Augment AI responses with relevant context by retrieving and processing documentation through vector search.
Ragdoc provides an MCP server implementation designed to enhance AI assistants by augmenting their responses with context from relevant documentation. Utilizing vector-based search, it retrieves and processes information from multiple documentation sources. Key tools include documentation search, source listing, URL extraction, and queue management for processing and indexing documentation, making it ideal for building documentation-aware AI assistants and implementing semantic documentation search.
Key Features
01Vector-based documentation search and retrieval
02Semantic search capabilities
03Automated documentation processing
040 GitHub stars
05Supports multiple documentation sources
06Real-time context augmentation for LLMs
Use Cases
01Building documentation-aware AI assistants
02Enhancing AI responses with relevant documentation
