Hyper-RAG is a hypergraph-driven Retrieval-Augmented Generation (RAG) system designed to reduce LLM hallucinations by modeling beyond-pairwise correlations in domain knowledge. The repository provides both a Python library (`hyperrag`) and a full-stack web application with a FastAPI backend and React frontend. The core innovation is the use of hypergraphs to represent complex relationships among entities, outperforming traditional graph-based RAG methods like Graph RAG and Light RAG. The system supports multiple query modes: hyper (full hypergraph), hyper-lite, graph, naive, and direct LLM, with streaming responses. Architecture: The Python library (`hyperrag/`) implements the RAG pipeline: text chunking, entity extraction via LLM, hypergraph construction, vector embedding storage (NanoVectorDB), and query execution. The web UI (`web-ui/`) consists of a FastAPI backend (`main.py`) exposing REST endpoints for file upload, database CRUD, and HyperRAG queries, plus a React frontend with Ant Design and Graphin for interactive chat, hypergraph visualization, and settings management. Docker Compose orchestrates the frontend, backend, and Nginx reverse proxy. Key entry points: `hyperrag/hyperrag.py` (HyperRAG class), `service_api.py` (standalone API), `web-ui/backend/main.py` (FastAPI app). External dependencies include OpenAI/Azure/Bedrock LLM APIs, NanoVectorDB, tenacity, aiofiles, and various Python packages. The system is configurable via `config_temp.py` and `settings.json`. Data flows: Raw text (uploaded or provided) is chunked, entities and relationships are extracted via LLM, hyperedges are constructed, and embeddings are stored. Queries are processed by selecting relevant hyperedges and entities, constructing a context, and generating a response. The system caches LLM responses and supports real-time logging via WebSocket during embedding.