fix llm

ddms_compliance_suite/llm_utils/llm_service.py

@@ -5,7 +5,6 @@ import json
 import logging
 import re
 from typing import Optional, Dict, Any, List
-
 import requests
 from pydantic import BaseModel, Field
 from pydantic.json_schema import models_json_schema
@@ -59,7 +58,7 @@ class LLMService:
         self,
         messages: List[Dict[str, str]],
         max_tokens: int = 1024,
-        temperature: float = 0.7,
+        temperature: float = 0.1,
         # TODO: Consider adding a parameter like response_format_type: Optional[str] = None 
         # if the LLM API supports forcing JSON output (e.g., { "type": "json_object" })
     ) -> Optional[str]:
@@ -114,7 +113,7 @@ class LLMService:
         pydantic_model_class: type[BaseModel], 
         prompt_instruction: Optional[str] = None,
         max_tokens: int = 1024,
-        temperature: float = 0.7
+        temperature: float = 0.1
     ) -> Optional[Dict[str, Any]]:
         """
         根据给定的Pydantic模型类生成JSON Schema，并调用LLM生成符合该Schema的参数字典。
@@ -178,6 +177,65 @@ class LLMService:
         
         return None
 
+    def generate_data_from_schema(
+        self,
+        schema_dict: dict,
+        prompt_instruction: Optional[str] = None,
+        max_tokens: int = 1024,
+        temperature: float = 0.1
+    ) -> Optional[Dict[str, Any]]:
+        """
+        根据给定的JSON Schema字典，调用LLM生成符合该Schema的数据对象。
+        """
+        try:
+            schema_str = json.dumps(schema_dict, indent=2, ensure_ascii=False)
+            logger.debug(f"LLMService.generate_data_from_schema: 使用的JSON Schema:\n{schema_str}")
+
+            system_prompt = (
+                "你是一个API测试数据生成助手。你的任务是根据用户提供的JSON Schema和额外指令，"
+                "生成一个符合该Schema的JSON对象。请确保你的输出严格是一个JSON对象，"
+                "不包含任何额外的解释、注释或Markdown标记。"
+            )
+            user_prompt_content = f"请为以下JSON Schema生成一个有效的JSON对象实例:\n\n```json\n{schema_str}\n```\n"
+            if prompt_instruction:
+                user_prompt_content += f"\n请遵循以下额外指令：\n{prompt_instruction}"
+            messages = [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt_content}
+            ]
+
+            assistant_response_content = self._execute_chat_completion_request(
+                messages=messages,
+                max_tokens=max_tokens,
+                temperature=temperature
+            )
+
+            if assistant_response_content:
+                # 尝试从返回内容中提取JSON部分
+                json_match = re.search(r'```json\n(.*?)\n```', assistant_response_content, re.DOTALL)
+                if json_match:
+                    json_str = json_match.group(1)
+                else:
+                    first_brace = assistant_response_content.find('{')
+                    last_brace = assistant_response_content.rfind('}')
+                    if first_brace != -1 and last_brace != -1 and last_brace > first_brace:
+                        json_str = assistant_response_content[first_brace : last_brace+1]
+                    else:
+                        json_str = assistant_response_content
+
+                try:
+                    generated_data = json.loads(json_str)
+                    logger.info("成功从LLM生成并解析了数据。")
+                    return generated_data
+                except json.JSONDecodeError as e_json:
+                    logger.error(f"无法将LLM响应解析为JSON: {e_json}\n原始响应片段: '{json_str[:500]}'")
+            else:
+                logger.warning("从LLM获取的响应内容为空或请求失败。")
+
+        except Exception as e:
+            logger.error(f"执行LLM数据生成时发生未知错误: {e}", exc_info=True)
+        return None
+
 # --- 示例用法 (用于模块内测试) ---
 if __name__ == '__main__':
     logging.basicConfig(level=logging.DEBUG,