ZeroNet-RAG - Offline Fail-Closed Agentic RAG

ZeroNet-RAG is a production-ready, highly secure, and fully offline agentic Retrieval-Augmented Generation (RAG) framework custom-engineered for the AMD AI League Match 3 challenge. Operating under strict air-gapped constraints (--network none), the framework completely eliminates external API dependencies and cloud network calls during runtime. At its core, ZeroNet-RAG utilizes a robust dual-engine search mechanism combining deterministic sparse feature hashing with BM25 lexical ranking to ensure ultra-fast corpus indexing and sub-second query retrieval. The multi-format document parsing pipeline seamlessly ingests PDFs, DOCX, spreadsheets, text logs, and images via optical character recognition (OCR), featuring resilient error-handling that gracefully handles edge cases. Powered by a state-based LangGraph orchestration workflow (incorporating Planner, Retriever, Extractive Generator, and Guardrail nodes), the system enforces a strict cite-or-refuse policy to eliminate hallucinations. If direct supporting evidence is absent from the local corpus, it securely returns a structured empty response complying with the required JSON output contract (answer and citations). Fully containerized using an optimized lightweight Python environment, ZeroNet-RAG guarantees deterministic execution, strict data privacy, and robust performance under rigorous latency limits.