修复: DeepSeek reasoning_content 全链路透传(跨6 crate补字段+映射+agentic loop累积回传)

src-tauri/src/commands/ai/agentic.rs

@@ -131,6 +131,8 @@ enum StreamOutcome {
         usage: df_ai::provider::TokenUsage,
         incomplete: bool,
         resolved_model: String,
+        /// DeepSeek thinking 模式推理内容（需回传到下一轮请求）
+        reasoning_content: Option<String>,
     },
     InitFailedExhausted,
     Fatal,
@@ -164,6 +166,7 @@ async fn stream_one_provider(
     retry_deadline: tokio::time::Instant,
     model_override: &Option<String>,
     agentic_req: &TaskRequirements,
+    last_reasoning_content: &Option<String>,
 ) -> StreamOutcome {
     // resolve→ensure→build 三步(复用 secret::build_provider_for,DRY)。
     // key 缺失/损坏 → Err → 归 Fatal(stream_llm Fatal 也是 key 类错,语义一致)。
@@ -209,17 +212,19 @@ async fn stream_one_provider(
             stream: true,
             tools: if tool_defs.is_empty() { None } else { Some(tool_defs.to_vec()) },
             tool_choice: None,
+            reasoning_content: last_reasoning_content.clone(),
         };
 
         match stream_llm(&*provider, retry_request, app_handle, stop_flag, notify, conv_id).await {
-            StreamResult::Complete { text, tool_calls, usage } => {
+            StreamResult::Complete { text, tool_calls, usage, reasoning_content } => {
                 return StreamOutcome::Success {
                     text, tool_calls, usage,
                     incomplete: false,
                     resolved_model,
+                    reasoning_content,
                 };
             }
-            StreamResult::Partial { text, tool_calls, usage } => {
+            StreamResult::Partial { text, tool_calls, usage, reasoning_content } => {
                 // MidStream 保文不重试(决策 a1)。携带 incomplete=true 交调用方走保文路径。
                 tracing::warn!(
                     conv_id = %conv_id,
@@ -231,6 +236,7 @@ async fn stream_one_provider(
                     text, tool_calls, usage,
                     incomplete: true,
                     resolved_model,
+                    reasoning_content,
                 };
             }
             StreamResult::InitFailed { retryable } => {
@@ -435,6 +441,9 @@ pub(crate) async fn run_agentic_loop(
     // 区分"正常收敛退出"与"达 MAX 被截断退出"——后者末轮 tool_calls 仍非空(tool_result 不再回传 LLM),属异常
     let mut converged = false;
 
+    // BUG-260617-12: DeepSeek thinking 模式推理内容跨轮透传
+    let mut last_reasoning_content: Option<String> = None;
+
     // F-260616-13: system_prompt 是 run_agentic_loop 的不变参数(整个 loop 期间文本不变),
     // 其 token 估算在 loop 外算一次缓存复用,避免每轮/每次重试重复 estimate_text(低收益优化,行为不变)。
     let sys_tokens = TokenEstimator::default().estimate_text(&system_prompt);
@@ -656,9 +665,9 @@ pub(crate) async fn run_agentic_loop(
         let mut candidate_chain: Vec<AiProviderRecord> = Vec::with_capacity(1 + candidates.len());
         candidate_chain.push(provider_config.clone());
         candidate_chain.extend(candidates.iter().cloned());
-        let (full_text, tool_calls_acc, round_usage, incomplete) = {
+        let (full_text, tool_calls_acc, round_usage, incomplete, round_reasoning_content) = {
             // outcome 累积成功结果(Complete/Partial),InitFailed 不写入。
-            let mut outcome: Option<(String, std::collections::HashMap<u32, super::ToolCallDraft>, df_ai::provider::TokenUsage, bool)> = None;
+            let mut outcome: Option<(String, std::collections::HashMap<u32, super::ToolCallDraft>, df_ai::provider::TokenUsage, bool, Option<String>)> = None;
 
             'candidate: for candidate in &candidate_chain {
                 // per-provider permit(可选):set_provider_caps 未配置时返回 None(单 provider 零变化);
@@ -678,14 +687,16 @@ pub(crate) async fn run_agentic_loop(
                     retry_deadline,
                     &model_override,
                     &agentic_req,
+                    &last_reasoning_content,
                 ).await {
-                    StreamOutcome::Success { text, tool_calls, usage, incomplete, resolved_model: m } => {
+                    StreamOutcome::Success { text, tool_calls, usage, incomplete, resolved_model: m, reasoning_content: rc } => {
                         // 成功:更新迭代级 resolved_model + provider_config(供后续 push/save/标题)。
                         // mut 解构(已在顶部声明 mut):本轮后续 save/push 用「实际成功所用 provider」
                         // 而非主 candidate。compress 已在本轮 stream 之前用过 primary,不受影响。
                         resolved_model = m;
                         provider_config = candidate.clone();
-                        outcome = Some((text, tool_calls, usage, incomplete));
+                        last_reasoning_content = rc;
+                        outcome = Some((text, tool_calls, usage, incomplete, last_reasoning_content.clone()));
                         break 'candidate;
                     }
                     StreamOutcome::InitFailedExhausted => {
@@ -750,6 +761,8 @@ pub(crate) async fn run_agentic_loop(
                 if !full_text.is_empty() {
                     let mut msg = ChatMessage::assistant(&full_text);
                     msg.model = Some(resolved_model.clone());
+                    // BUG-260617-12: MidStream 保文也回填 reasoning_content
+                    msg.reasoning_content = round_reasoning_content.clone();
                     session.messages.push(msg);
                     // 追加系统提示消息:响应因网络中断不完整(对齐决策 a1 系统提示机制)
                     let mut notice = ChatMessage::system("⚠ 响应因网络中断不完整,以上为已接收的部分内容。可重新发送以获取完整回复。");
@@ -807,10 +820,13 @@ pub(crate) async fn run_agentic_loop(
                     .collect();
                 let mut msg = ChatMessage::assistant_with_tools(&full_text, ai_tool_calls);
                 msg.model = Some(resolved_model.clone());
+                // BUG-260617-12: 回填 reasoning_content 供下一轮请求透传
+                msg.reasoning_content = last_reasoning_content.clone();
                 session.messages.push(msg);
             } else if !full_text.is_empty() {
                 let mut msg = ChatMessage::assistant(&full_text);
                 msg.model = Some(resolved_model.clone());
+                msg.reasoning_content = last_reasoning_content.clone();
                 session.messages.push(msg);
             }
         }