# Alemno Payments

[![Python Version](https://img.shields.io/badge/python-3.14-blue?style=flat-square&logo=python)](file:///home/sortedcord/Projects/alemno_payments/pyproject.toml)
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An asynchronous transactional data cleaning, validation, and LLM powered categorization pipeline.

## 🚀 Features

- Reads multi format dates and handles missing transaction IDs by auto generating unique fallbacks, and parses messy amounts with currency signs and commas cleanly.
- Offloads validation, spend conversion, and analysis to a Celery background worker backed by Redis.
- Integrates with the Google GenAI SDK (`gemini-3.5-flash`) using structured output schemas to categorize transaction records into database-defined custom categories.
- Uses Redis caching with a 24-hour TTL to store live exchange rates, falling back to a hardcoded rate of `93.0` if offline.
- Statistical Anomaly Detection:
  - Flags transactions exceeding 3 times the account's median spend as statistical outliers.
  - Flags domestic transactions in USD currency.
  - Flags high value transactions and transactions with fraud indicative keywords.

## 🐳 Self Hosting & Deployment

We publish production-ready images to **GitHub Container Registry (GHCR)**. You can pull them directly or run them via Docker Compose.

### Option A: Docker Compose (Using Pre-built Images)

Create a `docker-compose.yml` file to self-host the entire stack:

```yaml
version: "3.8"

services:
  db:
    image: postgres:16-alpine
    container_name: alemno_db
    ports:
      - "5432:5432"
    environment:
      POSTGRES_USER: postgres
      POSTGRES_PASSWORD: postgres
      POSTGRES_DB: alemno_payments
    volumes:
      - postgres_data:/var/lib/postgresql/data
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U postgres -d alemno_payments"]
      interval: 5s
      timeout: 5s
      retries: 5

  redis:
    image: redis:7-alpine
    container_name: alemno_redis
    ports:
      - "6379:6379"

  web:
    image: ghcr.io/sortedcord/alemno-payments/web:latest
    container_name: alemno_web
    ports:
      - "3000:3000"
    environment:
      - POSTGRES_HOST=db
      - POSTGRES_PORT=5432
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres
      - POSTGRES_DB=alemno_payments
      - REDIS_HOST=redis
      - REDIS_PORT=6379
      - REDIS_DB=0
      - GEMINI_API_KEY=your-gemini-api-key
    depends_on:
      db:
        condition: service_healthy
      redis:
        condition: service_started

  worker:
    image: ghcr.io/sortedcord/alemno-payments/worker:latest
    container_name: alemno_worker
    environment:
      - POSTGRES_HOST=db
      - POSTGRES_PORT=5432
      - POSTGRES_USER=postgres
      - POSTGRES_PASSWORD=postgres
      - POSTGRES_DB=alemno_payments
      - REDIS_HOST=redis
      - REDIS_PORT=6379
      - REDIS_DB=0
      - GEMINI_API_KEY=your-gemini-api-key
    depends_on:
      db:
        condition: service_healthy
      redis:
        condition: service_started

volumes:
  postgres_data:
```

Launch the stack:

```bash
docker compose up -d
```

### Option B: Direct Pull (Manual Run)

You can pull and run individual containers manually from GHCR:

1. **Pull the images**:

   ```bash
   docker pull ghcr.io/sortedcord/alemno-payments/web:latest
   docker pull ghcr.io/sortedcord/alemno-payments/worker:latest
   ```

2. **Run Web (FastAPI API Server)**:

   ```bash
   docker run -d \
     --name alemno_web \
     -p 3000:3000 \
     -e POSTGRES_HOST=your-db-host \
     -e REDIS_HOST=your-redis-host \
     -e GEMINI_API_KEY=your-gemini-api-key \
     ghcr.io/sortedcord/alemno-payments/web:latest
   ```

3. **Run Worker (Celery Processing Worker)**:
   ```bash
   docker run -d \
     --name alemno_worker \
     -e POSTGRES_HOST=your-db-host \
     -e REDIS_HOST=your-redis-host \
     -e GEMINI_API_KEY=your-gemini-api-key \
     ghcr.io/sortedcord/alemno-payments/worker:latest celery -A app.worker.celery_app worker --loglevel=info
   ```