LangChain MariaDB

LangChain integration for MariaDB, providing vector stores, chat message history, and natural language query capabilities.

The langchain-mariadb package provides seamless integration between LangChain and MariaDB, enabling advanced AI and machine learning workflows with persistent storage.

Features

• Vector Store - Store and search embeddings using MariaDB's vector capabilities

• Chat Message History - Persistent conversation storage for chatbots and AI assistants

• Expression Filters - Advanced metadata filtering for vector search

• Natural Language Queries - Translate natural language to SQL queries

Installation

pip install langchain-mariadb

Quick Start

Vector Store Example

from langchain_mariadb import MariaDBStore
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
vectorstore = MariaDBStore(
    embeddings=embeddings,
    datasource="mariadb://user:password@localhost/database",
    collection_name="my_documents"
)

# Add documents
vectorstore.add_texts(["Hello world", "LangChain with MariaDB"])

# Search
results = vectorstore.similarity_search("greeting", k=1)

Chat Message History Example

from langchain_mariadb import MariaDBChatMessageHistory

history = MariaDBChatMessageHistory(
    session_id="user_123",
    connection_string="mariadb://user:password@localhost/database"
)

history.add_user_message("Hello!")
history.add_ai_message("Hi! How can I help you?")

Documentation

For complete documentation, guides, and tutorials, visit the official LangChain documentation:

• LangChain MariaDB Documentation

This section contains only the API reference:

• API Reference - Complete API documentation

Resources

• GitHub Repository

• PyPI Package

• LangChain Documentation

Version

Current version: v0.0.20

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