RAG
0
Provides a Retrieval-Augmented Generation (RAG) server for efficient document ingestion, vector storage, and AI-powered question answering.
关于
RAG is a robust server leveraging the Model Context Protocol (MCP) to enable advanced Retrieval-Augmented Generation capabilities. It streamlines the process of ingesting diverse document types (PDF, DOCX, TXT), converting them into searchable embeddings, and storing them in a Qdrant vector database. Integrated with Google Gemini for both embeddings and text generation, RAG allows AI assistants to retrieve relevant information from your custom knowledge base and generate accurate, context-aware answers to user queries, all built on an efficient async architecture.
主要功能
- 0 GitHub stars
- Multi-format document processing (PDF, DOCX, TXT)
- High-performance async/await architecture
- Full Model Context Protocol (MCP) compliance
- Google Gemini for AI embeddings and text generation
- Qdrant vector database for efficient similarity search
使用案例
- Building custom knowledge bases for AI-powered question answering
- Retrieving context-aware information from unstructured documents
- Integrating RAG capabilities into AI assistants (e.g., Claude Desktop)
Sponsored