feat: 对话历史页面租户分组展示功能
- 新增 ConversationHistoryManager.get_tenant_summary() 按租户聚合会话统计 - get_sessions_paginated() 和 get_conversation_analytics() 增加 tenant_id 过滤 - 新增 GET /api/conversations/tenants 租户汇总端点 - sessions 和 analytics API 端点支持 tenant_id 查询参数 - 前端实现租户卡片列表视图和租户详情会话表格视图 - 实现面包屑导航、搜索范围限定、统计面板上下文切换 - 会话删除后自动检测空租户并返回列表视图 - dashboard.html 添加租户视图 DOM 容器 - 交互模式与知识库租户分组视图保持一致
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@@ -14,7 +14,7 @@ from ..core.database import db_manager
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from ..core.models import Conversation, WorkOrder, WorkOrderSuggestion, KnowledgeEntry, ChatSession
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from ..core.redis_manager import redis_manager
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from src.config.unified_config import get_config
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from sqlalchemy import and_, or_, desc
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from sqlalchemy import and_, or_, desc, func, case
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logger = logging.getLogger(__name__)
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@@ -634,7 +634,8 @@ class ConversationHistoryManager:
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def get_conversation_analytics(
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self,
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work_order_id: Optional[int] = None,
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days: int = 7
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days: int = 7,
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tenant_id: Optional[str] = None
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) -> Dict[str, Any]:
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"""获取对话分析数据（包含AI建议统计）"""
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try:
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@@ -652,6 +653,8 @@ class ConversationHistoryManager:
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conv_query = session.query(Conversation)
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if work_order_id:
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conv_query = conv_query.filter(Conversation.work_order_id == work_order_id)
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if tenant_id is not None:
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conv_query = conv_query.filter(Conversation.tenant_id == tenant_id)
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conversations = conv_query.filter(
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Conversation.timestamp >= cutoff_date
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@@ -718,6 +721,49 @@ class ConversationHistoryManager:
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logger.error(f"获取对话分析数据失败: {e}")
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return {}
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# ==================== 租户汇总方法 ====================
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def get_tenant_summary(self) -> List[Dict[str, Any]]:
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"""
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按 tenant_id 聚合 ChatSession，返回租户汇总列表。
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按 last_active_time 降序排列。
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数据库异常或无记录时返回空列表。
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"""
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try:
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with db_manager.get_session() as session:
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results = session.query(
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ChatSession.tenant_id,
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func.count(ChatSession.id).label('session_count'),
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func.coalesce(func.sum(ChatSession.message_count), 0).label('message_count'),
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func.sum(
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case(
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(ChatSession.status == 'active', 1),
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else_=0
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)
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).label('active_session_count'),
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func.max(ChatSession.updated_at).label('last_active_time')
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).group_by(
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ChatSession.tenant_id
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).order_by(
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desc('last_active_time')
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).all()
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summary = []
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for row in results:
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summary.append({
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'tenant_id': row.tenant_id,
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'session_count': row.session_count,
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'message_count': int(row.message_count),
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'active_session_count': int(row.active_session_count),
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'last_active_time': row.last_active_time.isoformat() if row.last_active_time else None
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})
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return summary
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except Exception as e:
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logger.error(f"获取租户汇总失败: {e}")
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return []
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# ==================== 会话管理方法 ====================
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def get_sessions_paginated(
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@@ -726,13 +772,17 @@ class ConversationHistoryManager:
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per_page: int = 20,
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status: Optional[str] = None,
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search: str = '',
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date_filter: str = ''
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date_filter: str = '',
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tenant_id: Optional[str] = None
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) -> Dict[str, Any]:
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"""分页获取会话列表"""
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try:
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with db_manager.get_session() as session:
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query = session.query(ChatSession)
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if tenant_id is not None:
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query = query.filter(ChatSession.tenant_id == tenant_id)
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if status:
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query = query.filter(ChatSession.status == status)
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@@ -232,6 +232,98 @@ class RealtimeChatManager:
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"confidence": 0.1,
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"ai_suggestions": []
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}
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def _generate_response_stream(self, user_message: str, knowledge_results: List[Dict], context: List[Dict], work_order_id: Optional[int] = None):
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"""流式生成回复，yield 每个 token 片段"""
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try:
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ai_suggestions = self._get_workorder_ai_suggestions(work_order_id)
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prompt = self._build_chat_prompt(user_message, knowledge_results, context, ai_suggestions)
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for chunk in self.llm_client.chat_completion_stream(
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messages=[{"role": "user", "content": prompt}],
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temperature=0.7,
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max_tokens=1000,
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):
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yield chunk
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except Exception as e:
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logger.error(f"流式生成回复失败: {e}")
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yield "抱歉，系统出现错误，请稍后再试。"
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def process_message_stream(self, session_id: str, user_message: str, ip_address: str = None, invocation_method: str = "http_stream"):
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"""流式处理用户消息，yield SSE 事件"""
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import time as _time
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if session_id not in self.active_sessions:
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yield f"data: {json.dumps({'error': '会话不存在'}, ensure_ascii=False)}\n\n"
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return
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session = self.active_sessions[session_id]
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session["last_activity"] = datetime.now()
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session["message_count"] += 1
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session["ip_address"] = ip_address
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session["invocation_method"] = invocation_method
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user_msg = ChatMessage(
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role="user",
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content=user_message,
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timestamp=datetime.now(),
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message_id=f"msg_{int(_time.time())}_{session['message_count']}"
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)
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self.message_history[session_id].append(user_msg)
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# 搜索知识 + VIN
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knowledge_results = self._search_knowledge(user_message)
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vin = self._extract_vin(user_message)
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if vin:
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latest = self.vehicle_manager.get_latest_vehicle_data_by_vin(vin)
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if latest:
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knowledge_results = [{
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"question": f"VIN {vin} 的最新实时数据",
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"answer": json.dumps(latest, ensure_ascii=False),
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"similarity_score": 1.0,
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"source": "vehicle_realtime"
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}] + knowledge_results
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knowledge_results = knowledge_results[:5]
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# 流式生成
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full_content = []
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for chunk in self._generate_response_stream(
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user_message, knowledge_results, session["context"], session["work_order_id"]
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):
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full_content.append(chunk)
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yield f"data: {json.dumps({'chunk': chunk}, ensure_ascii=False)}\n\n"
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# 拼接完整回复
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content = "".join(full_content)
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confidence = self._calculate_confidence(knowledge_results, content)
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# 创建助手消息并保存
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assistant_msg = ChatMessage(
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role="assistant",
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content=content,
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timestamp=datetime.now(),
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message_id=f"msg_{int(_time.time())}_{session['message_count'] + 1}",
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work_order_id=session["work_order_id"],
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knowledge_used=knowledge_results,
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confidence_score=confidence,
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)
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self.message_history[session_id].append(assistant_msg)
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session["context"].append({"role": "user", "content": user_message})
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session["context"].append({"role": "assistant", "content": content})
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if len(session["context"]) > 20:
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session["context"] = session["context"][-20:]
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self._save_conversation(session_id, user_msg, assistant_msg, ip_address, invocation_method)
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if knowledge_results:
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used_ids = [r["id"] for r in knowledge_results if r.get("id")]
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if used_ids:
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self.knowledge_manager.update_usage_count(used_ids)
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# 发送完成事件
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yield f"data: {json.dumps({'done': True, 'confidence_score': confidence, 'message_id': assistant_msg.message_id}, ensure_ascii=False)}\n\n"
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def _build_chat_prompt(self, user_message: str, knowledge_results: List[Dict], context: List[Dict], ai_suggestions: List[str] = None) -> str:
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"""构建聊天提示词"""
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