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"Understanding RAG (Retrieval-Augmented Generation or related topics)" matching MCP servers:

  • Retrieval-Augmented Thinking

    stat-guy
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    Enhances AI model capabilities with structured, retrieval-augmented thinking processes that enable dynamic thought chains, parallel exploration paths, and recursive refinement cycles for improved reasoning.
    Last updated
    23
    MIT

  • MCP RAG Server

    LuizDoPc
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    Provides local Retrieval-Augmented Generation (RAG) capabilities using Ollama for embeddings and ChromaDB for vector storage. It enables users to ingest and perform semantic searches across PDF, Markdown, and TXT documents within MCP-compatible clients.
    Last updated
    4
    36
    1
    MIT

  • MCP RAG

    santis84
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    A 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.
    Last updated
    11
    11
    1
    MIT

  • MCP-RAG Server

    apatoliya
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    A server that implements Retrieval-Augmented Generation using GroundX and OpenAI, enabling semantic search and document retrieval with Modern Context Processing for enhanced context handling.
    Last updated
    3

  • RAG MCP Server

    ali-48
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    Combines a knowledge graph with RAG (Retrieval-Augmented Generation) capabilities for semantic code indexing and search. Enables creating entity relationships, managing observations, and performing semantic searches across indexed codebases.
    Last updated
    13

  • RAG-MCP

    Luxter77
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    A Python server that enables retrieval-augmented generation through semantic, question/answer, and style search modalities using PostgreSQL and pgvector for embedding storage and retrieval.
    Last updated
    2
    Apache 2.0

  • MCP RAG Server

    karaage0703
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    Enables 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.
    Last updated
    70
    MIT

  • MCP-RAG

    AnuragB7
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    An MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.
    Last updated
    9
    MIT

  • Markdown RAG

    ashrobertsdragon
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    A 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.
    Last updated
    1
    MIT

  • RAG MCP Server

    NSANTRA
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    Enables Claude to perform retrieval-augmented generation using LangChain, ChromaDB, and HuggingFace models for domain-aware reasoning with PDF embedding, smart retrieval, reranking, and citation-based responses.
    Last updated
    2
    MIT

  • RAG-MCP Server

    plaban1981
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    A server that integrates Retrieval-Augmented Generation (RAG) with the Model Control Protocol (MCP) to provide web search capabilities and document analysis for AI assistants.
    Last updated
    4
    Apache 2.0

  • RAG Anything MCP Server

    jesse-merhi
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    An MCP server that provides comprehensive multimodal Retrieval-Augmented Generation (RAG) capabilities for processing and querying document directories, supporting text, images, tables, and equations.
    Last updated
    27
    MIT

  • MCP RAG with ChromaDB

    CyprianFusi
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    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.
    Last updated
    MIT