668 lines
36 KiB
Rust
668 lines
36 KiB
Rust
//! Agentic 循环 — 流式接收 → 工具执行 → 结果回传 LLM → 循环
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use std::sync::Arc;
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use std::sync::atomic::Ordering;
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use tauri::{AppHandle, Emitter};
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use tokio::sync::Mutex;
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use df_ai::ai_tools::AiToolRegistry;
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use df_ai::context::TokenEstimator;
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use df_ai::provider::{ChatMessage, CompletionRequest, LlmProvider};
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// CR-30-1: 复用 retry::backoff_delay(jitter 1s→2s→4s) + is_status_retryable(Fatal 分类)
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// 实现流前失败重试退避对齐(决策 F-260616-07 a1),避免重写退避逻辑。
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use df_ai::retry;
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use df_storage::db::Database;
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use df_storage::models::AiProviderRecord;
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use crate::state::{AppState, LlmConcurrency};
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use super::conversation::{save_conversation, TokenAccumulator};
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use super::knowledge_inject::maybe_spawn_extraction;
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use super::prompt::{build_system_prompt, get_active_provider};
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use super::stream_recv::{stream_llm, StreamResult};
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use super::title::{ensure_conversation_title, spawn_ensure_title};
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use super::audit::process_tool_calls;
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use super::{AiChatEvent, AiSession, ErrorType};
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/// Agentic 循环默认最大迭代次数（可配置项的默认值）
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///
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/// 默认 10 轮。F-260616-01 已接入配置:AppState.agent_max_iterations(Arc<AtomicUsize>) +
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/// ai_set_agent_max_iterations command + Settings.vue 数字配置。调用方在 loop 入口
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/// load AtomicUsize 快照后透传 `max_iterations: usize` 形参，当前 loop 锁定边界，
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/// 热改下次发消息生效（与 llm_concurrency 传 Arc 实时反映的区别）。
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pub const DEFAULT_MAX_AGENT_ITERATIONS: usize = 10;
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/// 流式对话失败自动重试默认次数（F-260616-07 / 决策 a1）
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///
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/// 默认 3 次（初次 + 2 次重试）。复用 retry::backoff_delay 退避(1s→2s→4s+jitter) +
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/// retry::is_status_retryable Fatal 分类(4xx 非429 立即放弃) + 30s 总预算。
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/// 只重试流前失败（Init Err：未输出任何 token）；流中途失败（MidStream：已输出
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/// partial_text）不重试——保文入库 + AiCompleted(incomplete=true) + 系统提示网络中断。
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pub const DEFAULT_MAX_AGENT_RETRIES: usize = 3;
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// ============================================================
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// B-260615-09: generating 状态 RAII guard
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// ============================================================
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/// generating 复位 RAII guard,取代散布的手动 `session.generating = false`。
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///
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/// 两路复位:
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/// - 正常路径:exit 点显式 `reset().await` 即时复位(emit 前调,保证"复位→emit"顺序,
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/// 前端收事件时后端已可接下一条)。
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/// - 异常路径(panic/未走正常 return):Drop 兜底 spawn 复位,防 generating 永真卡死前端。
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///
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/// 注:try_continue_agent_loop 不用 guard——其 should_continue=false 路径需保持
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/// generating=true(审批等待态),全函数 guard 会误复位;该函数单点 provider-Err 复位保持手动。
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struct GeneratingGuard {
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session: Arc<Mutex<AiSession>>,
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done: bool,
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}
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impl GeneratingGuard {
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fn new(session: Arc<Mutex<AiSession>>) -> Self {
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Self { session, done: false }
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}
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/// 显式复位 generating=false。emit 前调用保证顺序。幂等。
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async fn reset(&mut self) {
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if !self.done {
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self.session.lock().await.generating = false;
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self.done = true;
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}
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}
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/// 解除 Drop 兜底复位但不复位 generating。审批等待 return 路径调用:
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/// 保持 generating=true 留 try_continue 续生成,同时 Drop 因 done=true 跳过复位 spawn。
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/// (B-260615-26: 修复审批执行后对话不续生成回归)
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fn disarm(&mut self) {
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self.done = true;
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}
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}
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impl Drop for GeneratingGuard {
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fn drop(&mut self) {
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if !self.done {
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let session = self.session.clone();
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tauri::async_runtime::spawn(async move {
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session.lock().await.generating = false;
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});
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}
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}
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}
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/// Agentic 循环：流式接收 → 工具执行 → 结果回传 LLM → 循环
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///
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/// 退出条件：
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/// - LLM 只返回文本（无 tool_calls）→ 正常结束
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/// - 有工具需要审批 → 暂停循环（generating 保持 true），等 ai_approve 恢复
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/// - 达到最大迭代次数 → 正常结束
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pub(crate) async fn run_agentic_loop(
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session_arc: Arc<Mutex<AiSession>>,
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tools_arc: Arc<AiToolRegistry>,
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db: Arc<Database>,
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app_handle: AppHandle,
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provider_config: AiProviderRecord,
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system_prompt: String,
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conv_id: String,
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knowledge_config: crate::state::KnowledgeConfig,
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llm_concurrency: LlmConcurrency,
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max_iterations: usize,
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max_retries: usize,
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) {
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// B-260615-09: generating 状态由 RAII guard 收敛复位(正常 exit 显式 reset;panic/异常 Drop 兜底)
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let mut guard = GeneratingGuard::new(session_arc.clone());
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// FR-S1: resolve→ensure_resolved_key(空 key 早失败)→build_provider 三步统一走工厂
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// 空 key 早失败(逻辑见 secret::ensure_resolved_key 单测):避免空 key 发请求吃 401,错误伪装成"API Key 无效"
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//
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// B-260615-17:resolve 一次复用——原实现 build_provider_for 成功后又独立调 resolve_provider_secret
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// 取 key_len(重复 keyring resolve)。现 resolve 一次:既供 key_len 诊断日志,又供 build_provider,
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// 去重复 keyring resolve 调用。逻辑等价于 secret::build_provider_for(resolve→ensure→build 三步),
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// 仅因 build_provider_for 隐藏 resolved key 无法复用而在此内联(未改 secret.rs 锁边界)。
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let resolved_key = super::secret::resolve_provider_secret(&provider_config);
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let key_len = resolved_key.len();
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let provider: Box<dyn LlmProvider> = match super::secret::ensure_resolved_key(
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&provider_config.name, &resolved_key,
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) {
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Ok(()) => df_ai::build_provider(
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&provider_config.provider_type,
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&provider_config.base_url,
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&resolved_key,
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&provider_config.default_model,
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),
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Err(msg) => {
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guard.reset().await;
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let _ = app_handle.emit("ai-chat-event", AiChatEvent::AiError {
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error: msg,
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// ensure_resolved_key 失败 = key 缺失/钥匙串损坏,归 Auth
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error_type: Some(ErrorType::Auth),
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conversation_id: Some(conv_id.clone()),
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});
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return;
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}
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};
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// 诊断日志:401/错误时据此定位是 url/type/model/key 哪项问题(只记长度不记明文)
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tracing::info!(
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provider = %provider_config.name,
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provider_type = %provider_config.provider_type,
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base_url = %provider_config.base_url,
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model = %provider_config.default_model,
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key_len = key_len,
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"[ai] 发起 LLM 请求"
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);
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let tool_defs = tools_arc.tool_definitions();
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// 停止信号副本：stream_llm 与每轮迭代共享读取，避免重复加锁
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// notify 同取一份 Arc 引用（B-260615-14）：stream_llm select! 监听 notified() 即时唤醒
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let (stop_flag, notify) = {
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let session = session_arc.lock().await;
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(session.stop_flag.clone(), session.notify.clone())
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};
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// token 累加器:loop 生命周期内各轮叠加,退出时传 save_conversation(累加模式落库)
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let mut tokens = TokenAccumulator::default();
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// 收敛标志:仅当 LLM 末轮无 tool_calls 自行 break(正常收敛)时置 true;
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// 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
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let mut converged = false;
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for iteration in 0..max_iterations {
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// 用户请求停止 → 收尾退出（已生成文本已在上一轮入库）
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if stop_flag.load(Ordering::SeqCst) {
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let usage = df_ai::provider::TokenUsage {
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prompt_tokens: tokens.prompt(),
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completion_tokens: tokens.completion(),
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total_tokens: tokens.total(),
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};
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// 入口 stop:本轮可能尚未 stream(首轮即停),不记 model——避免把未实际生成的 model 写入 models 数组
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save_conversation(&session_arc, &db, &conv_id, Some(&usage), None).await;
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// 标题生成后台化：不阻塞 Completed emit（失败有 extract_title 兜底）
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spawn_ensure_title(&provider_config, &db, &conv_id, &app_handle, &session_arc, &llm_concurrency);
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guard.reset().await;
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// generating 复位后再 emit Completed：保证前端收事件时后端已可接下一条(发送队列续发不被"正在生成中"拒绝)
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let _ = app_handle.emit("ai-chat-event", AiChatEvent::AiCompleted { total_tokens: usage.total_tokens, prompt_tokens: tokens.prompt(), completion_tokens: tokens.completion(), incomplete: None, conversation_id: Some(conv_id.clone()) });
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return;
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}
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// B-260615-11: 旧 loop 污染防护——每轮开始校验对话一致性。
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// 用户新建/切换对话后 active_conversation_id 变更,本 loop(conv_id 快照)成陈旧,
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// 继续跑会往新对话 push 消息/pending 造成污染。检测到即退出(guard Drop 复位 generating)。
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{
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let session = session_arc.lock().await;
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if session.active_conversation_id.as_deref() != Some(conv_id.as_str()) {
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tracing::warn!(
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stale_conv = %conv_id,
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active_conv = ?session.active_conversation_id,
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"[ai] 对话已切换,旧 loop 退出(B-260615-11)避免污染新对话"
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);
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return;
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}
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}
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// 新一轮通知前端（第二轮起），前端需新建 assistant 消息
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if iteration > 0 {
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let _ = app_handle.emit("ai-chat-event", AiChatEvent::AiAgentRound {
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round: (iteration + 1) as u32,
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conversation_id: Some(conv_id.clone()),
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});
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}
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// 构建请求消息（超预算时自动裁剪旧消息，保护工具调用三元组 + 最近 6 条）
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let messages = {
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let session = session_arc.lock().await;
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let sys_tokens = TokenEstimator::default().estimate_text(&system_prompt);
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let (history_msgs, _trimmed) = session.messages.build_for_request(sys_tokens);
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let mut msgs = vec![ChatMessage::system(&system_prompt)];
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msgs.extend(history_msgs);
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msgs
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};
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// 预估输入 token(兜底:部分 provider 如 GLM 流式 usage 不报 prompt_tokens,后段用它补)
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// 注:F-260616-07 重试循环内每次重建 retry_request(因 provider.stream 消费 body),
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// 此处不再预构建 request(旧 request 变量已废弃),仅保留 messages 供 estimated_prompt。
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let estimated_prompt: u32 = {
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let est = TokenEstimator::default();
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messages.iter().map(|m| est.estimate_message(m)).sum()
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};
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// LLM 并发限流（全局 + 单对话双层），仅覆盖 stream_llm 调用本身;
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// 工具执行(process_tool_calls)是本地操作无 RPM 成本,permit 在 stream 后立即释放避免占槽
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//
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// CR-30-1 / F-260616-07 / 决策 a1: 流前失败(Init Err)重试,流中途失败(MidStream
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// Partial)不重试保文。重试退避复用 retry::backoff_delay(1s→2s→4s±20% jitter) +
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// retry::is_status_retryable Fatal 分类(stream_recv classify_status_or_class 镜像,
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// 4xx 非429 立即放弃) + 30s 总挂钟预算。重试期间持有 permit 不释放(防新请求挤占)。
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let _global_permit = llm_concurrency.acquire_global().await;
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let _per_conv_permit = llm_concurrency.acquire_per_conv().await;
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// 重试总预算(挂钟,含 sleep + 各次请求耗时),对齐 retry::MAX_TOTAL_BUDGET 30s。
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// 超预算直接放弃重试交最终错误/保文路径。
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let retry_deadline = tokio::time::Instant::now() + std::time::Duration::from_secs(30);
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let (full_text, tool_calls_acc, round_usage, incomplete) = {
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// outcome 累积最后一次成功/保文结果(Complete 或 Partial)。InitFailed 不写入,
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// 走重试或耗尽 return。
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let mut outcome: Option<(String, std::collections::HashMap<u32, super::ToolCallDraft>, df_ai::provider::TokenUsage, bool)> = None;
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for retry_attempt in 0..=max_retries {
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// 每次重试重建 request(CompletionRequest 无状态,但 provider.stream() 内部可能消费 body)
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let retry_request = CompletionRequest {
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model: provider_config.default_model.clone(),
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messages: {
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let session = session_arc.lock().await;
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let sys_tokens = TokenEstimator::default().estimate_text(&system_prompt);
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let (history_msgs, _trimmed) = session.messages.build_for_request(sys_tokens);
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let mut msgs = vec![ChatMessage::system(&system_prompt)];
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msgs.extend(history_msgs);
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msgs
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},
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temperature: Some(0.7),
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max_tokens: Some(8192),
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stream: true,
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tools: if tool_defs.is_empty() { None } else { Some(tool_defs.clone()) },
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tool_choice: None,
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};
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match stream_llm(&*provider, retry_request, &app_handle, &stop_flag, ¬ify, &conv_id).await {
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StreamResult::Complete { text, tool_calls, usage } => {
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// 正常完成(流尽 + finished,或用户主动停止)。incomplete=false。
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outcome = Some((text, tool_calls, usage, false));
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break;
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}
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StreamResult::Partial { text, tool_calls, usage } => {
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// CR-30-2 / UX-2025-04 / 决策 a1: 流中途失败保文,**不重试**。
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// 已 emit AiTextDelta(前端 currentText 已累积),此处保文入库 +
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// AiCompleted(incomplete=true) + 系统提示。currentText 不混乱(未重试无追加)。
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tracing::warn!(
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conv_id = %conv_id,
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text_len = text.len(),
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"[ai] 流中途失败,保文不重试(incomplete=true),入库 + AiCompleted + 系统提示",
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);
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outcome = Some((text, tool_calls, usage, true));
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break;
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}
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StreamResult::InitFailed { retryable } => {
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// stream_llm 已 emit AiError。决定是否重试(仅 Init 失败可重试,决策 a1)。
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let is_last = retry_attempt >= max_retries;
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let now = tokio::time::Instant::now();
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let budget_exhausted = now >= retry_deadline;
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// Fatal(4xx 非429/鉴权/参数错)立即放弃,不浪费预算重试
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if !retryable {
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tracing::warn!(
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conv_id = %conv_id,
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attempt = retry_attempt + 1,
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"[ai] 流前失败 Fatal(4xx/鉴权),立即放弃不重试",
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);
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guard.reset().await;
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return;
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}
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if is_last || budget_exhausted {
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// 重试耗尽或预算耗尽:错误已 emit,结束
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tracing::warn!(
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conv_id = %conv_id,
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attempt = retry_attempt + 1,
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budget_exhausted = budget_exhausted,
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"[ai] 流前失败重试{}({}次),放弃",
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if budget_exhausted { "预算耗尽" } else { "耗尽" },
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max_retries + 1,
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);
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guard.reset().await;
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return;
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}
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// CR-30-1: 退避复用 retry::backoff_delay(attempt+1)(1s→2s→4s + ±20% jitter),
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// 不再使用纯指数 `1u64 << retry_attempt`。attempt 传 retry_attempt+1
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// (retry::backoff_delay 是 1-based,第 1 次重试 ~1s,第 2 次 ~2s,第 3 次 ~4s)。
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let delay = retry::backoff_delay((retry_attempt + 1) as u32)
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.min(retry_deadline.saturating_duration_since(now));
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let total_retry = retry_attempt + 1; // 1-based 当前是第几次尝试
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tracing::warn!(
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conv_id = %conv_id,
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attempt = total_retry,
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max_attempts = max_retries + 1,
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delay_ms = delay.as_millis() as u64,
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"[ai] 流前失败(Retryable),{}ms 后重试 ({}/{})",
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delay.as_millis(), total_retry, max_retries + 1
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);
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// F-260616-07(d): emit 重试提示事件,前端在错误气泡内显示「重试 n/m」
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// (对齐决策"错误气泡内更新")。CR-30-2: useAiEvents.ts 补 case 处理。
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let _ = app_handle.emit("ai-chat-event", AiChatEvent::AiStreamRetry {
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attempt: total_retry as u32,
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max_attempts: (max_retries + 1) as u32,
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conversation_id: Some(conv_id.clone()),
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});
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tokio::time::sleep(delay).await;
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continue;
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}
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}
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}
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match outcome {
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Some(r) => r,
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None => {
|
||
// 不应到达:InitFailed 非 Fatal/未耗尽即重试,耗尽/Fatal 已 return;
|
||
// Complete/Partial 写入 outcome 后 break。防御兜底。
|
||
guard.reset().await;
|
||
return;
|
||
}
|
||
}
|
||
};
|
||
|
||
// CR-30-2 / UX-2025-04 / 决策 a1: MidStream 保文路径——partial_text 已接收,
|
||
// 入库为正常 assistant 消息 + emit AiCompleted(incomplete=true) + 追加系统提示消息。
|
||
// 不走 AiError(非异常中断,已有可用文本),不重试(决策 a1)。
|
||
if incomplete {
|
||
let usage = df_ai::provider::TokenUsage {
|
||
prompt_tokens: if round_usage.prompt_tokens == 0 { estimated_prompt } else { round_usage.prompt_tokens },
|
||
completion_tokens: round_usage.completion_tokens,
|
||
total_tokens: if round_usage.prompt_tokens == 0 { estimated_prompt + round_usage.completion_tokens } else { round_usage.total_tokens },
|
||
};
|
||
tokens.add(usage.prompt_tokens, usage.completion_tokens);
|
||
|
||
// 追加 partial assistant 消息(若无 tool_calls 且有文本)
|
||
{
|
||
let mut session = session_arc.lock().await;
|
||
if session.active_conversation_id.as_deref() != Some(conv_id.as_str()) {
|
||
tracing::warn!(
|
||
stale_conv = %conv_id,
|
||
active_conv = ?session.active_conversation_id,
|
||
"[ai] MidStream 保文后对话已切换,丢弃本轮 push(B-260615-11)"
|
||
);
|
||
return;
|
||
}
|
||
if !full_text.is_empty() {
|
||
let mut msg = ChatMessage::assistant(&full_text);
|
||
msg.model = Some(provider_config.default_model.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。");
|
||
notice.model = Some(provider_config.default_model.clone());
|
||
session.messages.push(notice);
|
||
}
|
||
}
|
||
|
||
save_conversation(&session_arc, &db, &conv_id, Some(&usage), Some(&provider_config.default_model)).await;
|
||
// 标题生成后台化(失败有 extract_title 兜底)
|
||
spawn_ensure_title(&provider_config, &db, &conv_id, &app_handle, &session_arc, &llm_concurrency);
|
||
guard.reset().await;
|
||
// generating 复位后再 emit Completed(incomplete=true):前端据此标记消息为不完整
|
||
let _ = app_handle.emit("ai-chat-event", AiChatEvent::AiCompleted {
|
||
total_tokens: usage.total_tokens,
|
||
prompt_tokens: tokens.prompt(),
|
||
completion_tokens: tokens.completion(),
|
||
conversation_id: Some(conv_id.clone()),
|
||
incomplete: Some(true),
|
||
});
|
||
return;
|
||
}
|
||
// stream 结束立即释放 permit,后续工具执行不受限流(本地操作无 RPM 成本)
|
||
drop(_global_permit);
|
||
drop(_per_conv_permit);
|
||
|
||
// 累加本轮 token:provider 流式 usage 的 prompt_tokens 为 0 时(GLM 等),用预估输入兜底
|
||
let round_prompt = if round_usage.prompt_tokens == 0 { estimated_prompt } else { round_usage.prompt_tokens };
|
||
tokens.add(round_prompt, round_usage.completion_tokens);
|
||
|
||
// 追加 assistant 消息到历史
|
||
let has_tool_calls = !tool_calls_acc.is_empty();
|
||
{
|
||
let mut session = session_arc.lock().await;
|
||
// B-260615-11: push 前再校验(stream_llm 期间用户可能新建对话)。
|
||
// 读端读到被 clear 的空历史不致命,但 push 写回新对话是污染,必须挡。
|
||
if session.active_conversation_id.as_deref() != Some(conv_id.as_str()) {
|
||
tracing::warn!(
|
||
stale_conv = %conv_id,
|
||
active_conv = ?session.active_conversation_id,
|
||
"[ai] stream 后对话已切换,丢弃本轮 push(B-260615-11)避免污染新对话"
|
||
);
|
||
return;
|
||
}
|
||
if has_tool_calls {
|
||
let mut order: Vec<u32> = tool_calls_acc.keys().copied().collect();
|
||
order.sort_unstable();
|
||
let ai_tool_calls: Vec<df_ai::provider::ToolCall> = order.iter()
|
||
.map(|i| {
|
||
let draft = &tool_calls_acc[i];
|
||
df_ai::provider::ToolCall::new(&draft.id, &draft.name, &draft.args)
|
||
})
|
||
.collect();
|
||
let mut msg = ChatMessage::assistant_with_tools(&full_text, ai_tool_calls);
|
||
msg.model = Some(provider_config.default_model.clone());
|
||
session.messages.push(msg);
|
||
} else if !full_text.is_empty() {
|
||
let mut msg = ChatMessage::assistant(&full_text);
|
||
msg.model = Some(provider_config.default_model.clone());
|
||
session.messages.push(msg);
|
||
}
|
||
}
|
||
|
||
// 停止信号：已生成文本入库后退出，不再执行后续工具调用
|
||
if stop_flag.load(Ordering::SeqCst) {
|
||
let usage = df_ai::provider::TokenUsage {
|
||
prompt_tokens: tokens.prompt(),
|
||
completion_tokens: tokens.completion(),
|
||
total_tokens: tokens.total(),
|
||
};
|
||
save_conversation(&session_arc, &db, &conv_id, Some(&usage), Some(&provider_config.default_model)).await;
|
||
// 标题生成后台化：不阻塞 Completed emit（失败有 extract_title 兜底）
|
||
spawn_ensure_title(&provider_config, &db, &conv_id, &app_handle, &session_arc, &llm_concurrency);
|
||
guard.reset().await;
|
||
// generating 复位后再 emit Completed：保证前端收事件时后端已可接下一条(发送队列续发不被"正在生成中"拒绝)
|
||
let _ = app_handle.emit("ai-chat-event", AiChatEvent::AiCompleted { total_tokens: usage.total_tokens, prompt_tokens: tokens.prompt(), completion_tokens: tokens.completion(), incomplete: None, conversation_id: Some(conv_id.clone()) });
|
||
return;
|
||
}
|
||
|
||
// 无工具调用 → 最终文本响应，正常收敛退出
|
||
if !has_tool_calls { converged = true; break; }
|
||
|
||
// 处理工具调用（Low 自动执行 / Medium+High 待审批）
|
||
let pending_count = {
|
||
let mut session = session_arc.lock().await;
|
||
process_tool_calls(&mut session, tool_calls_acc, &tools_arc, &db, &app_handle, &conv_id).await
|
||
};
|
||
|
||
// 有待审批 → 暂停循环，等待用户审批后通过 ai_approve → try_continue_agent_loop 恢复
|
||
if pending_count > 0 {
|
||
let usage = df_ai::provider::TokenUsage {
|
||
prompt_tokens: tokens.prompt(),
|
||
completion_tokens: tokens.completion(),
|
||
total_tokens: tokens.total(),
|
||
};
|
||
save_conversation(&session_arc, &db, &conv_id, Some(&usage), Some(&provider_config.default_model)).await;
|
||
// B-260615-26: 审批等待 return 前 disarm guard——保持 generating=true 留 try_continue 续生成,
|
||
// 同时 Drop 因 done=true 跳过复位 spawn(避免误复位审批态 generating 致 ai_approve→try_continue 不续)
|
||
guard.disarm();
|
||
return; // generating 保持 true
|
||
}
|
||
|
||
// 全部自动执行完成 → 继续下一轮
|
||
}
|
||
|
||
// 达 MAX 未收敛(LLM 末轮仍想调工具被截断,末轮 tool_result 不再回传 LLM):转入暂停态询问用户
|
||
// F-260616-03:不再 emit AiError + 走完成流程,改为 emit AiMaxRoundsReached + 保持 generating=true
|
||
// (仿审批等待 L313-316),等用户点继续(ai_continue_loop → try_continue_agent_loop 再跑 max_iterations 轮)
|
||
// 或点停止(ai_stop_loop → 走完成流程)。try_continue 重新 spawn run_agentic_loop,iteration 从 0 重计,
|
||
// 故续跑天然再跑 max_iterations 轮(决策 a),无需 reset 任何计数器。
|
||
if !converged {
|
||
tracing::warn!(
|
||
conv_id = %conv_id,
|
||
max_iter = max_iterations,
|
||
"[ai] agentic 循环达最大轮次(max_iterations={})仍未收敛,转暂停态询问用户(F-260616-03)",
|
||
max_iterations,
|
||
);
|
||
// 轮 token 落库(保留末轮已生成内容,续跑/停止都据此累加)
|
||
let usage = df_ai::provider::TokenUsage {
|
||
prompt_tokens: tokens.prompt(),
|
||
completion_tokens: tokens.completion(),
|
||
total_tokens: tokens.total(),
|
||
};
|
||
save_conversation(&session_arc, &db, &conv_id, Some(&usage), Some(&provider_config.default_model)).await;
|
||
// 暂停态保持 generating=true(防其他 send 抢占,仿审批),disarm guard 跳过 Drop 兜底复位
|
||
guard.disarm();
|
||
let _ = app_handle.emit("ai-chat-event", AiChatEvent::AiMaxRoundsReached {
|
||
conversation_id: Some(conv_id.clone()),
|
||
});
|
||
return; // generating 保持 true,等 ai_continue_loop / ai_stop_loop
|
||
}
|
||
|
||
// 正常完成
|
||
let usage = df_ai::provider::TokenUsage {
|
||
prompt_tokens: tokens.prompt(),
|
||
completion_tokens: tokens.completion(),
|
||
total_tokens: tokens.total(),
|
||
};
|
||
// 落库 + 标题 + 知识提炼打包后台化：不阻塞 generating 复位与 Completed 事件
|
||
// save 先行(extract/title 都读已落库消息);extract 内部 fire-and-forget,与 title 可能并发
|
||
// (均受 per_conv 信号量约束,读写不同字段互不干扰)
|
||
// 并发取舍：与新对话新 loop 的 save 存在低概率并发 upsert，最多丢少量 token 累加（非功能错误，可接受）
|
||
let usage_total = usage.total_tokens;
|
||
{
|
||
let session_arc = session_arc.clone();
|
||
let db = db.clone();
|
||
let conv_id = conv_id.clone();
|
||
let provider_config = provider_config.clone();
|
||
let knowledge_config = knowledge_config.clone();
|
||
let app_handle = app_handle.clone();
|
||
let llm_concurrency = llm_concurrency.clone();
|
||
tauri::async_runtime::spawn(async move {
|
||
save_conversation(&session_arc, &db, &conv_id, Some(&usage), Some(&provider_config.default_model)).await;
|
||
// 知识提炼:需读已落库的对话消息,故在 save 之后
|
||
if let Err(e) = maybe_spawn_extraction(&session_arc, &db, &conv_id, &provider_config, &knowledge_config, llm_concurrency.clone()).await {
|
||
tracing::warn!("知识提炼触发失败(非阻断): {}", e);
|
||
}
|
||
ensure_conversation_title(&provider_config, &db, &conv_id, &app_handle, &session_arc, llm_concurrency).await;
|
||
});
|
||
}
|
||
|
||
guard.reset().await;
|
||
// generating 复位后再 emit Completed：落库/标题/提炼已在后台，前端立即感知完成
|
||
let _ = app_handle.emit("ai-chat-event", AiChatEvent::AiCompleted { total_tokens: usage_total, prompt_tokens: tokens.prompt(), completion_tokens: tokens.completion(), incomplete: None, conversation_id: Some(conv_id.clone()) });
|
||
}
|
||
|
||
/// 检查是否所有待审批已处理，如果是则恢复 agentic 循环
|
||
///
|
||
/// B-260615-08:所有静默 return 点显式 emit 收尾事件,避免前端 streaming=true 永久卡。
|
||
/// 各 return 点的语义判断:
|
||
/// 1) should_continue=false(generating 已复位 / pending_approvals 非空):
|
||
/// - generating=false → 用户点了停止(ai_chat_stop 复位)或会话已结束,emit AiCompleted 标当前轮收敛
|
||
/// (streaming=true 由 AiCompleted 清理)
|
||
/// - pending_approvals 非空 → 转入审批等待态(其他审批未决),emit AiCompleted 标当前轮结束
|
||
/// (前端审批态 watchdog 已 clear,不卡)
|
||
/// 2) get_active_provider Err → 无可用 provider(配置丢失/全删),无法续生成,emit AiError
|
||
/// (语义:配置错误,用户需设 provider;非 generating 复位可恢复)
|
||
///
|
||
/// R-PD-6: conv_id 来源从全局 active_conversation_id 解耦到审批所属会话。
|
||
/// 触发本函数的 ai_approve 已 remove 触发审批,但 pending_approvals 内剩余审批(若 has_pending)
|
||
/// 仍各自携带 conversation_id(审批产生时由 process_tool_calls 写入,业务真相源)。
|
||
/// 故 has_pending=true 分支(审批等待态)直接取剩余审批的 conversation_id 做 conv_id,
|
||
/// 不读 active_conversation_id 全局单例——该字段在审批等待态(非 generating-only 期)可被
|
||
/// ai_chat_stop/clear/switch 并发改写,属竞态耦合。has_pending=false(全部审批已处理,续生成)
|
||
/// 分支:审批已被 remove,改为以剩余 pending_approvals 任一 conversation_id 做一致性校验
|
||
/// (此处空,校验通过即沿用全局值,该期 generating=true 且 switch 为 readonly 不并发)。
|
||
pub(crate) async fn try_continue_agent_loop(app: &AppHandle, state: &AppState) {
|
||
let (is_generating, has_pending, pending_conv_id) = {
|
||
let session = state.ai_session.lock().await;
|
||
// pending_approvals 中任一审批的 conversation_id:审批等待态(has_pending)下作为 conv_id 来源,
|
||
// 取第一个非空值(同一对话的审批 conversation_id 一致,见 process_tool_calls 写入路径)。
|
||
let pending_conv_id = session.pending_approvals.values()
|
||
.find_map(|a| a.conversation_id.clone());
|
||
(session.generating, !session.pending_approvals.is_empty(), pending_conv_id)
|
||
};
|
||
let should_continue = is_generating && !has_pending;
|
||
|
||
if !should_continue {
|
||
// generating=false(被 stop)或仍有审批(pending_approvals 非空):
|
||
// 统一 emit AiCompleted 标当前轮收敛,清前端 streaming。
|
||
// 轮 token 已在前序 AiCompleted/AiApprovalResult 流程落库,此处零 token 上报仅作收敛信号。
|
||
if is_generating {
|
||
// pending_approvals 非空但 generating 仍 true:转审批态,前端审批态 watchdog 已 clear,不卡
|
||
tracing::info!("[ai] try_continue 跳过:仍有待审批,转审批等待态");
|
||
} else {
|
||
// generating 已复位(用户 stop 或前序循环已 emit Completed):补发 AiCompleted 防前端卡住
|
||
tracing::info!("[ai] try_continue 跳过:generating 已复位(被 stop/已结束),补发 AiCompleted 清前端 streaming");
|
||
// R-PD-6: 优先用审批所属 conversation_id(审批等待态被 stop 触发,审批仍在 pending_approvals),
|
||
// 仅当无任何审批(has_pending=false 且 generating=false)时回退 active_conversation_id。
|
||
let conv_id = match pending_conv_id {
|
||
Some(cid) => cid,
|
||
None => {
|
||
let session = state.ai_session.lock().await;
|
||
session.active_conversation_id.clone().unwrap_or_default()
|
||
}
|
||
};
|
||
let _ = app.emit("ai-chat-event", AiChatEvent::AiCompleted {
|
||
total_tokens: 0,
|
||
prompt_tokens: 0,
|
||
completion_tokens: 0,
|
||
incomplete: None,
|
||
conversation_id: Some(conv_id),
|
||
});
|
||
}
|
||
return;
|
||
}
|
||
|
||
let provider_config = match get_active_provider(state).await {
|
||
Ok(p) => p,
|
||
Err(e) => {
|
||
// 无可用 provider(配置丢失/全删):无法续生成,emit AiError。
|
||
// 语义:配置错误,用户需在 Settings 设 provider;generating 复位由 run_agentic_loop 内
|
||
// build_provider_for Err 分支处理(同样 emit AiError),此处与之一致。
|
||
// 不用 GeneratingGuard:try_continue 的 should_continue=false 路径需保 generating=true(审批等待态),
|
||
// 全函数 guard 会误复位。此点单点 provider-Err 复位,语义独立。
|
||
let mut session = state.ai_session.lock().await;
|
||
session.generating = false;
|
||
// R-PD-6: 续生成被拒(provider 缺失)回退 conv_id 优先审批所属;无审批再读全局。
|
||
let conv_id = session.pending_approvals.values()
|
||
.find_map(|a| a.conversation_id.clone())
|
||
.or_else(|| session.active_conversation_id.clone())
|
||
.unwrap_or_default();
|
||
drop(session);
|
||
tracing::warn!(error = %e, "[ai] try_continue 失败:无可用 provider");
|
||
let _ = app.emit("ai-chat-event", AiChatEvent::AiError {
|
||
error: e,
|
||
// 无可用 provider(配置丢失/全删):用户需在 Settings 设 provider,归 ProviderConfig
|
||
error_type: Some(ErrorType::ProviderConfig),
|
||
conversation_id: Some(conv_id),
|
||
});
|
||
return;
|
||
}
|
||
};
|
||
// R-PD-6: 续生成路径 conv_id 解耦——has_pending=false 时审批已 remove,无审批 conversation_id 可取;
|
||
// 此期 generating=true 且 switchConversation 为 readonly 不并发改 active_conversation_id,
|
||
// 故读全局值安全(非竞态期);若 has_pending=true 已在上面 return,不会到此。
|
||
let (lang, conv_id) = {
|
||
let session = state.ai_session.lock().await;
|
||
let lang = session.agent_language.clone().unwrap_or_else(|| "zh-CN".to_string());
|
||
let conv_id = session.active_conversation_id.clone().unwrap_or_default();
|
||
(lang, conv_id)
|
||
};
|
||
let system_prompt = build_system_prompt(state, &lang).await;
|
||
|
||
let session_arc = state.ai_session.clone();
|
||
let tools_arc = state.ai_tools.clone();
|
||
let db = state.db.clone();
|
||
let app_handle = app.clone();
|
||
let knowledge_config = state.knowledge_config.lock().await.clone();
|
||
let llm_concurrency = state.llm_concurrency.clone();
|
||
// F-260616-01: loop 入口 load 快照，当前续生成 loop 锁定边界（热改下次发消息生效）
|
||
let max_iterations = state.agent_max_iterations.load(Ordering::SeqCst);
|
||
// F-260616-07: 流式失败重试次数快照
|
||
let max_retries = state.agent_max_retries.load(Ordering::SeqCst);
|
||
|
||
// 恢复循环前通知前端新建 assistant 消息：审批(通过/拒绝)后新一轮文本
|
||
// 不应追加到发起工具调用的旧消息，用 AiAgentRound 隔开
|
||
let _ = app.emit("ai-chat-event", AiChatEvent::AiAgentRound {
|
||
round: 0,
|
||
conversation_id: Some(conv_id.clone()),
|
||
});
|
||
|
||
tauri::async_runtime::spawn(async move {
|
||
run_agentic_loop(session_arc, tools_arc, db, app_handle, provider_config, system_prompt, conv_id, knowledge_config, llm_concurrency, max_iterations, max_retries).await;
|
||
});
|
||
}
|