删除注释掉的请求函数
This commit is contained in:
UnCLAS-Prommer
@@ -178,395 +178,6 @@ class LLMRequest:
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output_cost = (completion_tokens / 1000000) * self.pri_out
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output_cost = (completion_tokens / 1000000) * self.pri_out
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return round(input_cost + output_cost, 6)
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return round(input_cost + output_cost, 6)
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'''
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async def _execute_request(
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self,
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endpoint: str,
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prompt: str = None,
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image_base64: str = None,
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image_format: str = None,
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payload: dict = None,
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retry_policy: dict = None,
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response_handler: callable = None,
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user_id: str = "system",
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request_type: str = None,
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):
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"""统一请求执行入口
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Args:
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endpoint: API端点路径 (如 "chat/completions")
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prompt: prompt文本
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image_base64: 图片的base64编码
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image_format: 图片格式
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payload: 请求体数据
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retry_policy: 自定义重试策略
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response_handler: 自定义响应处理器
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user_id: 用户ID
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request_type: 请求类型
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"""
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if request_type is None:
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request_type = self.request_type
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# 合并重试策略
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default_retry = {
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"max_retries": 3,
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"base_wait": 10,
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"retry_codes": [429, 413, 500, 503],
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"abort_codes": [400, 401, 402, 403],
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}
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policy = {**default_retry, **(retry_policy or {})}
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# 常见Error Code Mapping
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error_code_mapping = {
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400: "参数不正确",
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401: "API key 错误，认证失败，请检查/config/bot_config.toml和.env中的配置是否正确哦~",
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402: "账号余额不足",
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403: "需要实名,或余额不足",
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404: "Not Found",
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429: "请求过于频繁，请稍后再试",
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500: "服务器内部故障",
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503: "服务器负载过高",
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}
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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.stream
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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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if image_base64:
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payload = await self._build_payload(prompt, image_base64, image_format)
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elif payload is None:
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payload = await self._build_payload(prompt)
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# 流式输出标志
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# 先构建payload，再添加流式输出标志
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if stream_mode:
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payload["stream"] = stream_mode
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for retry in range(policy["max_retries"]):
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try:
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# 使用上下文管理器处理会话
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headers = await self._build_headers()
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# 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
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if stream_mode:
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headers["Accept"] = "text/event-stream"
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async with aiohttp.ClientSession() as session:
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try:
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async with session.post(api_url, headers=headers, json=payload) as response:
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# 处理需要重试的状态码
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if response.status in policy["retry_codes"]:
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wait_time = policy["base_wait"] * (2**retry)
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logger.warning(
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f"模型 {self.model_name} 错误码: {response.status}, 等待 {wait_time}秒后重试"
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)
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if response.status == 413:
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logger.warning("请求体过大，尝试压缩...")
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image_base64 = compress_base64_image_by_scale(image_base64)
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payload = await self._build_payload(prompt, image_base64, image_format)
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elif response.status in [500, 503]:
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logger.error(
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f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}"
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)
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raise RuntimeError("服务器负载过高，模型恢复失败QAQ")
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else:
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logger.warning(f"模型 {self.model_name} 请求限制(429)，等待{wait_time}秒后重试...")
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await asyncio.sleep(wait_time)
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continue
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elif response.status in policy["abort_codes"]:
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logger.error(
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f"模型 {self.model_name} 错误码: {response.status} - {error_code_mapping.get(response.status)}"
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)
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# 尝试获取并记录服务器返回的详细错误信息
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try:
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error_json = await response.json()
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if error_json and isinstance(error_json, list) and len(error_json) > 0:
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for error_item in error_json:
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if "error" in error_item and isinstance(error_item["error"], dict):
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error_obj = error_item["error"]
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error_code = error_obj.get("code")
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error_message = error_obj.get("message")
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error_status = error_obj.get("status")
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logger.error(
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f"服务器错误详情: 代码={error_code}, 状态={error_status}, "
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f"消息={error_message}"
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)
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elif isinstance(error_json, dict) and "error" in error_json:
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# 处理单个错误对象的情况
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error_obj = error_json.get("error", {})
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error_code = error_obj.get("code")
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error_message = error_obj.get("message")
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error_status = error_obj.get("status")
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logger.error(
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f"服务器错误详情: 代码={error_code}, 状态={error_status}, 消息={error_message}"
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)
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else:
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# 记录原始错误响应内容
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logger.error(f"服务器错误响应: {error_json}")
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except Exception as e:
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logger.warning(f"无法解析服务器错误响应: {str(e)}")
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if response.status == 403:
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# 只针对硅基流动的V3和R1进行降级处理
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if (
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self.model_name.startswith("Pro/deepseek-ai")
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and self.base_url == "https://api.siliconflow.cn/v1/"
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):
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old_model_name = self.model_name
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self.model_name = self.model_name[4:] # 移除"Pro/"前缀
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logger.warning(
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f"检测到403错误，模型从 {old_model_name} 降级为 {self.model_name}"
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)
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# 对全局配置进行更新
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if global_config.llm_normal.get("name") == old_model_name:
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global_config.llm_normal["name"] = self.model_name
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logger.warning(f"将全局配置中的 llm_normal 模型临时降级至{self.model_name}")
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if global_config.llm_reasoning.get("name") == old_model_name:
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global_config.llm_reasoning["name"] = self.model_name
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logger.warning(
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f"将全局配置中的 llm_reasoning 模型临时降级至{self.model_name}"
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)
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# 更新payload中的模型名
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if payload and "model" in payload:
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payload["model"] = self.model_name
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# 重新尝试请求
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retry -= 1 # 不计入重试次数
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continue
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raise RuntimeError(f"请求被拒绝: {error_code_mapping.get(response.status)}")
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response.raise_for_status()
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reasoning_content = ""
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# 将流式输出转化为非流式输出
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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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try:
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line = line_bytes.decode("utf-8").strip()
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if not line:
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continue
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if line.startswith("data:"):
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data_str = line[5:].strip()
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if data_str == "[DONE]":
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break
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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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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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if delta_content is None:
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delta_content = ""
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accumulated_content += delta_content
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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 delta.get("reasoning_content", None):
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reasoning_content += delta["reasoning_content"]
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if finish_reason == "stop":
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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 as e:
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logger.exception(f"模型 {self.model_name} 解析流式输出错误: {str(e)}")
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except GeneratorExit:
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logger.warning("模型 {self.model_name} 流式输出被中断，正在清理资源...")
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# 确保资源被正确清理
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await response.release()
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# 返回已经累积的内容
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result = {
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"choices": [
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{
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"message": {
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"content": accumulated_content,
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"reasoning_content": reasoning_content,
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# 流式输出可能没有工具调用，此处不需要添加tool_calls字段
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}
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}
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],
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"usage": usage,
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}
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return (
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response_handler(result)
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if response_handler
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else self._default_response_handler(result, user_id, request_type, endpoint)
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)
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except Exception as e:
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logger.error(f"模型 {self.model_name} 处理流式输出时发生错误: {str(e)}")
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# 确保在发生错误时也能正确清理资源
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try:
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await response.release()
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except Exception as cleanup_error:
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logger.error(f"清理资源时发生错误: {cleanup_error}")
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# 返回已经累积的内容
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result = {
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"choices": [
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{
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"message": {
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"content": accumulated_content,
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"reasoning_content": reasoning_content,
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# 流式输出可能没有工具调用，此处不需要添加tool_calls字段
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}
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}
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],
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"usage": usage,
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}
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return (
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response_handler(result)
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if response_handler
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else self._default_response_handler(result, user_id, request_type, endpoint)
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)
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content = accumulated_content
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think_match = re.search(r"<think>(.*?)</think>", content, re.DOTALL)
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if think_match:
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reasoning_content = think_match.group(1).strip()
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content = re.sub(r"<think>.*?</think>", "", content, flags=re.DOTALL).strip()
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# 构造一个伪result以便调用自定义响应处理器或默认处理器
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result = {
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"choices": [
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{
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"message": {
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"content": content,
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"reasoning_content": reasoning_content,
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# 流式输出可能没有工具调用，此处不需要添加tool_calls字段
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}
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}
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],
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"usage": usage,
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}
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return (
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response_handler(result)
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if response_handler
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else self._default_response_handler(result, user_id, request_type, endpoint)
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)
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else:
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result = await response.json()
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# 使用自定义处理器或默认处理
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return (
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response_handler(result)
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if response_handler
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else self._default_response_handler(result, user_id, request_type, endpoint)
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)
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except (aiohttp.ClientError, asyncio.TimeoutError) as e:
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if retry < policy["max_retries"] - 1:
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wait_time = policy["base_wait"] * (2**retry)
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logger.error(f"模型 {self.model_name} 网络错误，等待{wait_time}秒后重试... 错误: {str(e)}")
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await asyncio.sleep(wait_time)
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continue
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else:
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logger.critical(f"模型 {self.model_name} 网络错误达到最大重试次数: {str(e)}")
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raise RuntimeError(f"网络请求失败: {str(e)}") from e
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except Exception as e:
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logger.critical(f"模型 {self.model_name} 未预期的错误: {str(e)}")
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raise RuntimeError(f"请求过程中发生错误: {str(e)}") from e
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except aiohttp.ClientResponseError as e:
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# 处理aiohttp抛出的响应错误
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if retry < policy["max_retries"] - 1:
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wait_time = policy["base_wait"] * (2**retry)
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logger.error(
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f"模型 {self.model_name} 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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)
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try:
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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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if "error" in error_item and isinstance(error_item["error"], dict):
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error_obj = error_item["error"]
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logger.error(
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f"模型 {self.model_name} 服务器错误详情: 代码={error_obj.get('code')}, "
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f"状态={error_obj.get('status')}, "
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f"消息={error_obj.get('message')}"
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)
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elif isinstance(error_json, dict) and "error" in error_json:
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error_obj = error_json.get("error", {})
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logger.error(
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f"模型 {self.model_name} 服务器错误详情: 代码={error_obj.get('code')}, "
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f"状态={error_obj.get('status')}, "
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f"消息={error_obj.get('message')}"
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)
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else:
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logger.error(f"模型 {self.model_name} 服务器错误响应: {error_json}")
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except (json.JSONDecodeError, TypeError) as json_err:
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logger.warning(
|
|
||||||
f"模型 {self.model_name} 响应不是有效的JSON: {str(json_err)}, 原始内容: {error_text[:200]}"
|
|
||||||
)
|
|
||||||
except (AttributeError, TypeError, ValueError) as parse_err:
|
|
||||||
logger.warning(f"模型 {self.model_name} 无法解析响应错误内容: {str(parse_err)}")
|
|
||||||
|
|
||||||
await asyncio.sleep(wait_time)
|
|
||||||
else:
|
|
||||||
logger.critical(
|
|
||||||
f"模型 {self.model_name} HTTP响应错误达到最大重试次数: 状态码: {e.status}, 错误: {e.message}"
|
|
||||||
)
|
|
||||||
# 安全地检查和记录请求详情
|
|
||||||
if (
|
|
||||||
image_base64
|
|
||||||
and payload
|
|
||||||
and isinstance(payload, dict)
|
|
||||||
and "messages" in payload
|
|
||||||
and len(payload["messages"]) > 0
|
|
||||||
):
|
|
||||||
if isinstance(payload["messages"][0], dict) and "content" in payload["messages"][0]:
|
|
||||||
content = payload["messages"][0]["content"]
|
|
||||||
if isinstance(content, list) and len(content) > 1 and "image_url" in content[1]:
|
|
||||||
payload["messages"][0]["content"][1]["image_url"]["url"] = (
|
|
||||||
f"data:image/{image_format.lower() if image_format else 'jpeg'};base64,"
|
|
||||||
f"{image_base64[:10]}...{image_base64[-10:]}"
|
|
||||||
)
|
|
||||||
logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {payload}")
|
|
||||||
raise RuntimeError(f"模型 {self.model_name} API请求失败: 状态码 {e.status}, {e.message}") from e
|
|
||||||
except Exception as e:
|
|
||||||
if retry < policy["max_retries"] - 1:
|
|
||||||
wait_time = policy["base_wait"] * (2**retry)
|
|
||||||
logger.error(f"模型 {self.model_name} 请求失败，等待{wait_time}秒后重试... 错误: {str(e)}")
|
|
||||||
await asyncio.sleep(wait_time)
|
|
||||||
else:
|
|
||||||
logger.critical(f"模型 {self.model_name} 请求失败: {str(e)}")
|
|
||||||
# 安全地检查和记录请求详情
|
|
||||||
if (
|
|
||||||
image_base64
|
|
||||||
and payload
|
|
||||||
and isinstance(payload, dict)
|
|
||||||
and "messages" in payload
|
|
||||||
and len(payload["messages"]) > 0
|
|
||||||
):
|
|
||||||
if isinstance(payload["messages"][0], dict) and "content" in payload["messages"][0]:
|
|
||||||
content = payload["messages"][0]["content"]
|
|
||||||
if isinstance(content, list) and len(content) > 1 and "image_url" in content[1]:
|
|
||||||
payload["messages"][0]["content"][1]["image_url"]["url"] = (
|
|
||||||
f"data:image/{image_format.lower() if image_format else 'jpeg'};base64,"
|
|
||||||
f"{image_base64[:10]}...{image_base64[-10:]}"
|
|
||||||
)
|
|
||||||
logger.critical(f"请求头: {await self._build_headers(no_key=True)} 请求体: {payload}")
|
|
||||||
raise RuntimeError(f"模型 {self.model_name} API请求失败: {str(e)}") from e
|
|
||||||
|
|
||||||
logger.error(f"模型 {self.model_name} 达到最大重试次数，请求仍然失败")
|
|
||||||
raise RuntimeError(f"模型 {self.model_name} 达到最大重试次数，API请求仍然失败")
|
|
||||||
'''
|
|
||||||
|
|
||||||
async def _prepare_request(
|
async def _prepare_request(
|
||||||
self,
|
self,
|
||||||
endpoint: str,
|
endpoint: str,
|
||||||
|
|||||||
Reference in New Issue
Block a user