feat: add pgvector client

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

@@ -0,0 +1,66 @@
+from typing import Optional
+from langflow import CustomComponent
+
+from langchain.vectorstores.pgvector import PGVector
+from langchain.schema import Document
+from langchain.vectorstores.base import VectorStore
+from langchain.embeddings.base import Embeddings
+
+
+class PostgresqlVectorComponent(CustomComponent):
+    """
+    A custom component for implementing a Vector Store using PostgreSQL.
+    """
+
+    display_name: str = "PGVector"
+    description: str = "Implementation of Vector Store using PostgreSQL"
+    documentation = "https://python.langchain.com/docs/integrations/vectorstores/pgvector"
+    beta = True
+
+    def build_config(self):
+        """
+        Builds the configuration for the component.
+
+        Returns:
+        - dict: A dictionary containing the configuration options for the component.
+        """
+        return {
+            "index_name": {"display_name": "Index Name", "value": "your_index"},
+            "code": {"show": True, "display_name": "Code"},
+            "documents": {"display_name": "Documents", "is_list": True},
+            "embedding": {"display_name": "Embedding"},
+            "pg_server_url": {
+                "display_name": "PostgreSQL Server Connection String",
+                "advanced": False,
+            },
+            "collection_name": {"display_name": "Table", "advanced": False},
+        }
+
+    def build(
+        self,
+        embedding: Embeddings,
+        pg_server_url: str,
+        collection_name: str,
+        documents: Optional[Document] = None,
+    ) -> VectorStore:
+        """
+        Builds the Vector Store or BaseRetriever object.
+
+        Args:
+        - embedding (Embeddings): The embeddings to use for the Vector Store.
+        - documents (Optional[Document]): The documents to use for the Vector Store.
+        - collection_name (str): The name of the PG table.
+        - pg_server_url (str): The URL for the PG server.
+
+        Returns:
+        - VectorStore: The Vector Store object.
+        """
+
+        return PGVector.from_documents(
+            embedding=embedding,
+            documents=documents,
+            collection_name=collection_name,
+            connection_string=pg_server_url,
+        )
+
+