Calibre RAG MCP Server

Enhanced Calibre MCP server with RAG (Retrieval-Augmented Generation) capabilities for project-based vector search and contextual conversations.

Features

• RAG-Enhanced Search: Vector-based semantic search using FAISS and Transformers

• Project-Based Organization: Create isolated vector search projects for different contexts

• Multi-Format Support: Process books in various formats (EPUB, PDF, MOBI, etc.)

• OCR Capabilities: Extract text from images and scanned PDFs using Tesseract

• Advanced Text Processing: Natural language processing for better content understanding

• Windows Compatible: Designed specifically for Windows environments

Technologies Used

• Vector Search: FAISS for efficient similarity search

• Embeddings: Xenova Transformers for local embedding generation

• OCR: Tesseract for optical character recognition

• PDF Processing: Multiple PDF parsing libraries (pdf-parse, pdf-poppler, pdf2pic)

• Image Processing: Sharp for image manipulation

• NLP: Natural language processing with multiple libraries

Prerequisites

• Node.js >= 16.0.0

• Calibre installed on Windows

• ImageMagick (for enhanced image processing)

• Tesseract OCR (for text extraction from images)

Installation

1. Clone this repository:

2. Install dependencies:

3. Run setup (Windows):

Configuration

The server automatically detects your Calibre library location. For custom configurations, modify the settings in server.js.

Usage

Starting the Server

Available Tools

• search: Semantic search across your ebook library

• fetch: Retrieve specific content from books

• list_projects: List all RAG projects

• create_project: Create a new RAG project

• add_books_to_project: Add books to a project for vectorization

• search_project_context: Search within specific projects

Example MCP Configuration

Add to your MCP client configuration:

Project Structure

Testing

Run the test suite:

Individual test files:

• test-enhanced-server.js - Enhanced server functionality

• test-ocr-full.js - OCR capabilities

• test-pdf-approaches.js - PDF processing

• test-enhanced-auto.js - Automated testing

Documentation

• Configuration Guide

• Usage Examples

• Quick Test Guide

Requirements

System Requirements

• Windows 10/11

• Node.js 16+

• Calibre installed

• At least 4GB RAM (8GB+ recommended for large libraries)

Optional Dependencies

• ImageMagick (for enhanced image processing)

• Tesseract OCR (for text extraction from scanned documents)

Troubleshooting

Common Issues

1. FAISS Installation: If FAISS fails to install, ensure you have proper build tools

2. Tesseract Not Found: Install Tesseract and add to PATH

3. Memory Issues: Reduce batch sizes for large document processing

Debug Mode

Enable verbose logging by setting environment variable:

Contributing

1. Fork the repository

2. Create a feature branch

3. Make your changes

4. Add tests for new functionality

5. Submit a pull request

License

Licensed under the Apache License 2.0. See LICENSE file for details.

Support

For issues and questions, please open an issue on GitHub.

Changelog

v1.0.0

• Initial release with RAG capabilities

• Project-based vector search

• Multi-format document support

• OCR integration

• Windows optimization