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?
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?
Vectorize MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.
Why this server?
Basic Memory is a knowledge management system that allows you to build a persistent semantic graph from conversations with AI assistants. All knowledge is stored in standard Markdown files on your computer, giving you full control and ownership of your data. Integrates directly with Obsidan.md
Why this server?
A Python-based local indexing server that creates semantic search capabilities for codebases using ChromaDB, allowing Cursor IDE to perform vector searches on your code without sending data to external services.
Why this server?
An open-source platform for Retrieval-Augmented Generation (RAG). Upload documents and query them ⚡
