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