93 lines
3.4 KiB
Python
93 lines
3.4 KiB
Python
"""
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OpenAI适配器 (支持GPT系列和OpenAI兼容API)
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"""
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from typing import Dict, Any
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from ..base_adapter import BaseLLMAdapter
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from ..types import LLMRequest, LLMResponse, LLMUsage, DEFAULT_BASE_URLS, LLMProvider
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class OpenAIAdapter(BaseLLMAdapter):
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"""OpenAI适配器"""
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@property
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def base_url(self) -> str:
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return self.config.base_url or DEFAULT_BASE_URLS.get(LLMProvider.OPENAI, "https://api.openai.com/v1")
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async def complete(self, request: LLMRequest) -> LLMResponse:
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try:
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await self.validate_config()
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return await self.retry(lambda: self._send_request(request))
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except Exception as error:
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self.handle_error(error, "OpenAI API调用失败")
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async def _send_request(self, request: LLMRequest) -> LLMResponse:
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# 构建请求头
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headers = {
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"Authorization": f"Bearer {self.config.api_key}",
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}
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# 检测是否为推理模型（o1/o3系列）
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model_name = self.config.model.lower()
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is_reasoning_model = "o1" in model_name or "o3" in model_name
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# 构建请求体
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messages = [{"role": msg.role, "content": msg.content} for msg in request.messages]
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request_body: Dict[str, Any] = {
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"model": self.config.model,
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"messages": messages,
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"temperature": request.temperature if request.temperature is not None else self.config.temperature,
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"top_p": request.top_p if request.top_p is not None else self.config.top_p,
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"frequency_penalty": self.config.frequency_penalty,
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"presence_penalty": self.config.presence_penalty,
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}
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# 推理模型使用max_completion_tokens，其他模型使用max_tokens
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max_tokens = request.max_tokens if request.max_tokens is not None else self.config.max_tokens
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if is_reasoning_model:
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request_body["max_completion_tokens"] = max_tokens
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else:
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request_body["max_tokens"] = max_tokens
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url = f"{self.base_url.rstrip('/')}/chat/completions"
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response = await self.client.post(
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url,
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headers=self.build_headers(headers),
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json=request_body
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)
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if response.status_code != 200:
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error_data = response.json() if response.text else {}
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error_msg = error_data.get("error", {}).get("message", f"HTTP {response.status_code}")
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raise Exception(f"{error_msg}")
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data = response.json()
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choice = data.get("choices", [{}])[0]
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if not choice:
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raise Exception("API响应格式异常: 缺少choices字段")
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usage = None
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if "usage" in data:
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usage = LLMUsage(
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prompt_tokens=data["usage"].get("prompt_tokens", 0),
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completion_tokens=data["usage"].get("completion_tokens", 0),
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total_tokens=data["usage"].get("total_tokens", 0)
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)
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return LLMResponse(
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content=choice.get("message", {}).get("content", ""),
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model=data.get("model"),
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usage=usage,
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finish_reason=choice.get("finish_reason")
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)
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async def validate_config(self) -> bool:
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await super().validate_config()
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if not self.config.model:
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raise Exception("未指定OpenAI模型")
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return True
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