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dymk

AskDocs MCP Server

by dymk

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    • 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
      Enables intelligent search and question-answering over PDF documents using semantic similarity and keyword search. Supports OCR for scanned PDFs, persistent vector storage with ChromaDB, and maintains source tracking with page numbers.
      6
      MIT
    • 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 and retrieval-augmented generation (RAG) using Qdrant vector database. Supports indexing documents from URLs and local directories, with flexible embedding options using Ollama or OpenAI.
      2
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    • A
      license
      Not graded
      quality
      Not graded
      maintenance
      Provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings, allowing users to add, search, list, and delete documentation with metadata support.
      16 npm
      16
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