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Understanding Document Embeddings, Knowledge Graphs, and Vector Representations

  • Why this server?

    This server allows interaction with Neo4j graph databases, which aligns directly with the 'graph' and 'knowledge' terms in the query.

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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.
    Python
  • Why this server?

    This server allows LLMs to programmatically interact with Logseq knowledge graphs, aligning with the 'graph' and 'knowledge' terms in the query.

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    A server that enables LLMs to programmatically interact with Logseq knowledge graphs, allowing creation and management of pages and blocks.
    8
    Python
    MIT License
  • Why this server?

    This server is relevant because it provides a knowledge graph implementation using DuckDB, aligning with the 'graph' and 'knowledge' terms.

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    A memory server for Claude that stores and retrieves knowledge graph data in DuckDB, enhancing performance and query capabilities for conversations with persistent user information.
    8
    47
    21
    TypeScript
    MIT License
    • Apple
  • Why this server?

    This server allows retrieving content from different knowledge bases using semantic search, matching the 'knowledge' part of the request.

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    Provides tools for listing and retrieving content from different knowledge bases using semantic search capabilities.
    2
    6
    TypeScript
    The Unlicense
    • Linux
    • Apple
  • Why this server?

    Facilitates knowledge graph representation and uses vector embeddings for semantic similarity, aligning with 'graph', 'knowledge', and 'vector' terms.

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    Facilitates knowledge graph representation with semantic search using Qdrant, supporting OpenAI embeddings for semantic similarity and robust HTTPS integration with file-based graph persistence.
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    4
    TypeScript
    • Linux
    • Apple
  • Why this server?

    This server is relevant due to its 'vector' related capabilities along with general document processing features.

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    Vectorize MCP server for advanced retrieval, Private Deep Research, Anything-to-Markdown file extraction and text chunking.
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    17
    JavaScript
    MIT License
    • Apple
    • Linux
  • Why this server?

    The description mentions integration with Weaviate, which is a vector search engine.

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    The MCP Server for Weaviate facilitates integration with Weaviate using a customizable Python-based server, enabling interaction with Weaviate databases and OpenAI APIs via configurable URL and API keys.
    44
    Python
    GPL 3.0
    • Apple
  • Why this server?

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

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    Enables semantic search, image search, and cross-modal search functionalities through integration with Jina AI's neural search capabilities.
    1
    JavaScript
    MIT License
  • Why this server?

    Facilitates semantic analysis of chat conversations through vector embeddings and knowledge graphs, covering 'vector' and 'knowledge' aspects.

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    Facilitates semantic analysis of chat conversations through vector embeddings and knowledge graphs, offering tools for semantic search, concept extraction, and conversation pattern analysis.
    8
    Python
    MIT License
  • Why this server?

    This server's description mentions managing markdown documents and metadata handling capabilities aligns with search terms doc and knowledge

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    A Model Context Protocol implementation that enables AI assistants to interact with markdown documentation files, providing capabilities for document management, metadata handling, search, and documentation health analysis.
    14
    346
    11
    TypeScript
    MIT License
    • Apple
    • Linux