newtw66

core/agent_tools.py

@@ -11,6 +11,13 @@ from db.chat_log_db import get_conversation, get_customer_orders
 logger = logging.getLogger("cs_agent")
 
 
+class TransferSuccessException(Exception):
+    """转接成功后抛出此异常，用于提前终止 AI 处理流程"""
+    def __init__(self, transfer_cmd: str):
+        self.transfer_cmd = transfer_cmd
+        super().__init__(transfer_cmd)
+
+
 async def transfer_to_human_tool(ctx: RunContext[Any], reason: str = Field(description="转人工的原因")) -> str:
     """
     【核心工具】执行转人工逻辑。
@@ -22,8 +29,9 @@ async def transfer_to_human_tool(ctx: RunContext[Any], reason: str = Field(descr
 
     if designer_name:
         magic_cmd = f"正在为您转接|[转移会话],{designer_name},无原因"
-        logger.info(f"[Tool] 成功呼叫设计师: {designer_name}")
-        return magic_cmd
+        logger.info(f"[Tool] 成功呼叫设计师: {designer_name}，立即触发转接")
+        # 抛出异常以提前终止 AI 后续处理，节省等待时间
+        raise TransferSuccessException(magic_cmd)
     else:
         hour = datetime.now().hour
         logger.warning(f"[Tool] 派单失败：设计师们不在位 (当前{hour}点)")

core/orchestrator.py

@@ -37,6 +37,18 @@ _OUTBOUND_BLOCK_MARKERS = (
     '[{"name":',
 )
 
+# 历史记录格式检测模式（AI 转述历史时容易泄露）
+_HISTORY_LEAK_PATTERNS = [
+    r'\[\d{4}-\d{2}-\d{2}[^\]]*\]\s*(客户|客服)[：:]',  # [2026-03-07 12:00:00] 客户：
+    r'\[\d{2}:\d{2}:\d{2}\]\s*(客户|客服|我)[：:]',       # [12:00:00] 客户：
+    r'(根据|查看|查询|翻看)(历史|聊天|对话)(记录|内容)',   # 根据历史记录
+    r'历史(记录|对话|消息)(显示|表明|中)',                # 历史记录显示
+    r'之前的(聊天|对话|记录)(中|里|显示)',               # 之前的聊天中
+    r'共\d+条(历史|对话)?消息',                          # 共30条历史消息
+    r'订单号[:：]\s*\d{10,}',                            # 订单号:xxxxxxxxxx
+    r'(状态|金额|数量)[:：].*(状态|金额|数量)[:：]',     # 状态:xxx 金额:xxx 连续出现
+]
+
 class SystemOrchestrator:
     """
     全系统总编排：具备转接冷却、防抖合并、多消息去重、以及精准日志。
@@ -84,6 +96,11 @@ class SystemOrchestrator:
         if any(marker in cleaned for marker in _OUTBOUND_BLOCK_MARKERS):
             logger.warning("[Orchestrator] 拦截到内部内容外发，替换为安全兜底回复")
             return "我在帮你看记录，稍等哈"
+        # 检查历史记录泄露模式
+        for pattern in _HISTORY_LEAK_PATTERNS:
+            if re.search(pattern, cleaned):
+                logger.warning(f"[Orchestrator] 检测到历史记录泄露模式: {pattern[:30]}...")
+                return "我在帮你看记录，稍等哈"
         return cleaned
 
     async def on_raw_message_received(self, platform: str, raw_data: dict):
@@ -243,11 +260,17 @@ class SystemOrchestrator:
 
     async def _debounced_process(self, session_key: str, user_id: str, platform: str):
         try:
+            # 记录开始时间（防抖前）
+            process_start = time.time()
+            
             await asyncio.sleep(self._debounce_seconds)
             async with self._get_user_lock(session_key):
                 messages = self._pending_messages.pop(session_key, [])
                 if not messages: return
                 
+                debounce_elapsed = time.time() - process_start
+                logger.info(f"[计时] user={user_id} 防抖等待完成: {debounce_elapsed:.1f}s")
+                
                 # A. 合并与元数据修复（去重：同一防抖窗口内完全相同的内容只保留一条）
                 seen_contents = set()
                 unique_parts = []
@@ -278,9 +301,12 @@ class SystemOrchestrator:
                 )
 
                 # B. 持久化
+                db_start = time.time()
                 db_content = combined_content
                 if all_image_urls: db_content = f"【系统：已收到{len(all_image_urls)}张图】\n{combined_content}"
                 await repo.save_chat(platform, user_id, db_content, "in", acc_id=acc_id, image_urls=all_image_urls)
+                db_elapsed = time.time() - db_start
+                logger.info(f"[计时] user={user_id} 消息入库: {db_elapsed:.2f}s")
                 
                 # B2. 后台图片分析（不阻塞主流程，用于数据标定）
                 if all_image_urls:
@@ -302,9 +328,17 @@ class SystemOrchestrator:
                     )
                 else:
                     # D. 正常流程：调用AI思考
+                    history_start = time.time()
                     history = await repo.get_chat_history(user_id, limit=10, acc_id=acc_id)
                     if history and history[-1].get('content') == db_content: history = history[:-1]
+                    history_elapsed = time.time() - history_start
+                    logger.info(f"[计时] user={user_id} 查询历史: {history_elapsed:.2f}s (共{len(history)}条)")
+                    
+                    ai_start = time.time()
                     std_res = await self.brain.think_and_reply(final_msg, history=history)
+                    ai_elapsed = time.time() - ai_start
+                    total_elapsed = time.time() - process_start
+                    logger.info(f"[计时] user={user_id} AI思考: {ai_elapsed:.1f}s | 总耗时: {total_elapsed:.1f}s")
 
                 # E. 发送并记录时间
                 if std_res.should_reply:

core/pydantic_ai_agent_v2.py

@@ -2,15 +2,20 @@ import os
 import re
 import hashlib
 import logging
+import time
 from typing import List, Optional, Any, Dict
 from pydantic_ai import Agent, RunContext
 from pydantic_ai.models.openai import OpenAIChatModel
 from pydantic_ai.providers.openai import OpenAIProvider
 from core.schema import StandardMessage, StandardResponse
-from core.agent_tools import register_agent_tools
+from core.agent_tools import register_agent_tools, TransferSuccessException
 
 logger = logging.getLogger("cs_agent")
 
+# 日志详细程度：设置环境变量 AI_LOG_LEVEL=debug 可获得完整日志
+_LOG_FULL_PROMPT = os.getenv("AI_LOG_LEVEL", "").lower() == "debug"
+_LOG_CLIP_LIMIT = int(os.getenv("AI_LOG_CLIP", "2000"))  # 日志截断长度
+
 from core.skill_manager import skill_manager
 
 
@@ -21,6 +26,18 @@ _INTERNAL_TOOL_MARKERS = (
     "【订单详情】",
 )
 
+# 历史记录格式检测模式（AI 转述历史时容易泄露）
+_HISTORY_LEAK_PATTERNS = [
+    r'\[\d{4}-\d{2}-\d{2}[^\]]*\]\s*(客户|客服)[：:]',  # [2026-03-07 12:00:00] 客户：
+    r'\[\d{2}:\d{2}:\d{2}\]\s*(客户|客服|我)[：:]',       # [12:00:00] 客户：
+    r'(根据|查看|查询|翻看)(历史|聊天|对话)(记录|内容)',   # 根据历史记录
+    r'历史(记录|对话|消息)(显示|表明|中)',                # 历史记录显示
+    r'之前的(聊天|对话|记录)(中|里|显示)',               # 之前的聊天中
+    r'共\d+条(历史|对话)?消息',                          # 共30条历史消息
+    r'订单号[:：]\s*\d{10,}',                            # 订单号:xxxxxxxxxx
+    r'(状态|金额|数量)[:：].*(状态|金额|数量)[:：]',     # 状态:xxx 金额:xxx 连续出现
+]
+
 
 def _clip(text: str, limit: int = 1200) -> str:
     if text is None:
@@ -61,10 +78,17 @@ def _sanitize_reply_text(reply_text: str) -> str:
     text = re.sub(r'[\[\]]{2,}', '', text)
     text = text.strip()
 
+    # 检查固定标记
     if any(marker in text for marker in _INTERNAL_TOOL_MARKERS):
         logger.warning("[Brain] 拦截到工具原文泄露，降级为安全兜底回复")
         return "我在帮你看记录，稍等哈"
 
+    # 检查历史记录泄露模式（AI 转述历史内容）
+    for pattern in _HISTORY_LEAK_PATTERNS:
+        if re.search(pattern, text):
+            logger.warning(f"[Brain] 检测到历史记录泄露模式: {pattern[:30]}...")
+            return "我在帮你看记录，稍等哈"
+
     return text.strip()
 
 
@@ -187,12 +211,51 @@ class CustomerServiceBrain:
                 recent_context = "【近期对话回顾】\n" + "\n".join(lines) + "\n----------------\n"
 
             full_input = f"【当前客户ID：{msg.user_id}】\n{recent_context}现在的对话：{user_content}"
-            logger.info(
-                f"[PROMPT->AI] user={msg.user_id} acc={msg.acc_id} images={len(msg.image_urls)}\n"
-                f"{_clip(full_input)}"
-            )
+            start_time = time.time()
+            
+            # ===== 详细日志：发给 AI 的提示词 =====
+            logger.info(f"[AI提示词] user={msg.user_id} acc={msg.acc_id} images={len(msg.image_urls)}\n{full_input}")
+            if history:
+                history_preview = "\n".join([f"  {h.get('role','?')}: {str(h.get('content',''))[:50]}" for h in history[-4:]])
+                logger.info(f"[AI历史上下文] 共{len(history)}条:\n{history_preview}")
 
-            result = await self.agent.run(full_input, message_history=history)
+            # 尝试运行 AI，捕获转接成功异常以提前终止
+            try:
+                result = await self.agent.run(full_input, message_history=history)
+            except TransferSuccessException as e:
+                # 转接工具成功后立即返回，无需等待 AI 继续生成
+                elapsed = time.time() - start_time
+                logger.info(f"[Brain] 转接成功（提前终止，耗时{elapsed:.1f}s）: {e.transfer_cmd[:60]}")
+                return StandardResponse(
+                    reply_content=e.transfer_cmd,
+                    need_transfer=True,
+                    metadata={"acc_id": msg.acc_id, "acc_type": msg.acc_type}
+                )
+
+            elapsed = time.time() - start_time
+            logger.info(f"[Brain] AI处理完成，总耗时{elapsed:.1f}s")
+            
+            # ===== 详细日志：AI 的思考过程和工具调用 =====
+            try:
+                all_msgs = result.all_messages()
+                for idx, m in enumerate(all_msgs):
+                    msg_kind = getattr(m, 'kind', type(m).__name__)
+                    if hasattr(m, 'parts'):
+                        for part in m.parts:
+                            part_kind = getattr(part, 'part_kind', '')
+                            if part_kind == 'tool-call':
+                                tool_name = getattr(part, 'tool_name', '?')
+                                tool_args = getattr(part, 'args', {})
+                                logger.info(f"[AI思考] 步骤{idx+1} 调用工具: {tool_name}({tool_args})")
+                            elif part_kind == 'tool-return':
+                                content = str(getattr(part, 'content', ''))[:200]
+                                logger.info(f"[AI思考] 步骤{idx+1} 工具返回: {content}")
+                            elif part_kind == 'text':
+                                content = str(getattr(part, 'content', ''))[:150]
+                                if content.strip():
+                                    logger.info(f"[AI思考] 步骤{idx+1} 文本输出: {content}")
+            except Exception as log_err:
+                logger.debug(f"[AI思考日志] 解析失败: {log_err}")
 
             # --- 转接指令：直接从工具返回截获，不经过 AI 二次加工 ---
             transfer_cmd = ""