📚 MCP-RAG

MCP-RAG system built with the Model Context Protocol (MCP) that handles large files (up to 200MB) using intelligent chunking strategies, multi-format document support, and enterprise-grade reliability.

Python 3.11+ License: MIT MCP

🌟 Features

📄 Multi-Format Document Support

PDF: Intelligent page-by-page processing with table detection
DOCX: Paragraph and table extraction with formatting preservation
Excel: Sheet-aware processing with column context (.xlsx/.xls)
CSV: Smart row batching with header preservation
PPTX: Support for PPTX
IMAGE: Suppport for jpeg , png , webp , gif etc and OCR

🚀 Large File Processing

Adaptive chunking: Different strategies based on file size
Memory management: Streaming processing for 50MB+ files
Progress tracking: Real-time progress indicators
Timeout handling: Graceful handling of long-running operations

🧠 Advanced RAG Capabilities

Semantic search: Vector similarity with confidence scores
Cross-document queries: Search across multiple documents simultaneously
Source attribution: Citations with similarity scores
Hybrid retrieval: Combine semantic and keyword search

🔌 Model Context Protocol (MCP) Integration

Universal tool interface: Standardized AI-to-tool communication
Auto-discovery: LangChain agents automatically find and use tools
Secure communication: Built-in permission controls
Extensible architecture: Easy to add new document processors

🏢 Enterprise Ready

Custom LLM endpoints: Support for any OpenAI-compatible API
Vector database options: ChromaDB (local) + Milvus (production)
Batch processing: Handles API rate limits and batch size constraints
Error recovery: Retry logic and graceful degradation

Related MCP server: MCP Excel Reader

🏗️ Architecture

┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ Streamlit │ │ LangChain │ │ MCP Server │ │ Frontend │◄──►│ Agent │◄──►│ (Tools) │ └─────────────────┘ └──────────────────┘ └─────────────────┘ │ ┌────────────────────────┼────────────────────────┐ │ ▼ │ ┌───────▼────────┐ ┌─────────────────┐ ┌──────▼──────┐ │ Document │ │ Vector Database │ │ LLM API │ │ Processors │ │ (ChromaDB) │ │ Endpoint │ └────────────────┘ └─────────────────┘ └─────────────┘

🚀 Quick Start

Prerequisites

Python 3.11+
OpenAI API key or compatible LLM endpoint
8GB+ RAM (for large file processing)

Installation

Clone the repository

git clone https://github.com/yourusername/rag-large-file-processor.git cd rag-large-file-processor python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -r requirements.txt # Create .env file cat > .env << EOF OPENAI_API_KEY=your_openai_api_key_here BASE_URL=https://api.openai.com/v1 MODEL_NAME=gpt-4o VECTOR_DB_TYPE=chromadb streamlit run streamlit_app.py