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    Enables semantic search and contextual conversations with your Calibre ebook library using vector-based RAG technology. Supports project-based organization, multi-format book processing, and OCR capabilities for enhanced content extraction and retrieval.
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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.
    3
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    MIT
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    Integrates R2R (Retrieval-Augmented Generation) with Claude Desktop, enabling semantic search across knowledge bases and RAG-based question answering with support for vector, graph, web, and document search.
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    Provides advanced document search and processing capabilities through vector stores, including PDF processing, semantic search, web search integration, and file operations. Enables users to create searchable document collections and retrieve relevant information using natural language queries.
    MIT
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    Enables private, offline semantic search across local files (documents, images, videos) using OCR and vector search, and optionally performs web research with cited sources.
    AGPL 3.0
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    A database-agnostic MCP server that integrates web crawling with Retrieval Augmented Generation, supporting multiple AI providers and vector database backends for flexible and intelligent content retrieval.
    1
    MIT
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    Open-source MCP servers for ESG data extraction, analysis, and regulation management, providing 31 tools across 6 servers for tasks like metrics extraction, PDF processing, vector storage, and web scraping.
    MIT
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    Enables AI-powered semantic search and question-answering for LiveKit documentation using Pinecone vector search and real-time web search with Tavily, providing detailed responses with source attribution.
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    A fully-local, three-layer memory plugin for Claude Code with Chinese support, offering hybrid FTS5/trigram and HNSW vector search, self-iterating memory management, consolidation, and a web console.
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    A visual Retrieval-Augmented Generation MCP server that renders web pages, PDFs, and design screenshots into visual tiles and enables natural language querying over them via multimodal vision embeddings.
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    Provides a per-project memory layer with plain notes, semantic vector recall, and a knowledge graph, all hosted on Cloudflare's free tier. It includes a web dashboard for browsing projects and deleting them.
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    Enables hybrid search (dense + sparse) over self-hosted indexes of continuously ingested public datasets (news, GitHub, Wikipedia, arXiv, small web, devdocs, Hacker News) using your own embedding model, served via the Model Context Protocol.
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    Enables AI assistants to crawl websites, extract and store web content with semantic search capabilities using vector embeddings, and retrieve information through natural language queries with tag-based filtering and intelligent content cleaning.