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"Agentic RAG: Understanding or Exploring Its Meaning and Applications" matching MCP servers:

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    Enables persistent, searchable memory for Claude Code by storing session knowledge in a local PostgreSQL + pgvector database, with hybrid vector and full-text search, automatic session mining, and knowledge graph curation.
    MIT
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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.
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    A 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.
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    MIT
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    Agentic RAG Knowledge Assistant is a secure, tenant-isolated MCP server built with FastAPI, PostgreSQL, and pgvector that enables document ingestion, semantic retrieval, and vector search over PDF, DOCX, and text files through authenticated MCP tools.
    MIT
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    An intelligent Retrieval-Augmented Generation (RAG) application that uses the Model Context Protocol (MCP) to automatically decide between searching a private knowledge base or the web.
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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
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    MIT
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    A RAG-based knowledge base system supporting document processing, semantic search, and intelligent Q\&A with multiple AI model integrations.
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    A persistent, bitemporal, injection-safe memory MCP server for coding agents, offering unified retrieval across six memory layers, non-destructive belief revision, and multi-tenant namespace isolation.
    1
    MIT
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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
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    Enables retrieval-augmented generation over a local markdown corpus, allowing grounded, cited answers via an MCP tool or CLI.
    13
    MIT