335 lines
14 KiB
Python
335 lines
14 KiB
Python
import ast
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import json
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import logging
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import re
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import threading
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from functools import wraps
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from ratelimit import limits, sleep_and_retry
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import time
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from pathlib import Path
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from openai import OpenAI
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import os
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file_write_lock = threading.Lock()
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@sleep_and_retry
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@limits(calls=2, period=1)
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def upload_file(file_path,output_folder=""):
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"""
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Uploads a file to DashScope and returns the file ID.
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Additionally, saves the file ID to 'file_ids.txt' in the given output folder.
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"""
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if not output_folder:
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output_folder=os.path.dirname(file_path)
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client = OpenAI(
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api_key=os.getenv("DASHSCOPE_API_KEY"),
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
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)
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# 上传文件并获取 file_id
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file = client.files.create(file=Path(file_path), purpose="file-extract")
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file_id = file.id
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# 创建output_folder路径，如果它不存在
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if not os.path.exists(output_folder):
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os.makedirs(output_folder)
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# 确保文件写入是互斥的
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with file_write_lock: # 在这个代码块中，其他线程无法进入
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file_ids_path = os.path.join(output_folder, 'file_ids.txt')
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# 如果文件不存在，就创建它并写入 file_id
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with open(file_ids_path, 'a') as f:
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f.write(f'{file_id}\n')
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return file_id
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@sleep_and_retry
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@limits(calls=8, period=1) # 每秒最多调用4次
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def rate_limiter():
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pass # 这个函数本身不执行任何操作，只用于限流
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# 创建一个共享的装饰器
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def shared_rate_limit(func):
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@wraps(func)
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def wrapper(*args, **kwargs):
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rate_limiter() # 通过共享的限流器
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return func(*args, **kwargs)
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return wrapper
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def extract_error_details(error_message):
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"""
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从错误消息中提取错误代码和内部错误代码。
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假设错误消息的格式包含 'Error code: XXX - {...}'
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"""
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# 提取数值型错误代码
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error_code_match = re.search(r'Error code:\s*(\d+)', error_message)
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error_code = int(error_code_match.group(1)) if error_code_match else None
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# 提取内部错误代码字符串（如 'data_inspection_failed'）
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error_code_string = None
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error_dict_match = re.search(r'Error code:\s*\d+\s*-\s*(\{.*\})', error_message)
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if error_dict_match:
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error_dict_str = error_dict_match.group(1)
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try:
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# 使用 ast.literal_eval 解析字典字符串
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error_dict = ast.literal_eval(error_dict_str)
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error_code_string = error_dict.get('error', {}).get('code')
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print(error_code_string)
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except Exception as e:
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print(f"解析错误消息失败: {e}")
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return error_code, error_code_string
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@shared_rate_limit
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def qianwen_long(file_id, user_query, max_retries=2, backoff_factor=1.0, need_extra=False):
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logger = logging.getLogger('model_log') # 通过日志名字获取记录器
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"""
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基于上传的文件 ID 和用户查询生成响应，并在失败时自动重试。
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参数：
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- file_id: 上传文件的 ID
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- user_query: 用户查询
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- max_retries: 最大重试次数（默认 2 次）
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- backoff_factor: 指数退避的基础等待时间（默认 1.0 秒）
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- need_extra: 是否需要返回额外数据（默认 False）
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"""
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client = OpenAI(
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api_key=os.getenv("DASHSCOPE_API_KEY"),
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
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)
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for attempt in range(1, max_retries + 2): # +1 是为了包括初始调用
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try:
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# 调用 API
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completion = client.chat.completions.create(
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model="qwen-long",
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temperature=0.5,
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messages=[
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{
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'role': 'system',
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'content': f'fileid://{file_id}'
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},
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{
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'role': 'user',
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'content': user_query
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}
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],
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stream=False
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)
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token_usage = completion.usage.completion_tokens
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# 如果调用成功，返回响应内容
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if need_extra:
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return completion.choices[0].message.content, token_usage
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else:
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return completion.choices[0].message.content
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except Exception as exc:
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# 提取错误代码
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error_code, error_code_string = extract_error_details(str(exc))
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logger.error(f"第 {attempt} 次尝试失败，查询：'{user_query}'，错误：{exc}", exc_info=True)
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if error_code == 429: # 超 qps/qpm
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if attempt <= max_retries:
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sleep_time = backoff_factor * (2 ** (attempt - 1)) # 指数退避
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logger.warning(f"错误代码为 429，将在 {sleep_time} 秒后重试...")
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time.sleep(sleep_time)
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else:
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print(f"查询 '{user_query}' 的所有 {max_retries + 1} 次尝试均失败（429 错误）。")
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break
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elif error_code == 400 and error_code_string in [
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'data_inspection_failed', 'ResponseTimeout', 'DataInspectionFailed',
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'response_timeout', 'request_timeout', "RequestTimeOut"
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]:
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logger.warning(f"错误代码为 400 - {error_code_string}，将调用 qianwen_long_stream 执行一次...")
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try:
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# 超时就调用 qianwen_long_stream
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stream_result = qianwen_long_stream(file_id, user_query, max_retries=0) # 禁用内部重试
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if need_extra:
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if isinstance(stream_result, tuple) and len(stream_result) == 2:
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return stream_result[0], stream_result[1] # 返回内容和默认的 token_usage=0
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else:
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logger.error(f"qianwen_long_stream 返回值不符合预期（需要元组）。返回值: {stream_result}")
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return "", 0 # 处理异常返回
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else:
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return stream_result # need_extra=False，直接返回内容
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except Exception as stream_exc:
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logger.error(f"调用 qianwen_long_stream 时出错：{stream_exc}", exc_info=True)
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break # 跳出循环，不再重试
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else:
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# 对于非 429 和非特定 400 错误，不进行重试，直接抛出异常
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logger.error(f"遇到非 429 或非 'data_inspection_failed' 的 400 错误（错误代码：{error_code}），不进行重试。")
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break
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# 在所有重试失败的情况下返回
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if need_extra:
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return "", 0 # 返回默认值和 token_usage = 0
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else:
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return ""
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@shared_rate_limit
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def qianwen_long_stream(file_id, user_query, max_retries=2, backoff_factor=1.0, need_extra=False):
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logger = logging.getLogger('model_log') # 通过日志名字获取记录器
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"""
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使用之前上传的文件，根据用户查询生成响应，并实时显示流式输出。
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参数：
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- file_id: 上传文件的 ID
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- user_query: 用户查询
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- max_retries: 最大重试次数（默认 2 次）
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- backoff_factor: 指数退避的基础等待时间（默认 1.0 秒）
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- need_extra: 是否需要返回额外数据（默认 False）
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返回:
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- 当 need_extra=False 时: 返回响应内容 (str)
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- 当 need_extra=True 时: 返回 (响应内容, token_usage)
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"""
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print("调用 qianwen-long stream...")
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client = OpenAI(
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api_key=os.getenv("DASHSCOPE_API_KEY"),
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
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)
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for attempt in range(1, max_retries + 2): # +1 是为了包括初始调用
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try:
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completion_tokens = 0 # 初始化 completion_tokens 为 0
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# 生成基于文件ID的响应
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completion = client.chat.completions.create(
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model="qwen-long",
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temperature=0.4,
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messages=[
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{
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'role': 'system',
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'content': f'fileid://{file_id}'
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},
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{
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'role': 'user',
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'content': user_query
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}
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],
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stream=True, # 启用流式响应
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stream_options={"include_usage": True}
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)
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full_response = "" # 用于存储完整的响应内容
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for chunk in completion:
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# print(chunk.model_dump_json())
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if hasattr(chunk, 'to_dict'):
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chunk_data = chunk.to_dict()
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else:
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chunk_data = json.loads(chunk.model_dump_json())
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# 处理 usage 信息
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usage = chunk_data.get('usage')
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if usage is not None:
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completion_tokens = usage.get('completion_tokens', 0)
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# 处理 choices 信息
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choices = chunk_data.get('choices', [])
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if choices:
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choice = choices[0]
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delta = choice.get('delta', {})
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content = delta.get('content', '')
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if content:
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full_response += content
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# 实时打印内容（可以取消注释下面一行以实时输出）
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# print(content, end='', flush=True)
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if choice.get('finish_reason'):
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# 不再提前跳出循环，允许处理最后一个包含 usage 的块
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pass # 或者记录 finish_reason 以供后续使用
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if need_extra:
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return full_response, completion_tokens
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else:
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return full_response
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except Exception as exc:
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# 提取错误代码
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error_code, error_code_string = extract_error_details(str(exc))
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logger.error(f"第 {attempt} 次尝试失败，查询：'{user_query}'，错误：{exc}", exc_info=True)
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if error_code == 429:
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if attempt <= max_retries:
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sleep_time = backoff_factor * (2 ** (attempt - 1)) # 指数退避
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logger.warning(f"错误代码为 429，将在 {sleep_time} 秒后重试...")
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time.sleep(sleep_time)
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else:
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logger.error(f"查询 '{user_query}' 的所有 {max_retries + 1} 次尝试均失败（429 错误）。")
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break
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elif error_code == 400 and error_code_string in [
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'data_inspection_failed', 'ResponseTimeout', 'DataInspectionFailed',
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'response_timeout', 'request_timeout', "RequestTimeOut"
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]:
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if attempt == 1: # 只重试一次
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logger.warning(f"错误代码为 400 - {error_code_string}，将立即重试...")
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continue # 直接跳到下一次循环（即重试一次）
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else:
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logger.error(f"查询 '{user_query}' 的所有 {max_retries + 1} 次尝试均失败（400 - {error_code_string}）。")
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break
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else:
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# 对于非 429 和非特定 400 错误，不进行重试，直接抛出异常
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logger.error(f"遇到非 429 或非 'data_inspection_failed...' 的 400 错误（错误代码：{error_code}），不进行重试。")
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break
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# 如果所有尝试都失败了，返回空字符串
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if need_extra:
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return "", 0
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else:
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return ""
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@shared_rate_limit
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def qianwen_long_text(file_id, user_query):
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print("call qianwen-long text...")
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"""
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Uses a previously uploaded file to generate a response based on a user query.
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"""
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client = OpenAI(
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api_key=os.getenv("DASHSCOPE_API_KEY"),
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base_url="https://dashscope.aliyuncs.com/compatible-mode/v1"
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)
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# Generate a response based on the file ID
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completion = client.chat.completions.create(
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model="qwen-long",
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# top_p=0.5,
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temperature=0.5,
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messages=[
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{
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'role': 'system',
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'content': f'fileid://{file_id}'
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},
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{
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'role': 'user',
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'content': user_query
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}
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],
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stream=False
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)
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# Return the response content
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return completion.choices[0].message.content
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#TODO:若采购需求和评分那块响应超时比较多，考虑都改为流式
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if __name__ == "__main__":
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# Example file path - replace with your actual file path
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file_path = r"C:\Users\Administrator\Desktop\货物标\截取test\2-招标文件_before.pdf"
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file_id = upload_file(file_path)
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print(file_id)
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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"
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# res1,res2=qianwen_long_stream(file_id,user_query1,2,1,True)
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res1,res2= qianwen_long_stream(file_id, user_query1, 2, 1,True)
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print(res1)
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print(res2)
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#
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#
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# user_query2 = ("请提供文件中关于资格审查的具体内容和标准。")
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# start_time=time.time()
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# # First query
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# print("starting qianwen-long...")
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# result1 ,result2= qianwen_long(file_id, user_query1)
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# print("First Query Result:", result1)
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# print(type(result1))
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# print(result2)
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# # Second query
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# print("starting qianwen-long...")
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# result2 = qianwen_long(file_id, user_query2)
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# print("Second Query Result:", result2)
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# end_time=time.time()
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# print("elapsed time:"+str(end_time-start_time))
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