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64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
import os
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import sys
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import json
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import logging
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import openai
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import requests
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import importlib
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from datetime import datetime
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from core.utils.util import is_segment
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from core.utils.util import get_string_no_punctuation_or_emoji
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from core.utils.util import read_config, get_project_dir
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from abc import ABC, abstractmethod
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logger = logging.getLogger(__name__)
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def create_instance(class_name, *args, **kwargs):
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# 创建LLM实例
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if os.path.exists(os.path.join('core', 'providers', 'llm', class_name, f'{class_name}.py')):
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lib_name = f'core.providers.llm.{class_name}.{class_name}'
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if lib_name not in sys.modules:
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sys.modules[lib_name] = importlib.import_module(f'{lib_name}')
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return sys.modules[lib_name].LLMProvider(*args, **kwargs)
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raise ValueError(f"不支持的LLM类型: {class_name}，请检查该配置的type是否设置正确")
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if __name__ == "__main__":
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"""
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响应速度测试
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"""
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config = read_config(get_project_dir() + "config.yaml")
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llm = create_instance(
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config["selected_module"]["LLM"]
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if not "type" in config["LLM"][config["selected_module"]["LLM"]]
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else
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config["LLM"][config["selected_module"]["LLM"]]["type"],
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config["LLM"][config["selected_module"]["LLM"]]
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)
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start_time = datetime.now()
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dialogue = []
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dialogue.append({"role": "system", "content": config.get("prompt")})
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dialogue.append({"role": "user", "content": "你好小智"})
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llm_responses = llm.response("test", dialogue)
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response_message = []
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first_text = None
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start = 0
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for content in llm_responses:
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response_message.append(content)
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if is_segment(response_message):
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segment_text = "".join(response_message[start:])
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segment_text = get_string_no_punctuation_or_emoji(segment_text)
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if len(segment_text) > 0:
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if first_text is None:
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first_text = segment_text
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print("大模型首次返回耗时：" + str(datetime.now() - start_time))
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start = len(response_message)
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print("大模型返回总耗时：" + str(datetime.now() - start_time))
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