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| title: VisualRAG | |
| emoji: π | |
| colorFrom: purple | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 4.40.0 | |
| python_version: "3.10" | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # π VisualRAG β Multi-Modal AI System | |
| A production-grade **Retrieval-Augmented Generation (RAG)** system combining computer vision and natural language understanding. | |
| ## π§ Pipeline | |
| **Index:** `Image β YOLOv8 detection β CLIP ViT-B/32 embedding β FAISS vector store` | |
| **Query:** `Text β CLIP text embedding β cosine k-NN β Zephyr-7B answer generation` | |
| ## π Stack | |
| | Component | Technology | | |
| |---|---| | |
| | Object detection | YOLOv8n (Ultralytics) | | |
| | Visual embedding | CLIP ViT-B/32 (OpenAI) | | |
| | Vector index | FAISS IndexFlatIP | | |
| | LLM | Zephyr-7B-Ξ² (HF Serverless API) | | |
| | UI | Gradio 4.40.0 | | |
| ## π How to use | |
| 1. **Detect & Index** β upload images; YOLOv8 detects objects, CLIP stores 512-d embeddings in FAISS | |
| 2. **Query (RAG)** β ask a question; CLIP retrieves relevant images, Zephyr-7B answers | |
| 3. **How it works** β full architecture overview | |
| ## π Optional: HF token | |
| Settings β Variables and secrets β New secret β Name: `HF_TOKEN` |