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