chore(log): 缩减 INFO 日志量，高频诊断日志降级/删除
1. 删除缓存诊断日志 4 处（openai.rs）：排查已完结，字段已补齐，每次 LLM 调用都刷屏。 2. 删除 AgentPromptProvider 模型配置日志（agent_prompt_provider.rs）：排查遗留，每次请求刷。 3. AgentFactory 创建日志 info→debug（agent_factory.rs）：每轮对话刷，降级保留供偶发排查。 4. Calling tool / Tool calls detected info→debug（agent_loop.rs）：每次工具调用都刷，全量参数打印开销大，tracing 在未启用级别时不评估字段。 5. LoadTaskMessages 3 条日志 info→debug（load_task_messages.rs）：每次前端 trigger 都刷，一次 3 条。 6. 旧结果诊断日志 info→debug（execution.rs）：每条消息首次 agent 迭代都触发。 7. pending subagents 日志 info→debug（processor.rs）：子代理运行期高频率重复触发。 8. Updating current_session_id 仅变化时打（ws.rs）：old==new 时每条消息都刷，无信息量。 编译通过，测试全绿。
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oudecheng
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ca7dcabbde
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0f60b02ede
@ -1406,7 +1406,7 @@ impl AgentLoop {
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}
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// Execute tool calls
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tracing::info!(
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tracing::debug!(
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iteration,
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count = response.tool_calls.len(),
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"Tool calls detected, executing tools"
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@ -2088,7 +2088,7 @@ impl AgentLoop {
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serde_json::Value::Object(obj) if obj.is_empty() => "{}".to_string(),
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other => serde_json::to_string_pretty(other).unwrap_or_else(|_| other.to_string()),
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};
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tracing::info!(tool = %tool_call.name, args = %args_str, "Calling tool");
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tracing::debug!(tool = %tool_call.name, args = %args_str, "Calling tool");
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// Record ToolCallStart event
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if let Some(ref observer) = self.observer {
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@ -56,7 +56,7 @@ async fn handle_load_task_messages(
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task_id: String,
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ctx: CommandContext,
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) -> Result<CommandResponse, CommandError> {
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tracing::info!(
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tracing::debug!(
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task_id = %task_id,
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request_id = %ctx.request_id,
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"LoadTaskMessages: looking up task"
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@ -65,7 +65,7 @@ async fn handle_load_task_messages(
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// 1. Try in-memory repository first
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let task = match handler.task_repository.load_task_session(&task_id).await {
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Ok(Some(task)) => {
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tracing::info!(
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tracing::debug!(
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task_id = %task.id,
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session_id = %task.session_id,
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state = ?task.state,
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@ -74,7 +74,7 @@ async fn handle_load_task_messages(
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Some(task)
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}
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Ok(None) => {
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tracing::info!(
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tracing::debug!(
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task_id = %task_id,
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"LoadTaskMessages: task not in memory, searching database"
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);
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@ -233,8 +233,8 @@ impl AgentFactory {
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_ => expert_provider_config,
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};
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// 诊断日志：记录 agent 实际使用的配置和实例 ID
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tracing::info!(
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// 诊断日志（debug）：记录 agent 实际使用的配置和实例 ID
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tracing::debug!(
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instance_id = self.instance_id,
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channel = %request.channel_name,
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session_id = %session_id,
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@ -61,15 +61,6 @@ impl SystemPromptProvider for AgentPromptProvider {
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return None;
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}
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// 诊断日志：记录系统提示词实际使用的模型配置（用于排查"配置不生效"问题）
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tracing::info!(
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session_id = ?context.session_id,
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chat_id = %context.chat_id,
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provider = %self.provider_config.name,
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model_id = %self.provider_config.model_id,
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"AgentPromptProvider: building system prompt with model config"
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);
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// 加载 Agent 提示词（AGENT.md + builtin + MEMORY_SUMMARY.md）
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let agent_prompt = load_agent_prompt().ok().flatten()?;
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@ -167,7 +167,7 @@ impl AgentExecutionService {
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.as_deref()
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.unwrap_or(request.chat_id),
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);
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tracing::info!(
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tracing::debug!(
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channel = %request.channel_name,
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chat_id = %request.chat_id,
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user_message_id = %request.user_message.id,
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@ -594,7 +594,7 @@ impl InboundProcessor {
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.list_pending_subagents(topic_id, Some("running"))
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.unwrap_or_default();
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if !pending.is_empty() {
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tracing::info!(
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tracing::debug!(
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topic_id = %topic_id,
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pending_count = pending.len(),
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"Skipping ExecutionCompleted: pending subagents still running"
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@ -557,13 +557,15 @@ async fn handle_inbound(
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// 处理响应
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if response.success {
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// 更新当前会话 ID（如果是创建会话）
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// 更新当前会话 ID（如果是创建会话）；仅在变化时记录日志
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if let Some(session_id) = response.metadata.get("session_id") {
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tracing::info!(
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old_session_id = %current_session_id,
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new_session_id = %session_id,
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"Updating current_session_id"
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);
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if session_id != current_session_id {
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tracing::info!(
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old_session_id = %current_session_id,
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new_session_id = %session_id,
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"Updating current_session_id"
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);
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}
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*current_session_id = session_id.clone();
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let _ = state
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.channel_manager
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@ -96,7 +96,6 @@ impl StreamingAccumulator {
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/// 跳过 total_tokens=0 的占位帧，避免覆盖真实值。
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fn set_usage(&mut self, usage: OpenAIUsage) {
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if usage.total_tokens > 0 {
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usage.log_cache_diagnostics("stream_final_frame");
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self.usage = Some(usage);
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}
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}
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@ -725,7 +724,6 @@ impl OpenAIProvider {
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})
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.unwrap_or_default();
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// 回退场景下也从非流式响应提取 usage
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openai_resp.usage.log_cache_diagnostics("non_streaming_fallback");
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response.usage = Usage {
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prompt_tokens: openai_resp.usage.prompt_tokens,
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completion_tokens: openai_resp.usage.completion_tokens,
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@ -1108,19 +1106,6 @@ impl OpenAIUsage {
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})
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.unwrap_or(0)
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}
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/// 诊断日志：记录 API 是否返回缓存字段及解析后的值（排查"缓存一直不命中"问题）。
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/// field_present=false 说明 API/网关根本没返回缓存字段（中转服务剥离或模型不支持）。
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fn log_cache_diagnostics(&self, source: &str) {
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tracing::info!(
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source = %source,
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prompt_tokens = self.prompt_tokens,
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cached_tokens = self.cached_tokens(),
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deepseek_cache_field_present = self.prompt_cache_hit_tokens.is_some(),
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openai_cache_details_present = self.prompt_tokens_details.is_some(),
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"OpenAI usage cache diagnostics"
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);
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}
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}
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#[async_trait]
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@ -1279,7 +1264,6 @@ impl LLMProvider for OpenAIProvider {
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reasoning_content: openai_resp.choices[0].message.reasoning_content.clone(),
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tool_calls,
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usage: {
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openai_resp.usage.log_cache_diagnostics("non_streaming");
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Usage {
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prompt_tokens: openai_resp.usage.prompt_tokens,
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completion_tokens: openai_resp.usage.completion_tokens,
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