llm

custom_testcases/basic_checks.py

@@ -8,7 +8,7 @@ class StatusCode200Check(BaseAPITestCase):
     description = "验证 API 响应状态码是否为 200 OK。"
     severity = TestSeverity.CRITICAL
     tags = ["status_code", "smoke_test"]
-
+    use_llm_for_body = False
     # 适用于所有方法和路径 (默认)
     # applicable_methods = None
     # applicable_paths_regex = None
@@ -52,6 +52,7 @@ class HeaderExistenceCheck(BaseAPITestCase):
     description = "验证 API 响应是否包含 'X-Request-ID' 头。"
     severity = TestSeverity.MEDIUM
     tags = ["header", "observability"]
+    use_llm_for_body = False
 
     EXPECTED_HEADER = "X-Request-ID" # 示例，可以根据实际需要修改
 

ddms_compliance_suite/test_framework_core.py

@@ -83,6 +83,7 @@ class BaseAPITestCase:
     
     applicable_methods: Optional[List[str]] = None  
     applicable_paths_regex: Optional[str] = None    
+    use_llm_for_body: Optional[bool] = None  # 新增属性：控制此测试用例是否使用LLM生成请求体
 
     def __init__(self, endpoint_spec: Dict[str, Any], global_api_spec: Dict[str, Any]):
         """

ddms_compliance_suite/test_orchestrator.py

@@ -7,9 +7,15 @@
 import logging
 import json
 import time
-from typing import Dict, List, Any, Optional, Union, Tuple, Type
+import re # 添加 re 模块导入
+from typing import Dict, List, Any, Optional, Union, Tuple, Type, ForwardRef
 from enum import Enum
 import datetime
+import datetime as dt
+from uuid import UUID
+
+from pydantic import BaseModel, Field, create_model
+from pydantic.networks import EmailStr
 
 from .input_parser.parser import InputParser, YAPIEndpoint, SwaggerEndpoint, ParsedYAPISpec, ParsedSwaggerSpec
 from .api_caller.caller import APICaller, APIRequest, APIResponse
@@ -23,6 +29,9 @@ except ImportError:
     LLMService = None
     logging.getLogger(__name__).info("LLMService 未找到，LLM 相关功能将不可用。")
 
+# Cache for dynamically created Pydantic models to avoid redefinition issues
+_dynamic_model_cache: Dict[str, Type[BaseModel]] = {}
+
 class ExecutedTestCaseResult:
     """存储单个APITestCase在其适用的端点上执行后的结果。"""
     
@@ -78,13 +87,11 @@ class TestResult: # 原来的 TestResult 被重构为 EndpointExecutionResult
     def __init__(self,
                  endpoint_id: str, # 通常是 method + path
                  endpoint_name: str, # API 的可读名称/标题
-                 # api_spec_details: Dict[str, Any], # 包含该端点从YAPI/Swagger解析的原始信息，可选
                  overall_status: Status = Status.SKIPPED, # 默认为跳过，后续根据测试用例结果更新
                  start_time: Optional[datetime.datetime] = None
                  ):
         self.endpoint_id = endpoint_id
         self.endpoint_name = endpoint_name
-        # self.api_spec_details = api_spec_details
         self.overall_status = overall_status
         self.executed_test_cases: List[ExecutedTestCaseResult] = []
         self.start_time = start_time if start_time else datetime.datetime.now()
@@ -262,13 +269,13 @@ class TestSummary:
 
     def print_summary_to_console(self): # Renamed from print_summary
         # (Implementation can be more detailed based on the new stats)
-        print("\\n===== 测试运行摘要 =====")
+        print("\n===== 测试运行摘要 =====")
         print(f"开始时间: {self.start_time.isoformat()}")
         if self.end_time:
             print(f"结束时间: {self.end_time.isoformat()}")
         print(f"总耗时: {self.duration:.2f} 秒")
         
-        print("\\n--- 端点统计 ---")
+        print("\n--- 端点统计 ---")
         print(f"定义的端点总数: {self.total_endpoints_defined}")
         print(f"实际测试的端点数: {self.total_endpoints_tested}")
         print(f"  通过: {self.endpoints_passed}")
@@ -278,7 +285,7 @@ class TestSummary:
         print(f"  跳过执行: {self.endpoints_skipped}")
         print(f"  端点通过率: {self.endpoint_success_rate:.2f}%")
 
-        print("\\n--- 测试用例统计 ---")
+        print("\n--- 测试用例统计 ---")
         print(f"适用的测试用例总数 (计划执行): {self.total_test_cases_applicable}")
         print(f"实际执行的测试用例总数: {self.total_test_cases_executed}")
         print(f"  通过: {self.test_cases_passed}")
@@ -290,7 +297,7 @@ class TestSummary:
         # 可选：打印失败的端点和测试用例摘要
         failed_endpoints = [res for res in self.detailed_results if res.overall_status == TestResult.Status.FAILED]
         if failed_endpoints:
-            print("\\n--- 失败的端点摘要 ---")
+            print("\n--- 失败的端点摘要 ---")
             for ep_res in failed_endpoints:
                 print(f"  端点: {ep_res.endpoint_id} ({ep_res.endpoint_name}) - 状态: {ep_res.overall_status.value}")
                 for tc_res in ep_res.executed_test_cases:
@@ -371,6 +378,186 @@ class APITestOrchestrator:
         elif not self.use_llm_for_request_body:
              self.logger.info("配置为不使用LLM生成请求体。")
         
+    def _create_pydantic_model_from_schema(
+        self, 
+        schema: Dict[str, Any], 
+        model_name: str,
+        recursion_depth: int = 0
+    ) -> Optional[Type[BaseModel]]:
+        """
+        动态地从JSON Schema字典创建一个Pydantic模型类。
+        支持嵌套对象和数组。
+
+        Args:
+            schema: JSON Schema字典。
+            model_name: 要创建的Pydantic模型的名称。
+            recursion_depth: 当前递归深度，用于防止无限循环。
+
+        Returns:
+            一个Pydantic BaseModel的子类，如果创建失败则返回None。
+        """
+        MAX_RECURSION_DEPTH = 10
+        if recursion_depth > MAX_RECURSION_DEPTH:
+            self.logger.error(f"创建Pydantic模型 '{model_name}' 时达到最大递归深度 {MAX_RECURSION_DEPTH}。可能存在循环引用。")
+            return None
+
+        # 清理模型名称，使其成为有效的Python标识符
+        safe_model_name = "".join(c if c.isalnum() or c == '_' else '_' for c in model_name)
+        if not safe_model_name or not safe_model_name[0].isalpha() and safe_model_name[0] != '_':
+            safe_model_name = f"DynamicModel_{safe_model_name}"
+        
+        # 检查缓存 (使用清理后的名称)
+        if safe_model_name in _dynamic_model_cache:
+            self.logger.debug(f"从缓存返回动态模型: {safe_model_name}")
+            return _dynamic_model_cache[safe_model_name]
+
+        self.logger.debug(f"开始从Schema创建Pydantic模型: '{safe_model_name}' (原始名: '{model_name}', 深度: {recursion_depth})")
+        
+        if not isinstance(schema, dict) or schema.get('type') != 'object':
+            # Safely get type for logging if schema is not a dict or does not have 'type'
+            schema_type_for_log = schema.get('type') if isinstance(schema, dict) else type(schema).__name__
+            self.logger.error(f"提供的Schema用于模型 '{safe_model_name}' 的必须是 type 'object' 且是一个字典, 实际: {schema_type_for_log}")
+            return None
+
+        properties = schema.get('properties', {})
+        required_fields = set(schema.get('required', []))
+        field_definitions: Dict[str, Tuple[Any, Any]] = {}
+
+        for prop_name, prop_schema in properties.items():
+            if not isinstance(prop_schema, dict):
+                self.logger.warning(f"属性 '{prop_name}' 在模型 '{safe_model_name}' 中的Schema无效，已跳过。")
+                continue
+
+            python_type: Any = Any
+            field_args: Dict[str, Any] = {}
+            
+            default_value: Any = ... # Ellipsis for required fields with no default
+            if 'default' in prop_schema:
+                default_value = prop_schema['default']
+            elif prop_name not in required_fields:
+                default_value = None
+
+            if 'description' in prop_schema:
+                field_args['description'] = prop_schema['description']
+
+            json_type = prop_schema.get('type')
+            json_format = prop_schema.get('format')
+
+            if json_type == 'object':
+                nested_model_name_base = f"{safe_model_name}_{prop_name}"
+                python_type = self._create_pydantic_model_from_schema(prop_schema, nested_model_name_base, recursion_depth + 1)
+                if python_type is None:
+                    self.logger.warning(f"无法为 '{safe_model_name}' 中的嵌套属性 '{prop_name}' 创建模型，已跳过。")
+                    continue
+            elif json_type == 'array':
+                items_schema = prop_schema.get('items')
+                if not isinstance(items_schema, dict):
+                    self.logger.warning(f"数组属性 '{prop_name}' 在模型 '{safe_model_name}' 中的 'items' schema无效，已跳过。")
+                    continue
+                
+                item_type: Any = Any
+                item_json_type = items_schema.get('type')
+                item_json_format = items_schema.get('format')
+
+                if item_json_type == 'object':
+                    item_model_name_base = f"{safe_model_name}_{prop_name}_Item"
+                    item_type = self._create_pydantic_model_from_schema(items_schema, item_model_name_base, recursion_depth + 1)
+                    if item_type is None:
+                        self.logger.warning(f"无法为 '{safe_model_name}' 中的数组属性 '{prop_name}' 的项创建模型，已跳过。")
+                        continue
+                elif item_json_type == 'string':
+                    if item_json_format == 'date-time': item_type = dt.datetime
+                    elif item_json_format == 'date': item_type = dt.date
+                    elif item_json_format == 'email': item_type = EmailStr
+                    elif item_json_format == 'uuid': item_type = UUID
+                    else: item_type = str
+                elif item_json_type == 'integer': item_type = int
+                elif item_json_type == 'number': item_type = float
+                elif item_json_type == 'boolean': item_type = bool
+                else:
+                    self.logger.warning(f"数组 '{prop_name}' 中的项具有未知类型 '{item_json_type}'，默认为 Any。")
+                
+                python_type = List[item_type] # type: ignore
+            elif json_type == 'string':
+                if json_format == 'date-time': python_type = dt.datetime
+                elif json_format == 'date': python_type = dt.date
+                elif json_format == 'email': python_type = EmailStr
+                elif json_format == 'uuid': python_type = UUID
+                else: python_type = str
+                if 'minLength' in prop_schema: field_args['min_length'] = prop_schema['minLength']
+                if 'maxLength' in prop_schema: field_args['max_length'] = prop_schema['maxLength']
+                if 'pattern' in prop_schema: field_args['pattern'] = prop_schema['pattern']
+            elif json_type == 'integer':
+                python_type = int
+                if 'minimum' in prop_schema: field_args['ge'] = prop_schema['minimum']
+                if 'maximum' in prop_schema: field_args['le'] = prop_schema['maximum']
+            elif json_type == 'number':
+                python_type = float
+                if 'minimum' in prop_schema: field_args['ge'] = prop_schema['minimum']
+                if 'maximum' in prop_schema: field_args['le'] = prop_schema['maximum']
+            elif json_type == 'boolean':
+                python_type = bool
+            elif json_type is None and '$ref' in prop_schema:
+                self.logger.warning(f"Schema $ref '{prop_schema['$ref']}' in '{safe_model_name}.{prop_name}' not yet supported. Defaulting to Any.")
+                python_type = Any
+            else:
+                self.logger.warning(f"属性 '{prop_name}' 在模型 '{safe_model_name}' 中具有未知类型 '{json_type}'，默认为 Any。")
+                python_type = Any
+
+            if 'enum' in prop_schema:
+                enum_values = prop_schema['enum']
+                if enum_values:
+                    enum_desc = f" (Enum values: {', '.join(map(str, enum_values))})"
+                    field_args['description'] = field_args.get('description', '') + enum_desc
+            
+            current_field_is_optional = prop_name not in required_fields
+            if current_field_is_optional and python_type is not Any and default_value is None:
+                # For Pydantic v1/v2, if a field is not required and has no other default, it's Optional.
+                # The `python_type` itself might already be an `Optional` if it came from a nested optional model.
+                # We only wrap with Optional if it's not already wrapped effectively.
+                # A simple check: if the type name doesn't start with "Optional"
+                if not (hasattr(python_type, '__origin__') and python_type.__origin__ is Union and type(None) in python_type.__args__):
+                     python_type = Optional[python_type]
+
+
+            field_definitions[prop_name] = (python_type, Field(default_value, **field_args))
+
+        if not field_definitions:
+            self.logger.warning(f"模型 '{safe_model_name}' 没有有效的字段定义，无法创建。")
+            # Return a very basic BaseModel if no properties are defined but an object schema was given
+            # This might happen for an empty object schema {}
+            try:
+                EmptyModel = create_model(safe_model_name, __base__=BaseModel)
+                _dynamic_model_cache[safe_model_name] = EmptyModel
+                self.logger.info(f"创建了一个空的动态Pydantic模型: '{safe_model_name}' (由于无属性定义)")
+                return EmptyModel
+            except Exception as e_empty:
+                self.logger.error(f"尝试为 '{safe_model_name}' 创建空模型时失败: {e_empty}", exc_info=True)
+                return None
+
+
+        try:
+            # ForwardRef for self-referencing models is complex; not fully handled here yet.
+            # If a type in field_definitions is a string (e.g., a ForwardRef string), create_model handles it.
+            DynamicModel = create_model(safe_model_name, **field_definitions, __base__=BaseModel) # type: ignore
+            _dynamic_model_cache[safe_model_name] = DynamicModel
+            self.logger.info(f"成功创建/缓存了动态Pydantic模型: '{safe_model_name}'")
+            
+            # Attempt to update forward refs if any were string types that are now defined
+            # This is a simplified approach. Pydantic's update_forward_refs is usually called on the module or specific model.
+            # For dynamically created models, this might need careful handling if true circular deps are common.
+            # For now, we assume nested creation order mostly handles dependencies.
+            # if hasattr(DynamicModel, 'update_forward_refs'):
+            #    try:
+            #        DynamicModel.update_forward_refs(**_dynamic_model_cache)
+            #        self.logger.debug(f"Attempted to update forward refs for {safe_model_name}")
+            #    except Exception as e_fwd:
+            #        self.logger.warning(f"Error updating forward_refs for {safe_model_name}: {e_fwd}")
+
+            return DynamicModel
+        except Exception as e:
+            self.logger.error(f"使用Pydantic create_model创建 '{safe_model_name}' 时失败: {e}", exc_info=True)
+            return None
 
     def _execute_single_test_case(
         self,
@@ -385,7 +572,6 @@ class APITestOrchestrator:
         validation_points: List[ValidationResult] = []
         test_case_instance: Optional[BaseAPITestCase] = None
         
-        # 准备 endpoint_spec_dict
         endpoint_spec_dict: Dict[str, Any]
         if hasattr(endpoint_spec, 'to_dict') and callable(endpoint_spec.to_dict):
             endpoint_spec_dict = endpoint_spec.to_dict()
@@ -393,15 +579,15 @@ class APITestOrchestrator:
             endpoint_spec_dict = {
                 "method": getattr(endpoint_spec, 'method', 'UNKNOWN_METHOD'),
                 "path": getattr(endpoint_spec, 'path', 'UNKNOWN_PATH'),
-                "title": getattr(endpoint_spec, 'title', ''),
+                "title": getattr(endpoint_spec, 'title', getattr(endpoint_spec, 'summary', '')),
                 "summary": getattr(endpoint_spec, 'summary', ''),
+                "description": getattr(endpoint_spec, 'description', ''), # 确保description也被传递
                 "_original_object_type": type(endpoint_spec).__name__
             }
             if isinstance(endpoint_spec, YAPIEndpoint):
                 for attr_name in dir(endpoint_spec):
                     if not attr_name.startswith('_') and not callable(getattr(endpoint_spec, attr_name)):
                         try:
-                            # Test serializability before adding
                             json.dumps({attr_name: getattr(endpoint_spec, attr_name)})
                             endpoint_spec_dict[attr_name] = getattr(endpoint_spec, attr_name)
                         except (TypeError, OverflowError):
@@ -431,24 +617,22 @@ class APITestOrchestrator:
             )
             test_case_instance.logger.info(f"开始执行测试用例 '{test_case_instance.id}' for endpoint '{endpoint_spec_dict.get('method')} {endpoint_spec_dict.get('path')}'")
 
-            # 1. 请求构建阶段
-            initial_request_data = self._prepare_initial_request_data(endpoint_spec) # endpoint_spec 是原始对象
+            # 调用 _prepare_initial_request_data 时传递 test_case_instance
+            initial_request_data = self._prepare_initial_request_data(endpoint_spec, test_case_instance=test_case_instance)
 
             current_q_params = test_case_instance.generate_query_params(initial_request_data['query_params'])
             current_headers = test_case_instance.generate_headers(initial_request_data['headers'])
             current_body = test_case_instance.generate_request_body(initial_request_data['body'])
             
-            # 路径参数应该从 initial_request_data 中获取，因为 _prepare_initial_request_data 负责生成它们
             current_path_params = initial_request_data['path_params']
 
-            # 构建最终请求URL，使用 current_path_params 进行替换
             final_url = self.base_url + endpoint_spec_dict.get('path', '')
             for p_name, p_val in current_path_params.items():
                 placeholder = f"{{{p_name}}}"
                 if placeholder in final_url:
                     final_url = final_url.replace(placeholder, str(p_val))
                 else:
-                    self.logger.warning(f"路径参数 '{p_name}' 在路径模板 '{endpoint_spec_dict.get('path')}' 中未找到占位符，但为其生成了值。")
+                    self.logger.warning(f"路径参数 '{p_name}' 在路径模板 '{endpoint_spec_dict.get('path')}' 中未找到占位符。")
 
             api_request_context = APIRequestContext(
                 method=endpoint_spec_dict.get('method', 'GET').upper(),
@@ -460,79 +644,68 @@ class APITestOrchestrator:
                 endpoint_spec=endpoint_spec_dict
             )
 
-            # 1.5. 请求预校验
             validation_points.extend(test_case_instance.validate_request_url(api_request_context.url, api_request_context))
             validation_points.extend(test_case_instance.validate_request_headers(api_request_context.headers, api_request_context))
             validation_points.extend(test_case_instance.validate_request_body(api_request_context.body, api_request_context))
 
-            # 检查是否有严重预校验失败
             critical_pre_validation_failure = False
             failure_messages = []
             for vp in validation_points:
-                if not vp.passed and test_case_instance.severity in [TestSeverity.CRITICAL, TestSeverity.HIGH]:
+                if not vp.passed and test_case_instance.severity in [TestSeverity.CRITICAL, TestSeverity.HIGH]: # Check severity of the Test Case for pre-validation
                     critical_pre_validation_failure = True
                     failure_messages.append(vp.message)
             
             if critical_pre_validation_failure:
-                self.logger.warning(f"测试用例 '{test_case_instance.id}' 因请求预校验失败而中止 (严重级别: {test_case_instance.severity.value})。失败信息: {'; '.join(failure_messages)}")
+                self.logger.warning(f"测试用例 '{test_case_instance.id}' 因请求预校验失败而中止 (TC严重级别: {test_case_instance.severity.value})。失败信息: {'; '.join(failure_messages)}")
                 tc_duration = time.time() - tc_start_time
                 return ExecutedTestCaseResult(
                     test_case_id=test_case_instance.id,
                     test_case_name=test_case_instance.name,
                     test_case_severity=test_case_instance.severity,
-                    status=ExecutedTestCaseResult.Status.FAILED, # 预校验失败算作 FAILED
+                    status=ExecutedTestCaseResult.Status.FAILED, 
                     validation_points=validation_points,
                     message=f"请求预校验失败: {'; '.join(failure_messages)}",
                     duration=tc_duration
                 )
 
-            # ---- API 调用 ----
             api_request_obj = APIRequest(
                 method=api_request_context.method,
                 url=api_request_context.url,
                 params=api_request_context.query_params,
                 headers=api_request_context.headers,
-                json_data=api_request_context.body # Assuming JSON, APICaller might need to handle other types
+                json_data=api_request_context.body
             )
             
             response_call_start_time = time.time()
             api_response_obj = self.api_caller.call_api(api_request_obj)
             response_call_elapsed_time = time.time() - response_call_start_time
             
-            # ---- 响应验证 ----
-            # 3. 创建 APIResponseContext
-
             actual_text_content: Optional[str] = None
-            if hasattr(api_response_obj, 'text_content') and api_response_obj.text_content is not None: # 优先尝试直接获取
+            if hasattr(api_response_obj, 'text_content') and api_response_obj.text_content is not None:
                 actual_text_content = api_response_obj.text_content
             elif api_response_obj.json_content is not None:
-                if isinstance(api_response_obj.json_content, str):
+                if isinstance(api_response_obj.json_content, str): # Should not happen if json_content is parsed
                     actual_text_content = api_response_obj.json_content
                 else:
                     try:
                         actual_text_content = json.dumps(api_response_obj.json_content, ensure_ascii=False)
-                    except TypeError:
-                        actual_text_content = str(api_response_obj.json_content) # 最后手段
+                    except TypeError: # If json_content is not serializable (e.g. bytes)
+                        actual_text_content = str(api_response_obj.json_content)
 
-            # elapsed_time: 使用 response_call_elapsed_time
-            # original_response: 设置为 None 因为 api_response_obj 没有 raw_response
 
             api_response_context = APIResponseContext(
                 status_code=api_response_obj.status_code,
-                headers=api_response_obj.headers, # 假设这些直接在 api_response_obj 上
-                json_content=api_response_obj.json_content, # 这个根据之前的错误提示是存在的
+                headers=api_response_obj.headers,
+                json_content=api_response_obj.json_content,
                 text_content=actual_text_content,
                 elapsed_time=response_call_elapsed_time, 
-                original_response=None, # api_response_obj 没有 .raw_response 属性
+                original_response= getattr(api_response_obj, 'raw_response', None), # Pass raw if available
                 request_context=api_request_context
             )
 
-            # 4. 执行响应验证和性能检查
             validation_points.extend(test_case_instance.validate_response(api_response_context, api_request_context))
             validation_points.extend(test_case_instance.check_performance(api_response_context, api_request_context))
 
-            # ---- 结果判定 ----
-            # 5. 判断此测试用例的最终状态
             final_status = ExecutedTestCaseResult.Status.PASSED
             if any(not vp.passed for vp in validation_points):
                 final_status = ExecutedTestCaseResult.Status.FAILED
@@ -553,45 +726,32 @@ class APITestOrchestrator:
             return ExecutedTestCaseResult(
                 test_case_id=test_case_instance.id if test_case_instance else test_case_class.id if hasattr(test_case_class, 'id') else "unknown_tc_id",
                 test_case_name=test_case_instance.name if test_case_instance else test_case_class.name if hasattr(test_case_class, 'name') else "Unknown Test Case Name",
-                test_case_severity=test_case_instance.severity if test_case_instance else TestSeverity.CRITICAL, # Default to critical on error
+                test_case_severity=test_case_instance.severity if test_case_instance else TestSeverity.CRITICAL,
                 status=ExecutedTestCaseResult.Status.ERROR,
-                validation_points=validation_points, # 可能包含部分成功或失败的验证点
+                validation_points=validation_points,
                 message=f"测试用例执行时发生内部错误: {str(e)}",
                 duration=tc_duration
             )
 
-    def _prepare_initial_request_data(self, endpoint_spec: Union[YAPIEndpoint, SwaggerEndpoint]) -> Dict[str, Any]:
+    def _prepare_initial_request_data(self, endpoint_spec: Union[YAPIEndpoint, SwaggerEndpoint], test_case_instance: Optional[BaseAPITestCase] = None) -> Dict[str, Any]:
         """
         根据端点规格准备一个初始的请求数据结构。
         返回一个包含 'path_params', 'query_params', 'headers', 'body' 的字典。
+        Args:
+            endpoint_spec: 当前端点的规格。
+            test_case_instance: (可选) 当前正在执行的测试用例实例，用于细粒度控制LLM使用。
         """
         self.logger.debug(f"Preparing initial request data for: {endpoint_spec.method} {endpoint_spec.path}")
         
-        # path_params_spec: List[Dict] # 用于存储从Swagger等提取的路径参数定义
-        # query_params_spec: List[Dict]
-        # headers_spec: List[Dict]
-        # body_schema: Optional[Dict]
-        
-        # 重置/初始化这些变量，以避免跨调用共享状态（如果 APITestOrchestrator 实例被重用）
         path_params_spec_list: List[Dict[str, Any]] = [] 
         query_params_spec_list: List[Dict[str, Any]] = []
         headers_spec_list: List[Dict[str, Any]] = []
         body_schema_dict: Optional[Dict[str, Any]] = None
-
         path_str = getattr(endpoint_spec, 'path', '')
 
         if isinstance(endpoint_spec, YAPIEndpoint): 
             query_params_spec_list = endpoint_spec.req_query or []
             headers_spec_list = endpoint_spec.req_headers or []
-            # YAPI 的路径参数在 req_params 中，如果用户定义了的话
-            if endpoint_spec.req_params:
-                for p in endpoint_spec.req_params:
-                    # YAPI的req_params可能混合了路径参数和查询参数，这里只关心路径中实际存在的
-                    # 需要从 path_str 中解析出占位符，然后匹配 req_params 中的定义
-                    # 简化：我们假设 req_params 中的条目如果其 name 在路径占位符中，则是路径参数
-                    # 更好的做法是 YAPI 解析器能明确区分它们
-                    pass # 下面会统一处理路径参数
-            
             if endpoint_spec.req_body_type == 'json' and endpoint_spec.req_body_other:
                 try:
                     body_schema_dict = json.loads(endpoint_spec.req_body_other) if isinstance(endpoint_spec.req_body_other, str) else endpoint_spec.req_body_other
@@ -599,6 +759,23 @@ class APITestOrchestrator:
                     self.logger.warning(f"YAPI req_body_other for {path_str} is not valid JSON: {endpoint_spec.req_body_other}")
         
         elif isinstance(endpoint_spec, SwaggerEndpoint):
+            # 优先尝试 OpenAPI 3.0+ 的 requestBody
+            if endpoint_spec.request_body and 'content' in endpoint_spec.request_body:
+                json_content_spec = endpoint_spec.request_body['content'].get('application/json', {})
+                if 'schema' in json_content_spec:
+                    body_schema_dict = json_content_spec['schema']
+                    self.logger.debug("从 Swagger 3.0+ 'requestBody' 中提取到 body schema。")
+
+            # 如果没有从 requestBody 中找到，再尝试 Swagger 2.0 的 in: "body" 参数
+            if not body_schema_dict and endpoint_spec.parameters:
+                for param_spec in endpoint_spec.parameters:
+                    if param_spec.get('in') == 'body':
+                        if 'schema' in param_spec:
+                            body_schema_dict = param_spec['schema']
+                            self.logger.debug(f"从 Swagger 2.0 'in: body' 参数 '{param_spec.get('name')}' 中提取到 body schema (作为回退)。")
+                            break # 找到一个 body 参数就足够了
+            
+            # 处理 path, query, header 参数 (这部分逻辑需要保留并放在正确的位置)
             if endpoint_spec.parameters:
                 for param_spec in endpoint_spec.parameters:
                     param_in = param_spec.get('in')
@@ -608,68 +785,53 @@ class APITestOrchestrator:
                         query_params_spec_list.append(param_spec)
                     elif param_in == 'header':
                         headers_spec_list.append(param_spec)
-            if endpoint_spec.request_body and 'content' in endpoint_spec.request_body:
-                json_content_spec = endpoint_spec.request_body['content'].get('application/json', {})
-                if 'schema' in json_content_spec:
-                    body_schema_dict = json_content_spec['schema']
         
-        # --- 生成路径参数数据 --- 
         path_params_data: Dict[str, Any] = {}
         import re
-        # 从路径字符串中提取所有占位符名称，例如 /users/{id}/items/{itemId} -> ["id", "itemId"]
         path_param_names_in_url = re.findall(r'{(.*?)}', path_str) 
 
         for p_name in path_param_names_in_url:
             found_spec = None
-            # 尝试从 Swagger 的 path_params_spec_list 查找详细定义
             for spec in path_params_spec_list:
                 if spec.get('name') == p_name:
                     found_spec = spec
                     break
-            # 尝试从 YAPI 的 req_params (如果之前有解析并填充到类似 path_params_spec_list 的结构)
-            # (当前YAPI的req_params未直接用于填充path_params_spec_list, 需要改进InputParser或此处逻辑)
-            # TODO: YAPI的req_params需要更可靠地映射到路径参数
             
             if found_spec and isinstance(found_spec, dict):
-                # 如果找到参数的详细规格 (例如来自Swagger)
                 value = found_spec.get('example')
                 if value is None and found_spec.get('schema'):
                     value = self._generate_data_from_schema(found_spec['schema'])
-                path_params_data[p_name] = value if value is not None else f"example_{p_name}" # Fallback
+                path_params_data[p_name] = value if value is not None else f"example_{p_name}"
             else:
-                # 如果没有详细规格，生成一个通用占位符值
                 path_params_data[p_name] = f"example_{p_name}"
             self.logger.debug(f"Path param '{p_name}' generated value: {path_params_data[p_name]}")
 
-        # --- 生成查询参数数据 --- 
         query_params_data: Dict[str, Any] = {}
         for q_param_spec in query_params_spec_list: 
             name = q_param_spec.get('name')
             if name:
-                value = q_param_spec.get('example') # Swagger/OpenAPI style
-                if value is None and 'value' in q_param_spec: # YAPI style (value often holds example or default)
+                value = q_param_spec.get('example')
+                if value is None and 'value' in q_param_spec:
                     value = q_param_spec['value']
                 
-                if value is None and q_param_spec.get('schema'): # Swagger/OpenAPI schema for param
+                if value is None and q_param_spec.get('schema'):
                     value = self._generate_data_from_schema(q_param_spec['schema'])
-                elif value is None and q_param_spec.get('type'): # YAPI may define type directly
-                    # Simplified schema generation for YAPI direct type if no 'value' field
+                elif value is None and q_param_spec.get('type'):
                     value = self._generate_data_from_schema({'type': q_param_spec.get('type')})
                 
                 query_params_data[name] = value if value is not None else f"example_query_{name}"
 
-        # --- 生成请求头数据 --- 
         headers_data: Dict[str, str] = {"Content-Type": "application/json", "Accept": "application/json"}
         for h_param_spec in headers_spec_list:
             name = h_param_spec.get('name')
-            if name and name.lower() not in ['content-type', 'accept']: # 不要覆盖基础的Content-Type/Accept，除非明确
+            if name and name.lower() not in ['content-type', 'accept']:
                 value = h_param_spec.get('example')
-                if value is None and 'value' in h_param_spec: # YAPI
+                if value is None and 'value' in h_param_spec:
                     value = h_param_spec['value']
                 
-                if value is None and h_param_spec.get('schema'): # Swagger
+                if value is None and h_param_spec.get('schema'):
                     value = self._generate_data_from_schema(h_param_spec['schema'])
-                elif value is None and h_param_spec.get('type'): # YAPI
+                elif value is None and h_param_spec.get('type'):
                      value = self._generate_data_from_schema({'type': h_param_spec.get('type')})
 
                 if value is not None:
@@ -677,37 +839,85 @@ class APITestOrchestrator:
                 else:
                     headers_data[name] = f"example_header_{name}"
         
-        # --- 生成请求体数据 --- 
         body_data: Optional[Any] = None
         if body_schema_dict:
             generated_by_llm = False
-            if self.use_llm_for_request_body and self.llm_service:
-                self.logger.debug(f"尝试使用 LLM 为端点 {endpoint_spec.method} {endpoint_spec.path} 生成请求体。")
+            
+            # 决定是否应该为这个特定的情况尝试LLM
+            # 1. 全局开关 self.use_llm_for_request_body 必须为 True
+            # 2. LLM 服务 self.llm_service 必须可用
+            # 3. 测试用例级别配置 test_case_instance.use_llm_for_body (如果存在且不是None) 会覆盖全局配置
+            attempt_llm_globally = self.use_llm_for_request_body and self.llm_service
+            should_try_llm_for_this_run = attempt_llm_globally
+
+            if test_case_instance and hasattr(test_case_instance, 'use_llm_for_body') and test_case_instance.use_llm_for_body is not None:
+                should_try_llm_for_this_run = test_case_instance.use_llm_for_body
+                if should_try_llm_for_this_run and not self.llm_service:
+                    self.logger.warning(f"测试用例 '{test_case_instance.id}' 配置为使用LLM，但LLM服务不可用。将回退。")
+                    should_try_llm_for_this_run = False # LLM服务不可用时，即使TC要求也无法使用
+                self.logger.debug(f"测试用例 '{test_case_instance.id}' 的 use_llm_for_body 设置为 {test_case_instance.use_llm_for_body}，最终决策是否尝试LLM: {should_try_llm_for_this_run}")
+            elif not attempt_llm_globally and test_case_instance and hasattr(test_case_instance, 'use_llm_for_body') and test_case_instance.use_llm_for_body is True and not self.llm_service:
+                 # 特殊情况：全局LLM关闭，但测试用例希望开启，可是LLM服务不可用
+                 self.logger.warning(f"测试用例 '{test_case_instance.id}' 配置为使用LLM，但全局LLM服务不可用或未配置。将回退。")
+                 should_try_llm_for_this_run = False
+            
+            if should_try_llm_for_this_run: # 只有在最终决策为True时才尝试
+                self.logger.debug(f"尝试使用 LLM 为端点 {endpoint_spec.method} {endpoint_spec.path} 生成请求体 (TC覆盖: {test_case_instance.use_llm_for_body if test_case_instance else 'N/A'})。")
                 try:
-                    # TODO: 动态创建 Pydantic 模型 (步骤2的核心)
-                    # DynamicPydanticModel = self._create_pydantic_model_from_schema(body_schema_dict, "DynamicRequestBodyModel")
-                    # if DynamicPydanticModel:
-                    #     # TODO: 考虑是否需要从 endpoint_spec 中提取一些 prompt_instructions
-                    #     llm_generated_body = self.llm_service.generate_parameters_from_schema(
-                    #         pydantic_model_class=DynamicPydanticModel,
-                    #         prompt_instructions=f"为API端点 {endpoint_spec.title or endpoint_spec.path} 生成请求体。"
-                    #     )
-                    #     if llm_generated_body is not None:
-                    #         body_data = llm_generated_body
-                    #         generated_by_llm = True
-                    #         self.logger.info(f"LLM 成功为 {endpoint_spec.method} {endpoint_spec.path} 生成了请求体。")
-                    #     else:
-                    #         self.logger.warning(f"LLM未能为 {endpoint_spec.method} {endpoint_spec.path} 生成请求体，将回退到默认方法。")
-                    # else:
-                    #     self.logger.warning(f"未能从Schema动态创建Pydantic模型用于LLM请求体生成，将回退。")
-                    self.logger.info("LLM请求体生成部分尚未完全实现 (_create_pydantic_model_from_schema)。暂时回退。") # 临时日志
-                    pass # 占位，直到 _create_pydantic_model_from_schema 完成
+                    # 生成一个稍微独特但可预测的模型名称，以利于缓存和调试
+                    model_base_name = "".join(part.capitalize() for part in re.split(r'[^a-zA-Z0-9]+', endpoint_spec.path.strip('/')) if part)
+                    dynamic_model_name = f"{model_base_name}{endpoint_spec.method.capitalize()}Body"
+                    if not dynamic_model_name or not dynamic_model_name[0].isalpha(): # 确保名称有效
+                        dynamic_model_name = f"Dynamic{endpoint_spec.method.capitalize()}Body_{abs(hash(endpoint_spec.path))}"
+
+
+                    DynamicPydanticModel = self._create_pydantic_model_from_schema(body_schema_dict, dynamic_model_name)
+                    
+                    if DynamicPydanticModel:
+                        # 尝试获取端点的可读名称，优先顺序: title, summary, path
+                        readable_endpoint_name = getattr(endpoint_spec, 'title', None) or \
+                                                 getattr(endpoint_spec, 'summary', None) or \
+                                                 endpoint_spec.path
+                        
+                        prompt_instr = f"请为API端点 '{readable_endpoint_name}' (方法: {endpoint_spec.method}) 生成一个符合其定义的请求体。"
+                        
+                        # 可以进一步从 description 获取更详细的上下文给LLM
+                        ep_description = getattr(endpoint_spec, 'description', None)
+                        if ep_description:
+                            prompt_instr += f" API描述: {ep_description}"
+                        
+                        llm_generated_body = self.llm_service.generate_parameters_from_schema(
+                            pydantic_model_class=DynamicPydanticModel,
+                            prompt_instructions=prompt_instr
+                        )
+                        if llm_generated_body is not None:
+                            try:
+                                # 尝试用生成的模型验证LLM的输出，确保LLM确实遵循了schema
+                                DynamicPydanticModel.model_validate(llm_generated_body)
+                                body_data = llm_generated_body
+                                generated_by_llm = True
+                                self.logger.info(f"LLM 成功为 {endpoint_spec.method} {endpoint_spec.path} 生成并验证了请求体。")
+                            except Exception as p_val_error: # Catches Pydantic's ValidationError
+                                self.logger.warning(f"LLM为 {endpoint_spec.method} {endpoint_spec.path} 生成的请求体未能通过动态Pydantic模型验证: {p_val_error}. 将回退。LLM输出: {json.dumps(llm_generated_body, indent=2, ensure_ascii=False)[:500]}...")
+                        else:
+                            self.logger.warning(f"LLM未能为 {endpoint_spec.method} {endpoint_spec.path} 生成请求体内容，将回退到默认方法。")
+                    else:
+                        self.logger.warning(f"未能从Schema动态创建Pydantic模型用于LLM请求体生成 (端点: {endpoint_spec.method} {endpoint_spec.path})，将回退。")
                 except Exception as e:
                     self.logger.error(f"使用LLM生成请求体时发生错误: {e}。将回退到默认方法。", exc_info=True)
 
-            if not generated_by_llm: # 如果未使用LLM或LLM生成失败
-                if self.use_llm_for_request_body and self.llm_service: # 只有在尝试过LLM之后才打印这条回退日志
-                    self.logger.debug(f"LLM生成请求体失败或未启用，回退到基于规则的生成方法 for {endpoint_spec.method} {endpoint_spec.path}。")
+            if not generated_by_llm:
+                # 只有当确实尝试了LLM（should_try_llm_for_this_run为True）但失败了，或者测试用例强制不使用LLM才记录回退日志
+                log_fallback = False
+                if should_try_llm_for_this_run: # 如果本应尝试LLM但generated_by_llm是False，说明LLM失败了
+                    log_fallback = True
+                elif test_case_instance and hasattr(test_case_instance, 'use_llm_for_body') and test_case_instance.use_llm_for_body is False:
+                    # 如果测试用例明确禁用了LLM
+                    log_fallback = True 
+                    self.logger.debug(f"测试用例 '{test_case_instance.id}' 明确配置不使用LLM，使用基于规则的生成方法 for {endpoint_spec.method} {endpoint_spec.path}。")
+                
+                if log_fallback and not (test_case_instance and hasattr(test_case_instance, 'use_llm_for_body') and test_case_instance.use_llm_for_body is False) : # 避免重复日志
+                     self.logger.debug(f"LLM生成请求体失败或未启用 (最终决策: {should_try_llm_for_this_run}), 回退到基于规则的生成方法 for {endpoint_spec.method} {endpoint_spec.path}。")
                 body_data = self._generate_data_from_schema(body_schema_dict)
 
         return {
@@ -718,30 +928,23 @@ class APITestOrchestrator:
         }
 
     def run_test_for_endpoint(self, endpoint: Union[YAPIEndpoint, SwaggerEndpoint], 
-                              global_api_spec: Union[ParsedYAPISpec, ParsedSwaggerSpec] # 新增参数
-                              ) -> TestResult: # 返回类型更新为新的TestResult (EndpointExecutionResult)
-        """
-        运行单个API端点的所有适用测试用例。
-        """
+                              global_api_spec: Union[ParsedYAPISpec, ParsedSwaggerSpec]
+                              ) -> TestResult:
         endpoint_id = f"{getattr(endpoint, 'method', 'GET').upper()} {getattr(endpoint, 'path', '/')}"
         endpoint_name = getattr(endpoint, 'title', '') or getattr(endpoint, 'summary', '') or endpoint_id
         
         self.logger.info(f"开始为端点测试: {endpoint_id} ({endpoint_name})")
         
-        # 使用新的TestResult结构 (它现在代表 EndpointExecutionResult)
-        endpoint_test_result = TestResult( # 这是新的 TestResult
+        endpoint_test_result = TestResult(
                     endpoint_id=endpoint_id,
                     endpoint_name=endpoint_name,
-            # api_spec_details=endpoint.to_dict() if hasattr(endpoint, 'to_dict') else endpoint # 可选
         )
 
         if not self.test_case_registry:
             self.logger.warning(f"TestCaseRegistry 未初始化，无法为端点 '{endpoint_id}' 执行自定义测试用例。")
-            # TODO: 决定此时的行为，是跳过，还是执行旧的规则引擎（如果保留），或者标记为错误。
-            # 简化：如果只想运行新的测试用例，那么这里就直接结束此端点的测试。
-            endpoint_test_result.overall_status = TestResult.Status.SKIPPED # 或者 ERROR
+            endpoint_test_result.overall_status = TestResult.Status.SKIPPED
             endpoint_test_result.error_message = "TestCaseRegistry 未初始化。"
-            endpoint_test_result.finalize_endpoint_test() # 计算持续时间等
+            endpoint_test_result.finalize_endpoint_test()
             return endpoint_test_result
 
         applicable_test_case_classes = self.test_case_registry.get_applicable_test_cases(
@@ -751,10 +954,7 @@ class APITestOrchestrator:
 
         if not applicable_test_case_classes:
             self.logger.info(f"端点 '{endpoint_id}' 没有找到适用的自定义测试用例。")
-            # 同样，决定行为。如果只依赖自定义测试用例，则此端点可能算作 SKIPPED 或某种形式的通过/信息。
-            # endpoint_test_result.overall_status = TestResult.Status.SKIPPED # 或 INFO / PASSED_NO_CASES
-            # endpoint_test_result.message = "没有适用的自定义测试用例。"
-            endpoint_test_result.finalize_endpoint_test() # 会将状态设置为ERROR并附带消息
+            endpoint_test_result.finalize_endpoint_test()
             return endpoint_test_result
         
         self.logger.info(f"端点 '{endpoint_id}' 发现了 {len(applicable_test_case_classes)} 个适用的测试用例: {[tc.id for tc in applicable_test_case_classes]}")
@@ -769,35 +969,15 @@ class APITestOrchestrator:
             endpoint_test_result.add_executed_test_case_result(executed_case_result)
             self.logger.debug(f"测试用例 '{tc_class.id}' 执行完毕，状态: {executed_case_result.status.value}")
 
-        # 所有测试用例执行完毕后，最终确定此端点的状态
         endpoint_test_result.finalize_endpoint_test()
         self.logger.info(f"端点 '{endpoint_id}' 测试完成，最终状态: {endpoint_test_result.overall_status.value}")
         
-        # 旧的规则引擎逻辑 (self.rule_executor) 可以选择性地在这里调用，
-        # 或者完全被新的 APITestCase 机制取代。
-        # 如果要保留，需要决定它如何与新的结果结构集成。
-        # 目前，为了清晰和逐步迁移，我们假设主要依赖新的 APITestCase。
-
         return endpoint_test_result
     
     def run_tests_from_yapi(self, yapi_file_path: str, 
                             categories: Optional[List[str]] = None,
-                            custom_test_cases_dir: Optional[str] = None # 新增参数
+                            custom_test_cases_dir: Optional[str] = None
                             ) -> TestSummary:
-        """
-        从YAPI定义文件运行API测试
-        
-        Args:
-            yapi_file_path: YAPI定义文件路径
-            categories: 要测试的API分类列表（如果为None，则测试所有分类）
-            custom_test_cases_dir: 自定义测试用例的目录。如果 Orchestrator 初始化时已提供，则此参数可选。
-                                   如果 Orchestrator 未提供，则必须在此处提供以加载测试用例。
-                                   如果 Orchestrator 初始化和此处都提供了，此处的优先。
-            
-        Returns:
-            TestSummary: 测试结果摘要
-        """
-        # 如果调用时传入了 custom_test_cases_dir，则重新初始化/更新 TestCaseRegistry
         if custom_test_cases_dir and (not self.test_case_registry or self.test_case_registry.test_cases_dir != custom_test_cases_dir):
             self.logger.info(f"从 run_tests_from_yapi 使用新的目录重新初始化 TestCaseRegistry: {custom_test_cases_dir}")
             try:
@@ -805,14 +985,10 @@ class APITestOrchestrator:
                 self.logger.info(f"TestCaseRegistry (re)initialization complete, found {len(self.test_case_registry.get_all_test_case_classes())} test case classes.")
             except Exception as e:
                  self.logger.error(f"从 run_tests_from_yapi 重新初始化 TestCaseRegistry 失败: {e}", exc_info=True)
-                 # 决定是中止还是继续（可能不运行自定义测试）
-                 # For now, if it fails here, it might proceed without custom tests if registry becomes None
-
 
         self.logger.info(f"从YAPI文件加载API定义: {yapi_file_path}")
         parsed_yapi = self.parser.parse_yapi_spec(yapi_file_path)
-        
-        summary = TestSummary() # 使用新的 TestSummary
+        summary = TestSummary()
         
         if not parsed_yapi:
             self.logger.error(f"解析YAPI文件失败: {yapi_file_path}")
@@ -825,7 +1001,6 @@ class APITestOrchestrator:
         
         summary.set_total_endpoints_defined(len(endpoints_to_test))
         
-        # 计算总的适用测试用例数量 (粗略估计，实际执行时可能会因内部逻辑跳过)
         total_applicable_tcs = 0
         if self.test_case_registry:
             for endpoint_spec in endpoints_to_test:
@@ -836,30 +1011,17 @@ class APITestOrchestrator:
                 )
         summary.set_total_test_cases_applicable(total_applicable_tcs)
 
-
         for endpoint in endpoints_to_test:
-            # 将完整的 parsed_yapi 作为 global_api_spec 传递
             result = self.run_test_for_endpoint(endpoint, global_api_spec=parsed_yapi)
-            summary.add_endpoint_result(result) # 使用新的 TestSummary 方法
+            summary.add_endpoint_result(result)
             
-        summary.finalize_summary() # 使用新的 TestSummary 方法
+        summary.finalize_summary()
         return summary
     
     def run_tests_from_swagger(self, swagger_file_path: str, 
                                tags: Optional[List[str]] = None,
-                               custom_test_cases_dir: Optional[str] = None # 新增参数
+                               custom_test_cases_dir: Optional[str] = None
                                ) -> TestSummary:
-        """
-        从Swagger定义文件运行API测试
-        
-        Args:
-            swagger_file_path: Swagger定义文件路径
-            tags: 要测试的API标签列表（如果为None，则测试所有标签）
-            custom_test_cases_dir: 自定义测试用例的目录。 (逻辑同 yapi 方法)
-            
-        Returns:
-            TestSummary: 测试结果摘要
-        """
         if custom_test_cases_dir and (not self.test_case_registry or self.test_case_registry.test_cases_dir != custom_test_cases_dir):
             self.logger.info(f"从 run_tests_from_swagger 使用新的目录重新初始化 TestCaseRegistry: {custom_test_cases_dir}")
             try:
@@ -868,11 +1030,9 @@ class APITestOrchestrator:
             except Exception as e:
                  self.logger.error(f"从 run_tests_from_swagger 重新初始化 TestCaseRegistry 失败: {e}", exc_info=True)
 
-
         self.logger.info(f"从Swagger文件加载API定义: {swagger_file_path}")
         parsed_swagger = self.parser.parse_swagger_spec(swagger_file_path)
-        
-        summary = TestSummary() # 使用新的 TestSummary
+        summary = TestSummary()
         
         if not parsed_swagger:
             self.logger.error(f"解析Swagger文件失败: {swagger_file_path}")
@@ -896,105 +1056,70 @@ class APITestOrchestrator:
         summary.set_total_test_cases_applicable(total_applicable_tcs)
 
         for endpoint in endpoints_to_test:
-            # 将完整的 parsed_swagger 作为 global_api_spec 传递
             result = self.run_test_for_endpoint(endpoint, global_api_spec=parsed_swagger)
-            summary.add_endpoint_result(result) # 使用新的 TestSummary 方法
+            summary.add_endpoint_result(result)
             
-        summary.finalize_summary() # 使用新的 TestSummary 方法
+        summary.finalize_summary()
         return summary
 
     def _generate_data_from_schema(self, schema: Dict[str, Any]) -> Any:
         """
         根据JSON Schema生成测试数据 (此方法基本保持不变，可能被测试用例或编排器内部使用)
-        
-        Args:
-            schema: JSON Schema
-            
-        Returns:
-            生成的测试数据
         """
-        if not schema or not isinstance(schema, dict): # 添加检查 schema 是否为 dict
+        if not schema or not isinstance(schema, dict):
             self.logger.debug(f"_generate_data_from_schema: 提供的 schema 无效或为空: {schema}")
             return None
             
         schema_type = schema.get('type')
         
-        # 优先使用 example 或 default
         if 'example' in schema:
             return schema['example']
         if 'default' in schema:
             return schema['default']
         
         if schema_type == 'object':
-            # ... (内容与旧版本相同，此处省略以便简洁) ...
             result = {}
             properties = schema.get('properties', {})
-            required_fields = schema.get('required', [])
             
             for prop_name, prop_schema in properties.items():
-                # 如果字段是必需的，或者我们想为所有字段生成值
-                # 为了生成更完整的请求体，我们通常会为所有定义的属性生成值
-                # if prop_name in required_fields or True: # 改为总是尝试生成
                 result[prop_name] = self._generate_data_from_schema(prop_schema)
-                    
-            # 确保所有必需字段都有值，即使它们在 properties 中没有 schema（不常见，但可能）
-            # for req_field in required_fields:
-            #    if req_field not in result:
-            #        result[req_field] = "example_required_value" # 或 None
-            return result if result else {} # 确保返回字典
+            return result if result else {}
             
         elif schema_type == 'array':
             items_schema = schema.get('items', {})
-            # 尝试生成一个或多个项，可以使用 minItems/maxItems (简化：生成一项)
-            min_items = schema.get('minItems', 1 if schema.get('default') is None and schema.get('example') is None else 0) # 如果有默认或示例空数组，则可以为0
+            min_items = schema.get('minItems', 1 if schema.get('default') is None and schema.get('example') is None else 0)
             if min_items == 0 and (schema.get('default') == [] or schema.get('example') == []):
                  return []
             
-            num_items_to_generate = max(1, min_items) # 至少生成一项，除非minItems显式为0且无内容
-            
+            num_items_to_generate = max(1, min_items)
             generated_array = [self._generate_data_from_schema(items_schema) for _ in range(num_items_to_generate)]
-            # 过滤掉生成失败的 None 值，除非 schema 允许 null
-            # if items_schema.get('type') != 'null' and not ('null' in items_schema.get('type', []) if isinstance(items_schema.get('type'), list) else False):
-            #    generated_array = [item for item in generated_array if item is not None]
             return generated_array
             
         elif schema_type == 'string':
             string_format = schema.get('format', '')
-            if 'enum' in schema and schema['enum']: # 确保 enum 非空
+            if 'enum' in schema and schema['enum']:
                 return schema['enum'][0]
-
-            # ... (其他格式处理与旧版类似) ...
             if string_format == 'date': return '2023-01-01'
             if string_format == 'date-time': return datetime.datetime.now().isoformat()
             if string_format == 'email': return 'test@example.com'
             if string_format == 'uuid': import uuid; return str(uuid.uuid4())
-            # pattern, minLength, maxLength 等可以进一步细化
             return schema.get('default', schema.get('example', 'example_string'))
                 
         elif schema_type == 'number' or schema_type == 'integer':
-            # ... (与旧版类似，优先 default/example) ...
             val = schema.get('default', schema.get('example'))
             if val is not None: return val
             
             minimum = schema.get('minimum')
-            maximum = schema.get('maximum')
+            maximum = schema.get('maximum') # Not used yet for generation, but could be
             if minimum is not None: return minimum
-            if maximum is not None: return maximum # (如果只有max，可能需要调整)
             return 0 if schema_type == 'integer' else 0.0
                 
         elif schema_type == 'boolean':
-            return schema.get('default', schema.get('example', False)) # 默认为 False
+            return schema.get('default', schema.get('example', False))
             
         elif schema_type == 'null':
             return None
             
         self.logger.debug(f"_generate_data_from_schema: 未知或不支持的 schema 类型 '{schema_type}' for schema: {schema}")
-        return None # 对于未知类型，返回None
-    
-# ... (旧的 _build_api_request 和 _validate_response 基本可以移除了，因为它们的功能被新的流程覆盖) ...
-# 确保删除或注释掉旧的 `_build_api_request` 和 `_validate_response` 方法，
-# 因为它们的功能现在被 `_execute_single_test_case` 和 `_prepare_initial_request_data` 中的逻辑所取代或整合。
-
-# python run_api_tests.py --base-url http://127.0.0.1:4523/m1/6386850-6083489-default  --yapi assets/doc/井筒API示例.json --custom-test-cases-dir ./custom_testcases
-# (示例命令行调用，需要更新以匹配新的参数)
+        return None
 

rules/apidesign/restful-url-pattern/1.0.0.json

@@ -1,18 +0,0 @@
-{
-  "id": "restful-url-pattern",
-  "name": "RESTful URL设计规则",
-  "description": "验证API URL是否符合RESTful设计规范",
-  "category": "APIDesign",
-  "version": "1.0.0",
-  "severity": "warning",
-  "source": null,
-  "is_enabled": true,
-  "tags": null,
-  "target_type": "APIRequest",
-  "target_identifier": null,
-  "lifecycle": "请求准备阶段",
-  "scope": "请求URL",
-  "code": null,
-  "design_aspect": "URL设计",
-  "pattern": "^/api/v\\d+/[a-z0-9-]+(/[a-z0-9-]+)*$"
-}

rules/errorhandling/standard-error-response/1.0.0.json

@@ -1,19 +0,0 @@
-{
-  "id": "standard-error-response",
-  "name": "标准错误响应格式规则",
-  "description": "验证API错误响应是否符合标准格式",
-  "category": "ErrorHandling",
-  "version": "1.0.0",
-  "severity": "warning",
-  "source": null,
-  "is_enabled": true,
-  "tags": null,
-  "target_type": "APIResponse",
-  "target_identifier": null,
-  "lifecycle": "响应验证阶段",
-  "scope": "响应体",
-  "code": null,
-  "error_code": "*",
-  "expected_status": 400,
-  "expected_message": null
-}

rules/performance/response-time-max-500ms/1.0.0.json

@@ -1,19 +0,0 @@
-{
-  "id": "response-time-max-500ms",
-  "name": "响应时间不超过500毫秒",
-  "description": "验证API响应时间不超过500毫秒",
-  "category": "Performance",
-  "version": "1.0.0",
-  "severity": "warning",
-  "source": null,
-  "is_enabled": true,
-  "tags": null,
-  "target_type": "APIResponse",
-  "target_identifier": null,
-  "lifecycle": "响应验证阶段",
-  "scope": "响应时间",
-  "code": null,
-  "threshold": 500.0,
-  "metric": "response_time",
-  "unit": "ms"
-}

rules/security/https-only-rule/1.0.0.json

@@ -1,18 +0,0 @@
-{
-  "id": "https-only-rule",
-  "name": "HTTPS强制使用规则",
-  "description": "验证API请求是否使用了HTTPS协议",
-  "category": "Security",
-  "version": "1.0.0",
-  "severity": "error",
-  "source": null,
-  "is_enabled": true,
-  "tags": null,
-  "target_type": "APIRequest",
-  "target_identifier": null,
-  "lifecycle": "请求准备阶段",
-  "scope": "安全性",
-  "code": null,
-  "check_type": "transport_security",
-  "expected_value": "https"
-}

run_api_tests.py

@@ -53,6 +53,22 @@ def parse_args():
                                  default=None, # 或者 './custom_testcases' 如果想设为默认
                                  help='存放自定义APITestCase Python文件的目录路径。如果未提供，则不加载自定义测试。')
 
+    # 新增：LLM 配置选项
+    llm_group = parser.add_argument_group('LLM 配置选项 (可选)')
+    llm_group.add_argument('--llm-api-key',
+                           default=os.environ.get("OPENAI_API_KEY"), # 尝试从环境变量获取
+                           help='LLM服务的API密钥 (例如 OpenAI API Key)。默认从环境变量 OPENAI_API_KEY 读取。')
+    llm_group.add_argument('--llm-base-url',
+                           default="https://dashscope.aliyuncs.com/compatible-mode/v1",
+                           help='LLM服务的自定义基础URL (例如 OpenAI API代理)。')
+    llm_group.add_argument('--llm-model-name',
+                           default="qwen-plus", # 设置一个常用的默认模型
+                           help='要使用的LLM模型名称 (例如 "gpt-3.5-turbo", "gpt-4")。')
+    llm_group.add_argument('--use-llm-for-request-body',
+                           action='store_true',
+                           default=True,
+                           help='是否启用LLM为API请求生成请求体数据。')
+
     return parser.parse_args()
 
 def list_yapi_categories(yapi_file: str):
@@ -190,12 +206,17 @@ def main():
     # 解析分类/标签过滤器
     categories = args.categories.split(',') if args.categories else None
     tags = args.tags.split(',') if args.tags else None
+    logger.info(f"args.api_key: {args.llm_api_key}")
     
     # 实例化测试编排器
     # 将 custom_test_cases_dir 参数传递给 APITestOrchestrator 的构造函数
     orchestrator = APITestOrchestrator(
         base_url=args.base_url, 
-        custom_test_cases_dir=args.custom_test_cases_dir # 新增参数
+        custom_test_cases_dir=args.custom_test_cases_dir, # 新增参数
+        llm_api_key=args.llm_api_key,
+        llm_base_url=args.llm_base_url,
+        llm_model_name=args.llm_model_name,
+        use_llm_for_request_body=args.use_llm_for_request_body
     )
     
     # 运行测试

tests/test_test_orchestrator.py

@@ -0,0 +1,438 @@
+import unittest
+import logging
+from typing import Optional, List, Dict, Any, Type, Union
+from uuid import UUID
+import datetime as dt
+
+# 调整导入路径以适应测试文件在 tests/ 目录下的情况
+import sys
+import os
+current_file_dir = os.path.dirname(os.path.abspath(__file__))
+project_root = os.path.dirname(current_file_dir)
+if project_root not in sys.path:
+    sys.path.insert(0, project_root)
+
+from pydantic import BaseModel, Field, ValidationError
+from pydantic.networks import EmailStr
+
+from ddms_compliance_suite.test_orchestrator import APITestOrchestrator, _dynamic_model_cache
+from ddms_compliance_suite.llm_utils.llm_service import LLMService # For orchestrator init if needed
+
+# 基本的 Orchestrator 初始化参数，如果测试中需要实例化 Orchestrator
+BASE_URL_FOR_TEST = "http://fakeapi.com"
+
+# 全局禁用或设置较低级别的日志，以便测试输出更干净
+# logging.basicConfig(level=logging.ERROR)
+# logging.getLogger(\"ddms_compliance_suite.test_orchestrator\").setLevel(logging.WARNING)
+
+# Helper functions to extract constraint values from FieldInfo.metadata
+def get_metadata_constraint_value(metadata_list: list, constraint_attr_name: str) -> Any:
+    for m_obj in metadata_list:
+        if hasattr(m_obj, constraint_attr_name):
+            return getattr(m_obj, constraint_attr_name)
+    return None
+
+class TestDynamicModelCreation(unittest.TestCase):
+    """
+    专门测试 APITestOrchestrator._create_pydantic_model_from_schema 方法。
+    """
+
+    def setUp(self):
+        """清除动态模型缓存，确保每个测试的独立性。"""
+        _dynamic_model_cache.clear()
+        # 创建一个Orchestrator实例，_create_pydantic_model_from_schema是它的方法
+        # 对于仅测试 _create_pydantic_model_from_schema，LLM配置可以为None
+        self.orchestrator = APITestOrchestrator(base_url=BASE_URL_FOR_TEST)
+        # 可以通过 self.orchestrator._create_pydantic_model_from_schema 调用
+
+    def tearDown(self):
+        """再次清除缓存，以防万一。"""
+        _dynamic_model_cache.clear()
+
+    def test_simple_object(self):
+        """测试基本对象创建，包含不同类型的字段和必需字段。"""
+        schema = {
+            "type": "object",
+            "properties": {
+                "name": {"type": "string", "description": "User name"},
+                "age": {"type": "integer", "minimum": 0},
+                "email": {"type": "string", "format": "email"},
+                "is_active": {"type": "boolean", "default": True},
+                "height": {"type": "number"}
+            },
+            "required": ["name", "age"]
+        }
+        model_name = "SimpleUserModel"
+        DynamicModel = self.orchestrator._create_pydantic_model_from_schema(schema, model_name)
+        self.assertIsNotNone(DynamicModel)
+        self.assertTrue(issubclass(DynamicModel, BaseModel))
+        self.assertEqual(DynamicModel.__name__, model_name)
+
+        fields = DynamicModel.model_fields
+        self.assertIn("name", fields)
+        self.assertEqual(fields["name"].annotation, str)
+        self.assertTrue(fields["name"].is_required())
+        self.assertEqual(fields["name"].description, "User name")
+
+        self.assertIn("age", fields)
+        age_field_info = fields["age"]
+        self.assertEqual(age_field_info.annotation, int)
+        self.assertTrue(age_field_info.is_required())
+        self.assertEqual(get_metadata_constraint_value(age_field_info.metadata, 'ge'), 0)
+
+        self.assertIn("email", fields)
+        self.assertEqual(fields["email"].annotation, Optional[EmailStr]) # Not required, so Optional
+        self.assertFalse(fields["email"].is_required())
+
+        self.assertIn("is_active", fields)
+        self.assertEqual(fields["is_active"].annotation, bool) # Corrected: Has default, so it's bool
+        self.assertEqual(fields["is_active"].default, True)
+        self.assertFalse(fields["is_active"].is_required()) # Fields with defaults are not strictly required from user input
+
+        self.assertIn("height", fields)
+        self.assertEqual(fields["height"].annotation, Optional[float]) # Not required
+
+        # 测试实例化和验证
+        valid_data = {"name": "Test", "age": 30, "email": "test@example.com", "height": 1.75}
+        instance = DynamicModel(**valid_data)
+        self.assertEqual(instance.name, "Test")
+        self.assertEqual(instance.is_active, True) # Default value
+
+        with self.assertRaises(ValidationError):
+            DynamicModel(age=-5, email="bademail") # name missing, age invalid
+
+    def test_nested_object(self):
+        """测试嵌套对象的创建。"""
+        schema = {
+            "type": "object",
+            "properties": {
+                "id": {"type": "string"},
+                "profile": {
+                    "type": "object",
+                    "properties": {
+                        "user_email": {"type": "string", "format": "email"},
+                        "score": {"type": "integer", "default": 0}
+                    },
+                    "required": ["user_email"]
+                }
+            },
+            "required": ["id"]
+        }
+        model_name = "NestedOuterModel"
+        DynamicModel = self.orchestrator._create_pydantic_model_from_schema(schema, model_name)
+        self.assertIsNotNone(DynamicModel)
+        fields = DynamicModel.model_fields
+        self.assertIn("profile", fields)
+        
+        ProfileModel = fields["profile"].annotation
+        self.assertTrue(hasattr(ProfileModel, '__origin__') and ProfileModel.__origin__ is Union)
+        self.assertIn(type(None), ProfileModel.__args__)
+        NestedProfileModel = [arg for arg in ProfileModel.__args__ if arg is not type(None)][0]
+
+        self.assertTrue(issubclass(NestedProfileModel, BaseModel))
+        self.assertEqual(NestedProfileModel.__name__, f"{model_name}_profile")
+
+        nested_fields = NestedProfileModel.model_fields
+        self.assertIn("user_email", nested_fields)
+        self.assertEqual(nested_fields["user_email"].annotation, EmailStr)
+        self.assertTrue(nested_fields["user_email"].is_required())
+
+        self.assertIn("score", nested_fields)
+        self.assertEqual(nested_fields["score"].annotation, int)
+        self.assertEqual(nested_fields["score"].default, 0)
+        
+        # Test instantiation
+        valid_data = {"id": "abc", "profile": {"user_email": "nested@example.com"}}
+        instance = DynamicModel(**valid_data)
+        self.assertEqual(instance.id, "abc")
+        self.assertEqual(instance.profile.user_email, "nested@example.com")
+        self.assertEqual(instance.profile.score, 0)
+
+    def test_array_of_simple_types(self):
+        schema = {
+            "type": "object",
+            "properties": {
+                "tags": {"type": "array", "items": {"type": "string"}},
+                "scores": {"type": "array", "items": {"type": "integer"}, "default": []}
+            }
+        }
+        model_name = "ArraySimpleModel"
+        DynamicModel = self.orchestrator._create_pydantic_model_from_schema(schema, model_name)
+        self.assertIsNotNone(DynamicModel)
+        fields = DynamicModel.model_fields
+
+        self.assertIn("tags", fields)
+        self.assertEqual(fields["tags"].annotation, Optional[List[str]])
+
+        self.assertIn("scores", fields)
+        self.assertEqual(fields["scores"].annotation, List[int])
+        self.assertEqual(fields["scores"].default, [])
+
+        valid_data = {"tags": ["a", "b"], "scores": [1,2,3]}
+        instance = DynamicModel(**valid_data)
+        self.assertEqual(instance.tags, ["a", "b"])
+
+        # Test default for scores when tags is provided
+        instance2 = DynamicModel(tags=["c"])
+        self.assertEqual(instance2.scores, [])
+
+
+    def test_array_of_objects(self):
+        schema = {
+            "type": "object",
+            "properties": {
+                "users": {
+                    "type": "array",
+                    "items": {
+                        "type": "object",
+                        "properties": {
+                            "username": {"type": "string"},
+                            "user_id": {"type": "integer"}
+                        },
+                        "required": ["username"]
+                    }
+                }
+            }
+        }
+        model_name = "ArrayObjectModel"
+        DynamicModel = self.orchestrator._create_pydantic_model_from_schema(schema, model_name)
+        self.assertIsNotNone(DynamicModel)
+        fields = DynamicModel.model_fields
+        self.assertIn("users", fields)
+        
+        # users is Optional[List[UserModel_users_Item]]
+        UserListItemType = fields["users"].annotation
+        self.assertTrue(hasattr(UserListItemType, '__origin__') and UserListItemType.__origin__ is Union)
+        UserListType = [arg for arg in UserListItemType.__args__ if arg is not type(None)][0]
+
+        self.assertEqual(UserListType.__origin__, list) # Check it's a List
+        ItemModel = UserListType.__args__[0] # Get the item type from List[ItemType]
+        
+        self.assertTrue(issubclass(ItemModel, BaseModel))
+        self.assertEqual(ItemModel.__name__, f"{model_name}_users_Item")
+
+        item_fields = ItemModel.model_fields
+        self.assertEqual(item_fields["username"].annotation, str)
+        self.assertTrue(item_fields["username"].is_required())
+        self.assertEqual(item_fields["user_id"].annotation, Optional[int])
+
+        valid_data = {"users": [{"username": "a", "user_id":1}, {"username": "b"}]}
+        instance = DynamicModel(**valid_data)
+        self.assertEqual(len(instance.users), 2)
+        self.assertEqual(instance.users[0].username, "a")
+
+
+    def test_field_constraints(self):
+        schema = {
+            "type": "object",
+            "properties": {
+                "quantity": {"type": "integer", "minimum": 1, "maximum": 100},
+                "code": {"type": "string", "minLength": 3, "maxLength": 5, "pattern": "^[A-Z]+$"},
+                "percentage": {"type": "number", "minimum": 0.0, "maximum": 1.0}
+            }
+        }
+        model_name = "ConstraintsModel"
+        DynamicModel = self.orchestrator._create_pydantic_model_from_schema(schema, model_name)
+        self.assertIsNotNone(DynamicModel)
+        fields = DynamicModel.model_fields
+
+        # Quantity (int)
+        quantity_field_info = fields["quantity"]
+        self.assertEqual(get_metadata_constraint_value(quantity_field_info.metadata, 'ge'), 1)
+        self.assertEqual(get_metadata_constraint_value(quantity_field_info.metadata, 'le'), 100)
+        
+        # Code (str)
+        code_field_info = fields["code"]
+        self.assertEqual(get_metadata_constraint_value(code_field_info.metadata, 'min_length'), 3)
+        self.assertEqual(get_metadata_constraint_value(code_field_info.metadata, 'max_length'), 5)
+        self.assertEqual(get_metadata_constraint_value(code_field_info.metadata, 'pattern'), "^[A-Z]+$")
+
+        # Percentage (float/number)
+        percentage_field_info = fields["percentage"]
+        self.assertEqual(get_metadata_constraint_value(percentage_field_info.metadata, 'ge'), 0.0)
+        self.assertEqual(get_metadata_constraint_value(percentage_field_info.metadata, 'le'), 1.0)
+
+        # Test validation
+        with self.assertRaises(ValidationError): DynamicModel(quantity=0)
+        with self.assertRaises(ValidationError): DynamicModel(code="ab")
+        with self.assertRaises(ValidationError): DynamicModel(code="ABCDEF")
+        with self.assertRaises(ValidationError): DynamicModel(code="ab1")
+        with self.assertRaises(ValidationError): DynamicModel(percentage=1.1)
+        DynamicModel(quantity=50, code="XYZ", percentage=0.5) # Should be valid
+
+
+    def test_enum_in_description(self):
+        schema = {
+            "type": "object",
+            "properties": {
+                "status": {"type": "string", "enum": ["active", "inactive", "pending"], "description": "Current status."}
+            }
+        }
+        model_name = "EnumDescModel"
+        DynamicModel = self.orchestrator._create_pydantic_model_from_schema(schema, model_name)
+        self.assertIsNotNone(DynamicModel)
+        fields = DynamicModel.model_fields
+        self.assertIn("status", fields)
+        self.assertIn("Enum values: active, inactive, pending", fields["status"].description)
+        self.assertIn("Current status.", fields["status"].description)
+
+
+    def test_datetime_formats(self):
+        schema = {
+            "type": "object",
+            "properties": {
+                "created_at": {"type": "string", "format": "date-time"},
+                "event_date": {"type": "string", "format": "date"},
+                "uid": {"type": "string", "format": "uuid"}
+            }
+        }
+        model_name = "DateTimeUUIDModel"
+        DynamicModel = self.orchestrator._create_pydantic_model_from_schema(schema, model_name)
+        self.assertIsNotNone(DynamicModel)
+        fields = DynamicModel.model_fields
+
+        self.assertEqual(fields["created_at"].annotation, Optional[dt.datetime])
+        self.assertEqual(fields["event_date"].annotation, Optional[dt.date])
+        self.assertEqual(fields["uid"].annotation, Optional[UUID])
+
+        valid_data = {
+            "created_at": "2024-01-15T10:30:00Z",
+            "event_date": "2024-01-15",
+            "uid": "a0eebc99-9c0b-4ef8-bb6d-6bb9bd380a11"
+        }
+        instance = DynamicModel(**valid_data)
+        self.assertIsInstance(instance.created_at, dt.datetime)
+        self.assertIsInstance(instance.event_date, dt.date)
+        self.assertIsInstance(instance.uid, UUID)
+
+
+    def test_empty_object_schema(self):
+        schema = {"type": "object", "properties": {}} # Empty properties
+        model_name = "EmptyPropertiesModel"
+        DynamicModel = self.orchestrator._create_pydantic_model_from_schema(schema, model_name)
+        self.assertIsNotNone(DynamicModel)
+        self.assertEqual(len(DynamicModel.model_fields), 0)
+        DynamicModel() # Should instantiate
+
+        schema2 = {"type": "object"} # No properties field at all
+        model_name2 = "NoPropertiesFieldModel"
+        DynamicModel2 = self.orchestrator._create_pydantic_model_from_schema(schema2, model_name2)
+        self.assertIsNotNone(DynamicModel2)
+        self.assertEqual(len(DynamicModel2.model_fields), 0)
+        DynamicModel2()
+
+    def test_invalid_top_level_schema(self):
+        schema = {"type": "string"} # Not an object
+        DynamicModel = self.orchestrator._create_pydantic_model_from_schema(schema, "InvalidSchemaModel")
+        self.assertIsNone(DynamicModel)
+
+        schema2 = [{"type": "object"}] # Not a dict
+        DynamicModel2 = self.orchestrator._create_pydantic_model_from_schema(schema2, "InvalidSchemaModel2")
+        self.assertIsNone(DynamicModel2)
+
+
+    def test_model_caching(self):
+        schema = {"type": "object", "properties": {"name": {"type": "string"}}}
+        model_name = "CachedModel"
+        
+        Model1 = self.orchestrator._create_pydantic_model_from_schema(schema, model_name)
+        self.assertIsNotNone(Model1)
+        self.assertIn(model_name, _dynamic_model_cache)
+        
+        Model2 = self.orchestrator._create_pydantic_model_from_schema(schema, model_name) # Should be from cache
+        self.assertIs(Model1, Model2) # Check they are the same object
+
+    def test_recursion_depth_limit(self):
+        # Construct a schema that would recurse indefinitely if not limited
+        # A: { "prop_b": B }, B: { "prop_a": A } - this is hard with current naming
+        # Easier: A: { "prop_a": A_prop_a }
+        # Let's try A: { "next": A }
+        # The _create_pydantic_model_from_schema method itself uses model_name + prop_name for nested models,
+        # so a direct self-reference in schema like {"type": "object", "properties": {"self": {"$ref": "#/"}}}
+        # is not fully handled yet and would rely on ForwardRef if schema was static.
+        # For dynamic creation, the depth limit is the main guard.
+        
+        # Create a schema that nests deeply
+        deep_schema: Dict[str, Any] = {"type": "object", "properties": {}}
+        current_level = deep_schema["properties"]
+        
+        # MAX_RECURSION_DEPTH in APITestOrchestrator is 10
+        # We create a schema of depth 11 (0 to 10 for properties)
+        # property name level_0 contains object with property level_1 etc.
+        for i in range(12): # Go a bit beyond the limit
+            current_level[f"level_{i}"] = {"type": "object", "properties": {}}
+            if i < 11: # Don't add properties to the very last one
+                 current_level = current_level[f"level_{i}"]["properties"]
+            
+        with self.assertLogs(level='ERROR') as log_watcher:
+            GeneratedModel = self.orchestrator._create_pydantic_model_from_schema(deep_schema, "DeepRecursiveModel")
+            self.assertTrue(any("达到最大递归深度" in msg for msg in log_watcher.output))
+            self.assertIsNotNone(GeneratedModel)
+
+    def test_name_sanitization(self):
+        schema = {"type": "object", "properties": {"test": {"type": "string"}}}
+        # Valid name
+        Model1 = self.orchestrator._create_pydantic_model_from_schema(schema, "ValidName123")
+        self.assertIsNotNone(Model1)
+        self.assertEqual(Model1.__name__, "ValidName123")
+
+        # Name with spaces and hyphens
+        Model2 = self.orchestrator._create_pydantic_model_from_schema(schema, "Invalid Name-Test")
+        self.assertIsNotNone(Model2)
+        self.assertEqual(Model2.__name__, "Invalid_Name_Test") # Check sanitized name
+
+        # Name starting with number
+        Model3 = self.orchestrator._create_pydantic_model_from_schema(schema, "123InvalidStart")
+        self.assertIsNotNone(Model3)
+        self.assertEqual(Model3.__name__, "DynamicModel_123InvalidStart")
+
+        # Empty name - should get a default prefix
+        Model4 = self.orchestrator._create_pydantic_model_from_schema(schema, "")
+        self.assertIsNotNone(Model4)
+        self.assertTrue(Model4.__name__.startswith("DynamicModel_"))
+
+        # Name that is just underscores
+        Model5 = self.orchestrator._create_pydantic_model_from_schema(schema, "___")
+        self.assertIsNotNone(Model5)
+        self.assertEqual(Model5.__name__, "___") # Underscores are valid but Pydantic might mangle if it's a dunder name. create_model seems to keep it.
+
+    def test_optional_logic_for_fields(self):
+        schema = {
+            "type": "object",
+            "properties": {
+                "required_field": {"type": "string"},
+                "optional_field_no_default": {"type": "integer"},
+                "optional_field_with_default": {"type": "boolean", "default": False},
+                "optional_nested_object": {
+                    "type": "object",
+                    "properties": {"value": {"type": "string"}}
+                }
+            },
+            "required": ["required_field"]
+        }
+        model_name = "OptionalFieldsModel"
+        DynamicModel = self.orchestrator._create_pydantic_model_from_schema(schema, model_name)
+        self.assertIsNotNone(DynamicModel)
+        fields = DynamicModel.model_fields
+
+        self.assertEqual(fields["required_field"].annotation, str)
+        self.assertTrue(fields["required_field"].is_required())
+
+        self.assertEqual(fields["optional_field_no_default"].annotation, Optional[int])
+        self.assertFalse(fields["optional_field_no_default"].is_required())
+        self.assertEqual(fields["optional_field_no_default"].default, None) # Pydantic default for Optional[T] is None
+
+        self.assertEqual(fields["optional_field_with_default"].annotation, bool)
+        self.assertFalse(fields["optional_field_with_default"].is_required())
+        self.assertEqual(fields["optional_field_with_default"].default, False)
+        
+        # optional_nested_object is not required
+        NestedType = fields["optional_nested_object"].annotation
+        self.assertTrue(hasattr(NestedType, '__origin__') and NestedType.__origin__ is Union)
+        self.assertIn(type(None), NestedType.__args__)
+        ActualNestedModel = [arg for arg in NestedType.__args__ if arg is not type(None)][0]
+        self.assertTrue(issubclass(ActualNestedModel, BaseModel))
+
+
+if __name__ == '__main__':
+    unittest.main()