PageIndex Hits 32K Stars — Vectorless RAG Framework Scores 98.7% on FinanceBench Without Embeddings or Chunking

GitHub·medium signal

VectifyAI's PageIndex has reached 32,418 stars with its radical approach to RAG: no vector database, no chunking, no embeddings. Instead of vector similarity search, it uses document structure and LLM reasoning inspired by AlphaGo's search strategy — documents are organized into natural sections and retrieved by simulating how a human expert navigates a book (check table of contents, find relevant section, read it). Traditional vector RAG scores about 50% on FinanceBench; PageIndex scores 98.7%. Open source under MIT license.