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"Information about RAG (Retrieval-Augmented Generation) or rag-related topics" matching MCP servers:

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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.
    4
    Apache 2.0
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    Implements Retrieval-Augmented Generation (RAG) using GroundX and OpenAI, allowing users to ingest documents and perform semantic searches with advanced context handling through Modern Context Processing (MCP).
    5
  • A
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    A lightweight knowledge base MCP server that enables full-text search and retrieval of markdown documents from indexed sites using Orama BM25.
    7
    Apache 2.0
  • F
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    A RAG-based knowledge base system supporting document processing, semantic search, and intelligent Q\&A with multiple AI model integrations.
    1
  • F
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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.
    3
  • A
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    A Model Context Protocol (MCP) server that provides powerful RAG (Retrieval-Augmented Generation) capabilities for PDF documents. This server uses ChromaDB for vector storage, sentence-transformers for embeddings, and semantic chunking for intelligent text segmentation.
    MIT
  • A
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    An MCP server that indexes documents and serves relevant context to LLMs via Retrieval Augmented Generation (RAG).
    115
    36
    MIT
  • A
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    Enables LLMs to perform conceptual search over local PDF/EPUB documents using a RAG pipeline with corpus-driven concept extraction and WordNet enrichment.
    3
    MIT
  • A
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    Enables AI agents to index and search local files, websites, GitHub repos, and packages using hybrid retrieval with reranking, all through IDE chat.
    4
    Apache 2.0
  • A
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    A Model Context Protocol (MCP) server with Retrieval-Augmented Generation (RAG) for answering questions about imaginary SuperNova documentation. Enables semantic search over documentation using HuggingFace embeddings.
    1
    MIT
  • A
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    MCP RAG Server is a Python MCP server that indexes documents in multiple formats (Markdown, text, PowerPoint, PDF) using multilingual-e5-large embeddings and enables vector search for retrieval-augmented generation.
    MIT
  • A
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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.
    71
    MIT
  • F
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    An MCP-based multi-agent retrieval-augmented generation system that enables question answering over academic papers with hybrid search, knowledge graph multi-hop reasoning, and source-cited answers.
  • F
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    Enables semantic search and call-graph exploration over decompiled Unity Mono C# codebases, with hybrid retrieval, LLM re-ranking, and lazy-loaded project indexes.
  • F
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    Local MCP server that provides a search_documents tool to query a RAG pipeline built with Ollama embeddings and ChromaDB, enabling Claude Desktop to retrieve relevant document chunks.
  • F
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    Hybrid RAG over Claude Code and Hermes session history via MCP tools for search, ingest, and context injection.
  • F
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    Provides read-only MCP tools for hybrid semantic and keyword search over locally indexed PDF documentation, with citations and context retrieval for LLM agents.