Rust Local RAG
Provides a high-performance, private Retrieval-Augmented Generation (RAG) server for searching PDF documents locally within Claude Desktop using Ollama embeddings.
About
Rust Local RAG is a powerful, privacy-first Retrieval-Augmented Generation (RAG) system engineered in Rust, designed to seamlessly integrate with Claude Desktop via the Model Context Protocol (MCP). It enables users to privately search and analyze their PDF documents directly within Claude conversations. By processing PDFs locally using poppler for text extraction, generating embeddings with local Ollama models, and utilizing a custom vector store, this system ensures no external API calls are made for document content, providing a secure, high-performance solution for personalized document intelligence.
Key Features
- Privacy-First Local Processing (no external API calls)
- Automatic PDF Text Extraction and Chunking
- High-Performance Rust Implementation
- Semantic Document Search with Ollama Embeddings
- 3 GitHub stars
- Seamless Claude Desktop Integration via MCP
Use Cases
- Extend AI assistant capabilities to access and analyze local document repositories without sending data to external services.
- Develop custom, high-performance RAG solutions integrated with AI models using the Model Context Protocol for specialized data sources.
- Conduct private, semantic searches of personal or sensitive PDF documents directly within Claude Desktop conversations.
