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Modular RAG MCP Server

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      A 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.
      1
      MIT
    • A
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      Enables advanced RAG with knowledge graphs, supporting document ingestion, multimodal extraction, and multiple query modes (naive, local, global, hybrid) via the Model Context Protocol.
      6
      MIT
    • A
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      A pluggable RAG framework that exposes hybrid search, ingestion, and evaluation tools via the Model Context Protocol, enabling AI assistants like Copilot and Claude to query knowledge bases directly.
      MIT
    • A
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      A pluggable and observable modular RAG framework that enables AI assistants to perform semantic search, document Q\&A, and knowledge base retrieval. It supports hybrid search, reranking, and multiple LLM backends through a standardized Model Context Protocol interface.
      8
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      A modular RAG (Retrieval-Augmented Generation) service framework with pluggable architecture and full observability, enabling AI assistants to perform document Q\&A, semantic search, and knowledge base construction through the Model Context Protocol.
      MIT
    • A
      license
      Not graded
      quality
      D
      maintenance
      Exposes RAG-related tools (query_knowledge_hub, list_collections, get_document_summary) via the Model Context Protocol, enabling AI assistants to perform hybrid search, document ingestion, and evaluation on knowledge bases.
      MIT