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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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"Tools for Automatically Indexing Code Files and RAG (Retrieval-Augmented Generation)" 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.Last updated13236MIT
- 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 updated4371MIT
- 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 updated1171MIT
- 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-qualityDmaintenanceA local vector database system that provides LLM coding agents with fast, efficient semantic search capabilities for software projects via the Message Control Protocol.Last updated7MIT
- 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 updated37MIT
- 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-qualityBmaintenanceMinimal RAG-over-a-corpus MCP retrieval: search_knowledge returns cited chunks. Local embeddings.Last updatedMIT
- Alicense-qualityDmaintenanceA very simple vector store that provides capability to watch a list of directories, and automatically index all the markdown, html and text files in the directory to a vector store to enhance context.Last updated2342MIT
- Alicense-qualityDmaintenance一个基于FastMCP协议的文件向量化服务，支持自动目录监控、多格式文件处理和高效向量搜索，帮助构建个人或团队知识库。Last updatedApache 2.0
- 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-qualityFmaintenanceEnables semantic search and retrieval-augmented generation (RAG) using Qdrant vector database. Supports indexing documents from URLs and local directories, with flexible embedding options using Ollama or OpenAI.Last updated2
- 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
- Flicense-quality-maintenanceA local Retrieval-Augmented Generation system that enables users to ingest markdown files into a FAISS-powered vector knowledge base for semantic search. It provides tools for document indexing and context retrieval to support informed LLM queries without external dependencies.Last updated