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
2026-04-01 16:11:02 +08:00
parent e14e3ee7a5
commit 7013e9db70
27 changed files with 2753 additions and 276 deletions
--- a/src/core/llm_client.py
+++ b/src/core/llm_client.py
@@ -1,35 +1,50 @@
+# -*- coding: utf-8 -*-
+"""
+统一 LLM 客户端
+兼容所有 OpenAI 格式 API（千问、Gemini、DeepSeek、本地 Ollama 等）
+通过 .env 中 LLM_PROVIDER / LLM_BASE_URL / LLM_MODEL 切换模型
+"""
+
 import requests
 import json
 import logging
-from typing import Dict, List, Optional, Any
+from typing import Dict, List, Optional, Any, Generator
 from datetime import datetime

 from src.config.unified_config import get_config

 logger = logging.getLogger(__name__)

-class QwenClient:
-    """阿里云千问API客户端"""

-    def __init__(self):
+class LLMClient:
+    """
+    统一大模型客户端
+    所有 OpenAI 兼容 API 都走这一个类，不再区分 provider。
+    """
+
+    def __init__(self, base_url: str = None, api_key: str = None,
+                 model: str = None, timeout: int = None):
        config = get_config()
-        self.base_url = config.llm.base_url or "https://dashscope.aliyuncs.com/compatible-mode/v1"
-        self.api_key = config.llm.api_key
-        self.model_name = config.llm.model
-        self.timeout = config.llm.timeout
+        self.base_url = (base_url or config.llm.base_url or
+                         "https://dashscope.aliyuncs.com/compatible-mode/v1")
+        self.api_key = api_key or config.llm.api_key
+        self.model_name = model or config.llm.model
+        self.timeout = timeout or config.llm.timeout
        self.headers = {
            "Authorization": f"Bearer {self.api_key}",
-            "Content-Type": "application/json"
+            "Content-Type": "application/json",
        }
-    
+
+    # ── 普通请求 ──────────────────────────────────────────
+
    def chat_completion(
        self,
        messages: List[Dict[str, str]],
        temperature: float = 0.7,
        max_tokens: int = 1000,
-        stream: bool = False
+        **kwargs,
    ) -> Dict[str, Any]:
-        """发送聊天请求"""
+        """标准聊天补全（非流式）"""
        try:
            url = f"{self.base_url}/chat/completions"
            payload = {
@@ -37,114 +52,146 @@ class QwenClient:
                "messages": messages,
                "temperature": temperature,
                "max_tokens": max_tokens,
-                "stream": stream
+                "stream": False,
            }
-            
+
            response = requests.post(
-                url,
-                headers=self.headers,
-                json=payload,
-                timeout=self.timeout
+                url, headers=self.headers, json=payload, timeout=self.timeout
            )
-            
+
            if response.status_code == 200:
-                result = response.json()
-                logger.info("API请求成功")
-                return result
+                return response.json()
            else:
-                logger.error(f"API请求失败: {response.status_code} - {response.text}")
+                logger.error(f"LLM API 失败: {response.status_code} - {response.text}")
                return {"error": f"API请求失败: {response.status_code}"}
-                
+
        except requests.exceptions.Timeout:
-            logger.error("API请求超时")
+            logger.error("LLM API 超时")
            return {"error": "请求超时"}
        except requests.exceptions.RequestException as e:
-            logger.error(f"API请求异常: {e}")
+            logger.error(f"LLM API 异常: {e}")
            return {"error": f"请求异常: {str(e)}"}
        except Exception as e:
-            logger.error(f"未知错误: {e}")
+            logger.error(f"LLM 未知错误: {e}")
            return {"error": f"未知错误: {str(e)}"}
-    
+
+    # ── 流式请求 ──────────────────────────────────────────
+
+    def chat_completion_stream(
+        self,
+        messages: List[Dict[str, str]],
+        temperature: float = 0.7,
+        max_tokens: int = 1000,
+    ) -> Generator[str, None, None]:
+        """流式聊天补全，逐 token yield 文本片段"""
+        try:
+            url = f"{self.base_url}/chat/completions"
+            payload = {
+                "model": self.model_name,
+                "messages": messages,
+                "temperature": temperature,
+                "max_tokens": max_tokens,
+                "stream": True,
+            }
+
+            response = requests.post(
+                url, headers=self.headers, json=payload,
+                timeout=self.timeout, stream=True,
+            )
+
+            if response.status_code != 200:
+                logger.error(f"流式 API 失败: {response.status_code}")
+                return
+
+            for line in response.iter_lines(decode_unicode=True):
+                if not line or not line.startswith("data: "):
+                    continue
+                data_str = line[6:]
+                if data_str.strip() == "[DONE]":
+                    break
+                try:
+                    chunk = json.loads(data_str)
+                    delta = chunk.get("choices", [{}])[0].get("delta", {})
+                    content = delta.get("content", "")
+                    if content:
+                        yield content
+                except (json.JSONDecodeError, IndexError, KeyError):
+                    continue
+
+        except requests.exceptions.Timeout:
+            logger.error("流式 API 超时")
+        except Exception as e:
+            logger.error(f"流式 API 异常: {e}")
+
+    # ── 便捷方法 ──────────────────────────────────────────
+
    def generate_response(
        self,
        user_message: str,
        context: Optional[str] = None,
-        knowledge_base: Optional[List[str]] = None
+        knowledge_base: Optional[List[str]] = None,
    ) -> Dict[str, Any]:
-        """生成回复"""
-        messages = []
-        
-        # 系统提示词
-        system_prompt = "你是一个专业的客服助手，请根据用户问题提供准确、 helpful的回复。"
+        """快捷生成回复"""
+        system_prompt = "你是一个专业的客服助手，请根据用户问题提供准确、有帮助的回复。"
        if context:
            system_prompt += f"\n\n上下文信息: {context}"
        if knowledge_base:
            system_prompt += f"\n\n相关知识库: {' '.join(knowledge_base)}"
-        
-        messages.append({"role": "system", "content": system_prompt})
-        messages.append({"role": "user", "content": user_message})
-        
+
+        messages = [
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_message},
+        ]
+
        result = self.chat_completion(messages)
-        
        if "error" in result:
            return result
-        
+
        try:
-            response_content = result["choices"][0]["message"]["content"]
            return {
-                "response": response_content,
+                "response": result["choices"][0]["message"]["content"],
                "usage": result.get("usage", {}),
                "model": result.get("model", ""),
-                "timestamp": datetime.now().isoformat()
+                "timestamp": datetime.now().isoformat(),
            }
        except (KeyError, IndexError) as e:
-            logger.error(f"解析API响应失败: {e}")
+            logger.error(f"解析响应失败: {e}")
            return {"error": f"解析响应失败: {str(e)}"}
-    
+
    def extract_entities(self, text: str) -> Dict[str, Any]:
        """提取文本中的实体信息"""
-        prompt = f"""
-        请从以下文本中提取关键信息，包括：
-        1. 问题类型/类别
-        2. 优先级（高/中/低）
-        3. 关键词
-        4. 情感倾向（正面/负面/中性）
-        
-        文本: {text}
-        
-        请以JSON格式返回结果。
-        """
-        
+        import re
+        prompt = (
+            f"请从以下文本中提取关键信息，包括：\n"
+            f"1. 问题类型/类别\n2. 优先级（高/中/低）\n"
+            f"3. 关键词\n4. 情感倾向（正面/负面/中性）\n\n"
+            f"文本: {text}\n\n请以JSON格式返回结果。"
+        )
        messages = [
            {"role": "system", "content": "你是一个信息提取专家，请准确提取文本中的关键信息。"},
-            {"role": "user", "content": prompt}
+            {"role": "user", "content": prompt},
        ]
-        
        result = self.chat_completion(messages, temperature=0.3)
-        
        if "error" in result:
            return result
-        
        try:
-            response_content = result["choices"][0]["message"]["content"]
-            # 尝试解析JSON
-            import re
-            json_match = re.search(r'\{.*\}', response_content, re.DOTALL)
-            if json_match:
-                return json.loads(json_match.group())
-            else:
-                return {"raw_response": response_content}
+            content = result["choices"][0]["message"]["content"]
+            json_match = re.search(r'\{.*\}', content, re.DOTALL)
+            return json.loads(json_match.group()) if json_match else {"raw_response": content}
        except Exception as e:
-            logger.error(f"解析实体提取结果失败: {e}")
            return {"error": f"解析失败: {str(e)}"}
-    
+
    def test_connection(self) -> bool:
-        """测试API连接"""
+        """测试连接"""
        try:
-            result = self.chat_completion([
-                {"role": "user", "content": "你好"}
-            ], max_tokens=10)
+            result = self.chat_completion(
+                [{"role": "user", "content": "你好"}], max_tokens=10
+            )
            return "error" not in result
-        except Exception as e:
-            logger.error(f"API连接测试失败: {e}")
+        except Exception:
            return False
+
+
+# ── 向后兼容别名 ──────────────────────────────────────────
+# 旧代码中 `from src.core.llm_client import QwenClient` 仍然能用
+QwenClient = LLMClient
--- a/src/core/models.py
+++ b/src/core/models.py
@@ -1,4 +1,4 @@
-from sqlalchemy import Column, Integer, String, Text, DateTime, Float, Boolean, ForeignKey
+from sqlalchemy import Column, Integer, String, Text, DateTime, Float, Boolean, ForeignKey, Index
 from sqlalchemy.ext.declarative import declarative_base
 from sqlalchemy.orm import relationship
 from datetime import datetime
@@ -6,11 +6,15 @@ import hashlib

 Base = declarative_base()

+# 默认租户ID，单租户部署时使用
+DEFAULT_TENANT = "default"
+
 class WorkOrder(Base):
    """工单模型"""
    __tablename__ = "work_orders"

    id = Column(Integer, primary_key=True)
+    tenant_id = Column(String(50), nullable=False, default=DEFAULT_TENANT, index=True)
    order_id = Column(String(50), unique=True, nullable=False)
    title = Column(String(200), nullable=False)
    description = Column(Text, nullable=False)
@@ -63,6 +67,7 @@ class ChatSession(Base):
    __tablename__ = "chat_sessions"

    id = Column(Integer, primary_key=True)
+    tenant_id = Column(String(50), nullable=False, default=DEFAULT_TENANT, index=True)
    session_id = Column(String(100), unique=True, nullable=False)  # 唯一会话标识
    user_id = Column(String(100), nullable=True)  # 用户标识
    work_order_id = Column(Integer, ForeignKey("work_orders.id"), nullable=True)
@@ -100,6 +105,7 @@ class Conversation(Base):
    __tablename__ = "conversations"

    id = Column(Integer, primary_key=True)
+    tenant_id = Column(String(50), nullable=False, default=DEFAULT_TENANT, index=True)
    session_id = Column(String(100), ForeignKey("chat_sessions.session_id"), nullable=True)  # 关联会话
    work_order_id = Column(Integer, ForeignKey("work_orders.id"))
    user_message = Column(Text, nullable=False)
@@ -124,6 +130,7 @@ class KnowledgeEntry(Base):
    __tablename__ = "knowledge_entries"

    id = Column(Integer, primary_key=True)
+    tenant_id = Column(String(50), nullable=False, default=DEFAULT_TENANT, index=True)
    question = Column(Text, nullable=False)
    answer = Column(Text, nullable=False)
    category = Column(String(100), nullable=False)
@@ -164,6 +171,7 @@ class Analytics(Base):
    __tablename__ = "analytics"

    id = Column(Integer, primary_key=True)
+    tenant_id = Column(String(50), nullable=False, default=DEFAULT_TENANT, index=True)
    date = Column(DateTime, nullable=False)
    total_orders = Column(Integer, default=0)
    resolved_orders = Column(Integer, default=0)
@@ -184,6 +192,7 @@ class Alert(Base):
    __tablename__ = "alerts"

    id = Column(Integer, primary_key=True)
+    tenant_id = Column(String(50), nullable=False, default=DEFAULT_TENANT, index=True)
    rule_name = Column(String(100), nullable=False)
    alert_type = Column(String(50), nullable=False)
    level = Column(String(20), nullable=False)  # info, warning, error, critical
@@ -242,6 +251,7 @@ class User(Base):
    __tablename__ = "users"

    id = Column(Integer, primary_key=True)
+    tenant_id = Column(String(50), nullable=False, default=DEFAULT_TENANT, index=True)
    username = Column(String(50), unique=True, nullable=False)
    password_hash = Column(String(128), nullable=False)
    email = Column(String(120), unique=True, nullable=True)