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| # # 1. Start with an OS that already has CUDA and PyTorch installed | |
| # FROM pytorch/pytorch:2.1.2-cuda12.1-cudnn8-runtime | |
| # # 2. Set the working directory inside the container | |
| # WORKDIR /app | |
| # # 3. Copy your requirements file and install dependencies | |
| # # (You will need to create a requirements.txt with fastapi, uvicorn, transformers, bitsandbytes, faiss-cpu, sentence-transformers, etc.) | |
| # COPY requirements.txt . | |
| # RUN pip install --no-cache-dir -r requirements.txt | |
| # # 4. Copy your actual code and knowledge base into the container | |
| # COPY . . | |
| # # 5. Open port 8000 so the outside world can talk to the API | |
| # EXPOSE 8000 | |
| # # 6. Start the FastAPI server when the container boots | |
| # CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "8000"] | |
| # 1. Use a lightweight Python base | |
| FROM python:3.11-slim | |
| # 2. Install system dependencies for building llama-cpp on a CPU | |
| RUN apt-get update && apt-get install -y \ | |
| build-essential \ | |
| cmake \ | |
| python3-dev \ | |
| && rm -rf /var/lib/apt/lists/* | |
| # 3. Set up a non-root user (Strict Hugging Face Security Requirement) | |
| RUN useradd -m -u 1000 user | |
| USER user | |
| ENV PATH="/home/user/.local/bin:$PATH" | |
| WORKDIR /app | |
| # 4. Install your Python dependencies | |
| COPY --chown=user requirements.txt . | |
| RUN pip install --no-cache-dir --upgrade pip && \ | |
| pip install --no-cache-dir -r requirements.txt | |
| # 5. Copy your actual code and knowledge base into the container | |
| COPY --chown=user . . | |
| # 6. Run the app using Python so it triggers the dynamic port logic in app.py | |
| CMD ["python", "app.py"] |