Overview

Introduction to MariaDB AI RAG

MariaDB AI RAG is an enterprise-grade Retrieval-Augmented Generation (RAG) solution that integrates with MariaDB to provide AI-powered document processing, semantic search, and natural language generation capabilities.

The system enables organizations to leverage their document repositories and databases for AI-powered search and generation. By combining the reliability of MariaDB with modern AI capabilities, AI RAG provides accurate, context-aware responses based on your organization's proprietary data.

System Architecture

MariaDB AI RAG follows a modular architecture with the following key components:

Core Components

1. Document Ingestion Module

   • Handles document uploads and text extraction

   • Supports multiple file formats (PDF, DOCX, TXT, etc.)

   • Processes metadata extraction

   • Manages document storage and versioning

2. Chunking Module

   • Divides documents into semantically meaningful segments

   • Supports multiple chunking strategies (fixed size, recursive, semantic)

   • Optimizes chunk size and overlap for retrieval quality

   • Preserves document structure and metadata

3. Vector Database Integration

   • Creates and stores vector embeddings for document chunks

   • Integrates with MariaDB's vector search capabilities

   • Enables efficient similarity search

   • Manages vector indexes and optimizations

4. Retrieval Module

   • Converts queries into vector embeddings

   • Performs semantic similarity search

   • Ranks and filters results based on relevance

   • Supports metadata-based filtering

5. Generation Module

   • Integrates with multiple LLM providers (OpenAI, Google, Anthropic)

   • Constructs prompts with retrieved context

   • Generates natural language responses

   • Supports streaming output for real-time display

6. Orchestration Module

   • Coordinates the end-to-end RAG pipeline

   • Manages asynchronous processing

   • Handles error recovery and retries

   • Provides status monitoring and reporting

7. API Interface

   • Exposes RESTful endpoints for all functionality

   • Implements JWT-based authentication

   • Manages user permissions and access control

   • Provides comprehensive error handling and logging

Architecture Diagram

Data Flow

1. Document Processing Flow

   • Documents are uploaded via the API

   • Text is extracted based on document format

   • Documents are divided into chunks

   • Chunks are converted to vector embeddings

   • Embeddings are stored in the vector database

2. Query Processing Flow

   • User submits a query via the API

   • Query is converted to a vector embedding

   • Similar document chunks are retrieved

   • Retrieved chunks are ranked by relevance

   • Top chunks are used as context for generation

   • LLM generates a response based on the context

3. Database Integration Flow

   • Database table/view is selected for ingestion

   • Rows are processed as individual documents

   • Column mapping defines content and metadata

   • Text is chunked and embedded

   • Embeddings are stored with source metadata

System Requirements

• Database: MariaDB 10.6+ or MySQL 8.0+

• Operating System: Linux (Debian/Ubuntu, RHEL/CentOS), Windows Server

• Hardware: Minimum 4 CPU cores, 8GB RAM, 100GB storage

• Network: Internet connectivity for external AI services

• Dependencies: Python 3.9+, FastAPI, SQLAlchemy, PyTorch (optional for local embeddings)

For detailed installation instructions, see the Installation Guide.

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