feat: 优化LLM服务的错误处理和模型配置逻辑
- 增强LLMService中的base_url和模型名称处理，确保兼容SiliconFlow格式 - 改进错误处理，详细记录异常信息并提供可能的原因 - 更新WorkflowEngine以使用LLMService的详细错误信息，避免重复信息
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eason
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@ -22,12 +22,34 @@ class LLMService:
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max_tokens: Optional[int] = None
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) -> str:
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"""调用大模型进行对话完成"""
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# 处理 base_url：如果包含 /chat/completions，需要移除
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base_url = model_config.base_url
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if base_url and '/chat/completions' in base_url:
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# 移除 /chat/completions 后缀，ChatOpenAI 会自动添加
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base_url = base_url.replace('/chat/completions', '').rstrip('/')
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logger.debug(f"调整 base_url: {model_config.base_url} -> {base_url}")
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# 处理 SiliconFlow 的模型名称格式
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model_name = model_config.model_name
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if 'siliconflow' in (base_url or '').lower() and '/' not in model_name:
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# SiliconFlow 需要 org/model 格式，尝试自动转换
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model_name_lower = model_name.lower()
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if 'deepseek' in model_name_lower or 'r1' in model_name_lower:
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# 尝试常见的 DeepSeek 模型格式
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if 'r1' in model_name_lower:
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model_name = 'deepseek-ai/DeepSeek-R1'
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elif 'v3' in model_name_lower:
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model_name = 'deepseek-ai/DeepSeek-V3'
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else:
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model_name = f'deepseek-ai/{model_name}'
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logger.debug(f"调整 SiliconFlow 模型名称: {model_config.model_name} -> {model_name}")
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try:
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# 创建LangChain ChatOpenAI实例
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llm = ChatOpenAI(
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model=model_config.model_name,
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model=model_name,
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api_key=model_config.api_key,
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base_url=model_config.base_url,
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base_url=base_url,
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temperature=temperature or model_config.temperature,
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max_tokens=max_tokens or model_config.max_tokens,
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streaming=False
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@ -53,8 +75,41 @@ class LLMService:
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return response.content
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except Exception as e:
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logger.error(f"LLM调用失败: {str(e)}")
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raise Exception(f"LLM调用失败: {str(e)}")
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# 提取详细的错误信息
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error_detail = str(e)
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# 尝试从异常对象中提取更多信息
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if hasattr(e, 'response'):
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# HTTP 响应错误
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if hasattr(e.response, 'status_code'):
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error_detail = f"HTTP {e.response.status_code}: {error_detail}"
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if hasattr(e.response, 'text'):
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try:
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import json
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error_body = json.loads(e.response.text)
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if isinstance(error_body, dict):
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if 'message' in error_body:
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error_detail = f"{error_detail} - {error_body['message']}"
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if 'error' in error_body:
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error_info = error_body['error']
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if isinstance(error_info, dict) and 'message' in error_info:
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error_detail = f"{error_detail} - {error_info['message']}"
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except:
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pass
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# 添加模型配置信息到错误消息中（使用处理后的配置）
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model_info = f"模型: {model_name}, base_url: {base_url}"
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if 'Not Found' in error_detail or '404' in error_detail:
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error_detail = f"{error_detail} ({model_info})。可能的原因：1) 模型名称格式不正确（SiliconFlow需要org/model格式，如deepseek-ai/DeepSeek-R1）；2) base_url配置错误；3) API端点不存在"
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elif '403' in error_detail or 'account balance' in error_detail.lower() or 'insufficient' in error_detail.lower():
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error_detail = f"{error_detail} ({model_info})。可能的原因：账户余额不足或API密钥权限不足"
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elif '401' in error_detail or 'authentication' in error_detail.lower():
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error_detail = f"{error_detail} ({model_info})。可能的原因：API密钥无效或已过期"
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else:
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error_detail = f"{error_detail} ({model_info})"
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logger.error(f"LLM调用失败: {error_detail}")
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raise Exception(f"LLM调用失败: {error_detail}")
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async def chat_completion_stream(
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self,
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@ -831,7 +831,23 @@ class WorkflowEngine:
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except Exception as e:
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error_msg = str(e)
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logger.error(f"LLM调用失败: {error_msg}")
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# LLMService 已经添加了详细的错误信息（包括处理后的模型名称和base_url），直接使用
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# 如果错误信息中已经包含了模型信息，就不再重复添加
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detailed_error = error_msg
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# 如果错误信息中还没有包含模型配置信息，则添加（使用原始配置作为补充）
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if "使用的模型:" not in error_msg and "模型:" not in error_msg:
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model_info = f"使用的模型: {llm_config.model_name} (ID: {llm_config.id}), base_url: {llm_config.base_url}"
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if "Not Found" in error_msg or "404" in error_msg:
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detailed_error = f"{detailed_error}。{model_info}。可能的原因：1) 模型名称格式不正确（SiliconFlow需要org/model格式）；2) base_url配置错误；3) API端点不存在"
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elif "403" in error_msg or "account balance" in error_msg.lower() or "insufficient" in error_msg.lower():
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detailed_error = f"{detailed_error}。{model_info}。可能的原因：账户余额不足或API密钥权限不足"
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elif "401" in error_msg or "authentication" in error_msg.lower():
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detailed_error = f"{detailed_error}。{model_info}。可能的原因：API密钥无效或已过期"
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else:
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detailed_error = f"{detailed_error}。{model_info}"
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logger.error(f"LLM调用失败: {detailed_error}")
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# 如果当前使用的不是默认模型，并且错误包含 Not Found / 404，则尝试回退到默认模型再调用一次
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if (not used_default_model) and ("Not Found" in error_msg or "404" in error_msg):
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@ -863,7 +879,7 @@ class WorkflowEngine:
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logger.error(f"使用默认模型重试LLM调用失败: {str(fallback_error)}")
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# 继续向下抛出原始错误
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raise ValueError(f"LLM调用失败: {error_msg}")
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raise ValueError(f"LLM调用失败: {detailed_error}")
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def _substitute_variables(self, template: str, input_data: Dict[str, Any]) -> str:
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"""变量替换函数"""
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