新增: AI Chat多项增强(审批去重/编辑重发/导出/实体引用/会话置顶搜索)+任务推进链df-nodes落地

src-tauri/src/commands/knowledge.rs

@@ -68,18 +68,22 @@ fn validate_transition(from: &str, to: &str) -> Result<(), String> {
 // CRUD
 // ============================================================
 
-/// 列出知识 — 可按 status 筛选,status=None 时默认排除 archived
+/// 列出知识 — 可按 status 筛选,status=None 时默认仅返回 published
+///
+/// 默认范围说明(F-260616-02 决策 a):
+///   - status=None → library tab 的数据源,语义为「已发布知识库」,仅 published。
+///     不再混杂 pending_review(pending_review 归 inbox 收件箱,见 knowledge_list_candidates)。
+///   - 显式传 status 时按该 status 过滤(含 archived)。
 #[tauri::command]
 pub async fn knowledge_list(
     state: State<'_, AppState>,
     status: Option<String>,
 ) -> Result<Vec<KnowledgeRecord>, String> {
     match status {
-        // 显式查 archived 时原样返回(含归档项)
-        Some(s) if s == "archived" => state.knowledge.list_by_status("archived").await.map_err(err_str),
+        // 显式传 status 时按该 status 过滤(含 archived)
         Some(s) => state.knowledge.list_by_status(&s).await.map_err(err_str),
-        // 默认:列出非 archived 的全部(单查询 status != 'archived')
-        None => state.knowledge.list_non_archived().await.map_err(err_str),
+        // 默认:library 仅 published(F-260616-02 决策 a,职责清晰:library=published,inbox=待处理)
+        None => state.knowledge.list_by_status("published").await.map_err(err_str),
     }
 }
 
@@ -208,16 +212,64 @@ pub async fn knowledge_record_reuse(
         .map_err(err_str)
 }
 
-/// 审核收件箱 — 列出 candidate(按 confidence 语义排序)
+/// 收件箱 — 列出待处理条目(candidate + pending_review),按 confidence 语义排序
+///
+/// 语义(F-260616-02 决策 a):inbox = 「待处理」收件箱,聚合 candidate(待评估)
+/// 与 pending_review(待发布审核)两种待处理状态。library 仅 published。
+///
+/// 实现:list_by_status 单状态查询,这里合并 candidate 与 pending_review 两路结果。
+/// 两路各自已按 `confidence DESC, created_at DESC` 排序,有序合并保持同一规则。
 #[tauri::command]
 pub async fn knowledge_list_candidates(
     state: State<'_, AppState>,
 ) -> Result<Vec<KnowledgeRecord>, String> {
-    state
-        .knowledge
-        .list_by_status("candidate")
-        .await
-        .map_err(err_str)
+    let (candidates, pending) = tokio::try_join!(
+        state.knowledge.list_by_status("candidate"),
+        state.knowledge.list_by_status("pending_review"),
+    )
+    .map_err(err_str)?;
+    Ok(merge_by_confidence(candidates, pending))
+}
+
+/// 有序合并两列(各自已按 confidence DESC, created_at DESC 排序),结果保持同序。
+///
+/// confidence 排序权重:high=3, medium=2, low=1, 其他=0;同权重按 created_at DESC
+/// (字符串毫秒时间戳字典序 = 时间序)。等价 SQL `ORDER BY CASE confidence ... DESC, created_at DESC`。
+fn merge_by_confidence(
+    mut a: Vec<KnowledgeRecord>,
+    mut b: Vec<KnowledgeRecord>,
+) -> Vec<KnowledgeRecord> {
+    use std::cmp::Ordering;
+    fn rank(c: &str) -> i8 {
+        match c {
+            "high" => 3,
+            "medium" => 2,
+            "low" => 1,
+            _ => 0,
+        }
+    }
+    let cmp = |x: &KnowledgeRecord, y: &KnowledgeRecord| -> Ordering {
+        let rx = rank(x.confidence.as_deref().unwrap_or(""));
+        let ry = rank(y.confidence.as_deref().unwrap_or(""));
+        ry.cmp(&rx) // confidence DESC
+            .then_with(|| y.created_at.cmp(&x.created_at)) // created_at DESC
+    };
+    a.sort_by(cmp);
+    b.sort_by(cmp);
+    let mut out = Vec::with_capacity(a.len() + b.len());
+    let (mut i, mut j) = (0, 0);
+    while i < a.len() && j < b.len() {
+        if cmp(&a[i], &b[j]) != Ordering::Greater {
+            out.push(a[i].clone());
+            i += 1;
+        } else {
+            out.push(b[j].clone());
+            j += 1;
+        }
+    }
+    out.extend_from_slice(&a[i..]);
+    out.extend_from_slice(&b[j..]);
+    out
 }
 
 /// 归档(软删除) — UPDATE status='archived'