A RAG Proof of Concept that delivers comprehensive, context-aware insights on healthcare data privacy through a novel knowledge tree.

Cancer-RAPTOR : GPU-accelerated hierarchical search system for cancer medical information

🌳 Open-source RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval - Complete open-source implementation with 100% local LLMs (Granite Code 8B + mxbai-embed-large)

Visual RAPTOR ColBERT Integration System - Multimodal document retrieval with SigLIP, PyMuPDF, and evaluation metrics.

An advanced RAG (Retrieval-Augmented Generation) system using RAPTOR algorithm to hierarchically organize and retrieve lessons from the 2011 Great East Japan Earthquake and Tsunami for educational purposes.

Transform liner LLM outputs into interactive, explorable knowledge trees and search

Multimodal RAPTOR for Disaster Documents using ColVBERT & BLIP. Hierarchical retrieval system over 46 tsunami-related PDFs (2378 pages), combining BLIP-based image captioning, ColVBERT embeddings, and GPT-OSS-20b long-context summarization. Optimized for fast multimodal tree construction and disaster knowledge preservation.

Treg免疫細胞系譜を例に、RAPTORアルゴリズムを実装したGPU加速対応のRAG（Retrieval-Augmented Generation）システムです。実際に、5ノードから14ノードを実現しました。