llm
This commit is contained in:
gongwenxin
parent
4a22060890
commit
4318cc103d
BIN
ddms_compliance_suite/__pycache__/llm_service.cpython-312.pyc
Normal file
BIN
ddms_compliance_suite/__pycache__/llm_service.cpython-312.pyc
Normal file
Binary file not shown.
Binary file not shown.
227
ddms_compliance_suite/llm_utils/llm_service.py
Normal file
227
ddms_compliance_suite/llm_utils/llm_service.py
Normal file
@ -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。")
|
||||
@ -16,6 +16,12 @@ from .api_caller.caller import APICaller, APIRequest, APIResponse
|
||||
from .json_schema_validator.validator import JSONSchemaValidator
|
||||
from .test_framework_core import ValidationResult, TestSeverity, APIRequestContext, APIResponseContext, BaseAPITestCase
|
||||
from .test_case_registry import TestCaseRegistry
|
||||
# 尝试导入 LLMService，如果失败则允许，因为 LLM 功能是可选的
|
||||
try:
|
||||
from .llm_utils.llm_service import LLMService
|
||||
except ImportError:
|
||||
LLMService = None
|
||||
logging.getLogger(__name__).info("LLMService 未找到，LLM 相关功能将不可用。")
|
||||
|
||||
class ExecutedTestCaseResult:
|
||||
"""存储单个APITestCase在其适用的端点上执行后的结果。"""
|
||||
@ -298,7 +304,11 @@ class APITestOrchestrator:
|
||||
"""API测试编排器"""
|
||||
|
||||
def __init__(self, base_url: str,
|
||||
custom_test_cases_dir: Optional[str] = None # 新的自定义测试用例目录路径
|
||||
custom_test_cases_dir: Optional[str] = None, # 新的自定义测试用例目录路径
|
||||
llm_api_key: Optional[str] = None,
|
||||
llm_base_url: Optional[str] = None,
|
||||
llm_model_name: Optional[str] = None,
|
||||
use_llm_for_request_body: bool = False
|
||||
):
|
||||
"""
|
||||
初始化API测试编排器
|
||||
@ -306,6 +316,10 @@ class APITestOrchestrator:
|
||||
Args:
|
||||
base_url: API基础URL
|
||||
custom_test_cases_dir: 存放自定义 APITestCase 的目录路径。如果为 None，则不加载自定义测试用例。
|
||||
llm_api_key: 大模型服务的API Key。
|
||||
llm_base_url: 大模型服务的兼容OpenAI的基础URL。
|
||||
llm_model_name: 要使用的具体模型名称。
|
||||
use_llm_for_request_body: 是否使用LLM生成请求体，默认为False。
|
||||
"""
|
||||
self.base_url = base_url.rstrip('/')
|
||||
self.logger = logging.getLogger(__name__)
|
||||
@ -324,9 +338,39 @@ class APITestOrchestrator:
|
||||
self.logger.info(f"TestCaseRegistry 初始化完成，发现 {len(self.test_case_registry.get_all_test_case_classes())} 个测试用例类。")
|
||||
except Exception as e:
|
||||
self.logger.error(f"初始化 TestCaseRegistry 失败: {e}", exc_info=True)
|
||||
# 即使注册表初始化失败，编排器本身仍可用于旧逻辑（如果保留）或不运行自定义测试
|
||||
else:
|
||||
self.logger.info("未提供 custom_test_cases_dir，不加载自定义 APITestCase。")
|
||||
|
||||
# 初始化 LLM 服务 (如果配置了)
|
||||
self.llm_service: Optional[LLMService] = None
|
||||
self.use_llm_for_request_body = use_llm_for_request_body
|
||||
|
||||
if LLMService is None: # 检查导入是否成功
|
||||
self.logger.warning("LLMService 类未能导入，LLM 相关功能将完全禁用。")
|
||||
self.use_llm_for_request_body = False # 强制禁用
|
||||
elif self.use_llm_for_request_body: # 只有当用户希望使用且类已导入时才尝试初始化
|
||||
if llm_api_key and llm_base_url and llm_model_name:
|
||||
try:
|
||||
self.llm_service = LLMService(
|
||||
api_key=llm_api_key,
|
||||
base_url=llm_base_url,
|
||||
model_name=llm_model_name
|
||||
)
|
||||
self.logger.info(f"LLMService 已成功初始化，模型: {llm_model_name}。将尝试使用LLM生成请求体。")
|
||||
except ValueError as ve: # LLMService init might raise ValueError for bad args
|
||||
self.logger.error(f"LLMService 初始化失败 (参数错误): {ve}。将回退到非LLM请求体生成。")
|
||||
self.llm_service = None
|
||||
self.use_llm_for_request_body = False # 初始化失败，禁用LLM使用
|
||||
except Exception as e:
|
||||
self.logger.error(f"LLMService 初始化时发生未知错误: {e}。将回退到非LLM请求体生成。", exc_info=True)
|
||||
self.llm_service = None
|
||||
self.use_llm_for_request_body = False # 初始化失败，禁用LLM使用
|
||||
else:
|
||||
self.logger.warning("希望使用LLM生成请求体，但未提供完整的LLM配置 (api_key, base_url, model_name)。将回退到非LLM请求体生成。")
|
||||
self.use_llm_for_request_body = False # 配置不全，禁用LLM使用
|
||||
elif not self.use_llm_for_request_body:
|
||||
self.logger.info("配置为不使用LLM生成请求体。")
|
||||
|
||||
|
||||
def _execute_single_test_case(
|
||||
self,
|
||||
@ -636,7 +680,35 @@ class APITestOrchestrator:
|
||||
# --- 生成请求体数据 ---
|
||||
body_data: Optional[Any] = None
|
||||
if body_schema_dict:
|
||||
body_data = self._generate_data_from_schema(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。")
|
||||
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
|
||||
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}。")
|
||||
body_data = self._generate_data_from_schema(body_schema_dict)
|
||||
|
||||
return {
|
||||
"path_params": path_params_data,
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user