feat(iit): Complete CRA Agent V3.0 P1 - ChatOrchestrator with LLM Function Calling

P1 Architecture: Lightweight ReAct (Function Calling loop, max 3 rounds)

Core changes:
- Add ToolDefinition/ToolCall types to LLM adapters (DeepSeek + CloseAI + Claude)
- Replace 6 old tools with 4 semantic tools: read_report, look_up_data, check_quality, search_knowledge
- Create ChatOrchestrator (~160 lines) replacing ChatService (1,442 lines)
- Wire WechatCallbackController to ChatOrchestrator, deprecate ChatService
- Fix nullable content (string | null) across 12+ LLM consumer files

E2E test results: 8/8 scenarios passed (100%)
- QC report query, critical issues, patient data, trend, on-demand QC
- Knowledge base search, project overview, data modification refusal

Net code reduction: ~1,100 lines
Tested: E2E P1 chat test 8/8 passed with DeepSeek API

Made-with: Cursor

backend/src/modules/rvw/services/methodologyService.ts

@@ -53,13 +53,14 @@ export async function reviewMethodology(
       temperature: 0.3,
       maxTokens: 8000,
     });
+    const methContent = response.content ?? '';
     logger.info('[RVW:Methodology] 评估完成', { 
       modelType, 
-      responseLength: response.content.length 
+      responseLength: methContent.length 
     });
 
     // 4. 解析JSON响应
-    const result = parseJSONFromLLMResponse<MethodologyReview>(response.content);
+    const result = parseJSONFromLLMResponse<MethodologyReview>(methContent);
 
     // 5. 验证响应格式
     if (!result || typeof result.overall_score !== 'number' || !Array.isArray(result.parts)) {