Pdf-Rag
Enables semantic search capabilities for PDF documents through a web interface or via the Model Context Protocol (MCP) for AI tool integration.
Acerca de
This system provides a powerful document knowledge base by leveraging PDF processing, vector storage, and the MCP protocol. It allows users to upload, process, and query PDF documents through a modern web interface or integrate with AI tools like Cursor via the MCP protocol, enabling efficient semantic search across processed documents.
Características Principales
- Include a React/Chakra UI frontend for document management and querying.
- 2 GitHub stars
- Offer MCP protocol support for integration with AI tools like Cursor.
- Enable vector-based semantic search across all processed documents.
- Upload and process PDF documents, extracting, chunking, and vectorizing content automatically.
- Provide WebSocket-based real-time status updates during document processing.
Casos de Uso
- Providing a searchable archive of PDF documents for research and analysis.
- Integrating a PDF knowledge base with Cursor for enhanced code generation and understanding.
- Creating a document Q&A system that responds to queries based on PDF content.
Sponsored