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+# 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. 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 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.