gem-hunter

langchain-postgres

Requirements

Installation

pip install -U langchain-postgres

Vectorstore

Warning

In v0.0.14+, PGVector is deprecated. Please migrate to PGVectorStore for improved performance and manageability. See the migration guide for details on how to migrate from PGVector to PGVectorStore.

Documentation

Tip

For developing, debugging, and deploying AI agents and LLM applications, see LangSmith.

Example

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+psycopg://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)

Tip

All synchronous functions have corresponding asynchronous functions

Hybrid Search with PGVectorStore

vs = PGVectorStore.create_sync(
    engine=engine,
    table_name=TABLE_NAME,
    embedding_service=embedding,
    hybrid_search_config=HybridSearchConfig(fusion_function=reciprocal_rank_fusion),
)
hybrid_docs = vector_store.similarity_search("products", k=5)

ChatMessageHistory

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)

Google Cloud Integrations