iflow-mcp_anuragb7-mcp-rag 0.1.1

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.

📚 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.

🌟 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

🏗️ 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