From 9bf1dd1210623406e9de1050c528851905fbe20a Mon Sep 17 00:00:00 2001
From: python <liji517@qq.com>
Date: Tue, 9 Dec 2025 15:01:31 +0800
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---
 .../__pycache__/ai_service.cpython-312.pyc    | Bin 6470 -> 50804 bytes
 services/ai_service.py                        | 303 +++++++++---------
 2 files changed, 159 insertions(+), 144 deletions(-)

diff --git a/services/__pycache__/ai_service.cpython-312.pyc b/services/__pycache__/ai_service.cpython-312.pyc
index 0f0bdb9bfd6d1e67a134b34b3cf7c0d04d9fd1c2..5df4db390be915150283d2f312dcb865c2e2c708 100644
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diff --git a/services/ai_service.py b/services/ai_service.py
index b41e0f9..6cc2ca0 100644
--- a/services/ai_service.py
+++ b/services/ai_service.py
@@ -309,165 +309,179 @@ class AIService:
                 raise Exception(error_message)
             
             result = response.json()
-        
-        # 提取AI返回的内容
-        if 'choices' in result and len(result['choices']) > 0:
-            raw_content = result['choices'][0]['message']['content']
             
-            # 调试：打印原始返回内容（前500字符）
-            print(f"[AI服务] API返回的原始内容（前500字符）: {raw_content[:500]}")
-            
-            # 处理思考过程标签（支持多种可能的标签格式）
-            content = raw_content
-            
-            # 处理 </think> 标签（DeepSeek-R1常用格式）
-            if '</think>' in content:
-                parts = content.split('</think>')
-                if len(parts) > 1:
-                    content = parts[-1].strip()
-                    print(f"[AI服务] 检测到 </think> 标签，提取标签后的内容")
-            
-            # 处理 </think> 标签
-            elif '</think>' in content:
-                parts = content.split('</think>')
-                if len(parts) > 1:
-                    content = parts[-1].strip()
-                    print(f"[AI服务] 检测到 </think> 标签，提取标签后的内容")
-            
-            # 处理 <reasoning>...</reasoning> 标签
-            elif '<reasoning>' in content and '</reasoning>' in content:
-                reasoning_start = content.find('</reasoning>')
-                if reasoning_start != -1:
-                    content = content[reasoning_start + 11:].strip()
-                    print(f"[AI服务] 检测到 <reasoning> 标签，提取标签后的内容")
-            
-            # 清理后的内容（前500字符）
-            print(f"[AI服务] 清理后的内容（前500字符）: {content[:500]}")
-            
-            # 尝试解析JSON（使用增强的修复机制）
-            extracted_data = self._extract_json_from_text(content)
-            if extracted_data:
-                print(f"[AI服务] JSON解析成功，提取到 {len(extracted_data)} 个字段")
-                print(f"[AI服务] 原始字段名: {list(extracted_data.keys())}")
-                # 规范化字段名并映射到正确的字段编码
-                normalized_data = self._normalize_field_names(extracted_data, output_fields)
-                print(f"[AI服务] 规范化后的字段名: {list(normalized_data.keys())}")
-                # 打印关键字段的值用于调试
-                for key in ['target_name', 'target_gender', 'target_age', 'target_date_of_birth']:
-                    if key in normalized_data:
-                        print(f"[AI服务] 规范化后 {key} = '{normalized_data[key]}'")
-                # 规范化日期格式
-                normalized_data = self._normalize_date_formats(normalized_data, output_fields)
-                # 再次打印关键字段的值用于调试
-                for key in ['target_name', 'target_gender', 'target_age', 'target_date_of_birth']:
-                    if key in normalized_data:
-                        print(f"[AI服务] 日期格式化后 {key} = '{normalized_data[key]}'")
-                # 后处理：从已有信息推断缺失字段
-                normalized_data = self._post_process_inferred_fields(normalized_data, output_fields)
-                # 打印后处理后的关键字段
-                for key in ['target_name', 'target_gender', 'target_age', 'target_date_of_birth', 'target_organization', 'target_position']:
-                    if key in normalized_data:
-                        print(f"[AI服务] 后处理后 {key} = '{normalized_data[key]}'")
-                # 即使提取的字段不完整，也返回结果（更宽容的处理）
-                if any(v for v in normalized_data.values() if v):  # 至少有一个非空字段
-                    print(f"[AI服务] 返回提取的数据（包含 {sum(1 for v in normalized_data.values() if v)} 个非空字段）")
-                    # 记录成功的对话
+            # 提取AI返回的内容
+            if 'choices' in result and len(result['choices']) > 0:
+                raw_content = result['choices'][0]['message']['content']
+                
+                # 调试：打印原始返回内容（前500字符）
+                print(f"[AI服务] API返回的原始内容（前500字符）: {raw_content[:500]}")
+                
+                # 处理思考过程标签（支持多种可能的标签格式）
+                content = raw_content
+                
+                # 处理 </think> 标签（DeepSeek-R1常用格式）
+                if '</think>' in content:
+                    parts = content.split('</think>')
+                    if len(parts) > 1:
+                        content = parts[-1].strip()
+                        print(f"[AI服务] 检测到 </think> 标签，提取标签后的内容")
+                
+                # 处理 </think> 标签
+                elif '</think>' in content:
+                    parts = content.split('</think>')
+                    if len(parts) > 1:
+                        content = parts[-1].strip()
+                        print(f"[AI服务] 检测到 </think> 标签，提取标签后的内容")
+                
+                # 处理 <reasoning>...</reasoning> 标签
+                elif '<reasoning>' in content and '</reasoning>' in content:
+                    reasoning_start = content.find('</reasoning>')
+                    if reasoning_start != -1:
+                        content = content[reasoning_start + 11:].strip()
+                        print(f"[AI服务] 检测到 <reasoning> 标签，提取标签后的内容")
+                
+                # 清理后的内容（前500字符）
+                print(f"[AI服务] 清理后的内容（前500字符）: {content[:500]}")
+                
+                # 尝试解析JSON（使用增强的修复机制）
+                extracted_data = self._extract_json_from_text(content)
+                if extracted_data:
+                    print(f"[AI服务] JSON解析成功，提取到 {len(extracted_data)} 个字段")
+                    print(f"[AI服务] 原始字段名: {list(extracted_data.keys())}")
+                    # 规范化字段名并映射到正确的字段编码
+                    normalized_data = self._normalize_field_names(extracted_data, output_fields)
+                    print(f"[AI服务] 规范化后的字段名: {list(normalized_data.keys())}")
+                    # 打印关键字段的值用于调试
+                    for key in ['target_name', 'target_gender', 'target_age', 'target_date_of_birth']:
+                        if key in normalized_data:
+                            print(f"[AI服务] 规范化后 {key} = '{normalized_data[key]}'")
+                    # 规范化日期格式
+                    normalized_data = self._normalize_date_formats(normalized_data, output_fields)
+                    # 再次打印关键字段的值用于调试
+                    for key in ['target_name', 'target_gender', 'target_age', 'target_date_of_birth']:
+                        if key in normalized_data:
+                            print(f"[AI服务] 日期格式化后 {key} = '{normalized_data[key]}'")
+                    # 后处理：从已有信息推断缺失字段
+                    normalized_data = self._post_process_inferred_fields(normalized_data, output_fields)
+                    # 打印后处理后的关键字段
+                    for key in ['target_name', 'target_gender', 'target_age', 'target_date_of_birth', 'target_organization', 'target_position']:
+                        if key in normalized_data:
+                            print(f"[AI服务] 后处理后 {key} = '{normalized_data[key]}'")
+                    # 即使提取的字段不完整，也返回结果（更宽容的处理）
+                    if any(v for v in normalized_data.values() if v):  # 至少有一个非空字段
+                        print(f"[AI服务] 返回提取的数据（包含 {sum(1 for v in normalized_data.values() if v)} 个非空字段）")
+                        # 记录成功的对话
+                        if self.ai_logger:
+                            self.ai_logger.log_conversation(
+                                prompt=prompt,
+                                api_request=api_request_info,
+                                api_response=result,
+                                extracted_data=normalized_data,
+                                error=None,
+                                session_id=session_id
+                            )
+                        return normalized_data
+                    else:
+                        print(f"[AI服务] 警告：提取的数据全部为空，但继续返回（允许部分字段为空）")
+                        # 记录对话（即使数据为空）
+                        if self.ai_logger:
+                            self.ai_logger.log_conversation(
+                                prompt=prompt,
+                                api_request=api_request_info,
+                                api_response=result,
+                                extracted_data=normalized_data,
+                                error="提取的数据全部为空",
+                                session_id=session_id
+                            )
+                        return normalized_data
+                
+                # 如果无法提取JSON，记录错误但尝试更宽容的处理
+                print(f"[AI服务] 警告：无法从内容中提取完整JSON，尝试备用解析方法")
+                print(f"[AI服务] 清理后的内容（前500字符）: {content[:500]}")
+                
+                # 尝试从文本中提取
+                parsed_data = self._parse_text_response(content, output_fields)
+                if parsed_data and any(v for v in parsed_data.values() if v):  # 至少有一个非空字段
+                    print(f"[AI服务] 使用备用方法解析成功，提取到 {len(parsed_data)} 个字段")
+                    # 记录对话
                     if self.ai_logger:
                         self.ai_logger.log_conversation(
                             prompt=prompt,
                             api_request=api_request_info,
                             api_response=result,
-                            extracted_data=normalized_data,
+                            extracted_data=parsed_data,
                             error=None,
                             session_id=session_id
                         )
-                    return normalized_data
-                else:
-                    print(f"[AI服务] 警告：提取的数据全部为空，但继续返回（允许部分字段为空）")
-                    # 记录对话（即使数据为空）
-                    if self.ai_logger:
-                        self.ai_logger.log_conversation(
-                            prompt=prompt,
-                            api_request=api_request_info,
-                            api_response=result,
-                            extracted_data=normalized_data,
-                            error="提取的数据全部为空",
-                            session_id=session_id
-                        )
-                    return normalized_data
-            
-            # 如果无法提取JSON，记录错误但尝试更宽容的处理
-            print(f"[AI服务] 警告：无法从内容中提取完整JSON，尝试备用解析方法")
-            print(f"[AI服务] 清理后的内容（前500字符）: {content[:500]}")
-            
-            # 尝试从文本中提取
-            parsed_data = self._parse_text_response(content, output_fields)
-            if parsed_data and any(v for v in parsed_data.values() if v):  # 至少有一个非空字段
-                print(f"[AI服务] 使用备用方法解析成功，提取到 {len(parsed_data)} 个字段")
-                # 记录对话
+                    return parsed_data
+                
+                # 如果所有方法都失败，尝试最后一次修复尝试
+                print(f"[AI服务] 所有解析方法都失败，尝试最后一次修复...")
+                # 尝试使用jsonrepair（如果可用）进行最后修复
+                if JSONREPAIR_AVAILABLE:
+                    try:
+                        repaired_content = repair_json(content)
+                        if repaired_content:
+                            try:
+                                extracted_data = json.loads(repaired_content)
+                                if extracted_data and isinstance(extracted_data, dict):
+                                    print(f"[AI服务] 使用jsonrepair最后修复成功，提取到 {len(extracted_data)} 个字段")
+                                    normalized_data = self._normalize_field_names(extracted_data, output_fields)
+                                    normalized_data = self._normalize_date_formats(normalized_data, output_fields)
+                                    normalized_data = self._post_process_inferred_fields(normalized_data, output_fields)
+                                    # 记录对话
+                                    if self.ai_logger:
+                                        self.ai_logger.log_conversation(
+                                            prompt=prompt,
+                                            api_request=api_request_info,
+                                            api_response=result,
+                                            extracted_data=normalized_data,
+                                            error=None,
+                                            session_id=session_id
+                                        )
+                                    return normalized_data
+                            except json.JSONDecodeError:
+                                pass
+                    except Exception as e:
+                        print(f"[AI服务] jsonrepair最后修复也失败: {e}")
+                
+                # 如果所有方法都失败，返回空字典而不是抛出异常（更宽容）
+                # 这样至少不会导致整个调用失败，前端可以显示部分结果
+                error_msg = f"无法从API返回内容中提取JSON数据。原始内容长度: {len(raw_content)}, 清理后内容长度: {len(content)}"
+                print(f"[AI服务] 警告：{error_msg}")
+                print(f"[AI服务] 完整内容: {content}")
+                # 返回一个包含所有输出字段的空字典，而不是抛出异常
+                empty_result = {field['field_code']: '' for field in output_fields}
+                print(f"[AI服务] 返回空结果（包含 {len(empty_result)} 个字段，全部为空）")
+                # 记录失败的对话
                 if self.ai_logger:
                     self.ai_logger.log_conversation(
                         prompt=prompt,
                         api_request=api_request_info,
                         api_response=result,
-                        extracted_data=parsed_data,
-                        error=None,
+                        extracted_data=empty_result,
+                        error=error_msg,
                         session_id=session_id
                     )
-                return parsed_data
-            
-            # 如果所有方法都失败，尝试最后一次修复尝试
-            print(f"[AI服务] 所有解析方法都失败，尝试最后一次修复...")
-            # 尝试使用jsonrepair（如果可用）进行最后修复
-            if JSONREPAIR_AVAILABLE:
-                try:
-                    repaired_content = repair_json(content)
-                    if repaired_content:
-                        try:
-                            extracted_data = json.loads(repaired_content)
-                            if extracted_data and isinstance(extracted_data, dict):
-                                print(f"[AI服务] 使用jsonrepair最后修复成功，提取到 {len(extracted_data)} 个字段")
-                                normalized_data = self._normalize_field_names(extracted_data, output_fields)
-                                normalized_data = self._normalize_date_formats(normalized_data, output_fields)
-                                normalized_data = self._post_process_inferred_fields(normalized_data, output_fields)
-                                # 记录对话
-                                if self.ai_logger:
-                                    self.ai_logger.log_conversation(
-                                        prompt=prompt,
-                                        api_request=api_request_info,
-                                        api_response=result,
-                                        extracted_data=normalized_data,
-                                        error=None,
-                                        session_id=session_id
-                                    )
-                                return normalized_data
-                        except json.JSONDecodeError:
-                            pass
-                except Exception as e:
-                    print(f"[AI服务] jsonrepair最后修复也失败: {e}")
-            
-            # 如果所有方法都失败，返回空字典而不是抛出异常（更宽容）
-            # 这样至少不会导致整个调用失败，前端可以显示部分结果
-            error_msg = f"无法从API返回内容中提取JSON数据。原始内容长度: {len(raw_content)}, 清理后内容长度: {len(content)}"
-            print(f"[AI服务] 警告：{error_msg}")
-            print(f"[AI服务] 完整内容: {content}")
-            # 返回一个包含所有输出字段的空字典，而不是抛出异常
-            empty_result = {field['field_code']: '' for field in output_fields}
-            print(f"[AI服务] 返回空结果（包含 {len(empty_result)} 个字段，全部为空）")
-            # 记录失败的对话
-            if self.ai_logger:
-                self.ai_logger.log_conversation(
-                    prompt=prompt,
-                    api_request=api_request_info,
-                    api_response=result,
-                    extracted_data=empty_result,
-                    error=error_msg,
-                    session_id=session_id
-                )
-            return empty_result
-        else:
-            error_msg = "API返回格式异常：未找到choices字段或choices为空"
-            # 记录错误
+                return empty_result
+            else:
+                error_msg = "API返回格式异常：未找到choices字段或choices为空"
+                # 记录错误
+                if self.ai_logger:
+                    self.ai_logger.log_conversation(
+                        prompt=prompt,
+                        api_request=api_request_info,
+                        api_response=result,
+                        extracted_data=None,
+                        error=error_msg,
+                        session_id=session_id
+                    )
+                raise Exception(error_msg)
+        
+        except Exception as e:
+            # 如果发生异常，记录错误日志
+            error_msg = str(e)
             if self.ai_logger:
                 self.ai_logger.log_conversation(
                     prompt=prompt,
@@ -477,7 +491,8 @@ class AIService:
                     error=error_msg,
                     session_id=session_id
                 )
-            raise Exception(error_msg)
+            # 重新抛出异常，让上层处理
+            raise
     
     def _extract_json_from_text(self, text: str) -> Optional[Dict]:
         """