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  • Simple Files Vector Store

    lishenxydlgzs
    A
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    quality
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    maintenance
    A very simple vector store that provides capability to watch a list of directories, and automatically index all the markdown, html and text files in the directory to a vector store to enhance context.
    15
    42
    MIT

  • Local RAG

    shinpr
    A
    license
    A
    quality
    A
    maintenance
    Privacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.
    9
    3,271
    365
    MIT

  • MCP RAG with ChromaDB

    CyprianFusi
    A
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    quality
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    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

  • mcp-rag-local

    renl
    A
    license
    B
    quality
    C
    maintenance
    Enables storing and retrieving text passages based on semantic meaning using local embeddings (Ollama) and vector storage (ChromaDB), allowing conversational memorization and retrieval of information.
    5
    18
    MIT

  • mem0-local-mcp

    sanchezvivi
    A
    license
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    quality
    C
    maintenance
    A fully local, self-hosted memory server for MCP clients (Claude Code, Cursor, etc.) that provides persistent memory storage with semantic search, using local embeddings and a local Qdrant vector store.
    MIT

  • ChromaDB Remote MCP Server

    meloncafe
    A
    license
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    quality
    A
    maintenance
    A Streamable HTTP MCP server that provides remote access to ChromaDB for AI assistants like Claude. Enables semantic search and vector database operations from mobile devices and remote locations.
    12
    MIT

  • local-RAG-backend

    suwa-sh
    A
    license
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    quality
    A
    maintenance
    A Docker-based local RAG backend that provides advanced document search capabilities using vector, graph, and full-text retrieval via the Model Context Protocol. It supports over 28 file formats and tracks evolving relationships between concepts using a Neo4j-backed graphiti implementation.
    1
    MIT

  • MCP PDF to PGVector

    sanskritiparmendranaval
    F
    license
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    quality
    C
    maintenance
    MCP server that ingests PDF documents into pgvector for semantic search and RAG pipelines. It handles extraction, chunking, local embeddings, and storage, enabling agents to make PDFs searchable via natural language.

  • local-rag-mcp-server

    n-irei
    F
    license
    -
    quality
    D
    maintenance
    A fully offline local RAG server that utilizes ChromaDB and Ollama to index and query PDF, text, and Markdown documents. It allows users to manage local knowledge bases and perform semantic searches with AI-generated responses.

  • Tiny Chat

    to-aoki
    A
    license
    D
    quality
    C
    maintenance
    A lightweight RAG system that provides an MCP server for searching and interacting with vector-based knowledge bases. It enables users to perform retrieval-augmented generation and search across Qdrant collections through a standardized interface.
    1
    2
    MIT

  • local-rag-mcp

    Orax0001
    A
    license
    -
    quality
    C
    maintenance
    A privacy-preserving local RAG system integrated with MCP, enabling natural language queries over ingested documents and a SQLite database through vector search and local database tools.
    MIT