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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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"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
- AlicenseAqualityBmaintenanceMinimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.1MIT
- AlicenseAqualityAmaintenanceRAG Vault lets your AI coding assistant search your private documents locally. Everything runs on your machine and your data stays private.92501MIT
- AlicenseAqualityDmaintenanceProvides local Retrieval-Augmented Generation (RAG) capabilities using Ollama for embeddings and ChromaDB for vector storage. It enables users to ingest and perform semantic searches across PDF, Markdown, and TXT documents within MCP-compatible clients.4115MIT
- AlicenseBqualityDmaintenanceA 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.11131MIT
- Alicense-qualityCmaintenanceEnables 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.115MIT
- Alicense-qualityDmaintenanceA 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.1MIT
- Alicense-qualityCmaintenanceA modular RAG server with hybrid search and LLM rerank, exposing 3 MCP tools for AI clients like Claude Desktop and Cursor.MIT
- Alicense-qualityDmaintenanceA Model Context Protocol (MCP) server for Retrieval-Augmented Generation (RAG) operations. It provides tools for building and querying vector-based knowledge bases from document collections, enabling semantic search and document retrieval capabilities.3MIT
- Alicense-qualityDmaintenanceMCP RAG Server is a Python MCP server that indexes documents in multiple formats (Markdown, text, PowerPoint, PDF) using multilingual-e5-large embeddings and enables vector search for retrieval-augmented generation.MIT
- Alicense-qualityCmaintenanceAn MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.10MIT
- Alicense-qualityDmaintenanceEnables 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.71MIT
- Flicense-qualityDmaintenanceA Retrieval Augmented Generation MCP server that ingests documents into a local vector database and enables semantic search queries.10
- Flicense-qualityBmaintenanceLocal MCP server that provides a search_documents tool to query a RAG pipeline built with Ollama embeddings and ChromaDB, enabling Claude Desktop to retrieve relevant document chunks.
- Flicense-qualityCmaintenanceMCP server that enables semantic search over PDF documents using RAG with ChromaDB and OpenAI embeddings.
- Flicense-qualityBmaintenanceA CLI tool and MCP server that turns markdown documentation into a searchable, queryable knowledge base.280
- Flicense-qualityCmaintenanceMCP bridge to a multimodal RAG service, enabling hybrid search and Q&A over documents with tools for knowledge base queries and health checks.
- Flicense-qualityDmaintenanceAn MCP server that implements Retrieval-Augmented Generation to efficiently retrieve and process important information from various sources, providing accurate and contextually relevant responses.
- Flicense-qualityDmaintenanceEnables semantic search across documents and code repositories using RAG (Retrieval-Augmented Generation) with vector embeddings. Automatically indexes PDF documents and performs relevance-scored lookups through ChromaDB and sentence transformers.