🏆 MariaDB AI Toolkit: The Unified Python Bridge for AI/RAG

The MariaDB AI Toolkit is a production-ready Python package that creates a seamless developer experience for building modern, context-aware AI applications. Submitted in the Integration Track, this toolkit directly exposes MariaDB's native capabilities (VECTOR, JSON) to the Python AI ecosystem, establishing MariaDB as a unified data platform for RAG.

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✨ Features: Deeply Integrated RAG Components

This toolkit provides three core modules that automate the entire AI-data workflow:

1. Schema-Aware Ingestor

• Automates Ingestion: Handles structured data (CSV, DB tables) into MariaDB for vector search.
• MariaDB Table Creation: Automatically creates tables with VECTOR and JSON columns, and sets up a high-performance VECTOR INDEX (M=10 DISTANCE=COSINE).
• Column Mapping: Specify which columns are used for HuggingFace embeddings and which are stored as JSON metadata.

2. HybridVectorStore (LangChain Integration)

• LangChain-Compatible: A custom vector store built for the LangChain framework.
• True Hybrid Search: Combines semantic similarity (VEC_DISTANCE_COSINE) with structured JSON filtering (JSON_VALUE) in a single, efficient query.
• Efficient Retrieval: Leverages MariaDB's unified data platform for scalable, precise search.

3. ChatHistoryManager

• Persistent Chat Memory: Manages conversational state for AI applications using MariaDB's flexible JSON type.
• Simple API: Provides easy add_message and get_history functions for any session ID.
• Fast and Flexible: Ideal for scalable conversational AI and state management.

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🛠️ Quickstart: Zero-Friction Setup

The included demonstration uses the Open Flights Dataset (or a derivative) to ensure the code's focus is on the features, not complex data setup.

1. Requirements: Python 3.8+ and MariaDB Server (11.8+) with VECTOR and JSON support.
2. Install Dependencies:

   pip install -r requirements.txt

3. Configure: Update the DB_CONNECTION_DETAILS in run_and_demo.py with your credentials.
4. Run the Complete Demo:

   python run_and_demo.py

Demonstration Flow

• Ingestor Demo: Loads sample OpenFlights data, automatically creating the vector index.
• Hybrid Search Demo: Performs a query ranked by similarity and filtered by structured metadata.
• Chat Manager Demo: Simulates a chat session, persisting history to MariaDB.

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📚 Project Structure & Documentation

mariadb-ai-toolkit/
├── mariadb_ai_toolkit/
│   ├── ingestor.py
│   ├── vectorstore.py
│   ├── chathistory.py
│   └── __init__.py
├── docs/
│   ├── ingestor.md
│   ├── vectorstore.md
│   └── chathistory.md
├── run_and_demo.py
├── requirements.txt
├── README.md
└── routes_demo.csv