import os
from openai import OpenAI

client = OpenAI(
    # 请用知识引擎原子能力API Key将下行替换为：api_key="sk-xxx",
    api_key="LKEAP_API_KEY", # 如何获取API Key：https://cloud.tencent.com/document/product/1772/115970
    base_url="https://api.lkeap.cloud.tencent.com/v1",
)

completion = client.chat.completions.create(
    model="deepseek-r1",  # 此处以 deepseek-r1 为例，可按需更换模型名称。
    messages=[
        {'role': 'user', 'content': '9.9和9.11谁大'}
        ]
)

# 通过reasoning_content字段打印思考过程
print("思考过程：")
print(completion.choices[0].message.reasoning_content)
# 通过content字段打印最终答案
print("最终答案：")
print(completion.choices[0].message.content)

import os
from openai import OpenAI

client = OpenAI(
    # 请用知识引擎原子能力API Key将下行替换为：api_key="sk-xxx",
    api_key="LKEAP_API_KEY", # 如何获取API Key：https://cloud.tencent.com/document/product/1772/115970
    base_url="https://api.lkeap.cloud.tencent.com/v1",
)

# 通过 messages 数组实现上下文管理
messages = [
    {'role': 'user', 'content': '你好'},
    {'role': 'assistant', 'content': '你好，有什么可以帮助你的？'},
    {'role': 'user', 'content': '9.9和9.11哪个大'}
]

completion = client.chat.completions.create(
    model="deepseek-r1",  # 此处以 deepseek-r1 为例，可按需更换模型名称。
    messages=messages
)

print("="*20+"第一轮对话"+"="*20)
# 通过reasoning_content字段打印思考过程
print("="*20+"思考过程"+"="*20)
print(completion.choices[0].message.reasoning_content)
# 通过content字段打印最终答案
print("="*20+"最终答案"+"="*20)
print(completion.choices[0].message.content)

messages.append({'role': 'assistant', 'content': completion.choices[0].message.content})
messages.append({'role': 'user', 'content': '你是谁'})
print("="*20+"第二轮对话"+"="*20)
completion = client.chat.completions.create(
    model="deepseek-r1",  # 此处以 deepseek-r1 为例，可按需更换模型名称。
    messages=messages
)
# 通过reasoning_content字段打印思考过程
print("="*20+"思考过程"+"="*20)
print(completion.choices[0].message.reasoning_content)
# 通过content字段打印最终答案
print("="*20+"最终答案"+"="*20)
print(completion.choices[0].message.content)

from openai import OpenAI
import os

# 初始化OpenAI客户端
client = OpenAI(
    # 请用知识引擎原子能力API Key将下行替换为：api_key="sk-xxx",
    api_key="LKEAP_API_KEY", # 如何获取API Key：https://cloud.tencent.com/document/product/1772/115970
    base_url="https://api.lkeap.cloud.tencent.com/v1",
)

def main():
    reasoning_content = ""  # 定义完整思考过程
    answer_content = ""     # 定义完整回复
    is_answering = False   # 判断是否结束思考过程并开始回复

    # 创建聊天完成请求
    stream = client.chat.completions.create(
        model="deepseek-r1",  # 此处以 deepseek-r1 为例，可按需更换模型名称
        messages=[
            {"role": "user", "content": "9.9和9.11谁大"}
        ],
        stream=True
    )

    print("\n" + "=" * 20 + "思考过程" + "=" * 20 + "\n")

    for chunk in stream:
        # 处理usage信息
        if not getattr(chunk, 'choices', None):
            print("\n" + "=" * 20 + "Token 使用情况" + "=" * 20 + "\n")
            print(chunk.usage)
            continue

        delta = chunk.choices[0].delta

        # 处理空内容情况
        if not getattr(delta, 'reasoning_content', None) and not getattr(delta, 'content', None):
            continue

        # 处理开始回答的情况
        if not getattr(delta, 'reasoning_content', None) and not is_answering:
            print("\n" + "=" * 20 + "完整回复" + "=" * 20 + "\n")
            is_answering = True

        # 处理思考过程
        if getattr(delta, 'reasoning_content', None):
            print(delta.reasoning_content, end='', flush=True)
            reasoning_content += delta.reasoning_content
        # 处理回复内容
        elif getattr(delta, 'content', None):
            print(delta.content, end='', flush=True)
            answer_content += delta.content

if __name__ == "__main__":
    try:
        main()
    except Exception as e:
        print(f"发生错误：{e}")