Vectorless, reasoning-based code index cho AI context retrieval. Instead of dumping your entire codebase into a prompt (50k+ tokens), codeindex builds a hierarchical tree index and uses LLM reasoning to find the exact context you need
AlphaGo-inspired MCTS for document retrieval. No vectors, no embeddings, no chunking — just reasoning. Upload PDFs, ask questions, get answers with citations.
A retrieval engine that reasons over document structure — not embeddings. No chunking, no top-K, no vector DB.
Code-aware search and navigation engine, powered by vectorless.
A vector-less RAG works fully in local NO internet needed, with webUI
Vectorless RAG via hierarchical tree indexing — Go reimplementation of PageIndex with zero external deps
A vectorless RAG pipeline that navigates PDF documents using a PageIndex tree structure and Gemini 2.0 Flash — no vector database, just LLM-guided tree search with auto-cited answers.
Deliver precise document retrieval using Monte Carlo Tree Search for PDF and folder-based question answering without vectors or embeddings.
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