion Data",
            is_list=True,
        ),
        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
        IntInput(
            name="number_of_results",
            display_name="Number of Results",
            info="Number of results to return.",
            value=4,
            advanced=True,
        ),
        HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]),
    ]

    @check_cached_vector_store
    def build_vector_store(self) -> PGVector:
        documents = []
        for _input in self.ingest_data or []:
            if isinstance(_input, Data):
                documents.append(_input.to_lc_document())
            else:
                documents.append(_input)

        connection_string_parsed = transform_connection_string(self.pg_server_url)

        if documents:
            pgvector = PGVector.from_documents(
                embedding=self.embedding,
                documents=documents,
                collection_name=self.collection_name,
                connection_string=connection_string_parsed,
            )
        else:
            pgvector = PGVector.from_existing_index(
                embedding=self.embedding,
                collection_name=self.collection_name,
                connection_string=connection_string_parsed,
            )

        return pgvector

    def search_documents(self) -> list[Data]:
        vector_store = self.build_vector_store()

        if self.search_query and isinstance(self.search_query, str) and self.search_query.strip():
            docs = vector_store.similarity_search(
                query=self.search_query,
                k=self.number_of_results,
            )

            data = docs_to_data(docs)
            self.status = data
            return data
        return []