DeepSeek API keys本地调用 [python]

首先需要安装python
再安装pycharm

打开pycharm终端

第一步是安装三个包，通过 pip install langchain 来安装LangChain，通过 pip install openai 来安装OpenAI，还需要通过 pip install langchain-openai 以便在 LangChain 中使用 OpenAI 模型

安装后可以直接开发
首先需要在deepseek官网申请密钥https://platform.deepseek.com/api_keys

首次调用API
from openai import OpenAI

client = OpenAI(api_key="<DeepSeek API Key>", base_url="https://api.deepseek.com")

response = client.chat.completions.create(
 model="deepseek-chat",
 messages=[
{"role":"system","content":"You are a helpful assistant"},
{"role":"user","content":"Hello"},
],
 stream=False
)

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

优化一下可以进行一对一对话

from openai import OpenAI

# 初始化 OpenAI 客户端
client = OpenAI(api_key=<你的密钥地址：str类型>, base_url="https://api.deepseek.com")

# 初始化对话消息列表
messages = [
    {"role": "assistant", "content": "你是一个AI专家"}
]

while True:
    # 获取用户输入
    user_input = input("你: ")
    if user_input.lower() == 'quit':
        break
    elif user_input.lower().startswith('set_system '):
        # 提取新的系统角色内容
        new_system_content = user_input[11:]
        # 找到并更新系统消息
        for message in messages:
            if message["role"] == "system":
                message["content"] = new_system_content
                break
        print(f"系统角色已更新为: {new_system_content}")
        continue

    # 将用户输入添加到消息列表
    messages.append({"role": "user", "content": user_input})

    # 调用模型获取回复
    try:
        response = client.chat.completions.create(
            model="deepseek-chat",
            messages=messages,
            max_tokens=1024,
            temperature=0.7,
            stream=True,
        )

        # 获取模型回复内容

        for s in response:
            content = s.choices[0].delta.content
            if content is not None:
                print(content, end="")
                assistant_reply = content
        print()
        # 将模型回复添加到消息列表
        messages.append({"role": "assistant", "content": assistant_reply})
    except Exception as e:
        print(f"发生错误: {e}")