重构：架构重组 + 6轮缺陷修复
Phase 1: 工具模块统一 - 新建 tools/executor.rs — 统一工具执行器，消除 main/worker/daemon 三处重复 - tools/builtin.rs → tools/registry.rs - tools/mod.rs 新增 build_tools_list() / build_env_map()，daemon/main 共用 Phase 2: 主模块重组 context/ → core/ - core/pipeline.rs — 消息处理流水线 (从 main.rs 抽取 listen_loop) - core/session.rs — ChatSession 会话管理 - core/context.rs — 上下文构建 + token 估算 - core/summary.rs — 摘要管理 (溢出/空闲触发) - core/memory.rs — MemoryStore 长期记忆 - main.rs 精简至 ~200 行 Phase 3: listen 模式全局会话锁 + Pipeline 收尾 - PipelineContext 持有全局 conversation_lock，所有消息串行处理防串话 Phase 4: wechat/ → channel/ 重命名 缺陷修复: - daemon send_frame 失败后消息重回 waiting 队列 - daemon 审批通过后按 code_hash 精确执行，不重跑 LLM - listen 模式执行器从 session.current_user_id 动态获取用户 - listen 模式 LLM 用量统计传入实际 user_id - send_text 响应校验改用 serde_json::Value 宽松解析 - pi_subagent 工具 (JSON 事件流模式, RiskLevel::High, timeout clamp 1-300) - listen 审批超时 300s→60s，cancel_by_code 精确保护 - approval.rs 新增 cancel_by_code 方法同步 DB
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use crate::core::session::{ChatSession, SummaryEntry, SummaryReason};
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use crate::llm::conversation::Summarizer;
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use crate::llm::types::Message;
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use std::sync::Arc;
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use tokio::sync::Mutex;
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/// 触发溢出摘要：压缩 checkpoint 到当前位置之间的新消息
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pub async fn trigger_overflow_summary(
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session: &Arc<Mutex<ChatSession>>,
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summarizer: Option<&Summarizer>,
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) -> bool {
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let mut s = session.lock().await;
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if s.messages.is_empty() {
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return false;
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}
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let prev_checkpoint = s.checkpoint;
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let end = s.messages.len();
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// checkpoint 始终在 user+assistant 对边界，直接用它作为摘要起点
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let start = prev_checkpoint;
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if start >= end {
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return false;
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}
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let to_summarize: Vec<_> = s.messages[start..end]
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.iter()
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.take(40) // 最多 40 条
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.cloned()
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.collect();
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if to_summarize.is_empty() {
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return false;
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}
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let summary_text = generate_summary(&to_summarize, summarizer).await;
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s.summaries.push(SummaryEntry {
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checkpoint: prev_checkpoint,
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text: summary_text.clone(),
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reason: SummaryReason::Overflow,
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created_at: chrono::Utc::now(),
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});
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// 持久化到数据库
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s.save_summary_to_db(&summary_text, "overflow", to_summarize.len() as i32)
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.await;
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s.checkpoint = end;
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// 清理太旧的消息（checkpoint 之前的保留最近 100 条用于调试）
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if s.checkpoint > 200 {
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let keep = s.checkpoint - 100;
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s.messages.drain(0..keep);
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// 调整 checkpoint 和 summary checkpoint
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let shift = keep;
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s.checkpoint -= shift;
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for sum in &mut s.summaries {
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if sum.checkpoint >= shift {
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sum.checkpoint -= shift;
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}
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}
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}
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true
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}
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/// 触发空闲摘要（不注入，保存到 summaries 供 LLM 工具查询）
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pub async fn trigger_idle_summary(
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session: &Arc<Mutex<ChatSession>>,
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summarizer: Option<&Summarizer>,
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) {
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let mut s = session.lock().await;
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if s.messages.is_empty() {
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return;
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}
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let summary_text = generate_summary(&s.messages, summarizer).await;
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let cp = s.messages.len();
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s.summaries.push(SummaryEntry {
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checkpoint: cp,
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text: summary_text.clone(),
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reason: SummaryReason::Timeout,
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created_at: chrono::Utc::now(),
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});
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let msg_count = cp as i32;
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s.save_summary_to_db(&summary_text, "timeout", msg_count)
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.await;
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s.checkpoint = s.messages.len();
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// 清理旧消息（checkpoint 之前的全部清除）
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if s.checkpoint > 0 {
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let shift = s.checkpoint;
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s.messages.drain(0..shift);
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s.checkpoint = 0;
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for sum in &mut s.summaries {
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if sum.checkpoint >= shift {
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sum.checkpoint -= shift;
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}
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}
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}
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}
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/// 生成摘要：优先使用 LLM，不可用时回退到简单截断
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async fn generate_summary(messages: &[Message], summarizer: Option<&Summarizer>) -> String {
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if let Some(summarizer) = summarizer {
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if messages.len() >= 3 {
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let prompt = build_summary_prompt(messages);
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match summarizer(prompt).await {
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Ok(text) if !text.is_empty() => {
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tracing::info!("LLM 摘要: {:.100}...", text);
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return text;
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}
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_ => tracing::warn!("LLM 摘要失败，回退到简单截断"),
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}
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}
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}
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summarize_messages(messages)
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}
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/// 构建给 LLM 的摘要 prompt
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fn build_summary_prompt(messages: &[Message]) -> String {
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let convo: String = messages
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.iter()
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.filter(|m| {
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m.role == crate::llm::types::Role::User
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|| m.role == crate::llm::types::Role::Assistant
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})
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.map(|m| {
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let role = if m.role == crate::llm::types::Role::User {
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"用户"
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} else {
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"助手"
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};
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let content: String = m.content.chars().take(200).collect();
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format!("{}: {}", role, content)
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})
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.collect::<Vec<_>>()
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.join("\n");
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format!(
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"请将以下对话压缩为简短摘要，保留关键事实、用户偏好、决定和待办事项。只输出摘要，不要其他内容。\n\n对话：\n{}",
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convo
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)
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}
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/// 简单的摘要生成（提取最近 N 条消息的关键内容）
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fn summarize_messages(messages: &[Message]) -> String {
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// 简单实现：提取用户消息的前 80 个字符作为摘要
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let lines: Vec<String> = messages
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.iter()
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.filter(|m| {
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m.role == crate::llm::types::Role::User
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|| m.role == crate::llm::types::Role::Assistant
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})
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.map(|m| {
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let role = match m.role {
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crate::llm::types::Role::User => "用户",
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crate::llm::types::Role::Assistant => "助手",
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_ => "",
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};
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let preview: String = m.content.chars().take(80).collect();
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format!("{}: {}", role, preview)
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})
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.collect();
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if lines.is_empty() {
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"暂无历史对话".to_string()
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} else {
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let result = format!(
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"历史对话摘要（{} 条消息）:\n{}",
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lines.len(),
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lines.join("\n")
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);
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if result.chars().count() > 2000 {
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format!(
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"{}...（已截断）",
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result.chars().take(2000).collect::<String>()
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)
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} else {
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result
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}
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}
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}
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