69a673e6c6
Chat 访谈 - 新增 persona 系统（default / warm_listener / curious_guide）与 background_voice 语气层 - 回复长度由 compute_reply_plan 统一决策（brief / standard / expanded），融合信息密度启发式 - 输入净稿（input_normalize）：编排层可选 rules/llm 归一用户口语后再喂模型与记忆检索 - 记忆证据注入：按用户话检索 memory evidence 并注入 prompt Memoir 回忆录 - 口述归一（oral_normalize）：segment 原文保留，story 管线取派生净稿作叙事输入 - segment 入队批次门闸：累计字数 + 最长等待秒数，减少零碎提交 - fidelity_check / prompts / narrative_agent 微调 - Alembic 0005：清理跨章节 story 外键 Infra - Dockerfile 加入 ffmpeg - pyproject.toml 新增依赖并同步 uv.lock - .env.example / .env.production 补全新配置项 Tests - 新增 test_background_voice、test_chat_input_normalize、test_experience_regressions - 扩展 test_interview_prompts、test_interview_reply_length、test_story_route_oral_invariant Made-with: Cursor
45 lines
1.3 KiB
Docker
45 lines
1.3 KiB
Docker
# Life Echo API Dockerfile
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FROM python:3.13-slim
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# 安装系统依赖 + uv
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RUN apt-get update && apt-get install -y --no-install-recommends \
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gcc \
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g++ \
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libc6-dev \
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libffi-dev \
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libxml2-dev \
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libxslt1-dev \
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libjpeg-dev \
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zlib1g-dev \
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libpng-dev \
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libfreetype6-dev \
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ffmpeg \
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&& rm -rf /var/lib/apt/lists/*
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COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv
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WORKDIR /app
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# 先复制依赖清单，利用 Docker 缓存
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COPY pyproject.toml uv.lock ./
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RUN uv sync --frozen --no-dev --no-install-project
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# 复制应用代码
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COPY . .
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# 构建时预下载 faster-whisper 模型
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ARG ASR_MODEL_SIZE=small
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RUN uv run python -c "from faster_whisper import WhisperModel; WhisperModel('${ASR_MODEL_SIZE}', device='cpu', compute_type='int8', download_root='/app/models/whisper')"
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# 非 root 用户
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RUN useradd -m -u 1000 appuser && chown -R appuser:appuser /app
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USER appuser
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EXPOSE 8000
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HEALTHCHECK --interval=30s --timeout=10s --start-period=10s --retries=3 \
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CMD python -c "import http.client; conn = http.client.HTTPConnection('localhost', 8000); conn.request('GET', '/health'); r = conn.getresponse(); exit(0 if r.status == 200 else 1)" || exit 1
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# 启动前执行 Alembic 迁移，再启动 uvicorn
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CMD ["sh", "-c", "uv run alembic upgrade head && uv run uvicorn main:app --host 0.0.0.0 --port 8000"]
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