A RAG Proof of Concept that delivers comprehensive, context-aware insights on healthcare data privacy through a novel knowledge tree.
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Updated
May 31, 2025 - Python
A RAG Proof of Concept that delivers comprehensive, context-aware insights on healthcare data privacy through a novel knowledge tree.
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.
🌳 Open-source RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval - Complete open-source implementation with 100% local LLMs (Granite Code 8B + mxbai-embed-large)
Cancer-RAPTOR : GPU-accelerated hierarchical search system for cancer medical information
Visual RAPTOR ColBERT Integration System - Multimodal document retrieval with SigLIP, PyMuPDF, and evaluation metrics.
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。
Transform liner LLM outputs into interactive, explorable knowledge trees and search
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.
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