Share personal context from documents, notes, and conversations across various AI environments and applications.
Sponsored
Personal RAG provides a powerful Model Context Protocol (MCP) server designed to centralize and share your personal knowledge base, complete with Retrieval-Augmented Generation (RAG) capabilities. It allows users to seamlessly integrate their private documents, notes, and conversations across diverse AI platforms like Claude Desktop, Claude Code, VS Code, and Open WebUI. By enabling the storage, semantic search, and contextual querying of personal data, it empowers AI models with rich, personalized information to generate more relevant and accurate responses.
Características Principales
01Dual Transport: stdio (for Claude Desktop/VS Code) + HTTP Streaming (for Open WebUI)
02Containerized: Runs in Docker, connects to existing Qdrant/Ollama/LiteLLM infrastructure
030 GitHub stars
04Rich Metadata: Comprehensive metadata capture for future extensibility
05Hybrid Storage: SQLite for full-text documents + Qdrant for semantic search
06Forward-Compatible: Strategy pattern allows adding advanced RAG features without refactoring
Casos de Uso
01Ingest and organize personal notes, documents, and snippets into a centralized knowledge base.
02Conduct semantic searches to retrieve relevant information from stored personal context.
03Ask questions to AI models that leverage your private data for retrieval-augmented generation.
