Enables AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently.
Enables semantic search over local Markdown documentation using hybrid retrieval combining embeddings, keyword search, and graph traversal with automatic file watching and zero-configuration setup.
Provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings, allowing users to add, search, list, and delete documentation with metadata support.
Enables context-aware semantic search across codebases using Qdrant vector database with intelligent GitHub issue resolution, Projects V2 management, and progressive context retrieval for 95%+ token reduction in AI-assisted development.
Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.
Provides a comprehensive Model Context Protocol interface for RAGFlow, enabling AI models to perform semantic retrieval, manage datasets, and handle document chunks. It supports advanced features like GraphRAG and RAPTOR for sophisticated knowledge base management and natural language querying.
Google PageRank for AI agents — live search across 25,000+ scored MCP servers and tools.
AgentRank gives your AI a live, ranked index of 25,000+ MCP servers and agent tools, scored daily from real GitHub signals (stars, freshness, issue health, contributors, dependents). Your AI's training data is months old — it can't tell you if a tool was abandoned last week or that something better shipped y
An open-source MCP server for RAG over personal documents. Supports three parallel strategies — Traditional, Contextual, and Graph RAG — with all data stored locally for privacy.