Advanced Agentic RAG System with LangGraph

Github: https://www.twine.net/signin
● Built an adaptive RAG system that auto-selects retrieval strategies, self-corrects, and detects hallucinations—achieving significant retrieval accuracy gains using only budget models (GPT-4o-mini).
● Agentic architecture: 7-node StateGraph with distributed routing; self-correction loops for retrieval (query rewrite, early strategy switch) and generation (HHEM hallucination verification + LLM-as-judge quality check).
● Retrieval & reranking: semantic/keyword/hybrid with two-stage rerank (CrossEncoder→top-10 → LLM-as-judge→top-4) and RRF multi-query fusion; research-backed patterns (CRAG, PreQRAG, RAG-Fusion, vRAG-Eval).
● Evaluation Framework: 4-tier architecture comparison (Basic → Multi-Agent) with curated golden datasets; comprehensive metrics (F1@K, MRR, nDCG) measuring retrieval gains from architectural improvements.