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Understanding Vector Search

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  • Why this server?

    Enables semantic search, image search, and cross-modal search functionalities through integration with Jina AI's neural search capabilities.

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    Enables semantic search, image search, and cross-modal search functionalities through integration with Jina AI's neural search capabilities.
    Last updated
    5
    MIT
  • Why this server?

    An MCP server that enables AI models to retrieve information from Ragie's knowledge base through a simple 'retrieve' tool, implying RAG capabilities which depend on a vector database.

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    An MCP server that enables AI models to retrieve information from Ragie's knowledge base through a simple 'retrieve' tool.
    Last updated
    1
    4,238
    86
    MIT
  • Why this server?

    A Model Context Protocol server providing vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage.

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    A Model Context Protocol server providing vector database capabilities through Chroma, enabling semantic document search, metadata filtering, and document management with persistent storage.
    Last updated
    6
    41
    MIT
  • Why this server?

    Aims to be portable, local, easy, and convenient to support semantic/graph-based retrieval of txtai "all in one" embeddings database, supporting vector search.

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    An MCP server aimed to be portable, local, easy and convenient to support semantic/graph based retrieval of txtai "all in one" embeddings database. Any txtai embeddings db in tar.gz form can be loaded
    Last updated
    69
    MIT
  • Why this server?

    Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes, a form of vector retrieval.

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    Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes.
    Last updated
    211
    398
    MIT
  • Why this server?

    A Model Context Protocol server that enables semantic search and retrieval of Apple Notes content, allowing AI assistants to access, search, and create notes using on-device embeddings.

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    A Model Context Protocol server that enables semantic search and retrieval of Apple Notes content, allowing AI assistants to access, search, and create notes using on-device embeddings.
    Last updated
    211
    2
    MIT
  • Why this server?

    A Python server that enables AI assistants to perform hybrid search queries against Apache Solr indexes through the Model Context Protocol, combining keyword precision with vector-based semantic understanding.

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    A Python server that enables AI assistants to perform hybrid search queries against Apache Solr indexes through the Model Context Protocol, combining keyword precision with vector-based semantic understanding.
    Last updated
    20
    MIT
  • Why this server?

    Model Context Protocol (MCP) server implementation for semantic search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memories with semantic search capabilities.

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    Model Context Protocol (MCP) server implementation for semantic search and memory management using TxtAI. This server provides a robust API for storing, retrieving, and managing text-based memories with semantic search capabilities. You can use Claude and Cline AI Also
    Last updated
    14
  • Why this server?

    This project is intended as an example of how to create a MCP server for Qdrant, a vector search engine.