5fc90903fb
- 新增 reasoning.py 文件，实现与 OpenAI API 的交互 - 添加 call_openai_api 函数，用于发送请求并处理响应 - 支持可选的 LLMParameters 参数，以定制化请求 - 处理 API 响应中的 tokens 使用情况 - 提供错误处理和缓存 token 字段的处理
82 lines
3.2 KiB
Python
82 lines
3.2 KiB
Python
import sys
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import asyncio
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import openai
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from pathlib import Path
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from datetime import datetime, timezone
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from typing import Optional
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sys.path.append(str(Path(__file__).resolve().parent.parent))
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from schema import APIProvider, LLMRequest, LLMResponse, TokensUsage, LLMParameters
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async def call_openai_api(llm_request: LLMRequest, llm_parameters: Optional[LLMParameters] = None) -> LLMResponse:
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start_time = datetime.now(timezone.utc)
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client = openai.AsyncOpenAI(
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api_key=llm_request.api_provider.api_key,
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base_url=llm_request.api_provider.base_url
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)
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try:
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messages = [{"role": "user", "content": llm_request.prompt}]
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response = await client.chat.completions.create(
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model=llm_request.api_provider.model_id,
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messages=messages,
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temperature=llm_parameters.temperature if llm_parameters else None,
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max_tokens=llm_parameters.max_tokens if llm_parameters else None,
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top_p=llm_parameters.top_p if llm_parameters else None,
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frequency_penalty=llm_parameters.frequency_penalty if llm_parameters else None,
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presence_penalty=llm_parameters.presence_penalty if llm_parameters else None,
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seed=llm_parameters.seed if llm_parameters else None
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)
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end_time = datetime.now(timezone.utc)
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duration = (end_time - start_time).total_seconds()
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# 处理可能不存在的缓存token字段
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usage = response.usage
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cache_hit = getattr(usage, 'prompt_cache_hit_tokens', None)
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cache_miss = getattr(usage, 'prompt_cache_miss_tokens', None)
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tokens_usage = TokensUsage(
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prompt_tokens=usage.prompt_tokens,
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completion_tokens=usage.completion_tokens,
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prompt_cache_hit_tokens=cache_hit,
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prompt_cache_miss_tokens=cache_miss if cache_miss is not None else usage.prompt_tokens
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)
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return LLMResponse(
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response_id=response.id,
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tokens_usage=tokens_usage,
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response_content={"content": response.choices[0].message.content},
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total_duration=duration,
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llm_parameters=llm_parameters
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)
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except Exception as e:
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end_time = datetime.now(timezone.utc)
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duration = (end_time - start_time).total_seconds()
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return LLMResponse(
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response_id="error",
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response_content={"error": str(e)},
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total_duration=duration
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)
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if __name__ == "__main__":
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from sqlmodel import Session, select
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from global_var import get_sql_engine, init_global_var
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init_global_var("workdir")
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api_state = "1 deepseek-chat"
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with Session(get_sql_engine()) as session:
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api_provider = session.exec(select(APIProvider).where(APIProvider.id == int(api_state.split(" ")[0]))).first()
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llm_request = LLMRequest(
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prompt="你好，世界！",
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api_provider=api_provider
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)
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# 不使用LLM参数调用
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result = asyncio.run(call_openai_api(llm_request))
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print(f"\n不使用LLM参数调用结果: {result}")
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# 使用LLM参数调用
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params = LLMParameters(
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temperature=0.7,
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max_tokens=100
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)
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result = asyncio.run(call_openai_api(llm_request, params))
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print(f"\nOpenAI API 响应: {result}") |