update:1.新增统一的JSON清洗和重试方法，避免AI响应json格式错误 2.重构提示词模板命名，优化大纲章节初始化提示词 3.移除布冯冗余代码，提高代码复用性 4.优化系统默认写作风格预设提示词和规则
This commit is contained in:
xiamuceer
@@ -8,9 +8,15 @@ from app.mcp.adapters import UniversalMCPAdapter, PromptInjectionAdapter
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import httpx
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import json
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import hashlib
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import re
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import asyncio
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logger = get_logger(__name__)
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# 全局请求限流器（使用信号量控制并发数）
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_global_semaphore = asyncio.Semaphore(5) # 最多5个并发请求
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_request_delay = 0.2 # 请求间隔200ms
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# 全局HTTP客户端池（按配置复用）
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_http_client_pool: Dict[str, httpx.AsyncClient] = {}
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_client_pool_lock = False # 简单的锁标志
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@@ -308,7 +314,7 @@ class AIService:
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max_tokens: int,
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system_prompt: Optional[str]
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) -> str:
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"""使用OpenAI生成文本"""
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"""使用OpenAI生成文本（带限流和重试）"""
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if not self.openai_http_client:
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raise ValueError("OpenAI客户端未初始化，请检查API key配置")
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@@ -317,84 +323,118 @@ class AIService:
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messages.append({"role": "system", "content": system_prompt})
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messages.append({"role": "user", "content": prompt})
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try:
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logger.info(f"🔵 开始调用OpenAI API（直接HTTP请求）")
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logger.info(f" - 模型: {model}")
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logger.info(f" - 温度: {temperature}")
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logger.info(f" - 最大tokens: {max_tokens}")
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logger.info(f" - Prompt长度: {len(prompt)} 字符")
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logger.info(f" - 消息数量: {len(messages)}")
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# 使用全局信号量限流
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async with _global_semaphore:
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# 请求间隔
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await asyncio.sleep(_request_delay)
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url = f"{self.openai_base_url}/chat/completions"
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headers = {
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"Authorization": f"Bearer {self.openai_api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": model,
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens
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}
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logger.debug(f" - 请求URL: {url}")
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logger.debug(f" - 请求头: Authorization=Bearer ***")
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response = await self.openai_http_client.post(url, headers=headers, json=payload)
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response.raise_for_status()
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data = response.json()
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logger.info(f"✅ OpenAI API调用成功")
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logger.info(f" - 响应ID: {data.get('id', 'N/A')}")
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logger.info(f" - 选项数量: {len(data.get('choices', []))}")
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logger.debug(f" - 完整API响应: {data}")
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if not data.get('choices'):
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logger.error("❌ OpenAI返回的choices为空")
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raise ValueError("API返回的响应格式错误：choices字段为空")
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choice = data['choices'][0]
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message = choice.get('message', {})
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finish_reason = choice.get('finish_reason')
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# DeepSeek R1特殊处理：只使用content（最终答案），忽略reasoning_content（思考过程）
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# reasoning_content是AI的思考过程，不是我们需要的JSON结果
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content = message.get('content', '')
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# 检查是否因达到长度限制而截断
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if finish_reason == 'length':
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logger.warning(f"⚠️ 响应因达到max_tokens限制而被截断")
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logger.warning(f" - 当前max_tokens: {max_tokens}")
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logger.warning(f" - 建议: 增加max_tokens参数（推荐2000+）")
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if content:
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logger.info(f" - 返回内容长度: {len(content)} 字符")
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logger.info(f" - 完成原因: {finish_reason}")
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logger.info(f" - 返回内容预览（前200字符）: {content[:200]}")
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return content
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else:
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logger.error("❌ AI返回了空内容")
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logger.error(f" - 完整响应: {data}")
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logger.error(f" - 完成原因: {finish_reason}")
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# 重试机制
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max_retries = 3
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for attempt in range(max_retries):
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try:
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if attempt > 0:
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wait_time = min(2 ** attempt, 10) # 指数退避
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logger.warning(f"⚠️ OpenAI API调用失败，{wait_time}秒后重试（第{attempt + 1}/{max_retries}次）")
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await asyncio.sleep(wait_time)
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logger.info(f"🔵 开始调用OpenAI API（尝试 {attempt + 1}/{max_retries}）")
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logger.info(f" - 模型: {model}")
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logger.info(f" - 温度: {temperature}")
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logger.info(f" - 最大tokens: {max_tokens}")
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logger.info(f" - Prompt长度: {len(prompt)} 字符")
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logger.info(f" - 消息数量: {len(messages)}")
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url = f"{self.openai_base_url}/chat/completions"
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headers = {
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"Authorization": f"Bearer {self.openai_api_key}",
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"Content-Type": "application/json"
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}
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payload = {
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"model": model,
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens
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}
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logger.debug(f" - 请求URL: {url}")
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logger.debug(f" - 请求头: Authorization=Bearer ***")
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response = await self.openai_http_client.post(url, headers=headers, json=payload)
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response.raise_for_status()
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data = response.json()
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logger.info(f"✅ OpenAI API调用成功")
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logger.info(f" - 响应ID: {data.get('id', 'N/A')}")
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logger.info(f" - 选项数量: {len(data.get('choices', []))}")
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logger.debug(f" - 完整API响应: {data}")
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if not data.get('choices'):
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logger.error("❌ OpenAI返回的choices为空")
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raise ValueError("API返回的响应格式错误：choices字段为空")
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choice = data['choices'][0]
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message = choice.get('message', {})
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finish_reason = choice.get('finish_reason')
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# DeepSeek R1特殊处理：只使用content（最终答案），忽略reasoning_content（思考过程）
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# reasoning_content是AI的思考过程，不是我们需要的JSON结果
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content = message.get('content', '')
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# 检查是否因达到长度限制而截断
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if finish_reason == 'length':
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logger.warning(f"⚠️ 响应因达到max_tokens限制而被截断")
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logger.warning(f" - 当前max_tokens: {max_tokens}")
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logger.warning(f" - 建议: 增加max_tokens参数（推荐2000+）")
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if content:
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logger.info(f" - 返回内容长度: {len(content)} 字符")
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logger.info(f" - 完成原因: {finish_reason}")
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logger.info(f" - 返回内容预览（前200字符）: {content[:200]}")
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return content
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else:
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logger.error("❌ AI返回了空内容")
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logger.error(f" - 完整响应: {data}")
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logger.error(f" - 完成原因: {finish_reason}")
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# 提供更详细的错误信息
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if finish_reason == 'length':
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raise ValueError(f"AI响应被截断且无有效内容。请增加max_tokens参数（当前: {max_tokens}，建议: 2000+）")
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else:
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raise ValueError(f"AI返回了空内容（finish_reason: {finish_reason}），请检查API配置或稍后重试")
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# 提供更详细的错误信息
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if finish_reason == 'length':
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raise ValueError(f"AI响应被截断且无有效内容。请增加max_tokens参数（当前: {max_tokens}，建议: 2000+）")
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else:
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raise ValueError(f"AI返回了空内容（finish_reason: {finish_reason}），请检查API配置或稍后重试")
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except httpx.HTTPStatusError as e:
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logger.error(f"❌ OpenAI API调用失败 (HTTP {e.response.status_code})")
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logger.error(f" - 错误信息: {e.response.text}")
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logger.error(f" - 模型: {model}")
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raise Exception(f"API返回错误 ({e.response.status_code}): {e.response.text}")
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except Exception as e:
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logger.error(f"❌ OpenAI API调用失败")
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logger.error(f" - 错误类型: {type(e).__name__}")
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logger.error(f" - 错误信息: {str(e)}")
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logger.error(f" - 模型: {model}")
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raise
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except httpx.ConnectError as e:
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logger.error(f"❌ OpenAI API连接失败 (尝试 {attempt + 1}/{max_retries}): {str(e)}")
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if attempt == max_retries - 1:
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raise Exception(f"连接失败，已重试{max_retries}次。请检查网络连接或API地址: {str(e)}")
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continue
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except httpx.HTTPStatusError as e:
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logger.error(f"❌ OpenAI API调用失败 (HTTP {e.response.status_code}, 尝试 {attempt + 1}/{max_retries})")
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logger.error(f" - 错误信息: {e.response.text}")
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# 某些错误不需要重试（如401、403）
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if e.response.status_code in [401, 403, 404]:
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raise Exception(f"API返回错误 ({e.response.status_code}): {e.response.text}")
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if attempt == max_retries - 1:
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raise Exception(f"API返回错误 ({e.response.status_code}): {e.response.text}")
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continue
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except httpx.TimeoutException as e:
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logger.error(f"❌ OpenAI API超时 (尝试 {attempt + 1}/{max_retries})")
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if attempt == max_retries - 1:
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raise Exception(f"API请求超时，已重试{max_retries}次: {str(e)}")
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continue
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except Exception as e:
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logger.error(f"❌ OpenAI API调用失败 (尝试 {attempt + 1}/{max_retries})")
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logger.error(f" - 错误类型: {type(e).__name__}")
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logger.error(f" - 错误信息: {str(e)}")
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if attempt == max_retries - 1:
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raise
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continue
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async def _generate_openai_with_tools(
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@@ -1044,6 +1084,297 @@ class AIService:
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**kwargs
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):
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yield chunk
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# ========== JSON 统一调用和自动重试 ==========
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@staticmethod
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def _clean_json_response(text: str) -> str:
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"""
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清洗 AI 返回的 JSON 响应
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去除常见的格式问题：
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- markdown 代码块标记 (```json ```)
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- 前后空白字符
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- 注释文字
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Args:
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text: AI 返回的原始文本
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Returns:
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清洗后的 JSON 字符串
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"""
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if not text:
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return text
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# 去除 markdown 代码块标记
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text = re.sub(r'^```json\s*\n?', '', text, flags=re.MULTILINE | re.IGNORECASE)
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text = re.sub(r'^```\s*\n?', '', text, flags=re.MULTILINE)
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text = re.sub(r'\n?```\s*$', '', text, flags=re.MULTILINE)
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# 去除前后空白
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text = text.strip()
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# 尝试提取第一个完整的 JSON 对象或数组
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# 查找第一个 { 或 [
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start_idx = -1
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for i, char in enumerate(text):
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if char in ('{', '['):
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start_idx = i
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break
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if start_idx == -1:
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return text
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# 从第一个括号开始提取
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text = text[start_idx:]
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# 查找匹配的结束括号
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bracket_stack = []
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end_idx = -1
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in_string = False
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escape_next = False
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for i, char in enumerate(text):
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if escape_next:
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escape_next = False
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continue
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if char == '\\':
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escape_next = True
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continue
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if char == '"':
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in_string = not in_string
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continue
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if in_string:
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continue
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if char in ('{', '['):
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bracket_stack.append(char)
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elif char == '}':
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if bracket_stack and bracket_stack[-1] == '{':
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bracket_stack.pop()
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if not bracket_stack:
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end_idx = i + 1
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break
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elif char == ']':
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if bracket_stack and bracket_stack[-1] == '[':
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bracket_stack.pop()
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if not bracket_stack:
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end_idx = i + 1
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break
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if end_idx > 0:
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return text[:end_idx]
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return text
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@staticmethod
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def _add_json_format_hint(original_prompt: str, failed_response: str, attempt: int) -> str:
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"""
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重试时添加格式纠正提示
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Args:
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original_prompt: 原始提示词
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failed_response: 上次失败的响应（截断显示）
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attempt: 当前尝试次数
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Returns:
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增强后的提示词
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"""
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error_preview = failed_response[:300] if failed_response else "无响应"
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return f"""{original_prompt}
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⚠️ 【第 {attempt} 次重试】上一次返回格式错误，请严格遵守以下规则：
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🔴 格式要求（必须严格遵守）：
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1. 只返回纯 JSON 对象或数组，不要有任何其他文字
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2. 不要使用 ```json``` 或 ``` 包裹 JSON
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3. 不要添加任何解释、说明或注释
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4. 确保 JSON 格式完全正确：
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- 所有括号必须匹配 {{}} []
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- 所有字符串必须用双引号 ""
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- 键值对用冒号分隔 :
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- 多个元素用逗号分隔 ,
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- 不要有多余的逗号
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❌ 上一次的错误返回示例：
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{error_preview}...
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✅ 请现在重新生成正确的 JSON 格式内容。"""
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async def call_with_json_retry(
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self,
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prompt: str,
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system_prompt: Optional[str] = None,
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max_retries: int = 3,
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temperature: Optional[float] = None,
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max_tokens: Optional[int] = None,
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provider: Optional[str] = None,
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model: Optional[str] = None,
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expected_type: Optional[str] = None # "object" 或 "array"
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) -> Dict[str, Any] | List[Dict[str, Any]]:
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"""
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统一的 JSON 调用方法，自动重试和格式修复
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这是一个专门用于需要返回 JSON 格式的 AI 调用封装，会自动：
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1. 清洗 AI 返回的内容（去除 markdown 标记等）
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2. 解析 JSON 并验证格式
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3. 失败时自动重试，并在提示词中添加纠正指引
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Args:
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prompt: 用户提示词
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system_prompt: 系统提示词（可选）
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max_retries: 最大重试次数，默认 3 次
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temperature: 温度参数（可选，使用默认值）
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max_tokens: 最大 token 数（可选，使用默认值）
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provider: AI 提供商（可选，使用默认值）
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model: 模型名称（可选，使用默认值）
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expected_type: 期望的 JSON 类型 "object" 或 "array"（可选，用于额外验证）
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Returns:
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解析后的 JSON 对象（dict）或数组（list）
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Raises:
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ValueError: 重试次数用尽仍未获得有效 JSON
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Examples:
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>>> # 获取 JSON 对象
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>>> result = await ai_service.call_with_json_retry(
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... prompt="生成一个角色",
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... expected_type="object"
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... )
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>>> print(result["name"])
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>>> # 获取 JSON 数组
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>>> results = await ai_service.call_with_json_retry(
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... prompt="生成3个角色",
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... expected_type="array"
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... )
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>>> print(len(results))
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"""
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last_error = None
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last_response = ""
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for attempt in range(1, max_retries + 1):
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try:
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logger.info(f"🔄 JSON 调用尝试 {attempt}/{max_retries}")
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# 第一次使用原始提示词，之后使用增强提示词
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current_prompt = prompt if attempt == 1 else self._add_json_format_hint(
|
||||
prompt, last_response, attempt
|
||||
)
|
||||
|
||||
# 调用 AI 生成内容
|
||||
if provider == "openai" and self.openai_client:
|
||||
response = await self._generate_openai(
|
||||
prompt=current_prompt,
|
||||
model=model or self.default_model,
|
||||
temperature=temperature or self.default_temperature,
|
||||
max_tokens=max_tokens or self.default_max_tokens,
|
||||
system_prompt=system_prompt
|
||||
)
|
||||
elif provider == "anthropic" and self.anthropic_client:
|
||||
response = await self._generate_anthropic(
|
||||
prompt=current_prompt,
|
||||
model=model or self.default_model,
|
||||
temperature=temperature or self.default_temperature,
|
||||
max_tokens=max_tokens or self.default_max_tokens,
|
||||
system_prompt=system_prompt
|
||||
)
|
||||
else:
|
||||
# 使用默认提供商
|
||||
if self.api_provider == "openai":
|
||||
response = await self._generate_openai(
|
||||
prompt=current_prompt,
|
||||
model=model or self.default_model,
|
||||
temperature=temperature or self.default_temperature,
|
||||
max_tokens=max_tokens or self.default_max_tokens,
|
||||
system_prompt=system_prompt
|
||||
)
|
||||
else:
|
||||
response = await self._generate_anthropic(
|
||||
prompt=current_prompt,
|
||||
model=model or self.default_model,
|
||||
temperature=temperature or self.default_temperature,
|
||||
max_tokens=max_tokens or self.default_max_tokens,
|
||||
system_prompt=system_prompt
|
||||
)
|
||||
|
||||
last_response = response
|
||||
|
||||
# 清洗响应内容
|
||||
cleaned = self._clean_json_response(response)
|
||||
logger.debug(f"清洗后的内容: {cleaned[:200]}...")
|
||||
|
||||
# 解析 JSON
|
||||
try:
|
||||
data = json.loads(cleaned)
|
||||
except json.JSONDecodeError as e:
|
||||
logger.warning(f"⚠️ JSON 解析失败: {e}")
|
||||
logger.debug(f"原始响应: {response[:500]}")
|
||||
logger.debug(f"清洗后: {cleaned[:500]}")
|
||||
raise
|
||||
|
||||
# 可选：验证 JSON 类型
|
||||
if expected_type:
|
||||
if expected_type == "object" and not isinstance(data, dict):
|
||||
raise ValueError(f"期望 JSON 对象，但得到 {type(data).__name__}")
|
||||
elif expected_type == "array" and not isinstance(data, list):
|
||||
raise ValueError(f"期望 JSON 数组，但得到 {type(data).__name__}")
|
||||
|
||||
logger.info(f"✅ JSON 解析成功 (尝试 {attempt}/{max_retries})")
|
||||
if isinstance(data, dict):
|
||||
logger.info(f" 返回对象，包含 {len(data)} 个键")
|
||||
elif isinstance(data, list):
|
||||
logger.info(f" 返回数组，包含 {len(data)} 个元素")
|
||||
|
||||
return data
|
||||
|
||||
except json.JSONDecodeError as e:
|
||||
last_error = e
|
||||
logger.warning(f"⚠️ 第 {attempt} 次尝试失败: JSON 解析错误")
|
||||
logger.warning(f" 错误位置: {e.msg} at line {e.lineno} column {e.colno}")
|
||||
|
||||
if attempt < max_retries:
|
||||
logger.info(f" 准备第 {attempt + 1} 次重试...")
|
||||
continue
|
||||
else:
|
||||
logger.error(f"❌ JSON 解析失败，已达到最大重试次数 {max_retries}")
|
||||
logger.error(f" 最后的响应内容:\n{last_response[:1000]}")
|
||||
raise ValueError(
|
||||
f"AI 返回内容无法解析为 JSON，已重试 {max_retries} 次。\n"
|
||||
f"最后错误: {e}\n"
|
||||
f"响应预览: {last_response[:200]}..."
|
||||
)
|
||||
|
||||
except ValueError as e:
|
||||
last_error = e
|
||||
logger.warning(f"⚠️ 第 {attempt} 次尝试失败: {e}")
|
||||
|
||||
if attempt < max_retries:
|
||||
logger.info(f" 准备第 {attempt + 1} 次重试...")
|
||||
continue
|
||||
else:
|
||||
logger.error(f"❌ 验证失败，已达到最大重试次数 {max_retries}")
|
||||
raise ValueError(
|
||||
f"AI 返回的 JSON 格式不符合要求，已重试 {max_retries} 次。\n"
|
||||
f"错误: {e}"
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"❌ 第 {attempt} 次调用出现未预期错误: {type(e).__name__}: {e}")
|
||||
if attempt < max_retries:
|
||||
logger.info(f" 准备第 {attempt + 1} 次重试...")
|
||||
last_error = e
|
||||
continue
|
||||
else:
|
||||
raise
|
||||
|
||||
# 理论上不会到达这里，但以防万一
|
||||
raise ValueError(f"JSON 调用失败，已重试 {max_retries} 次。最后错误: {last_error}")
|
||||
|
||||
|
||||
# 创建全局AI服务实例
|
||||
|
||||
Reference in New Issue
Block a user