适配业务

ddms_compliance_suite/utils/data_generator.py

@@ -20,15 +20,17 @@ class DataGenerator:
 
     def generate_data_from_schema(self, schema: Dict[str, Any],
                                   context_name: Optional[str] = None,
-                                  operation_id: Optional[str] = None) -> Any:
+                                  operation_id: Optional[str] = None,
+                                  llm_service=None) -> Any:
         """
         Generates test data from a JSON Schema.
         This method was extracted and generalized from APITestOrchestrator.
-        
+
         Args:
             schema: The JSON schema to generate data from.
             context_name: A name for the context (e.g., 'requestBody'), for logging.
             operation_id: The operation ID, for logging.
+            llm_service: Optional LLM service for intelligent data generation.
 
         Returns:
             Generated data that conforms to the schema.
@@ -66,17 +68,28 @@ class DataGenerator:
 
         # Handle both 'object' and 'Object' (case-insensitive)
         if schema_type and schema_type.lower() == 'object':
+            # 尝试使用LLM智能生成（如果可用且schema包含描述信息）
+            if llm_service and self._should_use_llm_for_schema(schema):
+                try:
+                    llm_data = self._generate_with_llm(schema, llm_service, context_name, operation_id)
+                    if llm_data is not None:
+                        self.logger.debug(f"{log_prefix}LLM successfully generated data for{context_log}")
+                        return llm_data
+                except Exception as e:
+                    self.logger.debug(f"{log_prefix}LLM generation failed for{context_log}: {e}, falling back to traditional generation")
+
+            # 传统生成方式
             result = {}
             properties = schema.get('properties', {})
             self.logger.debug(f"{log_prefix}Generating object data for{context_log}. Properties: {list(properties.keys())}")
             for prop_name, prop_schema in properties.items():
                 nested_context = f"{context_name}.{prop_name}" if context_name else prop_name
-                result[prop_name] = self.generate_data_from_schema(prop_schema, nested_context, operation_id)
-            
+                result[prop_name] = self.generate_data_from_schema(prop_schema, nested_context, operation_id, llm_service)
+
             additional_properties = schema.get('additionalProperties')
             if isinstance(additional_properties, dict):
                 self.logger.debug(f"{log_prefix}Generating an example property for additionalProperties for{context_log}")
-                result['additionalProp1'] = self.generate_data_from_schema(additional_properties, f"{context_name}.additionalProp1", operation_id)
+                result['additionalProp1'] = self.generate_data_from_schema(additional_properties, f"{context_name}.additionalProp1", operation_id, llm_service)
             return result
 
         # Handle both 'array' and 'Array' (case-insensitive)
@@ -117,3 +130,78 @@ class DataGenerator:
         
         self.logger.warning(f"{log_prefix}Unsupported schema type '{schema_type}' in {context_log}. Schema: {schema}")
         return None
+
+    def _should_use_llm_for_schema(self, schema: Dict[str, Any]) -> bool:
+        """判断是否应该使用LLM来生成数据"""
+
+        # 检查schema是否包含足够的描述信息来让LLM理解
+        properties = schema.get('properties', {})
+
+        # 如果有字段包含描述信息，就使用LLM
+        for prop_name, prop_schema in properties.items():
+            if isinstance(prop_schema, dict):
+                # 检查是否有描述信息
+                if prop_schema.get('description') or prop_schema.get('title'):
+                    return True
+
+                # 检查是否有特殊的业务字段（如bsflag）
+                if prop_name in ['bsflag', 'dataSource', 'dataRegion', 'surveyType', 'siteType']:
+                    return True
+
+        return False
+
+    def _generate_with_llm(self, schema: Dict[str, Any], llm_service, context_name: str, operation_id: str) -> Any:
+        """使用LLM生成数据"""
+
+        # 构建包含字段描述的提示
+        prompt = self._build_llm_prompt(schema, context_name, operation_id)
+
+        # 调用LLM服务
+        if hasattr(llm_service, 'generate_data_from_schema'):
+            return llm_service.generate_data_from_schema(
+                schema,
+                prompt_instruction=prompt,
+                max_tokens=512,
+                temperature=0.1
+            )
+        else:
+            # 如果LLM服务没有专门的方法，返回None让其回退到传统生成
+            return None
+
+    def _build_llm_prompt(self, schema: Dict[str, Any], context_name: str, operation_id: str) -> str:
+        """构建LLM提示，包含字段描述信息"""
+
+        properties = schema.get('properties', {})
+
+        prompt = f"""请为以下JSON Schema生成合理的测试数据。
+
+操作上下文: {operation_id or 'unknown'}
+数据上下文: {context_name or 'unknown'}
+
+字段说明：
+"""
+
+        for prop_name, prop_schema in properties.items():
+            if isinstance(prop_schema, dict):
+                prop_type = prop_schema.get('type', 'unknown')
+                title = prop_schema.get('title', '')
+                description = prop_schema.get('description', '')
+
+                prompt += f"- {prop_name} ({prop_type})"
+                if title:
+                    prompt += f" - {title}"
+                if description:
+                    prompt += f": {description}"
+                prompt += "\n"
+
+        prompt += """
+请根据字段的描述信息生成合理的测试数据：
+1. 严格遵守字段描述中的业务规则
+2. 生成真实、有意义的测试数据
+3. 对于有特定取值范围的字段，请选择合适的值
+4. 日期字段使用合理的日期格式
+5. 返回一个完整的JSON对象
+
+请只返回JSON数据，不要包含其他说明文字。"""
+
+        return prompt