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Notebook Library MCP Server

by clotho2

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    • F
      license
      Not graded
      quality
      D
      maintenance
      Enables semantic search across documents and code repositories using RAG (Retrieval-Augmented Generation) with vector embeddings. Automatically indexes PDF documents and performs relevance-scored lookups through ChromaDB and sentence transformers.
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    • F
      license
      Not graded
      quality
      D
      maintenance
      Enables semantic search over local notes and documents using natural language queries. Supports multiple file types (Markdown, Python, HTML, JSON, CSV, text) with fast local embeddings and persistent ChromaDB vector storage.
      1
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    • A
      license
      A
      quality
      C
      maintenance
      Enables local semantic search over PDF, DOCX, PPTX, and EPUB documents by converting them to Markdown, indexing them in ChromaDB, and retrieving complete structure-aware sections with tables and equations.
      6
      MIT
    • F
      license
      Not graded
      quality
      D
      maintenance
      Enables AI assistants to search and query PDF documents through a local RAG system with vector embeddings. Provides semantic document search capabilities while keeping all data stored locally without external dependencies.
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    • A
      license
      Not graded
      quality
      D
      maintenance
      Provides retrieval-augmented generation (RAG) capabilities by ingesting various document formats into a persistent ChromaDB vector store. It enables semantic search and retrieval using either OpenAI or Ollama embeddings for processing local files, directories, and URLs.
      1
      MIT
    • F
      license
      A
      quality
      D
      maintenance
      Enables indexing local documents (PDF, Markdown, text, code) into a knowledge base and querying them via semantic search using local embeddings, all running privately on your machine.
      4
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