diff --git a/apps/df-miniapp/src/composables/useAiChat.ts b/apps/df-miniapp/src/composables/useAiChat.ts index 1b2da92..8f4e054 100644 --- a/apps/df-miniapp/src/composables/useAiChat.ts +++ b/apps/df-miniapp/src/composables/useAiChat.ts @@ -812,6 +812,7 @@ export function handleEvent(event: AiChatEvent): void { cache_hit: event.prompt_cache_hit_tokens, cache_miss: event.prompt_cache_miss_tokens, reasoning: event.reasoning_tokens, + is_estimated: event.is_estimated, } // 不完整标记(网络中断保文) if (event.incomplete) { diff --git a/apps/df-miniapp/src/manifest.json b/apps/df-miniapp/src/manifest.json deleted file mode 100644 index e602de7..0000000 --- a/apps/df-miniapp/src/manifest.json +++ /dev/null @@ -1,53 +0,0 @@ -{ - "name": "DevFlow", - "appid": "PLACEHOLDER", - "description": "DevFlow 远程 AI Chat — 跨端操作桌面端开发助手", - "versionName": "0.1.0", - "versionCode": 100, - "transformPx": false, - "app-plus": { - "usingComponents": true, - "splashscreen": { - "alwaysShowBeforeRender": true, - "waiting": true, - "autoclose": true, - "delay": 0 - }, - "modules": {}, - "distribute": { - "android": { - "permissions": [ - "" - ] - }, - "ios": {}, - "sdkConfigs": {} - } - }, - "quickapp": {}, - "mp-weixin": { - "appid": "PLACEHOLDER", - "setting": { - "urlCheck": false, - "es6": true, - "minified": true, - "postcss": true - }, - "usingComponents": true, - "permission": {}, - "requiredBackgroundModes": [], - "requiredPrivateInfos": [] - }, - "h5": { - "title": "DevFlow Mini", - "router": { - "mode": "hash", - "base": "./" - }, - "devServer": { - "port": 8081, - "https": false - } - }, - "vueVersion": "3" -} diff --git a/apps/df-miniapp/src/types/events.ts b/apps/df-miniapp/src/types/events.ts index 9ec95f2..22dd7c5 100644 --- a/apps/df-miniapp/src/types/events.ts +++ b/apps/df-miniapp/src/types/events.ts @@ -53,6 +53,8 @@ export type AiChatEvent = prompt_cache_hit_tokens: number prompt_cache_miss_tokens: number reasoning_tokens: number + /** G4.3:该轮 token 用量是否估算值(provider 未报 prompt_tokens → estimated 兜底打标) */ + is_estimated: boolean incomplete?: boolean | null conversation_id?: string | null } @@ -170,6 +172,8 @@ export interface TokenUsage { cache_hit?: number cache_miss?: number reasoning?: number + /** 该轮 token 是否估算值(AiCompleted.is_estimated 透传,reload 历史消息可能无) */ + is_estimated?: boolean } // ============================================================ diff --git a/crates/df-ai-core/src/provider.rs b/crates/df-ai-core/src/provider.rs index ed05a01..f953a9b 100644 --- a/crates/df-ai-core/src/provider.rs +++ b/crates/df-ai-core/src/provider.rs @@ -42,25 +42,25 @@ impl ContentPart { impl ChatMessage { pub fn system(content: impl Into) -> Self { - Self { id: Some(new_message_id()), role: MessageRole::System, content: content.into(), parts: None, tool_call_id: None, tool_calls: None, model: None, status: None, reasoning_content: None, prompt_tokens: None, completion_tokens: None, prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, timestamp: Some(now_millis_i64()) } + Self { id: Some(new_message_id()), role: MessageRole::System, content: content.into(), parts: None, tool_call_id: None, tool_calls: None, model: None, status: None, reasoning_content: None, prompt_tokens: None, completion_tokens: None, prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, is_estimated: None, timestamp: Some(now_millis_i64()) } } pub fn user(content: impl Into) -> Self { - Self { id: Some(new_message_id()), role: MessageRole::User, content: content.into(), parts: None, tool_call_id: None, tool_calls: None, model: None, status: None, reasoning_content: None, prompt_tokens: None, completion_tokens: None, prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, timestamp: Some(now_millis_i64()) } + Self { id: Some(new_message_id()), role: MessageRole::User, content: content.into(), parts: None, tool_call_id: None, tool_calls: None, model: None, status: None, reasoning_content: None, prompt_tokens: None, completion_tokens: None, prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, is_estimated: None, timestamp: Some(now_millis_i64()) } } pub fn assistant(content: impl Into) -> Self { - Self { id: Some(new_message_id()), role: MessageRole::Assistant, content: content.into(), parts: None, tool_call_id: None, tool_calls: None, model: None, status: None, reasoning_content: None, prompt_tokens: None, completion_tokens: None, prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, timestamp: Some(now_millis_i64()) } + Self { id: Some(new_message_id()), role: MessageRole::Assistant, content: content.into(), parts: None, tool_call_id: None, tool_calls: None, model: None, status: None, reasoning_content: None, prompt_tokens: None, completion_tokens: None, prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, is_estimated: None, timestamp: Some(now_millis_i64()) } } pub fn assistant_with_tools(content: impl Into, tool_calls: Vec) -> Self { - Self { id: Some(new_message_id()), role: MessageRole::Assistant, content: content.into(), parts: None, tool_call_id: None, tool_calls: Some(tool_calls), model: None, status: None, reasoning_content: None, prompt_tokens: None, completion_tokens: None, prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, timestamp: Some(now_millis_i64()) } + Self { id: Some(new_message_id()), role: MessageRole::Assistant, content: content.into(), parts: None, tool_call_id: None, tool_calls: Some(tool_calls), model: None, status: None, reasoning_content: None, prompt_tokens: None, completion_tokens: None, prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, is_estimated: None, timestamp: Some(now_millis_i64()) } } pub fn tool_result(call_id: impl Into, content: impl Into) -> Self { - Self { id: Some(new_message_id()), role: MessageRole::Tool, content: content.into(), parts: None, tool_call_id: Some(call_id.into()), tool_calls: None, model: None, status: None, reasoning_content: None, prompt_tokens: None, completion_tokens: None, prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, timestamp: Some(now_millis_i64()) } + Self { id: Some(new_message_id()), role: MessageRole::Tool, content: content.into(), parts: None, tool_call_id: Some(call_id.into()), tool_calls: None, model: None, status: None, reasoning_content: None, prompt_tokens: None, completion_tokens: None, prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, is_estimated: None, timestamp: Some(now_millis_i64()) } } /// 多模态 user 消息：content 文本 + parts（含 Image 片）。 /// content 作为人类可读文本（也作非 vision 端点降级载荷）；parts 透传给 vision 端点。 pub fn user_parts(content: impl Into, parts: Vec) -> Self { - Self { id: Some(new_message_id()), role: MessageRole::User, content: content.into(), parts: Some(parts), tool_call_id: None, tool_calls: None, model: None, status: None, reasoning_content: None, prompt_tokens: None, completion_tokens: None, prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, timestamp: Some(now_millis_i64()) } + Self { id: Some(new_message_id()), role: MessageRole::User, content: content.into(), parts: Some(parts), tool_call_id: None, tool_calls: None, model: None, status: None, reasoning_content: None, prompt_tokens: None, completion_tokens: None, prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, is_estimated: None, timestamp: Some(now_millis_i64()) } } /// 是否含图片片（供 provider 判定走多模态分支）。 @@ -320,6 +320,7 @@ mod tests { prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, + is_estimated: None, timestamp: None, }; assert_eq!(m.content, "字面量构造"); @@ -377,6 +378,7 @@ mod tests { prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, + is_estimated: None, timestamp: None, }; let json = serde_json::to_string(&m).unwrap(); @@ -437,6 +439,7 @@ mod tests { prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, + is_estimated: None, timestamp: None, }; let json = serde_json::to_string(&m).unwrap(); diff --git a/crates/df-ai-core/src/types.rs b/crates/df-ai-core/src/types.rs index 19eb176..f7b6e4f 100644 --- a/crates/df-ai-core/src/types.rs +++ b/crates/df-ai-core/src/types.rs @@ -138,6 +138,10 @@ pub struct ChatMessage { /// 前端仅 > 0 时显示(reason 后缀);老 JSON 反序列化为 None。 #[serde(default, skip_serializing_if = "Option::is_none")] pub reasoning_tokens: Option, + /// 本轮 prompt 是否估算值(round_usage.prompt_tokens==0 → estimated_prompt 兜底打标)。 + /// 供 reload 逐条回显「估算」标注,对齐 live 态 AiCompleted.is_estimated;老消息 None(向前兼容)。 + #[serde(default, skip_serializing_if = "Option::is_none")] + pub is_estimated: Option, } /// 当前 Unix 毫秒(ChatMessage 打戳用;df-ai-core 不依赖 df-types,内联避免新增依赖)。 diff --git a/crates/df-ai/src/anthropic_helpers.rs b/crates/df-ai/src/anthropic_helpers.rs index 2fdf544..86816c2 100644 --- a/crates/df-ai/src/anthropic_helpers.rs +++ b/crates/df-ai/src/anthropic_helpers.rs @@ -165,7 +165,7 @@ pub(crate) fn apply_anthropic_event(data: &str, usage_accum: &mut Option { @@ -173,9 +173,11 @@ pub(crate) fn apply_anthropic_event(data: &str, usage_accum: &mut Option { diff --git a/crates/df-ai/src/openai_helpers.rs b/crates/df-ai/src/openai_helpers.rs index 467eb95..f1083f0 100644 --- a/crates/df-ai/src/openai_helpers.rs +++ b/crates/df-ai/src/openai_helpers.rs @@ -256,17 +256,19 @@ pub(crate) fn apply_openai_sse(data: &str, usage_accum: &mut Option) delta: delta_text, finished, tool_calls, - usage: None, + // 加固:usage 与 finish_reason 同帧的端点(部分兼容实现),此处已累积则挂上, + // 使下游 stream_recv 不必等到 [DONE] 帧即可拿到真实 usage。 + usage: usage_accum.clone(), error: None, reasoning_content: choice.delta.reasoning_content, } } else { - // choices 为空 = usage-only chunk，不输出文本（usage 已累积） + // choices 为空 = usage-only chunk，不输出文本（usage 已累积），usage 一并带出 StreamChunk { delta: String::new(), finished: false, tool_calls: None, - usage: None, + usage: usage_accum.clone(), error: None, reasoning_content: None, } diff --git a/crates/df-storage/src/crud/message_repo.rs b/crates/df-storage/src/crud/message_repo.rs index acb7293..158c6ef 100644 --- a/crates/df-storage/src/crud/message_repo.rs +++ b/crates/df-storage/src/crud/message_repo.rs @@ -46,6 +46,7 @@ fn ai_message_from_row(row: &Row<'_>) -> std::result::Result Result<()> { // V33 = 审批重启恢复:ai_conversations 加 pending_approvals TEXT 列,持久化挂起审批快照, // 重启后从 DB 恢复 pending_approvals 内存态,使待审批不丢。 // V41 = 任务关联工程模块:tasks.module_id 列(工程系统打底,项目多工程下任务落到具体 module)。 - let steps: [(i32, fn(&Connection) -> Result<()>); 41] = [ + // V42 = ai_messages 加 is_estimated 列(消息级估算标记,reload 逐条回显「估算」标注)。 + let steps: [(i32, fn(&Connection) -> Result<()>); 42] = [ (1, migrate_v1), (2, migrate_v2), (3, migrate_v3), @@ -88,6 +89,7 @@ pub fn run(conn: &Connection) -> Result<()> { (39, migrate_v39), (40, migrate_v40), (41, migrate_v41), + (42, migrate_v42), ]; for (version, migrate_fn) in steps { @@ -1257,6 +1259,22 @@ fn migrate_v41(conn: &Connection) -> Result<()> { Ok(()) } +/// V42: ai_messages 加 is_estimated 列(消息级估算标记) +/// +/// 消息级 token 持久化(V38/V39)已落 prompt/completion/cache/reasoning,但「该轮 prompt 是否 +/// estimated 兜底」未存——reload 逐条回显时无法对齐 live 态 AiCompleted.is_estimated 标注。 +/// 本迁移补 INTEGER 列(NULL=老消息未标记,向前兼容;0=false 真实,1=true 估算)。 +/// 用 PRAGMA 探测列存在性,缺失才 ALTER(同 v38/v39 模式),对新库/老库均安全。 +fn migrate_v42(conn: &Connection) -> Result<()> { + if !column_exists(conn, "ai_messages", "is_estimated") { + conn.execute("ALTER TABLE ai_messages ADD COLUMN is_estimated INTEGER", [])?; + tracing::info!("v42: ai_messages 加 is_estimated 列(消息级估算标记)"); + } + conn.execute("INSERT INTO schema_version (version) VALUES (?)", [42])?; + tracing::info!("迁移 v42 完成: ai_messages 加 is_estimated 列"); + Ok(()) +} + /// V21 建表 SQL — 消息拆分存储 ai_messages 表 /// /// 与 V9_SQL 中的 ai_messages 镜像(V9 给新库,此 const 给老库 V21 迁移用 IF NOT EXISTS)。 @@ -1281,6 +1299,7 @@ CREATE TABLE IF NOT EXISTS ai_messages ( prompt_cache_hit_tokens INTEGER, prompt_cache_miss_tokens INTEGER, reasoning_tokens INTEGER, + is_estimated INTEGER, UNIQUE(conversation_id, seq) ); @@ -1532,6 +1551,7 @@ CREATE TABLE IF NOT EXISTS ai_messages ( prompt_cache_hit_tokens INTEGER, prompt_cache_miss_tokens INTEGER, reasoning_tokens INTEGER, + is_estimated INTEGER, UNIQUE(conversation_id, seq) ); @@ -2023,7 +2043,7 @@ mod tests { "tasks.module_id 列缺失(V41 加)" ); - // 3. schema_version 应推进到 41(全量迁移成功落版本号) + // 3. schema_version 应推进到 42(全量迁移成功落版本号) let max_version: i64 = conn .query_row( "SELECT COALESCE(MAX(version), 0) FROM schema_version", @@ -2032,8 +2052,8 @@ mod tests { ) .expect("查 schema_version 应成功"); assert_eq!( - max_version, 41, - "全量迁移后 schema_version 应为 41(实际 {}),说明某条 migrate_vN 链路断在中间", + max_version, 42, + "全量迁移后 schema_version 应为 42(实际 {}),说明某条 migrate_vN 链路断在中间", max_version ); @@ -2046,6 +2066,11 @@ mod tests { column_exists(&conn, "project_modules", "status"), "project_modules.status 列缺失(V40 加)" ); + // 5. V42 抽查:ai_messages.is_estimated 列存在(消息级估算标记) + assert!( + column_exists(&conn, "ai_messages", "is_estimated"), + "ai_messages.is_estimated 列缺失(V42 加)" + ); } // ============================================================ @@ -2105,7 +2130,7 @@ mod tests { |r| r.get(0), ) .unwrap(); - assert_eq!(max_v, 41, "首轮应推进到 41"); + assert_eq!(max_v, 42, "首轮应推进到 42"); // 清空版本表强制全链第二遍(每步 execute 第二次) conn.execute("DELETE FROM schema_version", []).unwrap(); @@ -2117,7 +2142,7 @@ mod tests { |r| r.get(0), ) .unwrap(); - assert_eq!(max_v2, 41, "重跑后应重新推进到 41"); + assert_eq!(max_v2, 42, "重跑后应重新推进到 42"); } // ============================================================ diff --git a/crates/df-storage/src/models.rs b/crates/df-storage/src/models.rs index 217c16b..8fb956d 100644 --- a/crates/df-storage/src/models.rs +++ b/crates/df-storage/src/models.rs @@ -440,6 +440,9 @@ pub struct AiMessageRecord { pub prompt_cache_miss_tokens: Option, /// 思考 token(deepseek-reasoner/o1 reasoning_tokens,隐藏输出)。 pub reasoning_tokens: Option, + /// 本轮 prompt 是否估算值(round_usage.prompt_tokens==0 → estimated_prompt 兜底打标)。 + /// reload 逐条回显「估算」标注;老消息 NULL → None(向前兼容)。 + pub is_estimated: Option, } // ============================================================ diff --git a/src-tauri/src/commands/ai/agentic/mod.rs b/src-tauri/src/commands/ai/agentic/mod.rs index ead3682..bc9e107 100644 --- a/src-tauri/src/commands/ai/agentic/mod.rs +++ b/src-tauri/src/commands/ai/agentic/mod.rs @@ -972,6 +972,11 @@ pub(crate) async fn run_agentic_loop( // 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) let mut tokens = TokenAccumulator::default(); + // 会话级 token 落库口径:save_conversation 内部做 old+add,各 save 传「自上次 save 的增量」 + // (usage_delta_since)。传累计快照会被同 loop 多次 save 重复累加(双计);增量口径同轮幂等, + // 审批暂停→恢复跨 loop 实例仍由 old+add 兜住(新实例 snapshot 归零)。 + let mut saved_token_snapshot = df_ai::provider::TokenUsage::default(); + // 收敛标志:仅当 LLM 末轮无 tool_calls 自行 break(正常收敛)时置 true; // 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; @@ -1357,7 +1362,9 @@ pub(crate) async fn run_agentic_loop( // 用户请求停止 或 已被新 loop 接管(stale)→ 收尾退出(已生成文本已在上一轮入库)。 // F1:stale 判定(epoch 不匹配)让旧 loop 在新 loop 启动后立即在此退出,不再继续跑。 if stop_flag.load(Ordering::SeqCst) || loop_epoch_arc.load(Ordering::SeqCst) != my_epoch { - let usage = df_ai::provider::TokenUsage { + // save_usage 增量(自上次 save 后新增,首轮即停为 0);emit_usage 累计快照 + let save_usage = usage_delta_since(&tokens, &mut saved_token_snapshot); + let emit_usage = df_ai::provider::TokenUsage { prompt_tokens: tokens.prompt(), completion_tokens: tokens.completion(), total_tokens: tokens.total(), @@ -1370,11 +1377,11 @@ pub(crate) async fn run_agentic_loop( // (stale 时 finish_round_exit 内部仅 disarm 跳过 save/reset/emit,不干扰新 loop) finish_round_exit( &session_arc, &db, &conv_id, - Some(&usage), None, + Some(&save_usage), None, true, &provider_config, &llm_concurrency, &mut guard, - &usage, + &emit_usage, // 入口 stop:本轮尚未 stream,last_round_estimated 仍为初始 false(无估算)。 last_round_estimated, None, None, true, @@ -1595,6 +1602,8 @@ pub(crate) async fn run_agentic_loop( // 预估输入 token(兜底:部分 provider 如 GLM 流式 usage 不报 prompt_tokens,后段用它补) // 注:stream_one_provider 内每次重试重建 request(因 provider.stream 消费 body), // 此处不再预构建 request(旧 request 变量已废弃),仅保留 messages 供 estimated_prompt。 + // 语义说明:每轮对全量历史重估并累加(GLM 类无 usage provider 多轮 prompt 总和偏高), + // 但「多轮累计」本就接近真实总量且标估算展示,保留现状(增量估算收益低,不动)。 let estimated_prompt: u32 = { let est = TokenEstimator::default(); messages.iter().map(|m| est.estimate_message(m)).sum() @@ -1802,7 +1811,8 @@ pub(crate) async fn run_agentic_loop( // G4.3:本轮 token 用量是否估算值(provider 未报 prompt_tokens → estimated_prompt 兜底), // 供 loop 内/loop 后各退出路径透传 AiCompleted(is_estimated)仅作展示标注。 - last_round_estimated = round_usage.prompt_tokens == 0; + // 语义修正:整 loop 只要任一轮估算即标估算(累计总量含估算成分),非仅末轮。 + last_round_estimated |= round_usage.prompt_tokens == 0; // F1 并发 epoch:流返回后若已被新 loop 接管(force_send 等),旧 loop 不再 push 消息 / // 保文 / 执行工具,立即退出(guard disarm 跳过复位,防 clobber 新 loop 的 Generating)。 @@ -1821,7 +1831,7 @@ pub(crate) async fn run_agentic_loop( 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 }, + total_tokens: if round_usage.prompt_tokens == 0 { estimated_prompt.saturating_add(round_usage.completion_tokens) } else { round_usage.total_tokens }, // 分项 token(2026-08-02):cache/reasoning 透传自 round_usage,落库 + 累加器都需 prompt_cache_hit_tokens: round_usage.prompt_cache_hit_tokens, prompt_cache_miss_tokens: round_usage.prompt_cache_miss_tokens, @@ -1858,6 +1868,8 @@ pub(crate) async fn run_agentic_loop( msg.prompt_cache_hit_tokens = Some(usage.prompt_cache_hit_tokens); msg.prompt_cache_miss_tokens = Some(usage.prompt_cache_miss_tokens); msg.reasoning_tokens = Some(usage.reasoning_tokens); + // 消息级估算标记:本轮 prompt 是否 estimated 兜底,reload 逐条回显对齐 live 态 + msg.is_estimated = Some(round_usage.prompt_tokens == 0); conv.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。"); @@ -1868,25 +1880,26 @@ pub(crate) async fn run_agentic_loop( // 统一走 finish_round_exit 收尾(save + spawn_title + reset + emit)。 // 注意:partial 文本+系统提示已先 push(上方 block),此 save 落库含本轮 partial,幂等覆盖。 - // emit_usage 用 tokens 快照(tokens.add 已累加本轮):total_tokens/prompt/completion 对齐原 emit 三元组。 + // save_usage 用增量(tokens 已累加本轮,减上次快照);emit_usage 用 tokens 快照累计。 // MidStream 分叉:emit_incomplete=Some(true)(前端标不完整),publish_incomplete=None(总线消费方), // do_publish=true(publish 走总线)。spawn_title=true(后台标题,失败 extract 兜底)。 + let save_usage = usage_delta_since(&tokens, &mut saved_token_snapshot); let emit_usage = df_ai::provider::TokenUsage { prompt_tokens: tokens.prompt(), completion_tokens: tokens.completion(), - total_tokens: usage.total_tokens, + total_tokens: tokens.total(), prompt_cache_hit_tokens: tokens.cache_hit(), prompt_cache_miss_tokens: tokens.cache_miss(), reasoning_tokens: tokens.reasoning(), }; finish_round_exit( &session_arc, &db, &conv_id, - Some(&usage), Some(&resolved_model), + Some(&save_usage), Some(&resolved_model), true, &provider_config, &llm_concurrency, &mut guard, &emit_usage, - round_usage.prompt_tokens == 0, + last_round_estimated, Some(true), None, true, &pinned_goals_snapshot, &app_handle, @@ -1933,6 +1946,7 @@ pub(crate) async fn run_agentic_loop( round_prompt, round_usage.completion_tokens, round_usage.prompt_cache_hit_tokens, round_usage.prompt_cache_miss_tokens, round_usage.reasoning_tokens, + round_usage.prompt_tokens == 0, ); if GOAL_PIN_ENABLED { update_pinned_goals(&mut session, &conv_id, &tool_calls_acc); @@ -1944,21 +1958,17 @@ pub(crate) async fn run_agentic_loop( // 或 session lock 竞争)永远到不了出口,用户重启后上轮回复丢失。此处出 push 锁作用域后 // 立即 save,幂等(每轮重复覆盖落库),即使后续工具卡住本轮消息已持久化。 { - let usage = df_ai::provider::TokenUsage { - prompt_tokens: tokens.prompt(), - completion_tokens: tokens.completion(), - total_tokens: tokens.total(), - prompt_cache_hit_tokens: tokens.cache_hit(), - prompt_cache_miss_tokens: tokens.cache_miss(), - reasoning_tokens: tokens.reasoning(), - }; + // 落库传「自上次 save 的增量」而非累计快照(累计会被同 loop 多次 save 重复累加双计) + let usage = usage_delta_since(&tokens, &mut saved_token_snapshot); save_conversation(&session_arc, &db, &conv_id, Some(&usage), Some(&resolved_model), true).await; } // 停止信号 或 已被新 loop 接管(stale):已生成文本入库后退出,不再执行后续工具调用。 // F1:stale 时旧 loop 在此退出,不执行工具(防重复工具执行);finish_round_exit 内部跳过 emit。 if stop_flag.load(Ordering::SeqCst) || loop_epoch_arc.load(Ordering::SeqCst) != my_epoch { - let usage = df_ai::provider::TokenUsage { + // save_usage 增量(自上次 save 后新增);emit_usage 累计快照(前端展示本 loop 总量) + let save_usage = usage_delta_since(&tokens, &mut saved_token_snapshot); + let emit_usage = df_ai::provider::TokenUsage { prompt_tokens: tokens.prompt(), completion_tokens: tokens.completion(), total_tokens: tokens.total(), @@ -1969,12 +1979,12 @@ pub(crate) async fn run_agentic_loop( // 统一走 finish_round_exit:save(Some usage, Some model) + spawn_title + emit(None,None,publish=true) finish_round_exit( &session_arc, &db, &conv_id, - Some(&usage), Some(&resolved_model), + Some(&save_usage), Some(&resolved_model), true, &provider_config, &llm_concurrency, &mut guard, - &usage, - round_usage.prompt_tokens == 0, + &emit_usage, + last_round_estimated, None, None, true, &pinned_goals_snapshot, &app_handle, @@ -2057,14 +2067,8 @@ pub(crate) async fn run_agentic_loop( guard.disarm(); return; } - let usage = df_ai::provider::TokenUsage { - prompt_tokens: tokens.prompt(), - completion_tokens: tokens.completion(), - total_tokens: tokens.total(), - prompt_cache_hit_tokens: tokens.cache_hit(), - prompt_cache_miss_tokens: tokens.cache_miss(), - reasoning_tokens: tokens.reasoning(), - }; + // 落库传「自上次 save 的增量」;审批暂停→恢复跨 loop 实例由 save_conversation old+add 兜住 + let usage = usage_delta_since(&tokens, &mut saved_token_snapshot); save_conversation(&session_arc, &db, &conv_id, Some(&usage), Some(&resolved_model), true).await; // 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) @@ -2088,7 +2092,9 @@ pub(crate) async fn run_agentic_loop( max_iter = max_iterations, "[ai] agentic 循环达最大轮次仍未收敛,自动完成(incomplete=true)", ); - let usage = df_ai::provider::TokenUsage { + // save_usage 增量;emit_usage 累计快照(前端展示本 loop 总量) + let save_usage = usage_delta_since(&tokens, &mut saved_token_snapshot); + let emit_usage = df_ai::provider::TokenUsage { prompt_tokens: tokens.prompt(), completion_tokens: tokens.completion(), total_tokens: tokens.total(), @@ -2101,11 +2107,11 @@ pub(crate) async fn run_agentic_loop( // (与其他 5 路径不一致是历史现状,本次仅收敛重复代码不改 publish 策略,语义零变更)。 finish_round_exit( &session_arc, &db, &conv_id, - Some(&usage), Some(&resolved_model), + Some(&save_usage), Some(&resolved_model), false, &provider_config, &llm_concurrency, &mut guard, - &usage, + &emit_usage, last_round_estimated, Some(true), None, false, &pinned_goals_snapshot, @@ -2122,19 +2128,10 @@ pub(crate) async fn run_agentic_loop( guard.disarm(); return; } - let usage = df_ai::provider::TokenUsage { - prompt_tokens: tokens.prompt(), - completion_tokens: tokens.completion(), - total_tokens: tokens.total(), - prompt_cache_hit_tokens: tokens.cache_hit(), - prompt_cache_miss_tokens: tokens.cache_miss(), - reasoning_tokens: tokens.reasoning(), - }; - // 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; + // save_usage 增量(自上次 save 后新增,正常收敛时上轮 save 已落,此处通常为 0); + // emit_usage 用 tokens 累计快照。normal_usage.total 直接取 tokens.total() 保持三字段一致。 + let save_usage = usage_delta_since(&tokens, &mut saved_token_snapshot); + let usage_total = tokens.total(); { let session_arc = session_arc.clone(); let db = db.clone(); @@ -2145,7 +2142,7 @@ pub(crate) async fn run_agentic_loop( let llm_concurrency = llm_concurrency.clone(); let resolved_model = resolved_model.clone(); tauri::async_runtime::spawn(async move { - save_conversation(&session_arc, &db, &conv_id, Some(&usage), Some(&resolved_model), true).await; + save_conversation(&session_arc, &db, &conv_id, Some(&save_usage), Some(&resolved_model), true).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); @@ -2707,6 +2704,43 @@ async fn emit_ai_completed_once( } } +// ── usage_delta_since: 会话级 token 落库「增量」口径 helper ── +// +// save_conversation 对 usage 做 old+add(accumulate_tokens),故每次 save 只能传 +// 「自上次 save 以来的增量」——传累计快照会被同 loop 多次 save(每轮 + 各退出路径) +// 重复累加致 ai_conversations token 双计。本函数取 tokens 当前值减上次快照得增量, +// 并推进快照,同轮多次 save 幂等(第二次增量=0)。 +fn usage_delta_since( + tokens: &TokenAccumulator, + last_saved: &mut df_ai::provider::TokenUsage, +) -> df_ai::provider::TokenUsage { + let cur = df_ai::provider::TokenUsage { + prompt_tokens: tokens.prompt(), + completion_tokens: tokens.completion(), + total_tokens: tokens.total(), + prompt_cache_hit_tokens: tokens.cache_hit(), + prompt_cache_miss_tokens: tokens.cache_miss(), + reasoning_tokens: tokens.reasoning(), + }; + let delta = df_ai::provider::TokenUsage { + prompt_tokens: cur.prompt_tokens.saturating_sub(last_saved.prompt_tokens), + completion_tokens: cur.completion_tokens.saturating_sub(last_saved.completion_tokens), + total_tokens: 0, // 下方按 prompt+completion 增量重算,保持三字段一致 + prompt_cache_hit_tokens: cur + .prompt_cache_hit_tokens + .saturating_sub(last_saved.prompt_cache_hit_tokens), + prompt_cache_miss_tokens: cur + .prompt_cache_miss_tokens + .saturating_sub(last_saved.prompt_cache_miss_tokens), + reasoning_tokens: cur.reasoning_tokens.saturating_sub(last_saved.reasoning_tokens), + }; + *last_saved = cur; + df_ai::provider::TokenUsage { + total_tokens: delta.prompt_tokens.saturating_add(delta.completion_tokens), + ..delta + } +} + // ── finish_round_exit: run_agentic_loop 收尾统一入口(抽自 5 处退出路径重复代码) ── // // 收敛各退出点的「save_conversation + spawn_ensure_title + guard.reset + emit AiCompleted」序列。 @@ -2786,6 +2820,7 @@ async fn finish_round_exit( // prompt_tokens/completion_tokens: 本轮 LLM 调用 token 用量(消息级持久化,解 reload/压缩/切会话后 // 历史 assistant 消息 token 不显)。两构造分支都设。 // 分项 token(2026-08-02):cache_hit/cache_miss/reasoning 透传自 round_usage,前端分计费展示。 +// estimated:本轮 prompt 是否估算兜底(round_usage.prompt_tokens==0),落库供 reload 逐条回显。 fn push_assistant_message( session: &mut AiSession, conv_id: &str, @@ -2799,6 +2834,7 @@ fn push_assistant_message( cache_hit: u32, cache_miss: u32, reasoning: u32, + estimated: bool, ) { // 根治「空工具轮 assistant 消息落库」:LLM 仅返回 tool_calls 无文本时 full_text 可能为 // 空/纯空白(assistant("")/assistant("\n") 均合法落库),前端渲染空气泡。入口统一 trim: @@ -2831,6 +2867,7 @@ fn push_assistant_message( msg.prompt_cache_hit_tokens = Some(cache_hit); msg.prompt_cache_miss_tokens = Some(cache_miss); msg.reasoning_tokens = Some(reasoning); + msg.is_estimated = Some(estimated); session.conv(conv_id).messages.push(msg); } else if !full_text.is_empty() { let mut msg = ChatMessage::assistant(full_text); @@ -2841,6 +2878,7 @@ fn push_assistant_message( msg.prompt_cache_hit_tokens = Some(cache_hit); msg.prompt_cache_miss_tokens = Some(cache_miss); msg.reasoning_tokens = Some(reasoning); + msg.is_estimated = Some(estimated); session.conv(conv_id).messages.push(msg); } } diff --git a/src-tauri/src/commands/ai/commands/conversation.rs b/src-tauri/src/commands/ai/commands/conversation.rs index 7d42674..8b92127 100644 --- a/src-tauri/src/commands/ai/commands/conversation.rs +++ b/src-tauri/src/commands/ai/commands/conversation.rs @@ -106,6 +106,7 @@ pub fn record_to_message(rec: &AiMessageRecord) -> ChatMessage { prompt_cache_hit_tokens: rec.prompt_cache_hit_tokens, prompt_cache_miss_tokens: rec.prompt_cache_miss_tokens, reasoning_tokens: rec.reasoning_tokens, + is_estimated: rec.is_estimated, } } @@ -155,6 +156,7 @@ pub fn message_to_record( prompt_cache_hit_tokens: msg.prompt_cache_hit_tokens, prompt_cache_miss_tokens: msg.prompt_cache_miss_tokens, reasoning_tokens: msg.reasoning_tokens, + is_estimated: msg.is_estimated, } } @@ -836,6 +838,7 @@ mod tests { prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, + is_estimated: None, } } diff --git a/src-tauri/src/commands/ai/conversation.rs b/src-tauri/src/commands/ai/conversation.rs index 5ef3192..78353ff 100644 --- a/src-tauri/src/commands/ai/conversation.rs +++ b/src-tauri/src/commands/ai/conversation.rs @@ -706,6 +706,7 @@ mod tests { prompt_cache_hit_tokens: None, prompt_cache_miss_tokens: None, reasoning_tokens: None, + is_estimated: None, timestamp: None, } } diff --git a/src-tauri/src/commands/ai/stream_recv.rs b/src-tauri/src/commands/ai/stream_recv.rs index 4937118..0acb5be 100644 --- a/src-tauri/src/commands/ai/stream_recv.rs +++ b/src-tauri/src/commands/ai/stream_recv.rs @@ -328,6 +328,11 @@ pub(crate) async fn stream_llm( ), }; } + // 已收到 finish_reason 仅剩等 usage 帧:慢 provider 拖过 idle 窗口不降 Partial, + // 按正常完成收尾(usage 缺失用默认 0,文本/tool_calls 已完整)。 + if finished_received { + break; + } warn!( provider = %provider.name(), conv_id = %conv_id, @@ -379,6 +384,11 @@ pub(crate) async fn stream_llm( ), }; } + // 已收到 finish_reason 仅剩等 usage 帧:慢/坏 provider 拖过 15s 不降 Partial, + // 按正常完成收尾(与 idle_deadline 分支同口径,usage 缺失用默认 0)。 + if finished_received { + break; + } warn!( provider = %provider.name(), conv_id = %conv_id, @@ -461,7 +471,14 @@ pub(crate) async fn stream_llm( } if chunk.finished { finished_received = true; - break; + // usage 携带点:OpenAI 兼容流中 usage 挂在 [DONE] 帧(或 usage-only 帧), + // 而 finish_reason 帧(finished=true, usage=None)在其之前到达。 + // 若此刻 break 会错过 [DONE] 帧的 usage → 真实 completion_tokens 丢失, + // 前端 token 显示 0。拿到真实 usage 才停;usage 仍 None 则继续读到 + // usage-only/[DONE] 帧或通道关闭(Ok(None) 兜底退出,不断连误判)。 + if chunk.usage.is_some() { + break; + } } } Ok(Some(Err(err_str))) => { diff --git a/src/composables/ai/useAiConversations.ts b/src/composables/ai/useAiConversations.ts index 0812b03..54dde52 100644 --- a/src/composables/ai/useAiConversations.ts +++ b/src/composables/ai/useAiConversations.ts @@ -191,6 +191,8 @@ function parseConvMessages(rawMsgs: any[], fallbackPrefix: string): AiMessage[] cache_hit: m.prompt_cache_hit_tokens, cache_miss: m.prompt_cache_miss_tokens, reasoning: m.reasoning_tokens, + // 消息级估算标记(DB 列,老消息 null → undefined):reload 逐条回显对齐 live 态 AiCompleted + is_estimated: m.is_estimated, } : undefined, // 分项 token 消息级字段(详情面板直接读 msg.xxx,与实时态 useAiEvents 写入一致) diff --git a/src/composables/ai/useAiLifecycleEvents.ts b/src/composables/ai/useAiLifecycleEvents.ts index f1044d6..ef178ab 100644 --- a/src/composables/ai/useAiLifecycleEvents.ts +++ b/src/composables/ai/useAiLifecycleEvents.ts @@ -135,6 +135,9 @@ export function handleLifecycleEvent(event: AiChatEvent): boolean { state.convTokenTotal = { prompt: event.prompt_tokens, completion: event.completion_tokens, total: event.total_tokens } } // 每轮 token 写入最后一条 AI 消息,供 MessageList 逐条显示 + // 口径说明:后端 AiCompleted 每 loop 只发一次,携带本 loop 累计用量,故 live 态 + // 聚合到「最后一条 assistant」;reload(switchConversation)从 DB 逐条回显各轮本轮量 + // (消息级 token)。两者展示层级不同(live=loop 聚合,reload=单轮),属已接受口径。 for (let i = state.messages.length - 1; i >= 0; i--) { const m = state.messages[i] if (m.role === 'assistant' && !m.isError) {