This project implements an enhanced RAG methodology that generates multiple query variants via LLM, performs parallel vector searches, fuses results using Reciprocal Rank Fusion, and applies cross-encoder reranking. It supports hybrid search combining BM25 and dense vector retrieval, integrates with ChromaDB and OpenAI, and includes a full evaluation harness using NFCorpus and BEIR benchmarks with statistical confidence intervals.