新增: 批次工作落地(推进链/评估闭环/事件总线/并发/加固) + 技术债清理 + 文档整理
后端: - 工作流推进链(D-03):advance_task/状态机/闸门走 df-nodes Node trait,conditions 条件引擎扩展 - 想法评估闭环:启发式评分+对抗评估,df-ideas/scoring + df-storage/idea_eval_repo + idea 前端打通 - 全局事件数据总线:df-ai/context+context_helpers+augmentation 跨模块解耦 - AI planner/plan_hint/intent:aichat B 路线并行多轮基础 - patch_file 加固(TD-03/04):读改写整体锁防 lost update,expected_hash 合约闭环 - 压缩超时兜底(F-15 卡死根治) - F-09 多会话并发:LlmConcurrency per-conv + streamingGuard 前端守护 + verify 脚本 - 知识注入 DRY/skills/audit 扩展 清理: - aichat 技术债(误报 allow/死导入/过时注释 30 项) - URGENT.md 删除(11 项加急全解决/迁 todo) - 文档整理(todo/待决策/待审查/ARCHITECTURE/INDEX + 总线/技术债审查新文档)
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@@ -8,7 +8,8 @@ use tauri::State;
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use df_ai::provider::LlmProvider;
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use df_types::types::{new_id, Priority};
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use df_ideas::capture::Idea;
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use df_storage::models::{IdeaRecord, ProjectRecord};
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use df_storage::crud::is_unique_constraint_err;
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use df_storage::models::{IdeaEvaluationRecord, IdeaRecord, ProjectRecord};
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use crate::state::AppState;
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@@ -43,6 +44,16 @@ pub async fn list_ideas(
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}
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}
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/// 列出指定灵感的评估历史(version DESC,最新版本在前)。
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/// IdeaEvaluationRecord 已 Serialize,直接返回前端供历史面板渲染。
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#[tauri::command]
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pub async fn list_idea_evaluations(
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state: State<'_, AppState>,
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idea_id: String,
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) -> Result<Vec<IdeaEvaluationRecord>, String> {
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state.idea_evaluations.list_by_idea(&idea_id).await.map_err(err_str)
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}
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/// 创建灵感，返回完整记录
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#[tauri::command]
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pub async fn create_idea(
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@@ -62,6 +73,7 @@ pub async fn create_idea(
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promoted_to: None,
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ai_analysis: None,
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scores: None,
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related_ids: None,
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created_at: now.clone(),
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updated_at: now,
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};
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@@ -219,6 +231,7 @@ pub async fn evaluate_idea(
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"negative_strength": negative_strength,
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"net_sentiment": net_sentiment,
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"recommendation": recommendation,
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"evaluated_by": eval.evaluated_by,
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"final_score": final_score,
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"summary": analyst_summary,
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"action_items": action_items,
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@@ -239,10 +252,11 @@ pub async fn evaluate_idea(
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let score_value = (scores.overall * 10.0).round() as i64;
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// 构造完整记录后单次原子写回（update_full 保留 id 与 created_at）
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// 构造完整记录后单次原子写回（update_full 保留 id 与 created_at）。
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// ai_analysis/scores_json 按值 move 进主表记录后,下方历史快照仍需复用 → 此处 clone 保留绑定。
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let updated = IdeaRecord {
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scores: Some(scores_json),
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ai_analysis: Some(ai_analysis),
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scores: Some(scores_json.clone()),
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ai_analysis: Some(ai_analysis.clone()),
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score: Some(score_value as f64),
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status: "pending_review".to_string(),
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updated_at: now_millis(),
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@@ -254,6 +268,57 @@ pub async fn evaluate_idea(
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.await
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.map_err(err_str)?;
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// 追加一条评估历史快照(idea_evaluations 审计表,version 单调递增)。
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// 主表 update_full 成功后再追加,保证主表先落;历史表为额外冗余列(evaluated_by
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// 独立冗余,ai_analysis JSON 内的 evaluated_by 字段保留不删)。
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//
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// version 并发重复兜底(V25 唯一约束 + 重试):version 此前由
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// `list_by_idea().first().version + 1` 算出,读-改-写非原子,并发评估同一灵感
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// 可能写出相同 version。V25 在 idea_evaluations(idea_id, version) 上加了唯一索引,
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// 此处捕获唯一约束冲突 → 重新查最新 version 重算并重试(上限 3 次防死循环)。
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// 单用户桌面应用并发概率极低,但唯一约束 + 重试是数据完整性兜底,值得做。
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let mut attempt = 0;
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let max_attempts = 3;
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loop {
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attempt += 1;
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let version = state
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.idea_evaluations
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.list_by_idea(&id)
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.await
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.map_err(err_str)?
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.first()
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.map(|r| r.version + 1)
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.unwrap_or(1);
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let eval_record = IdeaEvaluationRecord {
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id: new_id(),
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idea_id: id.clone(),
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version,
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ai_analysis: Some(ai_analysis.clone()),
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scores: Some(scores_json.clone()),
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score: Some(score_value as f64),
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evaluated_by: Some(evaluated_by_str(&eval.evaluated_by).to_string()),
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evaluated_at: now_millis(),
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};
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match state.idea_evaluations.insert(eval_record).await {
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Ok(_) => break,
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Err(e) => {
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// 唯一约束冲突(SQLite extended code 2067 / SQLITE_CONSTRAINT_UNIQUE)
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// → version 并发重复,命中且未达上限则重试(重新查 version);否则向上抛错。
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// 检测逻辑收口到 df_storage::crud::is_unique_constraint_err,集中维护、
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// 大小写不敏感,不再散落脆弱的英文文案 contains。
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if is_unique_constraint_err(&e) && attempt < max_attempts {
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tracing::warn!(
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"灵感 {id} 评估历史 version 唯一约束冲突,重试 {}/{}",
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attempt,
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max_attempts
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);
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continue;
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}
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return Err(e.to_string());
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}
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}
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}
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Ok(updated)
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}
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@@ -347,3 +412,15 @@ fn action_items_for(r: &df_ideas::adversarial::Recommendation) -> Vec<String> {
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Monitor => vec!["持续跟踪相关指标".into(), "定期评估进展".into(), "等待更好时机".into()],
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}
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}
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/// EvaluatedBy 枚举 → 评估历史表 evaluated_by 列的字符串冗余值。
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/// (ai_analysis JSON 内的 evaluated_by 字段保留不删;此处为历史表独立冗余列,
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/// 便于不解析 JSON 即可直接按评估来源过滤/统计历史。)
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fn evaluated_by_str(e: &df_ideas::adversarial::EvaluatedBy) -> &'static str {
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use df_ideas::adversarial::EvaluatedBy::*;
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match e {
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Llm => "Llm",
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Heuristic => "Heuristic",
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HeuristicFallback => "HeuristicFallback",
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}
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}
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