關於gpt的使用

1.安裝環境(建議使用python3.9以及以上版本)

gradio==3.31.0
langchain==0.0.173
loguru==0.5.3
moviepy==1.0.3
openai==0.27.6
openai_whisper==20230314
pandas==1.3.4
pymongo==3.12.1
requests==2.28.1
retry==0.9.2
tqdm==4.65.0
whisper==1.1.10
redis
pymongo==3.12.1

2.簡單實用

import os
import openai
from langchain.chat_models import AzureChatOpenAI
from langchain import PromptTemplate, LLMChain
from common.logUtil_2 import log_debug

API_KEY = "sk-"
AZURE_OPENAI_API_KEY =  "xxxxxx"  # azure
AZURE_OPENAI_BASE_URL = "https://openaiyj.openai.azure.com/"
AZURE_OPENAI_AREA = "eastus"
AZURE_DEPLOYMENT_NAME = "gpt3"
AZURE_EMBEDDING_DEPLOYMENT_NAME = "embedding"
AZURE_API_VERSION = "2023-03-15-preview"
AZURE_API_TYPE = "azure"
AZURE_API_TEMPERATRUE = 0.3

def setup_llm_azure():
    """
    創建返回langchain azure的ChatOpenAI對象
    :return:
    """
    chat = AzureChatOpenAI(
        openai_api_base=AZURE_OPENAI_BASE_URL,
        openai_api_version=AZURE_API_VERSION,
        deployment_name=AZURE_DEPLOYMENT_NAME,
        openai_api_key=AZURE_OPENAI_API_KEY,
        openai_api_type=AZURE_API_TYPE,
        temperature=AZURE_API_TEMPERATRUE
    )
    return chat
    
TEXT_PROMPT = '''
提示內容
{變量1}{變量2}
'''
#使用方法一
async def checkup_report_format(text: str):
    """
    整理ocr提煉出的文本
    """
    llm = setup_llm_azure()
    prompt = PromptTemplate(template=TEXT_PROMPT, input_variables=["變量1"])
    #多個變量
    #prompt = PromptTemplate(template=TEXT_PROMPT, input_variables=["變量1", "變量2"])
    chain = LLMChain(llm=llm, prompt=prompt, output_key="result", verbose=True)
    result = None
    try:
        log_info("開始識別")
        print(text)
        result = await chain.arun(text)
        #多個變量
        #result = await chain.arun({"變量1": xxxx, "變量2": xxxx})
        log_info(f"結束識別-{result}")
        return result
    except Exception as e:
        log_error(f"checkup_result_format error:{e}")
        return result
        


if __name__ == '__main__':
    asyncio.run(checkup_report_format())