# RAG-Powered Brand Product Search Engine

This is the v2.0 upgrade of the Indian FMCG product catalog project: the
same discovery/enrichment pipeline (Ollama + pgvector + S3 image
storage), now with a **Retrieval-Augmented Generation (RAG) layer** and a
**React UI** in place of the old Streamlit app.

Two independent pieces:

- **`backend/`** - FastAPI service. Browse/search/chat endpoints over a
  pgvector-backed catalog; a RAG chat endpoint that retrieves relevant
  products and asks a local Ollama model to answer grounded in them.
- **`frontend/`** - React + Vite single-page app: product search/browse
  grid, a conversational "Ask AI" panel with source citations, and an
  admin page to ingest new brands.

See **`docs/`** for the full architecture write-up, setup guide, and API
reference. The two component READMEs (`backend/README.md`,
`frontend/README.md`) are quick local references once you've read the
main docs once.

> **v3.0 update:** this project now also includes a **Multi-Store
> Intelligence** layer - 5 simulated stores with independent pricing
> and inventory, ML-based dynamic discounts, trending-product
> detection, a hybrid recommendation engine, and a full analytics
> dashboard. See **`docs/CHANGES.docx`** for the complete write-up
> (architecture decisions, database schema, API reference, and setup
> guide), or jump straight to `backend/scripts/seed_store_intelligence.py`
> and `backend/scripts/train_ml_models.py` to try it.

> **v3.1 update:** added an **AI Nutritional Intelligence** module -
> verified nutrition facts retrieved from Open Food Facts (never LLM-
> generated), transparent/configurable health & nutrition scoring,
> allergen and diet-compatibility detection, ML-based nutritional
> similarity and clustering, healthier-alternative suggestions,
> personalized nutrition recommendations, and a nutrition analytics
> dashboard. See **`docs/NUTRITION_MODULE.docx`** for the complete
> write-up, or jump straight to `backend/scripts/enrich_nutrition.py`
> and `backend/scripts/train_nutrition_models.py` to try it.

## Fastest Path to Execute Project (Instant 1-Command Startup)

The project now includes a **unified single-command launcher** and **automated background initialization**. You no longer need to manually run multiple seed scripts or manage separate terminals:

```bash
# Single command from project root (starts Docker, Backend, and Frontend):
python run_project.py

# Or on Windows, double-click:
start_app.bat
```

The system automatically:
1. Starts the PostgreSQL container via Docker Desktop.
2. Checks database status and skips redundant seeding for instant boot (< 2 seconds).
3. Launches Backend API (`http://localhost:8000`) and Frontend UI (`http://localhost:5173`) concurrently.
4. Auto-seeds missing catalogs or store intelligence in the background if the database is empty.

### Individual Commands (Optional / Advanced)
- Backend: `start_backend.bat` or `python -m uvicorn app.main:app --reload --port 8000`
- Frontend: `start_frontend.bat` or `npm run dev`
- System Status API: `http://localhost:8000/api/system/status`

## Why this exists (what changed from v1.0)

The original project (`Project_LLM`) could discover, enrich, and store
products with embeddings in pgvector - but nothing ever read those
embeddings back out. There was no similarity-search function, no API
layer (`app/api/` was an empty folder), and the UI was a Streamlit app
that only ever queried the catalog by exact brand/category, never by
meaning. This project adds the missing retrieval layer
(`vector_store.semantic_search`), a RAG orchestration service
(`rag_service.py`), a full FastAPI surface, and a React UI to use it.

It also fixes a real security issue: `app/infrastructure/settings.py`
used to hard-code a live database host/port/password as Python literal
fallbacks. Every credential now comes only from environment
variables/`.env` - see `backend/app/infrastructure/settings.py` for
details, and `docs/` for the full write-up.