108 lines
3.9 KiB
Python
Executable File
108 lines
3.9 KiB
Python
Executable File
"""DeepSeek Provider - 使用 DeepSeek OpenAI-compatible API"""
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import asyncio
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from typing import AsyncIterator, Iterator
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from openai import OpenAI
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from ..base import BaseLLMProvider, LLMMessage, LLMResponse, LLMToolCall
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class DeepSeekProvider(BaseLLMProvider):
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"""DeepSeek模型Provider - 通过 DeepSeek OpenAI-compatible API 调用
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支持的模型:
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- deepseek-chat: 通用对话模型
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- deepseek-reasoner: 推理模型（适合复杂分析）
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"""
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provider_name = "deepseek"
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DEFAULT_BASE_URL = "https://api.deepseek.com"
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# 百炼平台模型名称映射
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MODEL_MAPPING = {
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"deepseek-chat": "deepseek-chat",
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"deepseek-reasoner": "deepseek-reasoner",
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"deepseek-v3": "deepseek-chat",
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"deepseek-r1": "deepseek-reasoner",
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"deepseek-v4-flash": "deepseek-v4-flash",
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"deepseek-v4-pro": "deepseek-v4-pro",
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}
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def __init__(
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self,
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api_key: str,
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model: str = "deepseek-chat",
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temperature: float = 0.7,
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max_tokens: int = 4096,
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api_base: str = None,
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):
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# 映射模型名称
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deepseek_model = self.MODEL_MAPPING.get(model, model)
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base_url = api_base or self.DEFAULT_BASE_URL
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super().__init__(
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api_key=api_key,
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api_base=base_url,
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model=deepseek_model,
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temperature=temperature,
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max_tokens=max_tokens,
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)
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self.client = OpenAI(api_key=api_key, base_url=base_url)
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def call(self, messages: list[LLMMessage], **kwargs) -> LLMResponse:
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request_kwargs = {
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"model": self.model,
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"messages": self._build_messages(messages),
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"temperature": kwargs.get("temperature", self.temperature),
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"max_tokens": kwargs.get("max_tokens", self.max_tokens),
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}
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tools = kwargs.get("tools")
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if tools is not None:
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request_kwargs["tools"] = tools
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if tools is not None or kwargs.get("disable_thinking", False):
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request_kwargs["extra_body"] = {"thinking": {"type": "disabled"}}
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tool_choice = kwargs.get("tool_choice")
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if tool_choice is not None:
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request_kwargs["tool_choice"] = tool_choice
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response = self.client.chat.completions.create(**request_kwargs)
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message = response.choices[0].message
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tool_calls = [
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LLMToolCall(
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id=tool_call.id,
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name=tool_call.function.name,
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arguments=tool_call.function.arguments,
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)
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for tool_call in (message.tool_calls or [])
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]
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return LLMResponse(
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content=message.content or "",
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model=self.model,
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provider=self.provider_name,
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usage={
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"prompt_tokens": response.usage.prompt_tokens,
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"completion_tokens": response.usage.completion_tokens,
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"total_tokens": response.usage.total_tokens,
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},
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finish_reason=response.choices[0].finish_reason,
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tool_calls=tool_calls,
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)
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async def acall(self, messages: list[LLMMessage], **kwargs) -> LLMResponse:
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return await asyncio.to_thread(self.call, messages, **kwargs)
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def stream(self, messages: list[LLMMessage], **kwargs) -> Iterator[str]:
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response = self.client.chat.completions.create(
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model=self.model,
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messages=self._build_messages(messages),
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temperature=kwargs.get("temperature", self.temperature),
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max_tokens=kwargs.get("max_tokens", self.max_tokens),
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stream=True,
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)
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for chunk in response:
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if chunk.choices[0].delta.content:
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yield chunk.choices[0].delta.content
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async def astream(self, messages: list[LLMMessage], **kwargs) -> AsyncIterator[str]:
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for content in self.stream(messages, **kwargs):
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yield content
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