# Kirana AI — 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 — including **`docs/SKU_RESOLVER.md`** for the Product
Resolver library (`POST /api/resolve`), which accepts any BigBasket
product ID, Amazon ASIN, or Internal SKU, resolves it against the
database or the matching external source, and stores it for next time.
The two component READMEs (`backend/README.md`,
`frontend/README.md`) are quick local references once you've read the
main docs once.

## Fastest path to a working demo

```bash
# 1. Backend
cd backend
python3 -m venv .venv && source .venv/bin/activate   # Windows: .venv\Scripts\activate
pip install -r requirements.txt
cp .env.example .env                                  # edit DB_PASSWORD, see docs
docker compose -f ../docker-compose.yml up -d          # local Postgres+pgvector (skip if you already have one)
python scripts/seed_sample_data.py                     # loads bundled sample catalogs - no LLM needed
ollama pull qwen2.5:1.5b                                # one-time
uvicorn app.main:app --reload --port 8000

# 2. Frontend (new terminal)
cd frontend
npm install
npm run dev
```

Open http://localhost:5173, pick a brand, try the search bar, then switch
to **Ask AI** and ask something like "recommend a low sugar biscuit".

## 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.