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【记录】Python调用大模型（以Deepseek和Qwen为例）

news 来源：原创 2025/4/30 8:51:26

代码

包

import requests
import json
from openai import OpenAI

预备（以BERTopic聚合主题为例）

prompt = """
I have a topic that is described by the following keywords: 
{INPUT}Based on the information above, extract a short but highly descriptive topic label of at most 5 words. Make sure it is in the following format:
topic: <topic label>
"""test = "xxx"msg = [{"role": "system", "content": prompt},{"role": "user",   "content": test}
]url = "https://api.siliconflow.cn/v1/chat/completions"model="deepseek-ai/DeepSeek-V3" # 替换为模型广场对应模型名client = OpenAI(api_key="sk-xxx",  # 从https://cloud.siliconflow.cn/i/Dyv2uzaz获取，链接为邀请。API密钥栏获取密钥，注意隐私保密。base_url="https://api.siliconflow.cn/v1"
)
# 或
headers = {"Authorization": "Bearer <token>","Content-Type": "application/json"
}

设定模型

payload = {"model": model,"messages": msg,"stream": False,   # 流输出"max_tokens": 4096,  #max_tokens必须小于等于16384"stop": None,"temperature": 0,  #求稳定的话尽可能温度为0"frequency_penalty": 0.5,"n": 1# 注意：根据API文档，你可能需要移除或适当地填充tools字段 # https://docs.siliconflow.cn/cn/api-reference/chat-completions/chat-completions
}

会话

response = client.chat.completions.create(**payload)
print(response)
#或
response = requests.request("POST", url, json=payload, headers=headers)
print(response.text)