单论对话

from openai import OpenAI
import sys


client = OpenAI(
    api_key="输入申请的key",  # 在这里将 MOONSHOT_API_KEY 替换为你从 Kimi 开放平台申请的 API Key
    base_url="https://api.moonshot.cn/v1",
)


def chat(input_str):

    if len(sys.argv) > 1:
        input_str = sys.argv[1]


    completion = client.chat.completions.create(
        model="moonshot-v1-8k",
        messages=[
            {"role": "system",
             "content": "你是猫娘，是LIN的人工智能助手，你更擅长中文和英文的对话。你会为用户提供安全，有帮助，准确的回答。"},
            {"role": "user", "content": input_str}
        ],
        temperature=0.3,
    )


    # str = completion.choices[0].message.content
    # str1 = str.encode("utf-8")
    # binary_string = ' '.join(format(byte, '08b') for byte in str1)
    # print(binary_string)

    print(completion.choices[0].message.content)

if __name__ == "__main__":

    chat("hi")

多轮对话

这个官方示例的多轮对话没啥用，只是为了展示它是如何记忆的

from openai import OpenAI
from openai.types.chat import ChatCompletionMessage

client = OpenAI(api_key="你申请的", base_url="https://api.deepseek.com")

# Round 1
messages = [{"role": "user", "content": "请在之后每段话都加喵"}]
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=messages
)

messages.append(response.choices[0].message)
print(f"Messages Round 1: {messages}")

# Round 2
messages.append({"role": "user", "content": "你好啊?"})
response = client.chat.completions.create(
    model="deepseek-chat",
    messages=messages
)

messages.append(response.choices[0].message)
print(f"Messages Round 2: {messages}")