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"Exploring RAG through defined workspaces" matching MCP servers:

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    Enables ingestion and semantic search over text documents using PostgreSQL + pgvector and OpenAI-compatible embeddings, allowing any LLM agent to retrieve relevant chunks for grounded answers.
    4
    AGPL 3.0
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    Intelligent knowledge base system that enables users to process documents in 25+ formats, perform semantic search and Q\&A through vector retrieval. Supports multiple AI models including OpenAI and DouBao with local processing capabilities.
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    A complete MCP server for Retrieval-Augmented Generation with file management and vector memory for agents. Supports multiple document formats (PDF, DOCX, TXT, MD, CSV, JSON) with semantic search using Hugging Face embeddings and ChromaDB for efficient vector storage.
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    MIT
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    MCP server that integrates a 1200-paper RAG database with six tools to support research workflows across stages like hypothesis, experiment, statistics, and writing. It routes requests to specialized skills and real-time frontier searches to provide evidence-grounded research mentoring.
    6
    Apache 2.0
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    Enables AI assistants to search and retrieve information from your knowledge base using RAG (Retrieval-Augmented Generation) with hybrid search, document indexing, and ChromaDB vector storage.
    115
    MIT
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    A Retrieval Augmented Generation system that enables AI assistants to perform semantic searches and manage document indices for markdown files. It supports PostgreSQL with pgvector and integrates both Google Gemini and Ollama for intelligent embedding generation.
    1
    MIT
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    An MCP knowledge server that enables saving and retrieving memory across sessions, with tools to ingest text, URLs, YouTube, and files, and perform semantic search.
    1
    MIT
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    An MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.
    10
    MIT
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    Enables retrieval-augmented generation (RAG) by indexing and searching through documents (Markdown, text, PowerPoint, PDF) using vector embeddings with multilingual-e5-large model and PostgreSQL pgvector. Supports contextual chunk retrieval and incremental indexing for efficient document management.
    71
    MIT
  • F
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    A local RAG knowledge base MCP server that exposes semantic document search as tools using zvec for vector storage and Qwen3-Embedding for text embedding.
  • F
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    MCP server exposing a RAG knowledge base as read-only tools (search_knowledge_base, ask_knowledge_base, kb_stats, kb_diagnostic) for AI clients like Claude Desktop and Cursor, enabling token-efficient document retrieval and Q&A.
  • F
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    An agentic AI system that orchestrates multiple specialized AI tools to perform business analytics and knowledge retrieval, allowing users to analyze data and access business information through natural language queries.
    4
  • F
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    A production-minded RAG service for MCP that answers questions over your documents with hybrid retrieval, PII redaction, and source citations, packaged for Docker/Kubernetes.
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    MCP server for Fathom Works RAG that exposes a self-hosted knowledge base as tools for any MCP-capable LLM to query documents, manage libraries, and ingest files or URLs.