Ragdoc
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Augment AI responses with relevant context by retrieving and processing documentation through vector search.
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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.
Características Principales
- Vector-based documentation search and retrieval
- Semantic search capabilities
- Automated documentation processing
- 0 GitHub stars
- Supports multiple documentation sources
- Real-time context augmentation for LLMs
Casos de Uso
- Building documentation-aware AI assistants
- Enhancing AI responses with relevant documentation
- Implementing semantic documentation search
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