This project implements a lightweight GraphRAG system for document question answering based on local LLMs. It supports multiple LLM providers including OpenAI, DeepSeek, and Ollama, along with various embedding models like text2vec and sentence-transformers. The system optimizes RAG accuracy through Chinese-aware text chunking, BM25 keyword matching combined with vector similarity search, and a reranker module to refine retrieved results.