5fc3b4950b
- 将 LLMResponse 类中的 response_content 字段重命名为 content - 更新字段类型从 dict 改为 str，以更准确地表示响应内容 - 在 reasoning.py 中相应地修改了调用 LLMResponse 时的参数
123 lines
5.4 KiB
Python
123 lines
5.4 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, rounds: int = 1, llm_parameters: Optional[LLMParameters] = None) -> LLMRequest:
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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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total_duration = 0.0
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total_tokens = TokensUsage()
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prompt = llm_request.prompt
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round_start = datetime.now(timezone.utc)
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if llm_request.format:
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prompt += "\n请以JSON格式返回结果" + llm_request.format
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for i in range(rounds):
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round_start = datetime.now(timezone.utc)
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try:
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messages = [{"role": "user", "content": prompt}]
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create_args = {
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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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} # 处理format参数
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if llm_request.format:
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create_args["response_format"] = {"type": "json_object"}
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response = await client.chat.completions.create(**create_args)
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round_end = datetime.now(timezone.utc)
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duration = (round_end - round_start).total_seconds()
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total_duration += duration
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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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# 累加总token使用量
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total_tokens.prompt_tokens += tokens_usage.prompt_tokens
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total_tokens.completion_tokens += tokens_usage.completion_tokens
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if tokens_usage.prompt_cache_hit_tokens:
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total_tokens.prompt_cache_hit_tokens = (total_tokens.prompt_cache_hit_tokens or 0) + tokens_usage.prompt_cache_hit_tokens
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if tokens_usage.prompt_cache_miss_tokens:
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total_tokens.prompt_cache_miss_tokens = (total_tokens.prompt_cache_miss_tokens or 0) + tokens_usage.prompt_cache_miss_tokens
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llm_request.response.append(LLMResponse(
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response_id=response.id,
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tokens_usage=tokens_usage,
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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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round_end = datetime.now(timezone.utc)
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duration = (round_end - round_start).total_seconds()
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total_duration += duration
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llm_request.response.append(LLMResponse(
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response_id=f"error-round-{i+1}",
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content={"error": str(e)},
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total_duration=duration
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))
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if llm_request.error is None:
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llm_request.error = []
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llm_request.error.append(str(e))
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# 更新总耗时和总token使用量
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llm_request.total_duration = total_duration
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llm_request.total_tokens_usage = total_tokens
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return llm_request
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if __name__ == "__main__":
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from json_example import generate_json_example
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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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from schema import DatasetItem
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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="测试，随便说点什么",
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api_provider=api_provider,
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format=generate_json_example(DatasetItem)
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)
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# # 单次调用示例
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# result = asyncio.run(call_openai_api(llm_request))
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# print(f"\n单次调用结果 - 响应数量: {len(result.response)}")
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# for i, resp in enumerate(result.response, 1):
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# print(f"响应{i}: {resp.response_content}")
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# 多次调用示例
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params = LLMParameters(temperature=0.7, max_tokens=100)
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result = asyncio.run(call_openai_api(llm_request, 3,params))
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print(f"\n3次调用结果 - 总耗时: {result.total_duration:.2f}s")
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print(f"总token使用: prompt={result.total_tokens_usage.prompt_tokens}, completion={result.total_tokens_usage.completion_tokens}")
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for i, resp in enumerate(result.response, 1):
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print(f"响应{i}: {resp.content}") |