Why this server?
Provides tools for retrieving and processing documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context
Why this server?
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 AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently.
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?
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. Uses Ollama or OpenAI to generate embeddings.
Why this server?
An open source platform for Retrieval-Augmented Generation (RAG). Upload documents and query them