gardel/Mofox-Core
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

refactor: 日志打印优化（终于改完了，爽了

Browse Source
This commit is contained in:
AL76
2025-03-10 02:25:03 +08:00
parent 8d99592b32
commit 052e67b576
15 changed files with 431 additions and 412 deletions
Download Patch File Download Diff File Expand all files Collapse all files

68
src/plugins/models/utils_model.py
View File

@@ -28,10 +28,10 @@ class LLM_request:
raise ValueError(f"配置错误：找不到对应的配置项 - {str(e)}") from e
self.model_name = model["name"]
self.params = kwargs
self.pri_in = model.get("pri_in", 0)
self.pri_out = model.get("pri_out", 0)
# 获取数据库实例
self.db = Database.get_instance()
self._init_database()
@@ -47,9 +47,9 @@ class LLM_request:
except Exception as e:
logger.error(f"创建数据库索引失败: {e}")
def _record_usage(self, prompt_tokens: int, completion_tokens: int, total_tokens: int,
user_id: str = "system", request_type: str = "chat",
endpoint: str = "/chat/completions"):
def _record_usage(self, prompt_tokens: int, completion_tokens: int, total_tokens: int,
user_id: str = "system", request_type: str = "chat",
endpoint: str = "/chat/completions"):
"""记录模型使用情况到数据库
Args:
prompt_tokens: 输入token数
@@ -140,12 +140,12 @@ class LLM_request:
}
api_url = f"{self.base_url.rstrip('/')}/{endpoint.lstrip('/')}"
#判断是否为流式
# 判断是否为流式
stream_mode = self.params.get("stream", False)
if self.params.get("stream", False) is True:
logger.info(f"进入流式输出模式，发送请求到URL: {api_url}")
logger.debug(f"进入流式输出模式，发送请求到URL: {api_url}")
else:
logger.info(f"发送请求到URL: {api_url}")
logger.debug(f"发送请求到URL: {api_url}")
logger.info(f"使用模型: {self.model_name}")
# 构建请求体
@@ -158,7 +158,7 @@ class LLM_request:
try:
# 使用上下文管理器处理会话
headers = await self._build_headers()
#似乎是openai流式必须要的东西,不过阿里云的qwq-plus加了这个没有影响
# 似乎是openai流式必须要的东西,不过阿里云的qwq-plus加了这个没有影响
if stream_mode:
headers["Accept"] = "text/event-stream"
@@ -184,29 +184,31 @@ class LLM_request:
logger.error(f"错误码: {response.status} - {error_code_mapping.get(response.status)}")
if response.status == 403:
# 尝试降级Pro模型
if self.model_name.startswith("Pro/") and self.base_url == "https://api.siliconflow.cn/v1/":
if self.model_name.startswith(
"Pro/") and self.base_url == "https://api.siliconflow.cn/v1/":
old_model_name = self.model_name
self.model_name = self.model_name[4:] # 移除"Pro/"前缀
logger.warning(f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}")
# 对全局配置进行更新
if hasattr(global_config, 'llm_normal') and global_config.llm_normal.get('name') == old_model_name:
if hasattr(global_config, 'llm_normal') and global_config.llm_normal.get(
'name') == old_model_name:
global_config.llm_normal['name'] = self.model_name
logger.warning(f"已将全局配置中的 llm_normal 模型降级")
# 更新payload中的模型名
if payload and 'model' in payload:
payload['model'] = self.model_name
# 重新尝试请求
retry -= 1 # 不计入重试次数
continue
raise RuntimeError(f"请求被拒绝: {error_code_mapping.get(response.status)}")
response.raise_for_status()
#将流式输出转化为非流式输出
# 将流式输出转化为非流式输出
if stream_mode:
accumulated_content = ""
async for line_bytes in response.content:
@@ -233,12 +235,15 @@ class LLM_request:
reasoning_content = think_match.group(1).strip()
content = re.sub(r'<think>.*?</think>', '', content, flags=re.DOTALL).strip()
# 构造一个伪result以便调用自定义响应处理器或默认处理器
result = {"choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}]}
return response_handler(result) if response_handler else self._default_response_handler(result, user_id, request_type, endpoint)
result = {
"choices": [{"message": {"content": content, "reasoning_content": reasoning_content}}]}
return response_handler(result) if response_handler else self._default_response_handler(
result, user_id, request_type, endpoint)
else:
result = await response.json()
# 使用自定义处理器或默认处理
return response_handler(result) if response_handler else self._default_response_handler(result, user_id, request_type, endpoint)
return response_handler(result) if response_handler else self._default_response_handler(
result, user_id, request_type, endpoint)
except Exception as e:
if retry < policy["max_retries"] - 1:
@@ -252,8 +257,8 @@ class LLM_request:
logger.error("达到最大重试次数，请求仍然失败")
raise RuntimeError("达到最大重试次数，API请求仍然失败")
async def _transform_parameters(self, params: dict) ->dict:
async def _transform_parameters(self, params: dict) -> dict:
"""
根据模型名称转换参数：
- 对于需要转换的OpenAI CoT系列模型（例如 "o3-mini"），删除 'temprature' 参数，
@@ -262,7 +267,8 @@ class LLM_request:
# 复制一份参数，避免直接修改原始数据
new_params = dict(params)
# 定义需要转换的模型列表
models_needing_transformation = ["o3-mini", "o1-mini", "o1-preview", "o1-2024-12-17", "o1-preview-2024-09-12", "o3-mini-2025-01-31", "o1-mini-2024-09-12"]
models_needing_transformation = ["o3-mini", "o1-mini", "o1-preview", "o1-2024-12-17", "o1-preview-2024-09-12",
"o3-mini-2025-01-31", "o1-mini-2024-09-12"]
if self.model_name.lower() in models_needing_transformation:
# 删除 'temprature' 参数（如果存在）
new_params.pop("temperature", None)
@@ -298,13 +304,13 @@ class LLM_request:
**params_copy
}
# 如果 payload 中依然存在 max_tokens 且需要转换，在这里进行再次检查
if self.model_name.lower() in ["o3-mini", "o1-mini", "o1-preview", "o1-2024-12-17", "o1-preview-2024-09-12", "o3-mini-2025-01-31", "o1-mini-2024-09-12"] and "max_tokens" in payload:
if self.model_name.lower() in ["o3-mini", "o1-mini", "o1-preview", "o1-2024-12-17", "o1-preview-2024-09-12",
"o3-mini-2025-01-31", "o1-mini-2024-09-12"] and "max_tokens" in payload:
payload["max_completion_tokens"] = payload.pop("max_tokens")
return payload
def _default_response_handler(self, result: dict, user_id: str = "system",
request_type: str = "chat", endpoint: str = "/chat/completions") -> Tuple:
def _default_response_handler(self, result: dict, user_id: str = "system",
request_type: str = "chat", endpoint: str = "/chat/completions") -> Tuple:
"""默认响应解析"""
if "choices" in result and result["choices"]:
message = result["choices"][0]["message"]
@@ -356,8 +362,8 @@ class LLM_request:
return {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
# 防止小朋友们截图自己的key
}
# 防止小朋友们截图自己的key
async def generate_response(self, prompt: str) -> Tuple[str, str]:
"""根据输入的提示生成模型的异步响应"""
@@ -404,6 +410,7 @@ class LLM_request:
Returns:
list: embedding向量，如果失败则返回None
"""
def embedding_handler(result):
"""处理响应"""
if "data" in result and len(result["data"]) > 0:
@@ -425,4 +432,3 @@ class LLM_request:
response_handler=embedding_handler
)
return embedding