dde0ecb9cd
Voice AI Features: - LiveKit Agents integration for real-time voice calls - Julia AI agent (Python) deployed to LiveKit Cloud - Token server for authentication - Debug screen with voice call testing - Voice call screen with full-screen UI Agent Configuration: - STT: Deepgram Nova-2 - LLM: OpenAI GPT-4o - TTS: Deepgram Aura Asteria (female voice) - Turn Detection: LiveKit Multilingual Model - VAD: Silero - Noise Cancellation: LiveKit BVC Files added: - julia-agent/ - Complete agent code and token server - app/voice-call.tsx - Full-screen voice call UI - services/livekitService.ts - LiveKit client service - contexts/VoiceTranscriptContext.tsx - Transcript state - polyfills/livekit-globals.ts - WebRTC polyfills 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
70 lines
2.7 KiB
Docker
70 lines
2.7 KiB
Docker
# syntax=docker/dockerfile:1
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# Use the official UV Python base image with Python 3.13 on Debian Bookworm
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# UV is a fast Python package manager that provides better performance than pip
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# We use the slim variant to keep the image size smaller while still having essential tools
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ARG PYTHON_VERSION=3.13
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FROM ghcr.io/astral-sh/uv:python${PYTHON_VERSION}-bookworm-slim AS base
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# Keeps Python from buffering stdout and stderr to avoid situations where
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# the application crashes without emitting any logs due to buffering.
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ENV PYTHONUNBUFFERED=1
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# Create a non-privileged user that the app will run under.
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# See https://docs.docker.com/develop/develop-images/dockerfile_best-practices/#user
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ARG UID=10001
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RUN adduser \
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--disabled-password \
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--gecos "" \
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--home "/app" \
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--shell "/sbin/nologin" \
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--uid "${UID}" \
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appuser
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# Install build dependencies required for Python packages with native extensions
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# gcc: C compiler needed for building Python packages with C extensions
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# python3-dev: Python development headers needed for compilation
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# We clean up the apt cache after installation to keep the image size down
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RUN apt-get update && apt-get install -y \
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gcc \
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g++ \
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python3-dev \
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&& rm -rf /var/lib/apt/lists/*
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# Create a new directory for our application code
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# And set it as the working directory
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WORKDIR /app
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# Copy just the dependency files first, for more efficient layer caching
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COPY pyproject.toml uv.lock ./
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RUN mkdir -p src
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# Install Python dependencies using UV's lock file
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# --locked ensures we use exact versions from uv.lock for reproducible builds
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# This creates a virtual environment and installs all dependencies
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# Ensure your uv.lock file is checked in for consistency across environments
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RUN uv sync --locked
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# Copy all remaining application files into the container
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# This includes source code, configuration files, and dependency specifications
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# (Excludes files specified in .dockerignore)
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COPY . .
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# Change ownership of all app files to the non-privileged user
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# This ensures the application can read/write files as needed
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RUN chown -R appuser:appuser /app
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# Switch to the non-privileged user for all subsequent operations
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# This improves security by not running as root
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USER appuser
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# Pre-download any ML models or files the agent needs
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# This ensures the container is ready to run immediately without downloading
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# dependencies at runtime, which improves startup time and reliability
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RUN uv run src/agent.py download-files
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# Run the application using UV
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# UV will activate the virtual environment and run the agent.
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# The "start" command tells the worker to connect to LiveKit and begin waiting for jobs.
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CMD ["uv", "run", "src/agent.py", "start"]
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