updates on the backend

README.md

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+# Backend - Brand Product Search Engine (RAG API)
+
+FastAPI service exposing the product catalog through:
+
+- **Browse** - plain listing endpoints (`/api/brands`, `/api/brands/{brand}/products`)
+- **Search** - pgvector semantic similarity search, no LLM (`/api/search`)
+- **Chat** - full RAG: retrieval + local Ollama generation (`/api/chat`)
+- **Admin** - trigger brand ingestion in the background (`/api/catalog/generate`)
+
+Full setup, architecture, and troubleshooting steps are in the project
+documentation (`docs/`). This file is just a fast local reference.
+
+## Quick start
+
+### One-click (Windows)
+
+Double-click **`start_backend.bat`**. It picks up `venv\Scripts\python.exe`
+if present (else falls back to system `python`), starts/creates the
+`catalog_rag_postgres` Docker container from `docker-compose.yml`,
+launches Ollama in the background if it's installed but not running,
+seeds sample data if needed, then starts uvicorn on port 8000.
+
+### Manual
+
+```bash
+cd backend
+python3 -m venv .venv
+source .venv/bin/activate          # Windows: .venv\Scripts\activate
+pip install -r requirements.txt
+
+cp .env.example .env               # then edit DB_PASSWORD etc.
+
+# Option A: already have a Postgres+pgvector catalog from the old project?
+#   Just point .env at it (DB_HOST/DB_PORT/DB_NAME/DB_USER/DB_PASSWORD) - done.
+# Option B: starting fresh locally?
+docker compose -f docker-compose.yml up -d
+python scripts/seed_sample_data.py     # loads bundled sample catalogs instantly
+
+uvicorn app.main:app --reload --port 8000
+```
+
+Then open http://localhost:8000/docs for interactive API docs, or run the
+frontend (`../frontend/README.md`) to use the React UI.
+
+## Pulling the local LLM (one-time)
+
+```bash
+ollama pull qwen2.5:1.5b
+ollama serve   # if not already running as a service
+```
+
+## Project layout
+
+```
+backend/
+├── app/
+│   ├── main.py                  # FastAPI app + router wiring
+│   ├── infrastructure/settings.py
+│   ├── api/
+│   │   ├── schemas.py           # Pydantic models
+│   │   ├── job_store.py         # in-memory background-job tracker
+│   │   └── routers/             # health, brands, search, chat, catalog
+│   ├── core/
+│   │   ├── catalog_engine.py    # discovery + enrichment + image pipeline
+│   │   └── ingestion.py         # thin wrapper used by API + CLI
+│   └── services/
+│       ├── embeddings_service.py  # sentence-transformers (lazy-loaded)
+│       ├── ollama_service.py      # local LLM calls (catalog + RAG answers)
+│       ├── vector_store.py        # pgvector reads/writes/SEMANTIC SEARCH
+│       ├── rag_service.py         # RAG orchestration (NEW)
+│       ├── image_search.py / s3_service.py / price_estimator.py / brand_registry.py
+├── cli/ingest_brand.py           # CLI: ingest one brand end-to-end
+├── scripts/seed_sample_data.py   # load bundled sample catalogs (no LLM needed)
+├── data/seed_catalogs/*.json     # bundled sample catalogs (Parle, Cadbury, ...)
+└── requirements.txt
+```
+
+## Running tests
+
+```bash
+pytest -q
+```