llm

2025-05-20 16:17:10 +08:00
parent 4a22060890
commit 4318cc103d
4 changed files with 302 additions and 3 deletions
@@ -0,0 +1,227 @@
+# -*- coding: utf-8 -*-
+# ddms_compliance_suite/llm_utils/llm_service.py
+import os
+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
+
+logger = logging.getLogger(__name__)
+
+# --- Pydantic 模型示例 (用于测试和生成Schema) ---
+class SampleUserAddress(BaseModel):
+    street: str = Field(..., description="街道地址")
+    city: str = Field(..., description="城市")
+    zip_code: str = Field(..., description="邮政编码", pattern=r"^[0-9]{5,6}$")
+
+class SampleUserProfile(BaseModel):
+    user_id: int = Field(..., description="用户ID", ge=1)
+    username: str = Field(..., description="用户名", min_length=3, max_length=50)
+    email: Optional[str] = Field(None, description="用户邮箱", pattern=r"^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\.[a-zA-Z0-9-.]+$")
+    is_active: bool = Field(True, description="账户是否激活")
+    address: Optional[SampleUserAddress] = Field(None, description="用户地址")
+    hobbies: Optional[list[str]] = Field(None, description="兴趣爱好列表")
+
+# --- LLM 服务类 ---
+class LLMService:
+    """
+    封装与大语言模型 (LLM) API的交互，用于智能参数生成和验证。
+    目前针对通义千问兼容OpenAI的模式进行实现。
+    """
+    def __init__(self, api_key: str, base_url: str, model_name: str = "qwen-plus"):
+        """
+        初始化LLM服务。
+
+        Args:
+            api_key: 大模型服务的API Key。
+            base_url: 大模型服务的兼容OpenAI的基础URL。
+            model_name: 要使用的具体模型名称。
+        """
+        if not api_key:
+            raise ValueError("API Key不能为空")
+        if not base_url:
+            raise ValueError("Base URL不能为空")
+            
+        self.api_key = api_key
+        self.base_url = base_url.rstrip('/')
+        self.model_name = model_name
+        self.headers = {
+            "Authorization": f"Bearer {self.api_key}",
+            "Content-Type": "application/json",
+        }
+        logger.info(f"LLMService initialized for model '{self.model_name}' at {self.base_url}")
+
+    def _execute_chat_completion_request(
+        self,
+        messages: List[Dict[str, str]],
+        max_tokens: int = 1024,
+        temperature: float = 0.7,
+        # 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]:
+        """
+        执行对LLM聊天补全端点的通用请求。
+
+        Args:
+            messages: 发送给LLM的消息列表 (例如, [{'role': 'system', 'content': '...'}, {'role': 'user', ...}]).
+            max_tokens: LLM生成内容的最大token数。
+            temperature: LLM生成的随机性。
+
+        Returns:
+            LLM助手返回的原始文本内容，如果发生错误或没有有效内容则返回None。
+        """
+        payload = {
+            "model": self.model_name,
+            "messages": messages,
+            "max_tokens": max_tokens,
+            "temperature": temperature,
+        }
+        # if response_format_type:
+        #     payload["response_format"] = {"type": response_format_type}
+
+        logger.debug(f"LLM API Request Payload:\n{json.dumps(payload, indent=2, ensure_ascii=False)}")
+
+        try:
+            response = requests.post(f"{self.base_url}/chat/completions", headers=self.headers, json=payload, timeout=60)
+            response.raise_for_status() # 如果HTTP状态码表示错误，则抛出异常
+            
+            response_data = response.json()
+            logger.debug(f"LLM API Response:\n{json.dumps(response_data, indent=2, ensure_ascii=False)}")
+
+            if response_data.get("choices") and len(response_data["choices"]) > 0 and response_data["choices"][0].get("message"):
+                assistant_message = response_data["choices"][0]["message"]
+                assistant_response_content = assistant_message.get("content")
+                if assistant_response_content:
+                    return assistant_response_content
+                else:
+                    logger.warning("LLM响应中消息内容为空。")
+            else:
+                logger.warning(f"LLM响应格式不符合预期或choices为空: {response_data}")
+        
+        except requests.exceptions.RequestException as e_req:
+            logger.error(f"请求LLM API时发生网络错误: {e_req}", exc_info=True)
+        except Exception as e:
+            logger.error(f"执行LLM聊天补全请求时发生未知错误: {e}", exc_info=True)
+        
+        return None
+
+    def generate_parameters_from_schema(
+        self,
+        pydantic_model_class: type[BaseModel], 
+        prompt_instructions: Optional[str] = None,
+        max_tokens: int = 1024,
+        temperature: float = 0.7
+    ) -> 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。
+        """
+        try:
+            # 1. 从Pydantic模型生成JSON Schema
+            model_schema = pydantic_model_class.model_json_schema(ref_template='{model}')
+            main_model_name = pydantic_model_class.__name__ 
+            schema_str = json.dumps(model_schema, indent=2, ensure_ascii=False)
+            logger.debug(f"Generated JSON Schema for '{main_model_name}':\n{schema_str}")
+
+            # 2. 构建Prompt
+            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_instructions:
+                user_prompt_content += f"\n请遵循以下额外指令：\n{prompt_instructions}"
+            messages = [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt_content}
+            ]
+            
+            # 3. 调用通用LLM请求方法获取原始响应
+            assistant_response_content = self._execute_chat_completion_request(
+                messages=messages,
+                max_tokens=max_tokens,
+                temperature=temperature
+            )
+
+            # 4. 解析响应
+            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_params = json.loads(json_str)
+                    pydantic_model_class.model_validate(generated_params)
+                    logger.info(f"成功从LLM生成并验证了 '{main_model_name}' 的参数。")
+                    return generated_params
+                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]}'")
+                except Exception as e_val: # Pydantic ValidationError
+                    logger.error(f"LLM生成的参数未能通过Pydantic模型验证: {e_val}\n生成的参数: {json_str}")
+            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参数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,
+                        format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+
+    DASH_API_KEY = os.environ.get("DASHSCOPE_API_KEY") or "sk-YOUR_DASHSCOPE_API_KEY"
+    DASH_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1"
+    
+    if "YOUR_DASHSCOPE_API_KEY" in DASH_API_KEY:
+        logger.warning("请将 DASH_API_KEY 替换为您的有效API密钥，或设置 DASHSCOPE_API_KEY 环境变量。")
+
+    llm_service_instance = LLMService(api_key=DASH_API_KEY, base_url=DASH_BASE_URL)
+
+    logger.info("\n--- 测试 SampleUserProfile 参数生成 ---")
+    generated_profile = llm_service_instance.generate_parameters_from_schema(
+        pydantic_model_class=SampleUserProfile,
+        prompt_instructions="请生成一个表示非活跃用户的配置文件，用户名包含 \"test_user\" 字样，城市为上海，并包含至少一个兴趣爱好。"
+    )
+    
+    if generated_profile:
+        logger.info(f"成功生成的 UserProfile:\n{json.dumps(generated_profile, indent=2, ensure_ascii=False)}")
+        try:
+            SampleUserProfile.model_validate(generated_profile)
+            logger.info("生成的UserProfile通过了Pydantic验证。")
+        except Exception as e:
+            logger.error(f"生成的UserProfile未能通过Pydantic验证: {e}")
+    else:
+        logger.warning("未能生成UserProfile。")
+
+    logger.info("\n--- 测试 SampleUserAddress 参数生成 ---")
+    generated_address = llm_service_instance.generate_parameters_from_schema(
+        pydantic_model_class=SampleUserAddress,
+        prompt_instructions="生成一个位于北京市朝阳区的地址，邮编以1000开头。"
+    )
+    if generated_address:
+        logger.info(f"成功生成的 UserAddress:\n{json.dumps(generated_address, indent=2, ensure_ascii=False)}")
+    else:
+        logger.warning("未能生成UserAddress。")
+
+    logger.info("\n--- 测试 SampleUserProfile 无额外指令 ---")
+    generated_profile_no_instr = llm_service_instance.generate_parameters_from_schema(
+        pydantic_model_class=SampleUserProfile
+    )
+    if generated_profile_no_instr:
+        logger.info(f"成功生成的 (无指令) UserProfile:\n{json.dumps(generated_profile_no_instr, indent=2, ensure_ascii=False)}")
+    else:
+        logger.warning("未能生成 (无指令) UserProfile。")