fix: 修复utils_model.py潜在问题
- 将重复的模型列表提取为类变量 - 修复流式处理中变量未初始化的错误 - 改进错误响应处理的安全性 - 修复类型标注 - 优化重复的条件判断 - 将_init_database改为静态方法 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
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春河晴
@@ -18,6 +18,17 @@ config = driver.config
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class LLM_request:
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# 定义需要转换的模型列表，作为类变量避免重复
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MODELS_NEEDING_TRANSFORMATION = [
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"o3-mini",
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"o1-mini",
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"o1-preview",
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"o1-2024-12-17",
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"o1-preview-2024-09-12",
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"o3-mini-2025-01-31",
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"o1-mini-2024-09-12",
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]
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def __init__(self, model, **kwargs):
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# 将大写的配置键转换为小写并从config中获取实际值
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try:
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@@ -36,7 +47,8 @@ class LLM_request:
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# 获取数据库实例
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self._init_database()
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def _init_database(self):
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@staticmethod
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def _init_database():
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"""初始化数据库集合"""
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try:
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# 创建llm_usage集合的索引
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@@ -44,8 +56,8 @@ class LLM_request:
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db.llm_usage.create_index([("model_name", 1)])
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db.llm_usage.create_index([("user_id", 1)])
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db.llm_usage.create_index([("request_type", 1)])
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except Exception:
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logger.error("创建数据库索引失败")
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except Exception as e:
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logger.error(f"创建数据库索引失败: {str(e)}")
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def _record_usage(
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self,
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@@ -85,8 +97,8 @@ class LLM_request:
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f"提示词: {prompt_tokens}, 完成: {completion_tokens}, "
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f"总计: {total_tokens}"
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)
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except Exception:
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logger.error("记录token使用情况失败")
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except Exception as e:
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logger.error(f"记录token使用情况失败: {str(e)}")
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def _calculate_cost(self, prompt_tokens: int, completion_tokens: int) -> float:
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"""计算API调用成本
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@@ -152,10 +164,8 @@ class LLM_request:
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api_url = f"{self.base_url.rstrip('/')}/{endpoint.lstrip('/')}"
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# 判断是否为流式
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stream_mode = self.params.get("stream", False)
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if self.params.get("stream", False) is True:
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logger.debug(f"进入流式输出模式，发送请求到URL: {api_url}")
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else:
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logger.debug(f"发送请求到URL: {api_url}")
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logger_msg = "进入流式输出模式，" if stream_mode else ""
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logger.debug(f"{logger_msg}发送请求到URL: {api_url}")
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logger.info(f"使用模型: {self.model_name}")
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# 构建请求体
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@@ -255,6 +265,8 @@ class LLM_request:
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if stream_mode:
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flag_delta_content_finished = False
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accumulated_content = ""
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usage = None # 初始化usage变量，避免未定义错误
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async for line_bytes in response.content:
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line = line_bytes.decode("utf-8").strip()
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if not line:
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@@ -266,7 +278,9 @@ class LLM_request:
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try:
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chunk = json.loads(data_str)
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if flag_delta_content_finished:
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usage = chunk.get("usage", None) # 获取tokn用量
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chunk_usage = chunk.get("usage",None)
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if chunk_usage:
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usage = chunk_usage # 获取token用量
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else:
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delta = chunk["choices"][0]["delta"]
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delta_content = delta.get("content")
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@@ -276,14 +290,15 @@ class LLM_request:
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# 检测流式输出文本是否结束
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finish_reason = chunk["choices"][0].get("finish_reason")
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if finish_reason == "stop":
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usage = chunk.get("usage", None)
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if usage:
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chunk_usage = chunk.get("usage",None)
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if chunk_usage:
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usage = chunk_usage
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break
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# 部分平台在文本输出结束前不会返回token用量，此时需要再获取一次chunk
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flag_delta_content_finished = True
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except Exception:
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logger.exception("解析流式输出错误")
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except Exception as e:
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logger.exception(f"解析流式输出错误: {str(e)}")
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content = accumulated_content
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reasoning_content = ""
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think_match = re.search(r"<think>(.*?)</think>", content, re.DOTALL)
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@@ -315,8 +330,9 @@ class LLM_request:
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wait_time = policy["base_wait"] * (2**retry)
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logger.error(f"HTTP响应错误，等待{wait_time}秒后重试... 状态码: {e.status}, 错误: {e.message}")
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try:
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if hasattr(e, "history") and e.history and hasattr(e.history[0], "text"):
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error_text = await e.history[0].text()
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if hasattr(e, "response") and e.response and hasattr(e.response, "text"):
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error_text = await e.response.text()
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try:
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error_json = json.loads(error_text)
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if isinstance(error_json, list) and len(error_json) > 0:
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for error_item in error_json:
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@@ -332,15 +348,21 @@ class LLM_request:
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)
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else:
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logger.error(f"服务器错误响应: {error_json}")
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except Exception as parse_err:
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except (json.JSONDecodeError, TypeError) as json_err:
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logger.warning(f"响应不是有效的JSON: {str(json_err)}, 原始内容: {error_text[:200]}")
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except (AttributeError, TypeError, ValueError) as parse_err:
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logger.warning(f"无法解析响应错误内容: {str(parse_err)}")
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await asyncio.sleep(wait_time)
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else:
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logger.critical(f"HTTP响应错误达到最大重试次数: 状态码: {e.status}, 错误: {e.message}")
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if image_base64:
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# 安全地检查和记录请求详情
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if image_base64 and payload and isinstance(payload, dict) and "messages" in payload and len(payload["messages"]) > 0:
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if isinstance(payload["messages"][0], dict) and "content" in payload["messages"][0]:
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content = payload["messages"][0]["content"]
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if isinstance(content, list) and len(content) > 1 and "image_url" in content[1]:
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payload["messages"][0]["content"][1]["image_url"]["url"] = (
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f"data:image/{image_format.lower()};base64,{image_base64[:10]}...{image_base64[-10:]}"
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f"data:image/{image_format.lower() if image_format else 'jpeg'};base64,{image_base64[:10]}...{image_base64[-10:]}"
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)
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logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {payload}")
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raise RuntimeError(f"API请求失败: 状态码 {e.status}, {e.message}")
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@@ -351,9 +373,13 @@ class LLM_request:
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await asyncio.sleep(wait_time)
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else:
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logger.critical(f"请求失败: {str(e)}")
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if image_base64:
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# 安全地检查和记录请求详情
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if image_base64 and payload and isinstance(payload, dict) and "messages" in payload and len(payload["messages"]) > 0:
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if isinstance(payload["messages"][0], dict) and "content" in payload["messages"][0]:
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content = payload["messages"][0]["content"]
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if isinstance(content, list) and len(content) > 1 and "image_url" in content[1]:
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payload["messages"][0]["content"][1]["image_url"]["url"] = (
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f"data:image/{image_format.lower()};base64,{image_base64[:10]}...{image_base64[-10:]}"
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f"data:image/{image_format.lower() if image_format else 'jpeg'};base64,{image_base64[:10]}...{image_base64[-10:]}"
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)
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logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {payload}")
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raise RuntimeError(f"API请求失败: {str(e)}")
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@@ -364,23 +390,14 @@ class LLM_request:
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async def _transform_parameters(self, params: dict) -> dict:
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"""
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根据模型名称转换参数：
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- 对于需要转换的OpenAI CoT系列模型（例如 "o3-mini"），删除 'temprature' 参数，
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- 对于需要转换的OpenAI CoT系列模型（例如 "o3-mini"），删除 'temperature' 参数，
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并将 'max_tokens' 重命名为 'max_completion_tokens'
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"""
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# 复制一份参数，避免直接修改原始数据
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new_params = dict(params)
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# 定义需要转换的模型列表
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models_needing_transformation = [
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"o3-mini",
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"o1-mini",
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"o1-preview",
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"o1-2024-12-17",
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"o1-preview-2024-09-12",
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"o3-mini-2025-01-31",
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"o1-mini-2024-09-12",
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]
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if self.model_name.lower() in models_needing_transformation:
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# 删除 'temprature' 参数（如果存在）
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if self.model_name.lower() in self.MODELS_NEEDING_TRANSFORMATION:
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# 删除 'temperature' 参数（如果存在）
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new_params.pop("temperature", None)
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# 如果存在 'max_tokens'，则重命名为 'max_completion_tokens'
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if "max_tokens" in new_params:
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@@ -417,19 +434,7 @@ class LLM_request:
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**params_copy,
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}
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# 如果 payload 中依然存在 max_tokens 且需要转换，在这里进行再次检查
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if (
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self.model_name.lower()
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in [
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"o3-mini",
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"o1-mini",
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"o1-preview",
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"o1-2024-12-17",
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"o1-preview-2024-09-12",
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"o3-mini-2025-01-31",
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"o1-mini-2024-09-12",
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]
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and "max_tokens" in payload
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):
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if self.model_name.lower() in self.MODELS_NEEDING_TRANSFORMATION and "max_tokens" in payload:
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payload["max_completion_tokens"] = payload.pop("max_tokens")
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return payload
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@@ -466,7 +471,8 @@ class LLM_request:
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return "没有返回结果", ""
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def _extract_reasoning(self, content: str) -> tuple[str, str]:
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@staticmethod
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def _extract_reasoning(content: str) -> Tuple[str, str]:
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"""CoT思维链提取"""
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match = re.search(r"(?:<think>)?(.*?)</think>", content, re.DOTALL)
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content = re.sub(r"(?:<think>)?.*?</think>", "", content, flags=re.DOTALL, count=1).strip()
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@@ -506,6 +512,7 @@ class LLM_request:
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"messages": [{"role": "user", "content": prompt}],
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"max_tokens": global_config.max_response_length,
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**self.params,
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**kwargs,
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}
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content, reasoning_content = await self._execute_request(
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