zbparse/flask_app/general/通义千问long.py

import random
import time
from pathlib import Path
from openai import OpenAI
import os

def upload_file(file_path):
    """
    Uploads a file to DashScope and returns the file ID.
    """
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
    )
    file = client.files.create(file=Path(file_path), purpose="file-extract")
    return file.id

def qianwen_long(file_id, user_query):
    print("call qianwen-long...")
    """
    Uses a previously uploaded file to generate a response based on a user query.
    """
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
    )

    # Generate a response based on the file ID
    completion = client.chat.completions.create(
        model="qwen-long",
        # top_p=0.5,
        temperature=0.5,
        # response_format={"type":"json_object"},
        messages=[
            {
                'role': 'system',
                'content': f'fileid://{file_id}'
            },
            {
                'role': 'user',
                'content': user_query
            }
        ],
        stream=False
    )

    # Return the response content
    # return completion.choices[0].message.content,completion.usage
    return completion.choices[0].message.content

def qianwen_long_text(file_id, user_query):
    print("call qianwen-long text...")
    """
    Uses a previously uploaded file to generate a response based on a user query.
    """
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
    )

    # Generate a response based on the file ID
    completion = client.chat.completions.create(
        model="qwen-long",
        # top_p=0.5,
        temperature=0.5,
        messages=[
            {
                'role': 'system',
                'content': f'fileid://{file_id}'
            },
            {
                'role': 'user',
                'content': user_query
            }
        ],
        stream=False
    )

    # Return the response content
    return completion.choices[0].message.content

def qianwen_long_stream(file_id, user_query):
    print("call qianwen-long text...")
    """
    Uses a previously uploaded file to generate a response based on a user query.
    """
    client = OpenAI(
        api_key=os.getenv("DASHSCOPE_API_KEY"),
        base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
    )

    # Generate a response based on the file ID
    completion = client.chat.completions.create(
        model="qwen-long",
        # top_p=0.5,
        temperature=0.5,
        messages=[
            {
                'role': 'system',
                'content': f'fileid://{file_id}'
            },
            {
                'role': 'user',
                'content': user_query
            }
        ],
        stream=True
    )
    # Return the response content
    return completion.choices[0].message.content

if __name__ == "__main__":
    # Example file path - replace with your actual file path

    file_path = "C:\\Users\\Administrator\\Desktop\\货物标\\output4\\招标文件111_tobidders_notice_part1.docx"
    file_id = upload_file(file_path)

    user_query1 = "该招标文件前附表中的项目名称是什么，请以json格式返回给我"
    user_query2 = ("请提供文件中关于资格审查的具体内容和标准。")
    start_time=time.time()
    # First query
    print("starting qianwen-long...")
    result1 ,result2= qianwen_long(file_id, user_query1)
    print("First Query Result:", result1)
    print(type(result1))
    print(result2)
    # # Second query
    # print("starting qianwen-long...")
    # result2 = qianwen_long(file_id, user_query2)
    # print("Second Query Result:", result2)
    # end_time=time.time()
    # print("elapsed time:"+str(end_time-start_time))