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# syntax=docker/dockerfile:1
# Use the official UV Python base image with Python 3.13 on Debian Bookworm
# UV is a fast Python package manager that provides better performance than pip
# We use the slim variant to keep the image size smaller while still having essential tools
ARG PYTHON_VERSION=3.13
FROM ghcr.io/astral-sh/uv:python${PYTHON_VERSION}-bookworm-slim AS base
# Keeps Python from buffering stdout and stderr to avoid situations where
# the application crashes without emitting any logs due to buffering.
ENV PYTHONUNBUFFERED=1
# Create a non-privileged user that the app will run under.
# See https://docs.docker.com/develop/develop-images/dockerfile_best-practices/#user
ARG UID=10001
RUN adduser \
--disabled-password \
--gecos "" \
--home "/app" \
--shell "/sbin/nologin" \
--uid "${UID}" \
appuser
# Install build dependencies required for Python packages with native extensions
# gcc: C compiler needed for building Python packages with C extensions
# python3-dev: Python development headers needed for compilation
# We clean up the apt cache after installation to keep the image size down
RUN apt-get update && apt-get install -y \
gcc \
g++ \
python3-dev \
&& rm -rf /var/lib/apt/lists/*
# Create a new directory for our application code
# And set it as the working directory
WORKDIR /app
# Copy just the dependency files first, for more efficient layer caching
COPY pyproject.toml uv.lock ./
RUN mkdir -p src
# Install Python dependencies using UV's lock file
# --locked ensures we use exact versions from uv.lock for reproducible builds
# This creates a virtual environment and installs all dependencies
# Ensure your uv.lock file is checked in for consistency across environments
RUN uv sync --locked
# Copy all remaining application files into the container
# This includes source code, configuration files, and dependency specifications
# (Excludes files specified in .dockerignore)
COPY . .
# Change ownership of all app files to the non-privileged user
# This ensures the application can read/write files as needed
RUN chown -R appuser:appuser /app
# Switch to the non-privileged user for all subsequent operations
# This improves security by not running as root
USER appuser
# Pre-download any ML models or files the agent needs
# This ensures the container is ready to run immediately without downloading
# dependencies at runtime, which improves startup time and reliability
RUN uv run src/agent.py download-files
# Run the application using UV
# UV will activate the virtual environment and run the agent.
# The "start" command tells the worker to connect to LiveKit and begin waiting for jobs.
CMD ["uv", "run", "src/agent.py", "start"]