"Understanding Document Embeddings, Knowledge Graphs, and Vector Representations" matching MCP servers:

• MCP Qdrant Codebase Embeddings

  steiner385

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  A Model Context Protocol server that provides semantic understanding of codebases using Qdrant vector database, enabling AI assistants to search files by purpose, discover relationships between files, analyze architecture, and identify refactoring opportunities.

  Last updated 9 months ago

• Vector Memory MCP

  Xsaven

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  A secure vector-based memory server that provides persistent semantic memory for AI assistants using sqlite-vec and sentence-transformers. It enables semantic search and organization of coding experiences, solutions, and knowledge with features like auto-cleanup and deduplication.

  Last updated a month ago

  MIT

• knowledge-mcp

  olafgeibig

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  An MCP server that enables AI agents to query specialized, domain-specific knowledge bases built using the LightRAG framework for enhanced retrieval-augmented generation. It allows for managing and searching knowledge graphs and vector embeddings to provide accurate, context-aware information during an AI assistant's reasoning process.

  Last updated 2 months ago

  39

  MIT

• Vector Memory MCP Server

  aeriondyseti

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  Provides local vector-based semantic memory storage for AI assistants to persist context and decisions across sessions using local embeddings and LanceDB. It enables private semantic search and session handoff capabilities to maintain long-term project context.

  Last updated 4 days ago

  1,271

  4

  MIT

• Docs Vector MCP

  MeteorGeminy

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  Enables AI agents to semantically search GitHub repository documentation by automatically fetching, vectorizing, and indexing content into an Upstash Vector database. It provides a standard MCP interface for agents to retrieve relevant documentation snippets through natural language queries.

  Last updated a day ago

• MCP Document Indexer

  yairwein

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  Enables real-time indexing and semantic search of local documents (PDF, Word, text, Markdown, RTF) using vector embeddings and local LLMs. Monitors folders for changes and provides natural language search capabilities through Claude Desktop integration.

  Last updated 8 months ago

  15

  MIT