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A vector database for efficient similarity search and AI applications

  • Why this server?

    This server provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings.

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    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.
    Last updated 4 months ago
    5
    4
    TypeScript
    Apache 2.0
  • Why this server?

    This server enables AI agents to perform Retrieval-Augmented Generation by querying a FAISS vector database containing Sui Move language documents.

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    A Machine Control Protocol (MCP) server that enables storing and retrieving information from a Qdrant vector database with semantic search capabilities.
    Last updated a month ago
    • Linux
    • Apple
  • Why this server?

    This server provides 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 4 months ago
    17
    Python
    MIT License
    • Apple
    • Linux
  • Why this server?

    This server provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.

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    A server that provides data retrieval capabilities powered by Chroma embedding database, enabling AI models to create collections over generated data and user inputs, and retrieve that data using vector search, full text search, and metadata filtering.
    Last updated 19 days ago
    71
    Python
    Apache 2.0
  • Why this server?

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

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    This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
    Last updated 16 days ago
    2
    448
    Python
    Apache 2.0
  • Why this server?

    A Model Context Protocol (MCP) server providing semantic search and memory mining server based on PubTator3, providing convenient access through the MCP interface.

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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 8 days ago
    4
    Python
    • Apple
  • Why this server?

    This server enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes.

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    Enables semantic search and RAG (Retrieval Augmented Generation) over your Apple Notes.
    Last updated 4 months ago
    158
    TypeScript
    • Apple
  • Why this server?

    An MCP server that provides tools to retrieve and process documentation through vector search, enabling AI assistants to augment their responses with relevant documentation context

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    A Model Context Protocol (MCP) server that enables semantic search and retrieval of documentation using a vector database (Qdrant). This server allows you to add documentation from URLs or local files and then search through them using natural language queries.
    Last updated 2 months ago
    14
    74
    JavaScript
    Apache 2.0
    • Apple
  • Why this server?

    A Node.js implementation for vector search using LanceDB and Ollama's embedding model.

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    A Node.js implementation for vector search using LanceDB and Ollama's embedding model.
    Last updated a month ago
    JavaScript
  • Why this server?

    Enables semantic search across multiple Qdrant vector database collections, supporting multi-query capability and providing semantically relevant document retrieval with configurable result counts.

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    Enables semantic search across multiple Qdrant vector database collections, supporting multi-query capability and providing semantically relevant document retrieval with configurable result counts.
    Last updated a month ago
    46
    TypeScript
    MIT License