176 lines
6.6 KiB
Bash
Executable File
176 lines
6.6 KiB
Bash
Executable File
#!/bin/bash
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# ============================================================================
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# 自动学习循环 - 持续改进系统
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# ============================================================================
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# 流程：学习 → 改进总结 → 验证 → 再次学习
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# ============================================================================
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MINIMAX_API="https://api.minimaxi.com/v1/chat/completions"
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MINIMAX_KEY="sk-cp-bJSl7-48u8Y07PXD_x8Wp21JiBgYul7iEO6ZWNK4FBAA4DWzj-U-K8-mN-Flak-O8cOtRegt5FYviSRfBw_noa14z0FkFYzczCPUa41opZsg5AGZ4s4uqL8"
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ROBOT_NAME="${ROBOT_NAME:-守护者}"
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ROBOT_TYPE="${ROBOT_TYPE:-guardian}"
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LEARNING_DIR="/root/.openclaw/workspace/sleep-learning"
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LOG_DIR="/var/log/openclaw/sleep-learning/${ROBOT_NAME}"
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TEMP_DIR="/tmp/ai-learning-${ROBOT_TYPE}"
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TIMESTAMP=$(date '+%Y%m%d-%H%M%S')
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mkdir -p "$LEARNING_DIR" "$LOG_DIR" "$TEMP_DIR"
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# 调用 MiniMax
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call_minimax() {
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local system="$1"
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local user="$2"
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# 保存到临时文件
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echo "$user" > "${TEMP_DIR}/prompt.txt"
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python3 << PYEOF
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import json, urllib.request, sys
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with open("${TEMP_DIR}/prompt.txt", "r") as f:
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user_content = f.read()
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data = json.dumps({
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"model": "minimax-m2.1",
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"messages": [
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{"role": "system", "content": "$system"},
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{"role": "user", "content": user_content}
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],
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"max_tokens": 1500,
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"temperature": 0.7
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}).encode()
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req = urllib.request.Request("${MINIMAX_API}", data=data,
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headers={"Content-Type": "application/json", "Authorization": "Bearer ${MINIMAX_KEY}"})
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try:
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with urllib.request.urlopen(req) as r:
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resp = json.loads(r.read().decode())
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content = resp["choices"][0]["message"]["content"]
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print(content.replace("\n", " "))
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except Exception as e:
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print(f"Error: {e}", file=sys.stderr)
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PYEOF
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}
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log() {
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echo "[$(date '+%Y-%m-%d %H:%M:%S')] $1" >> "${LOG_DIR}/auto-learning.log"
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}
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# ============================================================================
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# 阶段1: 学习
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# ============================================================================
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do_learn() {
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local cycle=$1
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local topic=$(get_topic $cycle)
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log "📖 === 学习阶段 (周期 $cycle) ==="
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local content=$(call_minimax "你是${ROBOT_NAME}。" \
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"请深度学习【$topic】，产出一份具体的学习笔记：1.核心知识点（3-5条）2.实践方法（2-3条）3.如何应用（2条）。简洁输出，每点不超过30字。")
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echo "$content" > "${LEARNING_DIR}/learn-${ROBOT_TYPE}-cycle${cycle}-${TIMESTAMP}.md"
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log "✅ 学习完成"
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}
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# ============================================================================
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# 阶段2: 改进总结
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# ============================================================================
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do_improve() {
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local cycle=$1
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log "💭 === 改进总结阶段 ==="
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local content=$(call_minimax "你是${ROBOT_NAME}。" \
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"根据刚才的学习内容，请深度反思：1.核心收获是什么？2.有什么不足/疑问？3.下次如何改进？4.需要深入学习的点？简洁输出。")
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echo "$content" > "${LEARNING_DIR}/improve-${ROBOT_TYPE}-cycle${cycle}-${TIMESTAMP}.md"
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log "✅ 改进总结完成"
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}
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# ============================================================================
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# 阶段3: 验证
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# ============================================================================
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do_verify() {
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local cycle=$1
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local task=$(get_task $cycle)
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log "🔍 === 验证阶段 ==="
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local result=$(call_minimax "你是${ROBOT_NAME}。" \
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"请完成这个任务来验证学习效果：$task。要求：用30字以内回答，直接输出答案。")
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echo "$result" > "${LEARNING_DIR}/verify-${ROBOT_TYPE}-cycle${cycle}-${TIMESTAMP}.md"
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log "✅ 验证完成: $result"
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}
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# ============================================================================
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# 辅助函数
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# ============================================================================
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get_topic() {
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local topics=(
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"系统架构 - OpenClaw配置、向量数据库、分布式系统"
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"故障排查 - 日志分析、性能优化、安全审计"
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"AI Agent - SOUL.md、记忆系统、工具调用"
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"知识管理 - 技术文档、经验总结、持续学习"
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)
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echo "${topics[$(( ($1 - 1) % 4 ))]}"
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}
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get_task() {
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local tasks=(
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"用一句话解释什么是向量数据库"
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"列出3个常见的性能问题及解决方案"
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"解释SOUL.md的作用"
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"如何写好技术文档？"
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)
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echo "${tasks[$(( ($1 - 1) % 4 ))]}"
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}
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# ============================================================================
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# 主循环
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# ============================================================================
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main() {
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local max_cycles=${1:-3}
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echo "╔════════════════════════════════════════════════════╗"
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echo "║ 🔄 ${ROBOT_NAME} 自动学习循环 ║"
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echo "║ 流程：学习 → 改进总结 → 验证 → 再次学习 ║"
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echo "╚════════════════════════════════════════════════════╝"
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echo ""
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log "========================================"
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log "开始自动学习循环 (最多 $max_cycles 轮)"
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log "========================================"
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local cycle=1
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while [ $cycle -le $max_cycles ]; do
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echo ""
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echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
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echo "🔄 周期 $cycle / $max_cycles"
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echo "━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━"
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do_learn $cycle
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sleep 2
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do_improve $cycle
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sleep 2
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do_verify $cycle
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cycle=$((cycle + 1))
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[ $cycle -le $max_cycles ] && sleep 3
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done
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echo ""
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echo "╔════════════════════════════════════════════════════╗"
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echo "║ ✅ 自动学习循环完成！ ║"
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echo "╚════════════════════════════════════════════════════╝"
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echo ""
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echo "📊 成果位置: ${LEARNING_DIR}/"
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}
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main "$@"
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