feat(pkb): Complete PKB module frontend migration with V3 design

Summary:
- Implement PKB Dashboard and Workspace pages based on V3 prototype
- Add single-layer header with integrated Tab navigation
- Implement 3 work modes: Full Text, Deep Read, Batch Processing
- Integrate Ant Design X Chat component for AI conversations
- Create BatchModeComplete with template selection and document processing
- Add compact work mode selector with dropdown design

Backend:
- Migrate PKB controllers and services to /modules/pkb structure
- Register v2 API routes at /api/v2/pkb/knowledge
- Maintain dual API routes for backward compatibility

Technical details:
- Use Zustand for state management
- Handle SSE streaming responses for AI chat
- Support document selection for Deep Read mode
- Implement batch processing with progress tracking

Known issues:
- Batch processing API integration pending
- Knowledge assets page navigation needs optimization

Status: Frontend functional, pending refinement

frontend-v2/src/modules/pkb/components/Workspace/DeepReadMode.tsx

@@ -0,0 +1,116 @@
+/**
+ * 逐篇精读模式组件 - ChatGPT风格全屏聊天
+ */
+
+import React from 'react';
+import { Empty } from 'antd';
+import { FileText } from 'lucide-react';
+import { ChatContainer } from '@/shared/components/Chat';
+import type { KnowledgeBase, Document } from '../../api/knowledgeBaseApi';
+
+interface DeepReadModeProps {
+  kbId: string;
+  kbInfo: KnowledgeBase;
+  selectedDocuments: Document[];
+}
+
+export const DeepReadMode: React.FC<DeepReadModeProps> = ({ 
+  kbId, 
+  selectedDocuments 
+}) => {
+  if (!selectedDocuments || selectedDocuments.length === 0) {
+    return (
+      <div className="h-full flex items-center justify-center bg-slate-50">
+        <div className="text-center">
+          <div className="w-16 h-16 mx-auto bg-purple-50 rounded-full flex items-center justify-center mb-4">
+            <FileText className="w-8 h-8 text-purple-400" />
+          </div>
+          <h3 className="text-lg font-semibold text-slate-700 mb-2">选择文档开始精读</h3>
+          <p className="text-sm text-slate-500 max-w-xs">
+            请在上方"逐篇精读"下拉框中选择一篇文档进行深度分析
+          </p>
+        </div>
+      </div>
+    );
+  }
+
+  const selectedDoc = selectedDocuments[0];
+  const selectedDocIds = selectedDocuments.map(d => d.id);
+
+  return (
+    <div className="h-full flex flex-col bg-white">
+      {/* Chat组件 - 全屏展开 */}
+      <div className="flex-1 overflow-hidden">
+        <ChatContainer
+          conversationType="pkb"
+          conversationKey={`kb-deepread-${kbId}-${selectedDoc.id}`}
+          defaultMessages={[{
+            id: 'welcome',
+            role: 'assistant',
+            content: `您好！我已准备好深度解读文档《${selectedDoc.filename}》。\n\n我可以帮您：\n- 📖 逐段解读文献内容\n- 🎯 提炼核心观点和结论\n- 💡 分析研究方法和局限性\n\n请告诉我您想深入了解哪方面？`,
+            status: 'success',
+            timestamp: Date.now(),
+          }]}
+          providerConfig={{
+            apiEndpoint: '/api/v1/chat/stream',
+            requestFn: async (message: string) => {
+              const response = await fetch('/api/v1/chat/stream', {
+                method: 'POST',
+                headers: { 
+                  'Content-Type': 'application/json',
+                  'Accept': 'text/event-stream',
+                },
+                body: JSON.stringify({
+                  content: message,
+                  modelType: 'qwen-long',
+                  knowledgeBaseIds: [kbId],
+                  documentIds: selectedDocIds, // 🌟 关键参数：限定文档范围
+                }),
+              });
+              
+              if (!response.ok) {
+                throw new Error(`API请求失败: ${response.status}`);
+              }
+              
+              // 处理流式响应
+              const reader = response.body?.getReader();
+              const decoder = new TextDecoder();
+              let fullContent = '';
+              
+              if (reader) {
+                while (true) {
+                  const { done, value } = await reader.read();
+                  if (done) break;
+                  
+                  const chunk = decoder.decode(value);
+                  const lines = chunk.split('\n');
+                  
+                  for (const line of lines) {
+                    if (line.startsWith('data: ')) {
+                      const data = line.slice(6);
+                      if (data === '[DONE]') break;
+                      
+                      try {
+                        const json = JSON.parse(data);
+                        if (json.content) {
+                          fullContent += json.content;
+                        }
+                      } catch (e) {
+                        // 忽略解析错误
+                      }
+                    }
+                  }
+                }
+              }
+              
+              return {
+                content: fullContent,
+                messageId: Date.now().toString(),
+              };
+            },
+          }}
+        />
+      </div>
+    </div>
+  );
+};