Ragdocs
Enables semantic search and retrieval of documentation using a vector database, allowing users to add and query documentation from URLs or local files using natural language.
About
Ragdocs is a Model Context Protocol (MCP) server designed to facilitate semantic search and retrieval of documentation. By leveraging a vector database (Qdrant), it allows users to ingest documentation from various sources, including URLs and local files. Once the documentation is stored, users can perform natural language queries to efficiently search and retrieve relevant information. This tool streamlines the process of accessing and understanding extensive documentation, making it a valuable asset for developers, researchers, and anyone working with large volumes of information.
Key Features
- Supports natural language queries for documentation search.
- 78 GitHub stars
- Adds documentation from URLs or local files.
- Lists all available documentation sources.
- Stores documentation in a vector database (Qdrant) for semantic search.
- Integrates with Cline, Roo-Code, and Claude Desktop via Model Context Protocol (MCP).
Use Cases
- Enable context-aware search within software documentation.
- Integrate documentation search capabilities into AI assistants and development environments.
- Quickly find specific information within a large codebase documentation.