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securityA
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qualityAccess any documentation indexed by RagRabbit Open Source AI site search
Last updated -
3
33
TypeScript
MIT License
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Why this server?
Access any documentation indexed by RagRabbit Open Source AI site search
Why this server?
An MCP server implementation that provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context
Why this server?
Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes.
Why this server?
A Model Context Protocol (MCP) server that enables semantic search and retrieval of documentation using a vector database (Qdrant). This server allows you to add documentation from URLs or local files and then search through them using natural language queries.
Why this server?
Provides curated documentation access via the Gemini API, enabling users to query and interact with technical docs effectively by overcoming context and search limitations.
Why this server?
A very simple vector store that provides capability to watch a list of directories, and automatically index all the markdown, html and text files in the directory to a vector store to enhance context.
Why this server?
Semantic memory layer that integrates LLMs with OpenSearch, enabling storage and retrieval of memories within the OpenSearch engine.
Why this server?
Persistent memory and RAG context provider for enhanced code understanding and management through vector embeddings, integrated with RooCode and Cline.
Why this server?
Helps refine AI-generated content to sound more natural and human-like. Built with advanced AI detection and text enhancement capabilities.
Why this server?
Transform your non-existent or unreadable docs into an intelligent, searchable knowledge base that actually answers those 'basic questions' before they're asked.