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⇱ JSON File Upload, Data Vectorization, and LLM Similarity Search | Glama


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JSON File Upload, Data Vectorization, and LLM Similarity Search

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  • Why this server?

    This server is designed for advanced retrieval using vectorization, enabling similarity searches based on uploaded data. It also supports file extraction and text chunking, which are useful for handling JSON data.

  • Why this server?

    This server can watch directories, index files (including JSON), and store them in a vector store. This facilitates similarity searches based on file content.

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    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.
    Last updated
    8
    42
    MIT
  • Why this server?

    This server enables efficient vector database operations, allowing for the storage of embeddings from uploaded JSON data and subsequent similarity searching.

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    Enables efficient vector database operations for embedding storage and similarity search through a Model Context Protocol interface.
    Last updated
    7
  • Why this server?

    Enables AI assistants to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB.

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    A Model Context Protocol (MCP) server that enables LLMs to interact directly the documents that they have on-disk through agentic RAG and hybrid search in LanceDB. Ask LLMs questions about the dataset as a whole or about specific documents.
    Last updated
    21
    79
    MIT
  • Why this server?

    Convex lets you introspect tables, call functions, and read/write data seamlessly. Agents can generate one-off queries safely—thanks to Convex’s sandboxed queries, ensuring data integrity.

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    Convex’s MCP server lets you introspect tables, call functions, and read/write data seamlessly. Agents can generate one-off queries safely—thanks to Convex’s sandboxed queries, ensuring data integrity. Perfect for AI automation, real-time apps, and dynamic data access.
    Last updated
    11,959
  • Why this server?

    Enables graph database interactions with Neo4j, allowing users to access and manipulate graph data through natural language commands.

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    An MCP server that enables graph database interactions with Neo4j, allowing users to access and manipulate graph data through natural language commands.
    Last updated
  • Why this server?

    Enables LLMs to interact directly with MongoDB databases, allowing users to query collections, inspect schemas, and manage data through natural language.

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    A Model Context Protocol server that enables LLMs to interact directly with MongoDB databases, allowing users to query collections, inspect schemas, and manage data through natural language.
    Last updated
    67
    2
    MIT
  • Why this server?

    Provides seamless access to multiple storage services including S3, Azure Blob Storage, and Google Cloud Storage through Apache OpenDAL™.

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    A Model Context Protocol server that provides seamless access to multiple storage services including S3, Azure Blob Storage, and Google Cloud Storage through Apache OpenDAL™.
    Last updated
    3
    34
    Apache 2.0
  • 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