README.md

metadata

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