README.md

metadata

title: Thoracic Radiology RAG System
emoji: 👀
colorFrom: green
colorTo: red
sdk: gradio
sdk_version: 6.1.0
app_file: app.py
pinned: false
license: mit
short_description: 'Ask questions about thoracic radiology and get answers with '

Overview

This repository contains a Hugging Face Spaces-ready RAG (Retrieval-Augmented Generation) demo for thoracic radiology Q&A.

• Default index (prebuilt): ZhangNy/radiology-index-qwen3-embedding-0.6b
• Raw public dataset: ZhangNy/radiology-dataset
• No image rendering in UI: references link to original pages where images can be viewed.

The Space uses external APIs for Embeddings / Reranker / LLM via Secrets.

Run (local)

cd LangGraphAgent/rebuild_1219
pip install -r requirements.txt

export EMBED_API_KEY="..."
export LLM_API_KEY="..."
# optional:
export RERANK_API_KEY="..."

python app.py --config config/default_config.yaml --host 0.0.0.0 --port 7860

Open http://localhost:7860.

Required Hugging Face Space Secrets

Required

• EMBED_API_KEY: embedding API key (OpenAI-compatible)
• LLM_API_KEY: LLM API key (OpenAI-compatible)

Recommended

• RERANK_API_KEY: reranker API key (OpenAI-compatible /rerank endpoint)

Optional (override defaults)

• EMBED_API_BASE_URL, EMBED_MODEL_NAME
• RERANK_API_BASE_URL, RERANK_MODEL_NAME
• LLM_BASE_URL, LLM_MODEL_NAME
• RAG_INDEX_REPO_ID (default: ZhangNy/radiology-index-qwen3-embedding-0.6b)
• RAG_STORAGE_DIR (default: /data/radiology_rag if /data exists, else ./storage)

Advanced: rebuild your own index (offline)

Install dev deps:

pip install -r requirements-dev.txt

The scripts/ folder (to be used locally) will support:

• Downloading ZhangNy/radiology-dataset to ./hf_dataset_prepared
• Building a new index with a different embedding model
• Publishing that index as a Hugging Face dataset repo

Fast path (no rebuild): publish your existing local index

If you already have a built index locally (e.g. rebuild_1217/storage contains chroma_db/ + doc_store.db), you can package it without images and upload it:

python scripts/package_existing_storage.py \
  --storage /home/zny/codes/radioagent_prepare/LangGraphAgent/rebuild_1217/storage \
  --output-dir ./index_out \
  --overwrite

python scripts/publish_index_to_hf.py \
  --repo ZhangNy/radiology-index-qwen3-embedding-0.6b \
  --folder ./index_out \
  --token $HF_TOKEN

Notes

• Do not commit API keys. This repo is configured to read them from environment variables / Space Secrets.
• Index compatibility: query-time embedding model should match the index embedding model for best retrieval quality.