# Dockerfile for Agent Worker / Learning Worker (adjust base image for GPU if needed)

# Use a base image with Python and potentially CUDA for GPU support (for learning worker)
# FROM python:3.10-slim
FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu20.04 # Example GPU base image

WORKDIR /app

# Install system dependencies (e.g., for psycopg2, git)
# RUN apt-get update && apt-get install -y --no-install-recommends gcc libpq-dev git && rm -rf /var/lib/apt/lists/*

COPY requirements.txt .
# Install Python dependencies (consider using a virtual environment)
# Ensure torch is installed correctly for CPU/GPU
RUN pip install --no-cache-dir -r requirements.txt
# OR specific torch version:
# RUN pip install --no-cache-dir torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
# RUN pip install --no-cache-dir -r requirements.txt

COPY . .

# Set environment variables (can also be done via docker-compose.yml)
ENV OPENAI_API_KEY=your_openai_key
ENV POSTGRES_DSN=postgresql://user:password@postgres:5432/agentdb # Use service name 'postgres'
ENV REDIS_URL=redis://redis:6379/0 # Use service name 'redis'
ENV BASE_MODEL_NAME=gpt-4o-mini
# Add other ENV vars as needed (NUM_WORKERS, etc.)

# Command to run the worker (or learning script)
# CMD ["python", "your_main_script.py"]
# Use different CMD for agent worker vs learning worker containers if separated