hey Pinecone community! sharing something we just open-sourced that takes a different approach to RAG entirely. NanoIndex skips the vector DB and embedding step. it indexes documents as trees, extracts entity relations, and navigates structure at query time instead of doing nearest-neighbor search over chunks. for financial and legal documents especially, this matters a lot. the answer to a real question is usually spread across multiple sections. chunk retrieval finds one. tree navigation finds all of them. 95% on FinanceBench. 96% on DocBench Legal. would be curious to hear from people here who've hit the limits of vector retrieval on structured documents. demo: https://nanoindex.nanonets.com/demo github: https://github.com/NanoNets/nanoindex