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⇱ Semantic search, RAG, and memory systems | Glama

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Semantic search, RAG, and memory systems

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  • Why this server?

    This server is a strong match as its description explicitly mentions 'semantic search' and 'ChromaDB vector storage', which are fundamental components of RAG and memory systems.

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    A Model Context Protocol server that enables intelligent document search and retrieval from PDF collections, providing semantic search capabilities powered by OpenAI embeddings and ChromaDB vector storage.
    Last updated
    13
    MIT

  • Why this server?

    This is an excellent fit as it explicitly mentions implementing a 'RAG (Retrieval-Augmented Generation) system' for querying documents with context.

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    A TypeScript MCP server that allows querying documents using LLMs with context from locally stored repositories and text files through a RAG (Retrieval-Augmented Generation) system.
    Last updated
    4
    17

  • Why this server?

    This server is highly relevant, mentioning both 'semantic search' and team 'memories', addressing two of the user's key search terms directly.

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    Enables teams to create a shared knowledge base where members can store, search, and validate information collectively. Provides semantic search across team memories with granular permissions and collaborative verification features.
    Last updated
    1

  • Why this server?

    This server explicitly covers 'RAG' capabilities with 'semantic code search' using AI 'embeddings', linking all three core themes.

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    Enables semantic search and retrieval of code files using embeddings stored in PostgreSQL. Supports intelligent codebase exploration through natural language queries, file listing, and content retrieval.
    Last updated
    16

  • Why this server?

    An excellent match as it describes a 'persistent memory system' with 'vector search' and 'semantic knowledge storage', covering memory, semantic search, and the technology (vectors) used in RAG.

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    🧠 High-performance persistent memory system for Model Context Protocol (MCP) powered by libSQL. Features vector search, semantic knowledge storage, and efficient relationship management - perfect for AI agents and knowledge graph applications.
    Last updated
    7
    MIT

  • Why this server?

    This server acts as a personal AI 'memory system' using a 'knowledge graph database' that enables 'semantic search', directly matching all concepts requested.

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    A personal AI memory system that creates a cognitive hub connecting to Notion, enabling semantic search and relationship navigation of your knowledge through a graph database for AI assistants.
    Last updated
    1
    MIT

  • Why this server?

    This server is explicitly designed around 'Retrieval-Augmented Generation (RAG)' integrated with the Model Control Protocol.

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    A server that integrates Retrieval-Augmented Generation (RAG) with the Model Control Protocol (MCP) to provide web search capabilities and document analysis for AI assistants.
    Last updated
    4
    Apache 2.0

  • Why this server?

    This server provides 'vector database' capabilities and enables 'semantic search', both of which are central to RAG and effective memory systems.

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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
    6
    41
    MIT

  • Why this server?

    This focuses on 'lightweight short-term memory' and recalling 'working context' and 'session state' for AI agents, which is essential for managing context and memory in RAG architectures.

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    A lightweight short-term memory MCP server that automatically stores and recalls working context, session state, and task progress for AI agents. Memories auto-expire after 24 hours and integrate seamlessly with workspace-aware storage across multiple projects.
    Last updated
    10
    MIT