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maintenanceEnables retrieval-augmented generation by embedding queries with a chosen provider (e.g., OpenAI) and searching supported vector stores (Pinecone, pgvector) to return relevant content.
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Apache 2.0
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"Information about RAG (Retrieval-Augmented Generation) or rag-related topics" matching MCP servers:
- AlicenseAqualityAmaintenanceLocal RAG system for Claude Code with hybrid search (semantic + BM25), cross-encoder reranking, markdown-aware chunking, and 12 MCP tools. Zero external servers, pure ONNX in-process.13252MIT
- AlicenseAqualityCmaintenanceEnables semantic search and question answering over a knowledge base using hybrid retrieval and grounded answers, all running offline with no API keys.4MIT
- FlicenseBqualityDmaintenanceA RAG-based knowledge base system supporting document processing, semantic search, and intelligent Q\&A with multiple AI model integrations.1
- FlicenseCqualityDmaintenanceA server that implements Retrieval-Augmented Generation using GroundX and OpenAI, enabling semantic search and document retrieval with Modern Context Processing for enhanced context handling.3
- AlicenseAqualityAmaintenanceA multi-agent Retrieval-Augmented Generation system exposed as an MCP server. Ask a question and a LangGraph pipeline plans the retrieval, pulls evidence from a pgvector knowledge base, optionally augments it with live web research, drafts a cited answer, and then self-critiques it for grounding — revising until the answer is supported by the sources.31MIT
- AlicenseAqualityCmaintenanceMCP server that integrates Apache Ranger authorization with RAG, enforcing Ranger policies to control access to knowledge bases before forwarding queries to RAG Studio.4Apache 2.0
- FlicenseAqualityDmaintenanceEnables indexing local documents (PDF, Markdown, text, code) into a knowledge base and querying them via semantic search using local embeddings, all running privately on your machine.4
- AlicenseAqualityBmaintenanceEnables 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.4AGPL 3.0
- AlicenseAqualityCmaintenanceOffline RAG MCP server that indexes documents into SQLite with hybrid vector and lexical search, and enables natural language queries via Claude.4MIT
- AlicenseAqualityAmaintenance"primitive" RAG-like web search model context protocol server that runs locally. ✨ no APIs ✨5133PythonMIT
- FlicenseCqualityCmaintenanceProvides RAG tools with local vector retrieval and web fallback using Firecrawl, enabling document ingestion and querying through MCP stdio transport.3
- AlicenseAqualityCmaintenanceMCP server that integrates with LM Studio to provide a search_notes tool, allowing the chat model to retrieve and answer from a local Obsidian vault.1MIT
- FlicenseAqualityDmaintenanceProvides semantic vector search over local codebases via MCP, enabling hybrid search (dense + sparse + RRF) for any MCP client like GitHub Copilot or Claude Desktop.58
- AlicenseAqualityBmaintenanceProvides governed retrieval over MCP with hybrid search, strict confidence gating, and access control, exposing three read-only tools.3Apache 2.0
- AlicenseAqualityBmaintenanceA local RAG MCP server that indexes project documentation and code into ChromaDB for semantic search, supporting multiple tech stacks and deployment modes.201Apache 2.0
- AlicenseAqualityDmaintenanceA Model Context Protocol (MCP) server that provides a local-first RAG engine for your markdown documents. It uses a file-based Milvus vector database to index your notes, enabling LLMs to perform semantic search and retrieve relevant content from your local files.355Apache 2.0
- FlicenseAqualityBmaintenanceA local-first document retrieval engine that mounts as an MCP tool for agents to index files, search for relevant passages, and let the agent's own LLM answer.4
- FlicenseAqualityCmaintenanceSearches local markdown files using TF-IDF retrieval and serves full documents as resources.1
- FlicenseAqualityAmaintenanceMCP server for semantic search in an Obsidian Second Brain vault using self-hosted Qdrant and Google Gemini embeddings.3