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.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
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 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.
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
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
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
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
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 RAG
┌─────────────────┐ ┌──────────────────┐ ┌─────────────────┐ │ Streamlit │ │ LangChain │ │ MCP Server │ │ Frontend │◄──►│ Agent │◄──►│ (Tools) │ └─────────────────┘ └──────────────────┘ └─────────────────┘ │ ┌────────────────────────┼────────────────────────┐ │ ▼ │ ┌───────▼────────┐ ┌─────────────────┐ ┌──────▼──────┐ │ Document │ │ Vector Database │ │ LLM API │ │ Processors │ │ (ChromaDB) │ │ Endpoint │ └────────────────┘ └─────────────────┘ └─────────────┘
Python 3.11+
OpenAI API key or compatible LLM endpoint
8GB+ RAM (for large file processing)
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.pyThis server cannot be deployed
The CustomGPT.ai MCP server is a fully managed, RAG-powered endpoint that connects large language models with private knowledge bases and external data sources. It provides tools for retrieval-augmented generation queries (send_message), data ingestion (upload_file), and source listing, enabling AI agents to query private documents like PDFs with high accuracy and real-time citations.
OCR, transcription, file extraction, and image generation for AI agents via MCP.
An MCP memory server. One memory your agents share — across models, devices and apps.
The Needle MCP server enables semantic search on documents stored in files like PDFs, DOCX, and XLSX by connecting AI applications to external data sources. It provides capabilities to create and manage document collections, perform natural language searches on stored content, and retrieve relevant information without requiring exact keyword matches.
