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from langchain_community.vectorstores import PGVector |
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from langflow.base.vectorstores.model import LCVectorStoreComponent, check_cached_vector_store |
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from langflow.helpers.data import docs_to_data |
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from langflow.io import DataInput, HandleInput, IntInput, MultilineInput, SecretStrInput, StrInput |
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from langflow.schema import Data |
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from langflow.utils.connection_string_parser import transform_connection_string |
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class PGVectorStoreComponent(LCVectorStoreComponent): |
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display_name = "PGVector" |
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description = "PGVector Vector Store with search capabilities" |
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documentation = "https://python.langchain.com/v0.2/docs/integrations/vectorstores/pgvector/" |
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name = "pgvector" |
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icon = "cpu" |
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inputs = [ |
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SecretStrInput(name="pg_server_url", display_name="PostgreSQL Server Connection String", required=True), |
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StrInput(name="collection_name", display_name="Table", required=True), |
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MultilineInput(name="search_query", display_name="Search Query"), |
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DataInput( |
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name="ingest_data", |
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display_name="Ingestion Data", |
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is_list=True, |
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), |
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HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]), |
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IntInput( |
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name="number_of_results", |
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display_name="Number of Results", |
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info="Number of results to return.", |
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value=4, |
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advanced=True, |
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), |
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HandleInput(name="embedding", display_name="Embedding", input_types=["Embeddings"]), |
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] |
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@check_cached_vector_store |
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def build_vector_store(self) -> PGVector: |
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documents = [] |
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for _input in self.ingest_data or []: |
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if isinstance(_input, Data): |
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documents.append(_input.to_lc_document()) |
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else: |
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documents.append(_input) |
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connection_string_parsed = transform_connection_string(self.pg_server_url) |
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if documents: |
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pgvector = PGVector.from_documents( |
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embedding=self.embedding, |
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documents=documents, |
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collection_name=self.collection_name, |
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connection_string=connection_string_parsed, |
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) |
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else: |
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pgvector = PGVector.from_existing_index( |
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embedding=self.embedding, |
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collection_name=self.collection_name, |
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connection_string=connection_string_parsed, |
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) |
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return pgvector |
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def search_documents(self) -> list[Data]: |
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vector_store = self.build_vector_store() |
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if self.search_query and isinstance(self.search_query, str) and self.search_query.strip(): |
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docs = vector_store.similarity_search( |
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query=self.search_query, |
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k=self.number_of_results, |
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) |
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data = docs_to_data(docs) |
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self.status = data |
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return data |
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return [] |
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