初始化医疗报告生成项目，添加核心代码文件

backend/services/report_integrator.py

@@ -0,0 +1,106 @@
+from typing import List, Dict, Any
+import json
+
+class ReportIntegrator:
+    """医疗报告整合分析器"""
+    
+    def __init__(self, llm_service):
+        self.llm_service = llm_service
+    
+    def integrate_reports(self, reports: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """整合多份医疗报告"""
+        
+        if len(reports) == 1:
+            return self._single_report_summary(reports[0])
+        
+        # 构建整合分析的提示词
+        prompt = self._build_integration_prompt(reports)
+        
+        # 调用LLM进行整合分析
+        if self.llm_service.llm_type == "openai":
+            result = self._call_openai_integration(prompt)
+        elif self.llm_service.llm_type == "ollama":
+            result = self._call_ollama_integration(prompt)
+        else:
+            result = self._mock_integration(reports)
+        
+        # 添加报告列表
+        result["reports_included"] = [
+            {"filename": report["filename"], "summary": report["analysis"].get("summary", "无摘要")}
+            for report in reports
+        ]
+        
+        return result
+    
+    def _build_integration_prompt(self, reports: List[Dict[str, Any]]) -> str:
+        """构建整合分析提示词"""
+        report_details = []
+        for i, report in enumerate(reports, 1):
+            analysis = report["analysis"]
+            report_details.append(f"【报告{i}: {report['filename']}】\n摘要: {analysis.get('summary', '无')}")
+        
+        prompt = f"""你是专业医疗分析专家。整合以下{len(reports)}份报告，提供综合评估。
+{chr(10).join(report_details)}
+
+请以JSON格式返回：
+{{"overall_summary": "整体摘要", "health_trends": ["趋势"], "priority_concerns": [{{"concern": "关注点", "severity": "低/中/高", "description": "描述"}}], "comprehensive_assessment": "综合评估", "integrated_recommendations": ["建议"], "follow_up_suggestions": ["后续建议"]}}"""
+        return prompt
+    
+    def _call_openai_integration(self, prompt: str) -> Dict[str, Any]:
+        """调用OpenAI进行整合分析"""
+        try:
+            response = self.llm_service.client.chat.completions.create(
+                model=self.llm_service.model,
+                messages=[{"role": "system", "content": "你是医疗分析专家。"}, {"role": "user", "content": prompt}],
+                temperature=0.7, max_tokens=3000
+            )
+            content = response.choices[0].message.content
+            return self.llm_service._parse_llm_response(content)
+        except Exception as e:
+            return self._create_error_result(f"OpenAI分析失败: {str(e)}")
+    
+    def _call_ollama_integration(self, prompt: str) -> Dict[str, Any]:
+        """调用Ollama进行整合分析"""
+        try:
+            import requests
+            response = requests.post(f"{self.llm_service.ollama_host}/api/generate",
+                json={"model": self.llm_service.model, "prompt": prompt, "stream": False}, timeout=90)
+            if response.status_code == 200:
+                return self.llm_service._parse_llm_response(response.json().get("response", ""))
+            raise Exception(f"Ollama错误: {response.status_code}")
+        except Exception as e:
+            return self._create_error_result(f"Ollama分析失败: {str(e)}")
+    
+    def _mock_integration(self, reports: List[Dict[str, Any]]) -> Dict[str, Any]:
+        """模拟整合分析结果"""
+        total_abnormal = sum(len(report["analysis"].get("abnormal_items", [])) for report in reports)
+        return {
+            "overall_summary": f"综合分析了{len(reports)}份报告，发现{total_abnormal}项异常指标。整体健康状况良好。",
+            "health_trends": ["各项指标整体稳定", "未发现明显恶化趋势", "建议持续监测"],
+            "priority_concerns": [{"concern": "定期体检", "severity": "低", "description": "建议保持定期体检"}] if total_abnormal == 0 else [{"concern": "异常指标", "severity": "中", "description": f"发现{total_abnormal}项异常"}],
+            "comprehensive_assessment": "整体健康状况可控，建议关注生活方式、定期复查。",
+            "integrated_recommendations": ["保持均衡饮食", "坚持适量运动", "保证充足睡眠", "定期体检"],
+            "follow_up_suggestions": ["3-6个月后复查关键指标", "如有不适及时就医", "保持健康记录"],
+            "note": "这是模拟结果。实际使用请配置OpenAI或Ollama。"
+        }
+    
+    def _single_report_summary(self, report: Dict[str, Any]) -> Dict[str, Any]:
+        """单个报告摘要"""
+        analysis = report["analysis"]
+        return {
+            "overall_summary": f"单份报告分析：{analysis.get('summary', '无摘要')}",
+            "reports_included": [{"filename": report["filename"], "summary": analysis.get("summary", "无")}],
+            "health_trends": analysis.get("key_findings", []),
+            "priority_concerns": [{"concern": item, "severity": "中", "description": "需关注"} for item in analysis.get("abnormal_items", [])[:3]],
+            "comprehensive_assessment": analysis.get("risk_assessment", "请查看详细分析"),
+            "integrated_recommendations": analysis.get("recommendations", []),
+            "follow_up_suggestions": ["定期复查", "咨询医生"]
+        }
+    
+    def _create_error_result(self, error_msg: str) -> Dict[str, Any]:
+        """创建错误结果"""
+        return {
+            "error": error_msg, "overall_summary": "分析失败", "health_trends": [],
+            "priority_concerns": [], "comprehensive_assessment": "无法完成分析",
+            "integrated_recommendations": [], "follow_up_suggestions": []
+        }