A
license-
qualityC
maintenanceAn MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.
10
MIT
72,431 servers. Updated
Search Relevance ↓
Recent Usage ↓
Date Added ↓
Date Updated ↓
Weekly Downloads ↓
GitHub Stars ↓
Recent GitHub Stars ↓
Matching MCP tools:
Matching MCP Connectors:
io.github.sercanmetalore
io.github.aa0101181514
io.github.maxi-moss
com.ai-rete-rag
"Information about RAG (Retrieval-Augmented Generation) or rag-related topics" matching MCP servers:
- AlicenseAqualityAmaintenanceRAG Vault lets your AI coding assistant search your private documents locally. Everything runs on your machine and your data stays private.92501MIT
- AlicenseBqualityDmaintenanceA complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.11131MIT
- FlicenseCqualityDmaintenanceA server that implements Retrieval-Augmented Generation using GroundX and OpenAI, enabling semantic search and document retrieval with Modern Context Processing for enhanced context handling.3
- Alicense-qualityCmaintenanceEnables AI assistants to search and retrieve information from your knowledge base using RAG (Retrieval-Augmented Generation) with hybrid search, document indexing, and ChromaDB vector storage.115MIT
- Alicense-qualityDmaintenanceA Retrieval Augmented Generation system that enables AI assistants to perform semantic searches and manage document indices for markdown files. It supports PostgreSQL with pgvector and integrates both Google Gemini and Ollama for intelligent embedding generation.1MIT
- Alicense-qualityCmaintenanceA modular RAG server with hybrid search and LLM rerank, exposing 3 MCP tools for AI clients like Claude Desktop and Cursor.MIT
- Alicense-qualityDmaintenanceEnables retrieval-augmented generation (RAG) by indexing and searching through documents (Markdown, text, PowerPoint, PDF) using vector embeddings with multilingual-e5-large model and PostgreSQL pgvector. Supports contextual chunk retrieval and incremental indexing for efficient document management.71MIT
- Flicense-qualityDmaintenanceAn agentic AI system that orchestrates multiple specialized AI tools to perform business analytics and knowledge retrieval, allowing users to analyze data and access business information through natural language queries.4
- Flicense-qualityCmaintenanceMCP server that enables semantic search over PDF documents using RAG with ChromaDB and OpenAI embeddings.
- Flicense-qualityCmaintenanceMCP server for Fathom Works RAG that exposes a self-hosted knowledge base as tools for any MCP-capable LLM to query documents, manage libraries, and ingest files or URLs.
- Flicense-qualityCmaintenanceMCP bridge to a multimodal RAG service, enabling hybrid search and Q&A over documents with tools for knowledge base queries and health checks.
- Flicense-qualityDmaintenanceAn MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.
- Flicense-qualityDmaintenanceEnables RAG (Retrieval-Augmented Generation) capabilities with document processing, vector storage, and intelligent Q\&A using OpenAI embeddings and semantic search.
- AlicenseAqualityAmaintenanceA multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.31MIT
- AlicenseAqualityBmaintenanceEnables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.4AGPL 3.0
- Alicense-quality-maintenanceA Python server that enables retrieval-augmented generation through semantic, question/answer, and style search modalities using PostgreSQL and pgvector for embedding storage and retrieval.2Apache 2.0
- Alicense-qualityAmaintenanceMCP server for searching, reading, listing, writing, and appending Markdown wiki content using RAG with ChromaDB.MIT
- Alicense-qualityDmaintenanceA modular Retrieval-Augmented Generation (RAG) framework that provides hybrid search and knowledge retrieval capabilities via the Model Context Protocol. It enables users to integrate document-based knowledge into LLM workflows with support for dense/sparse retrieval, reranking, and observability.1MIT