https://github.com/langchain-ai/langchain-postgres

LangChain abstractions backed by Postgres Backend
https://github.com/langchain-ai/langchain-postgres

langchain langchain-python postgres postgresql

Last synced: about 13 hours ago
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LangChain abstractions backed by Postgres Backend

• Host: GitHub
• URL: https://github.com/langchain-ai/langchain-postgres
• Owner: langchain-ai
• License: mit
• Created: 2024-04-08T13:38:40.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-12-10T19:01:00.000Z (11 days ago)
• Last Synced: 2024-12-14T13:02:37.190Z (8 days ago)
• Topics: langchain, langchain-python, postgres, postgresql
• Language: Python
• Homepage:
• Size: 399 KB
• Stars: 140
• Watchers: 9
• Forks: 52
• Open Issues: 57
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Security: security.md

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README

        
# langchain-postgres

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The `langchain-postgres` package implementations of core LangChain 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.

## Requirements

The package currently only supports the [psycogp3](https://www.psycopg.org/psycopg3/) driver.

## Installation

```bash

pip install -U langchain-postgres

```

## Change Log

0.0.6: 

- Remove langgraph as a dependency as it was causing dependency conflicts.

- Base interface for checkpointer changed in langgraph, so existing implementation would've broken regardless.

## 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()`.

```python

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)

```

### Vectorstore

See example for the [PGVector vectorstore here](https://github.com/langchain-ai/langchain-postgres/blob/main/examples/vectorstore.ipynb)