feat: 优化飞书集成、知识库、Agent、工单管理及AI建议功能，统一前端对话字体样式并移除工单模板文件。

src/agent_assistant.py

@@ -6,10 +6,12 @@ TSP Agent助手 - 简化版本
 
 import logging
 import asyncio
+import json
 from typing import Dict, Any, List, Optional
 from datetime import datetime
 from src.config.unified_config import get_config
 from src.agent.llm_client import LLMManager
+from src.web.service_manager import service_manager
 
 logger = logging.getLogger(__name__)
 
@@ -304,22 +306,65 @@ class TSPAgentAssistant:
             logger.error(f"获取LLM使用统计失败: {e}")
             return {}
     
+    def process_message_agent_sync(self, message: str, user_id: str = "admin", 
+                                 work_order_id: Optional[int] = None, 
+                                 enable_proactive: bool = True) -> Dict[str, Any]:
+        """处理消息（同步桥接）"""
+        try:
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            return loop.run_until_complete(self.process_message_agent(message, user_id, work_order_id, enable_proactive))
+        except Exception as e:
+            logger.error(f"同步处理消息失败: {e}")
+            return {"error": str(e)}
+
     async def process_message_agent(self, message: str, user_id: str = "admin", 
                                   work_order_id: Optional[int] = None, 
                                   enable_proactive: bool = True) -> Dict[str, Any]:
-        """处理消息"""
+        """处理消息 (实战化)"""
         try:
-            # 简化的消息处理
+            logger.info(f"Agent收到消息: {message}")
+            
+            # 1. 识别意图和推荐工具
+            prompt = f"用户消息: {message}\n请分析用户意图，并从工具列表中选择最合适的工具。工具列表: {json.dumps(self.get_available_tools())}\n请直接返回你的分析和建议响应。"
+            
+            response_text = await self.llm_manager.generate(prompt)
+            
+            # 2. 模拟动作生成
+            actions = []
+            if "工单" in message or "查询" in message:
+                actions.append({"type": "tool_call", "tool": "search_work_order", "status": "suggested"})
+            
             return {
                 "success": True,
-                "message": f"Agent收到消息: {message}",
+                "response": response_text,
+                "actions": actions,
                 "user_id": user_id,
                 "work_order_id": work_order_id,
+                "status": "completed",
                 "timestamp": datetime.now().isoformat()
             }
         except Exception as e:
             logger.error(f"处理消息失败: {e}")
             return {"error": str(e)}
+
+    def execute_tool_sync(self, tool_name: str, parameters: Dict[str, Any] = None) -> Dict[str, Any]:
+        """执行工具（同步桥接）"""
+        try:
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            return loop.run_until_complete(self.execute_tool(tool_name, parameters))
+        except Exception as e:
+            return {"error": str(e)}
+
+    def trigger_sample_actions_sync(self) -> Dict[str, Any]:
+        """触发示例动作（同步桥接）"""
+        try:
+            loop = asyncio.new_event_loop()
+            asyncio.set_event_loop(loop)
+            return loop.run_until_complete(self.trigger_sample_actions())
+        except Exception as e:
+            return {"success": False, "error": str(e)}
     
     async def trigger_sample_actions(self) -> Dict[str, Any]:
         """触发示例动作"""
@@ -336,7 +381,7 @@ class TSPAgentAssistant:
             logger.error(f"触发示例动作失败: {e}")
             return {"success": False, "error": str(e)}
     
-    def process_file_to_knowledge(self, file_path: str, filename: str) -> Dict[str, Any]:
+    async def process_file_to_knowledge(self, file_path: str, filename: str) -> Dict[str, Any]:
         """处理文件并生成知识库"""
         try:
             import os
@@ -356,20 +401,39 @@ class TSPAgentAssistant:
 
             logger.info(f"文件读取成功: {filename}, 字符数={len(content)}")
 
-            # 使用简化的知识提取
+            # 使用LLM进行知识提取 (异步调用)
             logger.info(f"正在对文件内容进行 AI 知识提取...")
-            knowledge_entries = self._extract_knowledge_from_content(content, filename)
+            knowledge_entries = await self._extract_knowledge_from_content(content, filename)
 
             logger.info(f"知识提取完成: 共提取出 {len(knowledge_entries)} 个潜在条目")
 
             # 保存到知识库
             saved_count = 0
+
+            # 获取知识库管理器
+            try:
+                knowledge_manager = service_manager.get_assistant().knowledge_manager
+            except Exception as e:
+                logger.error(f"无法获取知识库管理器: {e}")
+                knowledge_manager = None
+
             for i, entry in enumerate(knowledge_entries):
                 try:
                     logger.info(f"正在保存知识条目 [{i+1}/{len(knowledge_entries)}]: {entry.get('question', '')[:30]}...")
-                    # 这里在实际项目中应当注入知识库管理器的保存逻辑
-                    # 但在当前简化版本中仅记录日志
-                    saved_count += 1
+
+                    if knowledge_manager:
+                        # 实际保存到数据库
+                        knowledge_manager.add_knowledge_entry(
+                            question=entry.get('question'),
+                            answer=entry.get('answer'),
+                            category=entry.get('category', '文档导入'),
+                            confidence_score=entry.get('confidence_score', 0.8)
+                        )
+                        saved_count += 1
+                    else:
+                        # 如果无法获取管理器，仅记录日志（降级处理）
+                        logger.warning("知识库管理器不可用，跳过保存")
+
                 except Exception as save_error:
                     logger.error(f"保存知识条目 {i+1} 时出错: {save_error}")
 
@@ -402,26 +466,71 @@ class TSPAgentAssistant:
             logger.error(f"读取文件失败: {e}")
             return ""
 
-    def _extract_knowledge_from_content(self, content: str, filename: str) -> List[Dict[str, Any]]:
-        """从内容中提取知识"""
+    async def _extract_knowledge_from_content(self, content: str, filename: str) -> List[Dict[str, Any]]:
+        """从内容中提取知识 - 使用LLM"""
         try:
-            # 简化的知识提取逻辑
-            entries = []
-            
-            # 按段落分割内容
-            paragraphs = content.split('\n\n')
-            
-            for i, paragraph in enumerate(paragraphs[:5]):  # 最多提取5个
-                if len(paragraph.strip()) > 20:  # 过滤太短的段落
-                    entries.append({
-                        "question": f"关于{filename}的问题{i+1}",
-                        "answer": paragraph.strip(),
-                        "category": "文档知识",
-                        "confidence_score": 0.7
+            # 限制内容长度，避免超出token限制
+            # 假设每个汉字2个token，保留前8000个字符作为上下文
+            truncated_content = content[:8000]
+            if len(content) > 8000:
+                truncated_content += "\n...(后续内容已省略)"
+
+            prompt = f"""
+你是一个专业的知识库构建助手。请分析以下文档内容，提取出关键的"问题"和"答案"对，用于构建知识库。
+
+文档文件名：{filename}
+文档内容：
+{truncated_content}
+
+要求：
+1. 提取文档中的核心知识点，转化为"问题(question)"和"答案(answer)"的形式。
+2. "问题"应该清晰明确，方便用户搜索。
+3. "答案"应该准确、完整，直接回答问题。
+4. "分类(category)"请根据内容自动归类（如：故障排查、操作指南、系统配置、业务流程等）。
+5. 输出格式必须是合法的 JSON 数组，不要包含Markdown标记。
+
+JSON格式示例：
+[
+    {{"question": "如何重置密码？", "answer": "请访问设置页面，点击重置密码按钮...", "category": "操作指南"}},
+    {{"question": "系统支持哪些浏览器？", "answer": "支持Chrome, Edge, Firefox...", "category": "系统配置"}}
+]
+"""
+            # 调用LLM生成
+            logger.info("正在调用LLM进行知识提取...")
+            response_text = await self.llm_manager.generate(prompt, temperature=0.3)
+
+            # 清理响应中的Markdown标记（如果存在）
+            cleaned_text = response_text.strip()
+            if cleaned_text.startswith("```json"):
+                cleaned_text = cleaned_text[7:]
+            if cleaned_text.startswith("```"):
+                cleaned_text = cleaned_text[3:]
+            if cleaned_text.endswith("```"):
+                cleaned_text = cleaned_text[:-3]
+            cleaned_text = cleaned_text.strip()
+
+            # 解析JSON
+            try:
+                entries = json.loads(cleaned_text)
+            except json.JSONDecodeError:
+                # 尝试修复常见的JSON错误
+                logger.warning(f"JSON解析失败，尝试简单修复: {cleaned_text[:100]}...")
+                # 这里可以添加更复杂的修复逻辑，或者直接记录错误
+                return []
+
+            # 验证和标准化
+            valid_entries = []
+            for entry in entries:
+                if isinstance(entry, dict) and "question" in entry and "answer" in entry:
+                    valid_entries.append({
+                        "question": entry["question"],
+                        "answer": entry["answer"],
+                        "category": entry.get("category", "文档导入"),
+                        "confidence_score": 0.9  # LLM生成的置信度较高
                     })
-            
-            return entries
-            
+
+            return valid_entries
+
         except Exception as e:
             logger.error(f"提取知识失败: {e}")
             return []