Dockerfile

# Use a Python image with uv pre-installed
FROM ghcr.io/astral-sh/uv:python3.13-bookworm-slim

# Install the project into `/app`
WORKDIR /app

# Enable bytecode compilation
ENV UV_COMPILE_BYTECODE=1 

# Copy from the cache instead of linking since it's a mounted volume
ENV UV_LINK_MODE=copy

# Place executables in the environment at the front of the path
ENV PATH="/app/.venv/bin:$PATH"

# Install the project's dependencies using the lockfile and settings
RUN --mount=type=cache,target=/root/.cache/uv \
    --mount=type=bind,source=uv.lock,target=uv.lock \
    --mount=type=bind,source=pyproject.toml,target=pyproject.toml \
    uv sync --frozen --no-install-project --no-dev --no-editable

# Then, add the rest of the project source code and install it
# Installing separately from its dependencies allows optimal layer caching
COPY pyproject.toml /app
COPY uv.lock /app
COPY src /app/src
COPY .env.example /app/.env.example
COPY tests /app/tests
COPY LICENSE /app
COPY *.md /app
COPY .python-version /app

RUN --mount=type=cache,target=/root/.cache/uv \
    uv sync --frozen --no-dev --no-editable

ENV BRAVE_SEARCH_PYTHON_CLIENT_RUNNING_IN_CONTAINER=1

# When running the container, start the Starbridge MCP server
# But feel free to add arguments and options as needed when doing a docker run
ENTRYPOINT ["uv", "run", "--no-dev", "brave-search"]