Which integrations are available for this server?

Offers integration with GitHub for Model Context Protocol compatibility, linking to the modelcontextprotocol repository. Provides integration with LangChain, allowing agents to automatically discover and use tools through the MCP server for document processing and RAG capabilities. Offers Milvus as a production-grade vector database option for enterprise deployments, supporting advanced retrieval capabilities. Integrates with OpenAI API (or compatible endpoints) to power the language model capabilities needed for document processing and question answering. Provides a Streamlit-based frontend interface that connects with the MCP server, enabling user interaction with the RAG system.

How do I use MCP-RAG?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@MCP-RAG process this 150MB PDF report and find all mentions of quarterly revenue" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

📚 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: RAG Memory MCP

🏗️ 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