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60 lines
2.0 KiB
60 lines
2.0 KiB
from openai import OpenAI
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import os
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# 初始化OpenAI客户端
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client = OpenAI(
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# 请用知识引擎原子能力API Key将下行替换为：api_key="sk-xxx",
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api_key="sk-fGBYaQLHykBOQsFwVrQdIFTsYr8YDtDVDQWFU41mFsmvfNPc", # 如何获取API Key：https://cloud.tencent.com/document/product/1772/115970
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base_url="https://api.lkeap.cloud.tencent.com/v1",
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)
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def main():
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reasoning_content = "" # 定义完整思考过程
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answer_content = "" # 定义完整回复
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is_answering = False # 判断是否结束思考过程并开始回复
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# 创建聊天完成请求
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stream = client.chat.completions.create(
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model="deepseek-v3", # 此处以 deepseek-v3 为例，可按需更换模型名称
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messages=[
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{"role": "user", "content": "9.9和9.11谁大"}
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],
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stream=True
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)
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print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")
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for chunk in stream:
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# 处理usage信息
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if not getattr(chunk, 'choices', None):
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print("\n" + "=" * 20 + "Token 使用情况" + "=" * 20 + "\n")
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print(chunk.usage)
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continue
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delta = chunk.choices[0].delta
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# 处理空内容情况
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if not getattr(delta, 'reasoning_content', None) and not getattr(delta, 'content', None):
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continue
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# 处理开始回答的情况
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if not getattr(delta, 'reasoning_content', None) and not is_answering:
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print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
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is_answering = True
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# 处理思考过程
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if getattr(delta, 'reasoning_content', None):
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print(delta.reasoning_content, end='', flush=True)
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reasoning_content += delta.reasoning_content
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# 处理回复内容
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elif getattr(delta, 'content', None):
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print(delta.content, end='', flush=True)
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answer_content += delta.content
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if __name__ == "__main__":
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try:
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main()
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except Exception as e:
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print(f"发生错误：{e}")
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