mujahid609/Implementation-and-Evaluation-of-a-Basic-Retrieval--Augmented-Generation-RAG-System-in-Medical-field
A Google Colab-based AI medical chatbot using RAG to answer questions from 13 medical documents. It combines BM25 + semantic search with a RoBERTa QA model to generate accurate answers, confidence scores, and source citations in a Gradio web app — all running free on Google Colab GPU with no setup required.
SUMMARY AI summary by gpt-5-mini
このリポジトリは、医療分野向けのRetrieval Augmented Generation（RAG）システムの実用パッケージです。目的は医療知識ベースから根拠付きの回答を生成することで、研究者や開発者、データサイエンティスト、臨床教育用途のユーザが試作・カスタマイズして使えます。主な構成はColabで動く修正済みノートブック（Medical_RAG_System_Fixed.ipynb）、詳細なUSAGE_GUIDE・QUICK_REFERENCE・SUMMARY、JSONエラーを防ぐnotebook_generator.pyです。検索はBM25と意味検索（Sentence Transformers）のハイブリッド、生成はRoBERTaベースのQAモデルを用い、出力は回答・信頼度・参照文献を返します。GradioベースのWeb UIを備え、初回はモデルダウンロードで15–20分、通常の応答は5–10秒程度。資料の追加やUI・モデルの切替などカスタマイズが容易で、トラブルシューティングと実行手順も同梱されています。
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👋 Hi, I'm Mujahid Hussain – a Data Analyst & Aspiring Business Analyst with a passion for turning data into meaningful insights. 🔍 Skilled in Python, Panda
Dates
| Created on GitHub | 2026-05-09 |
| Last push | 2026-05-09 |
| First seen here | 2026-05-09 |
| Last fetched | 2026-05-09 14:22 |
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