# src/services/llm/qwen_client.py """Qwen LLM客户端 - 支持OpenAI兼容API格式""" import time import json from typing import List, Dict, Optional, Generator, AsyncGenerator from loguru import logger import httpx from .base_client import BaseLLMClient, LLMResponse, LLMConfig, LLMProvider class QwenClient(BaseLLMClient): """ Qwen API客户端（OpenAI兼容格式） 支持通过new-api等代理服务调用： - qwen-turbo - qwen-plus - qwen-max - qwen3.5-flash (推荐：快速响应) - qwen3.5-plus - qwen-long - qwen2.5系列 """ SUPPORTED_MODELS = [ "qwen-turbo", "qwen-plus", "qwen-max", "qwen-max-longcontext", "qwen-long", "qwen3.5-flash", "qwen3.5-plus", "qwen3-plus", "qwen2.5-72b-instruct", "qwen2.5-32b-instruct", "qwen2.5-14b-instruct", "qwen2.5-7b-instruct" ] def __init__(self, config: LLMConfig): if config.provider not in [LLMProvider.QWEN, LLMProvider.QWEN_VL]: raise ValueError(f"配置provider应为Qwen，实际为{config.provider}") super().__init__(config) self._init_client() def _init_client(self): """初始化HTTP客户端""" # OpenAI兼容API格式 self._client = httpx.Client( base_url=self.config.base_url, headers={ "Authorization": f"Bearer {self.config.api_key}", "Content-Type": "application/json" }, timeout=self.config.timeout ) logger.info(f"Qwen客户端初始化完成: {self.config.base_url} - {self.config.model}") def chat( self, messages: List[Dict[str, str]], max_tokens: Optional[int] = None, temperature: Optional[float] = None, **kwargs ) -> LLMResponse: """对话补全（OpenAI兼容格式）""" start_time = time.time() try: # OpenAI兼容格式的请求体 payload = { "model": self.config.model, "messages": messages, "max_tokens": max_tokens or self.config.max_tokens, "temperature": temperature or self.config.temperature, "top_p": kwargs.get("top_p", self.config.top_p), "stream": False } # OpenAI兼容接口路径 response = self._client.post("/chat/completions", json=payload) response.raise_for_status() data = response.json() latency_ms = int((time.time() - start_time) * 1000) # OpenAI兼容格式的响应解析 choices = data.get("choices", [{}]) message = choices[0].get("message", {}) return LLMResponse( content=message.get("content", ""), model=data.get("model", self.config.model), usage=data.get("usage", {}), finish_reason=choices[0].get("finish_reason", "stop"), latency_ms=latency_ms ) except httpx.HTTPStatusError as e: logger.error(f"Qwen API错误: {e.response.status_code} - {e.response.text}") return LLMResponse( content="", model=self.config.model, error=f"API错误: {e.response.status_code} - {e.response.text[:200]}" ) except Exception as e: logger.error(f"Qwen调用失败: {e}") return LLMResponse( content="", model=self.config.model, error=str(e) ) def stream_chat( self, messages: List[Dict[str, str]], max_tokens: Optional[int] = None, temperature: Optional[float] = None, **kwargs ) -> Generator[str, None, None]: """ 流式对话补全（SSE格式） Yields: str: 每次返回一个文本片段 使用示例: for chunk in client.stream_chat(messages): print(chunk, end="", flush=True) """ try: # OpenAI兼容格式的请求体，启用流式输出 payload = { "model": self.config.model, "messages": messages, "max_tokens": max_tokens or self.config.max_tokens, "temperature": temperature or self.config.temperature, "top_p": kwargs.get("top_p", self.config.top_p), "stream": True # 启用流式输出 } # 使用stream模式发送请求 with self._client.stream("POST", "/chat/completions", json=payload) as response: for line in response.iter_lines(): if line: line = line.strip() # SSE格式: data: {...} if line.startswith("data: "): data_str = line[6:] # 移除 "data: " 前缀 if data_str == "[DONE]": break try: data = json.loads(data_str) choices = data.get("choices", []) if not choices: continue # 跳过空的choices delta = choices[0].get("delta", {}) content = delta.get("content", "") if content: yield content except json.JSONDecodeError: continue except httpx.HTTPStatusError as e: logger.error(f"Qwen流式API错误: {e.response.status_code}") yield f"[ERROR: API返回错误 {e.response.status_code}]" except Exception as e: logger.error(f"Qwen流式调用失败: {e}") yield f"[ERROR: {str(e)}]" async def async_stream_chat( self, messages: List[Dict[str, str]], max_tokens: Optional[int] = None, temperature: Optional[float] = None, **kwargs ) -> AsyncGenerator[str, None]: """ 异步流式对话补全（用于FastAPI SSE响应） Yields: str: 每次返回一个文本片段 """ import asyncio # 使用同步流式方法，包装为异步 for chunk in self.stream_chat(messages, max_tokens, temperature, **kwargs): yield chunk # 给async循环一个小延迟，让其他任务有机会执行 await asyncio.sleep(0) def get_available_models(self) -> List[str]: """获取可用模型列表""" return self.SUPPORTED_MODELS def close(self): """关闭客户端""" if self._client: self._client.close() class QwenVLClient(BaseLLMClient): """ Qwen VL多模态客户端（OpenAI兼容格式） 支持模型： - qwen-vl-plus - qwen-vl-max - qwen3-vl-plus - qwen2-vl-7b-instruct - qwen2-vl-72b-instruct """ SUPPORTED_MODELS = [ "qwen-vl-plus", "qwen-vl-max", "qwen3-vl-plus", "qwen2-vl-7b-instruct", "qwen2-vl-72b-instruct" ] def __init__(self, config: LLMConfig): if config.provider != LLMProvider.QWEN_VL: raise ValueError(f"配置provider应为QWEN_VL，实际为{config.provider}") super().__init__(config) self._init_client() def _init_client(self): """初始化HTTP客户端""" self._client = httpx.Client( base_url=self.config.base_url, headers={ "Authorization": f"Bearer {self.config.api_key}", "Content-Type": "application/json" }, timeout=self.config.timeout ) logger.info(f"QwenVL客户端初始化完成: {self.config.base_url} - {self.config.model}") def chat( self, messages: List[Dict[str, str]], max_tokens: Optional[int] = None, temperature: Optional[float] = None, **kwargs ) -> LLMResponse: """多模态对话补全（OpenAI兼容格式） 支持图片输入，消息格式： { "role": "user", "content": [ {"type": "image_url", "image_url": {"url": "https://example.com/image.jpg"}}, {"type": "text", "text": "描述这张图片"} ] } """ start_time = time.time() try: # OpenAI兼容格式的请求体 payload = { "model": self.config.model, "messages": messages, "max_tokens": max_tokens or self.config.max_tokens, "temperature": temperature or self.config.temperature, "top_p": kwargs.get("top_p", self.config.top_p), "stream": False } response = self._client.post("/chat/completions", json=payload) response.raise_for_status() data = response.json() latency_ms = int((time.time() - start_time) * 1000) choices = data.get("choices", [{}]) message = choices[0].get("message", {}) return LLMResponse( content=message.get("content", ""), model=data.get("model", self.config.model), usage=data.get("usage", {}), finish_reason=choices[0].get("finish_reason", "stop"), latency_ms=latency_ms ) except httpx.HTTPStatusError as e: logger.error(f"QwenVL API错误: {e.response.status_code} - {e.response.text}") return LLMResponse( content="", model=self.config.model, error=f"API错误: {e.response.status_code} - {e.response.text[:200]}" ) except Exception as e: logger.error(f"QwenVL调用失败: {e}") return LLMResponse( content="", model=self.config.model, error=str(e) ) def stream_chat( self, messages: List[Dict[str, str]], max_tokens: Optional[int] = None, temperature: Optional[float] = None, **kwargs ) -> Generator[str, None, None]: """流式多模态对话补全""" try: payload = { "model": self.config.model, "messages": messages, "max_tokens": max_tokens or self.config.max_tokens, "temperature": temperature or self.config.temperature, "top_p": kwargs.get("top_p", self.config.top_p), "stream": True } with self._client.stream("POST", "/chat/completions", json=payload) as response: for line in response.iter_lines(): if line: line = line.strip() if line.startswith("data: "): data_str = line[6:] if data_str == "[DONE]": break try: data = json.loads(data_str) choices = data.get("choices", []) if not choices: continue # 跳过空的choices delta = choices[0].get("delta", {}) content = delta.get("content", "") if content: yield content except json.JSONDecodeError: continue except Exception as e: logger.error(f"QwenVL流式调用失败: {e}") yield f"[ERROR: {str(e)}]" def get_available_models(self) -> List[str]: """获取可用模型列表""" return self.SUPPORTED_MODELS def close(self): """关闭客户端""" if self._client: self._client.close() def create_qwen_client( api_key: str, model: str = "qwen3.5-flash", base_url: str = "http://6.86.80.4:30080/v1", **kwargs ) -> QwenClient: """便捷函数：创建Qwen客户端""" config = LLMConfig( provider=LLMProvider.QWEN, model=model, api_key=api_key, base_url=base_url, **kwargs ) return QwenClient(config) def create_qwen_vl_client( api_key: str, model: str = "qwen3-vl-plus", base_url: str = "http://6.86.80.4:30080/v1", **kwargs ) -> QwenVLClient: """便捷函数：创建QwenVL客户端""" config = LLMConfig( provider=LLMProvider.QWEN_VL, model=model, api_key=api_key, base_url=base_url, **kwargs ) return QwenVLClient(config)