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Creating a Graph on Vector Knowledge Base Using CozoDB

  • Why this server?

    This server provides a basic implementation of persistent memory using a local knowledge graph, which aligns with the user's request for graph creation.

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    A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.
    9
    9,068
    37,968
    JavaScript
    MIT License
  • Why this server?

    This server offers an improved implementation of persistent memory using a local knowledge graph, which can be beneficial for building a knowledge base.

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    An improved implementation of persistent memory using a local knowledge graph with a customizable --memory-path. This lets Claude remember information about the user across chats.
    9
    161
    91
    JavaScript
    MIT License
  • Why this server?

    This server leverages ChromaDB and sentence transformers for enhanced search and retrieval capabilities, which is essential for a vector knowledge base.

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    Provides semantic memory and persistent storage for Claude, leveraging ChromaDB and sentence transformers for enhanced search and retrieval capabilities.
    3
    167
    Python
    MIT License
    • Linux
  • Why this server?

    This server is an mcp server for vikingdb store and search. It directly relates to a knowledge base built with a database.

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    An mcp server for vikingdb store and search.
    2
    Python
    • Apple
  • Why this server?

    This server enables semantic search and document management using ChromaDB, aligning with the user's interest in a vector knowledge base.

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    Enables LLMs to perform semantic search and document management using ChromaDB, supporting natural language queries with intuitive similarity metrics for retrieval augmented generation applications.
    Python
    • Apple
  • Why this server?

    This server is an example of how to create a MCP server for Qdrant, a vector search engine, which relates to the user's goal.

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

    This server enables semantic search using a vector database (Qdrant), which is essential for building a vector knowledge base.

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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.
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    74
    JavaScript
    Apache 2.0
    • Apple
  • Why this server?

    This server provides Pinecone integration with vector search capabilities, which directly addresses the user's request for a vector knowledge base.

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    Pinecone integration with vector search capabilities
    51
    Python
    MIT License
    • Apple
  • Why this server?

    This server facilitates semantic analysis of chat conversations using vector embeddings and knowledge graphs, which can be used for building a knowledge base

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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 provides database interaction and business intelligence capabilities through SQLite, which can be used to store and query the knowledge graph.

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    A Model Context Protocol (MCP) server implementation that provides database interaction and business intelligence capabilities through SQLite. This server enables running SQL queries, analyzing business data, and automatically generating business insight memos.
    6
    37,968
    JavaScript
    MIT License