from langchain_core.documents import Document from langchain_core.embeddings import DeterministicFakeEmbedding from langchain_postgres import PGEngine, PGVectorStore # Replace the connection string with your own Postgres connection string CONNECTION_STRING = "postgresql+psycopg3://langchain:langchain@localhost:6024/langchain" engine = PGEngine.from_connection_string(url=CONNECTION_STRING) # Replace the vector size with your own vector size VECTOR_SIZE = 768 embedding = DeterministicFakeEmbedding(size=VECTOR_SIZE) TABLE_NAME = "my_doc_collection" engine.init_vectorstore_table( table_name=TABLE_NAME, vector_size=VECTOR_SIZE, ) store = PGVectorStore.create_sync( engine=engine, table_name=TABLE_NAME, embedding_service=embedding, ) docs = [ Document(page_content="Apples and oranges"), Document(page_content="Cars and airplanes"), Document(page_content="Train") ] store.add_documents(docs) query = "I'd like a fruit." docs = store.similarity_search(query) print(docs)

import uuid from langchain_core.messages import SystemMessage, AIMessage, HumanMessage from langchain_postgres import PostgresChatMessageHistory import psycopg # Establish a synchronous connection to the database # (or use psycopg.AsyncConnection for async) conn_info = ... # Fill in with your connection info sync_connection = psycopg.connect(conn_info) # Create the table schema (only needs to be done once) table_name = "chat_history" PostgresChatMessageHistory.create_tables(sync_connection, table_name) session_id = str(uuid.uuid4()) # Initialize the chat history manager chat_history = PostgresChatMessageHistory( table_name, session_id, sync_connection=sync_connection ) # Add messages to the chat history chat_history.add_messages([ SystemMessage(content="Meow"), AIMessage(content="woof"), HumanMessage(content="bark"), ]) print(chat_history.messages)