Vectorless RAG Meets Agent Memory: Running Hindsight + PageIndex Fully Local

Most RAG systems work the same way: chunk documents, embed them into vectors, run similarity search, and surface the closest match. It works — until it doesn't. Similarity is not relevance. On complex professional documents, that gap shows up quickly. A Different Retrieval Model PageIndex from VectifyAI skips chunking and embedding entirely. It builds a hierarchical tree index from the document structure — effectively an auto-generated table of contents — then uses LLM reasoning to navigate that structure. No vector database. No chunking pipeline. Reported accuracy: 98.7% on FinanceBench. Memory, Not Just Retrieval Hindsight by Vectorize.io handles long-term agent memory. It organises memory into three types: World facts Experiences Mental models ...accessed through a retain → recall → reflect API. It leads the LongMemEval benchmark for agent memory accuracy. The Problem Both systems are capable — but both depend on external APIs. I wanted the same functionality running fully local, of