| --- |
| title: Amazon Analytics Chatbot |
| emoji: 📦 |
| colorFrom: blue |
| colorTo: green |
| sdk: docker |
| app_port: 8501 |
| pinned: false |
| license: mit |
| short_description: RAG + SQL chatbot for Amazon seller analytics (demo data) |
| --- |
| |
| # 📦 Amazon Analytics Chatbot |
|
|
| A Streamlit chatbot (Docker-backed) that answers questions about Amazon seller |
| analytics using **SQL templates** for quantitative queries and **FAISS semantic |
| search (RAG)** for qualitative ones. |
|
|
| ## 🏗️ Architecture |
|
|
| - **SQL Engine** — SQLite + SQLAlchemy, template-based query generation |
| - **RAG** — `sentence-transformers/all-MiniLM-L6-v2` embeddings + FAISS index |
| - **LLM** — Hugging Face Inference API (default: `Qwen/Qwen2.5-7B-Instruct`) |
| - **UI** — Streamlit with a custom dark theme, served via Docker |
|
|
| ## 📁 Files |
|
|
| ``` |
| Dockerfile ← Build & run instructions |
| requirements.txt ← Python deps |
| src/ |
| ├── streamlit_app.py ← Streamlit UI (entry point) |
| ├── rag_core.py ← RAG + SQL engine |
| ├── company_data.db ← SQLite database (demo data) |
| ├── rag.index ← FAISS vector index |
| └── rag_chunks.parquet← Chunk metadata |
| ``` |
|
|
| ## 🔑 Secrets |
|
|
| In **Settings → Variables and secrets → New secret**, add: |
|
|
| - `HF_TOKEN` — your Hugging Face access token (read scope) |
|
|
| Optional: |
|
|
| - `HF_MODEL` — override default model, e.g. |
| `meta-llama/Llama-3.2-3B-Instruct` |
|
|
| ## 💡 Example Questions |
|
|
| - "Total revenue in 2023 Q1" |
| - "Monthly sessions trend last 30 days" |
| - "Top search terms by spend" |
| - "2024 H1 B2B revenue by state" |
|
|
| > The included database contains **demo / synthetic data** only. |
|
|
| ## 📝 License |
|
|
| MIT |
|
|
