修复AI建议逻辑和字段映射问题
- 修复AI建议基于问题描述而不是处理过程生成 - 修复工单详情页面显示逻辑 - 修复飞书时间字段处理（毫秒时间戳转换） - 优化字段映射和转换逻辑 - 添加飞书集成功能 - 改进对话历史合并功能 - 优化系统优化反馈机制
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赵杰 Jie Zhao （雄狮汽车科技）
@@ -101,26 +101,47 @@ class DialogueManager:
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# 性能优化分析
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optimization_result = self.system_optimizer.optimize_response_time(response_time)
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# 记录Token使用情况
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if success and "token_usage" in response_result:
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token_usage = response_result["token_usage"]
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# 计算成本
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# 记录Token使用情况（兼容多种返回格式）
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if success:
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# 兼容返回 usage: {prompt_tokens, completion_tokens}
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usage = response_result.get("usage", {}) or {}
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token_usage = response_result.get("token_usage", {}) or {}
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input_tokens = token_usage.get("input_tokens")
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output_tokens = token_usage.get("output_tokens")
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if input_tokens is None and isinstance(usage, dict):
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input_tokens = usage.get("prompt_tokens") or usage.get("input_tokens") or 0
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if output_tokens is None and isinstance(usage, dict):
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output_tokens = usage.get("completion_tokens") or usage.get("output_tokens") or 0
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# 若均为0，使用简易估算（避免记录缺失）
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if not input_tokens and user_message:
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try:
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input_tokens = max(1, len(user_message) // 4)
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except Exception:
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input_tokens = 0
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if not output_tokens and response_result.get("response"):
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try:
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output_tokens = max(1, len(response_result.get("response")) // 4)
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except Exception:
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output_tokens = 0
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model_name = response_result.get("model") or response_result.get("model_name") or "qwen-plus-latest"
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# 计算成本并限制
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estimated_cost = self.token_monitor._calculate_cost(
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response_result.get("model_name", "qwen-plus-latest"),
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token_usage.get("input_tokens", 0),
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token_usage.get("output_tokens", 0)
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model_name,
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int(input_tokens or 0),
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int(output_tokens or 0)
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)
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# 检查成本限制
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if not self.system_optimizer.check_cost_limit(estimated_cost):
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return {"error": "请求成本超限，请稍后再试"}
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self.token_monitor.record_token_usage(
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user_id=user_id or "anonymous",
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work_order_id=work_order_id,
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model_name=response_result.get("model_name", "qwen-plus-latest"),
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input_tokens=token_usage.get("input_tokens", 0),
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output_tokens=token_usage.get("output_tokens", 0),
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model_name=model_name,
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input_tokens=int(input_tokens or 0),
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output_tokens=int(output_tokens or 0),
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response_time=response_time,
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success=success,
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error_message=error_message
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@@ -140,7 +140,7 @@ class RealtimeChatManager:
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if len(session["context"]) > 20: # 保留最近10轮对话
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session["context"] = session["context"][-20:]
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# 保存到数据库
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# 保存到数据库（每轮一条，带会话标记）
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self._save_conversation(session_id, user_msg, assistant_msg)
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return {
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@@ -274,30 +274,32 @@ class RealtimeChatManager:
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"""保存对话到数据库"""
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try:
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with db_manager.get_session() as session:
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# 保存用户消息
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user_conversation = Conversation(
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work_order_id=user_msg.work_order_id,
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user_message=user_msg.content,
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assistant_response="", # 用户消息没有助手回复
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timestamp=user_msg.timestamp,
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confidence_score=None,
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knowledge_used=None,
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response_time=None
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)
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session.add(user_conversation)
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# 保存助手消息
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assistant_conversation = Conversation(
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work_order_id=assistant_msg.work_order_id,
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user_message="", # 助手消息没有用户输入
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assistant_response=assistant_msg.content,
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timestamp=assistant_msg.timestamp,
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# 统一为一条记录：包含用户消息与助手回复
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try:
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response_time = None
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if assistant_msg.timestamp and user_msg.timestamp:
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response_time = max(0.0, (assistant_msg.timestamp - user_msg.timestamp).total_seconds() * 1000.0)
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except Exception:
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response_time = None
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# 在知识字段中打上会话标记，便于结束时合并清理
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marked_knowledge = assistant_msg.knowledge_used or []
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try:
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marked_knowledge = list(marked_knowledge)
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marked_knowledge.append({"session_id": session_id, "type": "session_marker"})
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except Exception:
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pass
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conversation = Conversation(
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work_order_id=assistant_msg.work_order_id or user_msg.work_order_id,
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user_message=user_msg.content or "",
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assistant_response=assistant_msg.content or "",
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timestamp=assistant_msg.timestamp or user_msg.timestamp,
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confidence_score=assistant_msg.confidence_score,
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knowledge_used=json.dumps(assistant_msg.knowledge_used, ensure_ascii=False) if assistant_msg.knowledge_used else None,
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response_time=0.5 # 模拟响应时间
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knowledge_used=json.dumps(marked_knowledge, ensure_ascii=False) if marked_knowledge else None,
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response_time=response_time
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)
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session.add(assistant_conversation)
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session.add(conversation)
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session.commit()
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except Exception as e:
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@@ -385,6 +387,56 @@ class RealtimeChatManager:
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"""结束会话"""
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try:
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if session_id in self.active_sessions:
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session_meta = self.active_sessions[session_id]
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# 汇总本会话为一条记录
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history = self.message_history.get(session_id, [])
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if history:
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user_parts = []
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assistant_parts = []
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response_times = []
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first_ts = None
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last_ts = None
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for i in range(len(history)):
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msg = history[i]
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if first_ts is None:
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first_ts = msg.timestamp
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last_ts = msg.timestamp
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if msg.role == "user":
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user_parts.append(msg.content)
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# 计算到下一条助手回复的间隔
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if i + 1 < len(history) and history[i+1].role == "assistant":
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try:
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rt = max(0.0, (history[i+1].timestamp - msg.timestamp).total_seconds() * 1000.0)
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response_times.append(rt)
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except Exception:
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pass
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elif msg.role == "assistant":
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assistant_parts.append(msg.content)
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agg_user = "\n\n".join([p for p in user_parts if p])
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agg_assistant = "\n\n".join([p for p in assistant_parts if p])
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avg_rt = sum(response_times)/len(response_times) if response_times else None
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from ..core.database import db_manager as _db
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from ..core.models import Conversation as _Conv
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import json as _json
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with _db.get_session() as dbs:
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agg = _Conv(
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work_order_id=session_meta.get("work_order_id"),
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user_message=agg_user,
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assistant_response=agg_assistant,
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timestamp=last_ts or first_ts,
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confidence_score=None,
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knowledge_used=_json.dumps({"session_id": session_id, "aggregated": True}, ensure_ascii=False),
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response_time=avg_rt
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)
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dbs.add(agg)
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# 删除本会话标记的分散记录
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try:
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pattern = f'%"session_id":"{session_id}"%'
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dbs.query(_Conv).filter(_Conv.knowledge_used.like(pattern)).delete(synchronize_session=False)
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except Exception:
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pass
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dbs.commit()
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del self.active_sessions[session_id]
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if session_id in self.message_history:
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