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  • A
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    Local RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.
    13
    256
    MIT
  • A
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    quality
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    MCP server that integrates Apache Ranger authorization with RAG, enforcing Ranger policies to control access to knowledge bases before forwarding queries to RAG Studio.
    4
    Apache 2.0
  • A
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    quality
    B
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    A local RAG MCP server that indexes project documentation and code into ChromaDB for semantic search, supporting multiple tech stacks and deployment modes.
    20
    1
    Apache 2.0
  • A
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    A fully modular RAG system with MCP protocol integration, enabling hybrid retrieval, multimodal support, and dual-mode transport for connecting to AI assistants like GitHub Copilot and Claude Desktop.
    5
    1
    MIT
  • F
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    quality
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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
  • A
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    quality
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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 ingests Spring Boot REST microservices from GitHub, builds a request-flow ontology, and enables natural-language queries about code with flow-aware answers and citations.
    MIT
  • A
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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
  • 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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    Enables RAG over messy PDFs — extract, chunk, embed, and search scanned, multi-column, and table-heavy documents.
    MIT
  • A
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    A minimal RAG service that exposes a vector index for document retrieval via REST and MCP, allowing querying for relevant document chunks and returning a suggested LLM prompt.
    MIT
  • A
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    quality
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    A headless local knowledge library and RAG substrate that enables LLM clients to search, retrieve chunks, and list documentation packs through read-only MCP tools.
    MIT