langchain/libs/partners/postgres

langchain-postgres

The langchain-postgres package is an integration package managed by the core LangChain team.

This package contains implementations of core abstractions using Postgres.

The package is released under the MIT license.

Feel free to use the abstraction as provided or else modify them / extend them as appropriate for your own application.

Installation

pip install -U langchain-postgres

Usage

ChatMessageHistory

The chat message history abstraction helps to persist chat message history in a postgres table.

PostgresChatMessageHistory is parameterized using a table_name and a session_id.

The table_name is the name of the table in the database where the chat messages will be stored.

The session_id is a unique identifier for the chat session. It can be assigned by the caller using uuid.uuid4().

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_schema(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)

PostgresCheckpoint

An implementation of the Checkpoint abstraction in LangGraph using Postgres.

Async Usage:

from psycopg_pool import AsyncConnectionPool
from langchain_postgres import (
    PostgresCheckpoint, PickleCheckpointSerializer
)

pool = AsyncConnectionPool(
    # Example configuration
    conninfo="postgresql://user:password@localhost:5432/dbname",
    max_size=20,
)

# Uses the pickle module for serialization
# Make sure that you're only de-serializing trusted data
# (e.g., payloads that you have serialized yourself).
# Or implement a custom serializer.
checkpoint = PostgresCheckpoint(
    serializer=PickleCheckpointSerializer(),
    async_connection=pool,
)

# Use the checkpoint object to put, get, list checkpoints, etc.

Sync Usage:

from psycopg_pool import ConnectionPool
from langchain_postgres import (
    PostgresCheckpoint, PickleCheckpointSerializer
)

pool = ConnectionPool(
    # Example configuration
    conninfo="postgresql://user:password@localhost:5432/dbname",
    max_size=20,
)

# Uses the pickle module for serialization
# Make sure that you're only de-serializing trusted data
# (e.g., payloads that you have serialized yourself).
# Or implement a custom serializer.
checkpoint = PostgresCheckpoint(
    serializer=PickleCheckpointSerializer(),
    sync_connection=pool,
)

# Use the checkpoint object to put, get, list checkpoints, etc.