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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)" matching MCP servers:
- 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.Last updated
- AlicenseAqualityBmaintenanceMinimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.Last updated1MIT
- AlicenseAqualityDmaintenanceEnables creation and querying of knowledge bases using Google's Gemini API File Search feature, allowing AI applications to upload documents and retrieve information through RAG (Retrieval-Augmented Generation).Last updated3146MIT
- 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.Last updated4341MIT
- 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.Last updated11121MIT
- 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.Last updated3
- 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.Last updated34MIT
- 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.Last updated1MIT
- 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.Last updated3MIT
- 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.Last updatedMIT
- Alicense-qualityDmaintenanceAn MCP-compatible system that handles large files (up to 200MB) with intelligent chunking and multi-format document support for advanced retrieval-augmented generation.Last updated10MIT
- 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.Last updated71MIT
- Flicense-qualityDmaintenanceA Retrieval Augmented Generation MCP server that ingests documents into a local vector database and enables semantic search queries.Last updated9
- Flicense-qualityDmaintenanceProvides tools for ingesting documents into a local vector database and retrieving relevant information via semantic search, enabling retrieval-augmented generation for MCP clients.Last updated5
- 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.Last updated
- Flicense-qualityDmaintenanceEnables RAG (Retrieval-Augmented Generation) capabilities with document processing, vector storage, and intelligent Q\&A using OpenAI embeddings and semantic search.Last updated
- 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.Last updated13243MIT
- 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.Last updated31MIT
- 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.Last updated4AGPL 3.0