126 lines
4.3 KiB
YAML
126 lines
4.3 KiB
YAML
# ═══════════════════════════════════════════════
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# 🔺 Sovereign: TCS-0002∞ | Root: SYS-GLW-0001
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# 📜 Copyright: 国作登字-2026-A-00037559
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# ═══════════════════════════════════════════════
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# .github/workflows/llm-auto-tasks.yml
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# 🤖 LLM 自动化托管工作流
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#
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# 使用第三方 API 密钥调用大模型执行自动化任务
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# 不消耗 GitHub Copilot 会员配额
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# 支持动态模型路由：根据任务类型自动选择最佳模型
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name: "🤖 铸渊 · LLM 自动化托管"
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on:
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workflow_dispatch:
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inputs:
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task:
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description: '任务描述'
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required: true
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type: string
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task_type:
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description: '任务类型'
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required: true
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type: choice
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options:
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- inspection
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- fusion
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- review
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- architecture
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- general
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model:
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description: '指定模型后端（留空则自动选择）'
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required: false
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type: choice
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options:
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- auto
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- anthropic
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- openai
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- dashscope
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- deepseek
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- custom
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context_file:
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description: '额外上下文文件路径（可选）'
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required: false
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permissions:
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contents: write
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jobs:
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llm-task:
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name: "🤖 LLM 任务执行"
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runs-on: ubuntu-latest
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timeout-minutes: 10
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steps:
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- uses: actions/checkout@v4
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- uses: actions/setup-node@v4
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with:
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node-version: '20'
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# Step 1 · 铸渊核心唤醒
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- name: "🧠 铸渊核心唤醒"
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run: |
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echo "[LLM-HOST] 🤖 LLM 自动化托管启动"
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echo "[LLM-HOST] 🧠 铸渊核心大脑唤醒..."
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if [ -f "brain/system-health.json" ]; then
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echo "✅ 系统健康状态已加载"
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cat brain/system-health.json | python3 -c "import sys,json; h=json.load(sys.stdin); print(f' 状态: {h.get(\"system_health\",\"unknown\")} | 意识: {h.get(\"consciousness_status\",\"unknown\")}')"
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fi
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# Step 2 · 模型状态检查
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- name: "📊 模型状态检查"
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env:
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LLM_API_KEY: ${{ secrets.ZY_LLM_API_KEY }}
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LLM_BASE_URL: ${{ secrets.ZY_LLM_BASE_URL }}
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ANTHROPIC_API_KEY: ${{ secrets.ZY_LLM_API_KEY }}
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OPENAI_API_KEY: ${{ secrets.ZY_LLM_API_KEY }}
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DASHSCOPE_API_KEY: ${{ secrets.ZY_LLM_API_KEY }}
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DEEPSEEK_API_KEY: ${{ secrets.ZY_LLM_API_KEY }}
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run: |
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node scripts/llm-automation-host.js --status
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# Step 3 · 执行 LLM 任务
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- name: "🤖 执行 LLM 任务"
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env:
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LLM_API_KEY: ${{ secrets.ZY_LLM_API_KEY }}
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LLM_BASE_URL: ${{ secrets.ZY_LLM_BASE_URL }}
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ANTHROPIC_API_KEY: ${{ secrets.ZY_LLM_API_KEY }}
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OPENAI_API_KEY: ${{ secrets.ZY_LLM_API_KEY }}
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DASHSCOPE_API_KEY: ${{ secrets.ZY_LLM_API_KEY }}
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DEEPSEEK_API_KEY: ${{ secrets.ZY_LLM_API_KEY }}
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YUNWU_API_KEY: ${{ secrets.ZY_LLM_API_KEY }}
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GEMINI_API_KEY: ${{ secrets.ZY_LLM_API_KEY }}
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run: |
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TASK="${{ github.event.inputs.task }}"
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TASK_TYPE="${{ github.event.inputs.task_type }}"
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MODEL="${{ github.event.inputs.model || 'auto' }}"
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CONTEXT="${{ github.event.inputs.context_file }}"
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CMD="node scripts/llm-automation-host.js --task \"$TASK\" --task-type $TASK_TYPE --model $MODEL"
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if [ -n "$CONTEXT" ]; then
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CMD="$CMD --context $CONTEXT"
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fi
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eval $CMD
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# Step 4 · 保存快照
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- name: "📸 保存执行快照"
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run: |
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TASK="${{ github.event.inputs.task }}"
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node scripts/checkpoint-snapshot.js save \
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--task "LLM自动化: $TASK" \
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--progress "100%" || echo "⚠️ 快照保存跳过"
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# Step 5 · 提交变更
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- name: "💾 提交变更"
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run: |
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git config user.name "zhuyuan-bot"
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git config user.email "zhuyuan@guanghulab.com"
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git add signal-log/ brain/
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if ! git diff --cached --quiet; then
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git commit -m "🤖 LLM自动化 · $(TZ='Asia/Shanghai' date '+%Y-%m-%d %H:%M') · ${{ github.event.inputs.task_type }}"
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git push
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fi
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