常见大模型API python调用一览_python调用大模型api

1. openai

from openai import OpenAI
 
def chatgpt_api(query):
 
    client = OpenAI(
        base_url='https://api.openai-proxy.live/v1',
        api_key='sk-xxxxxxxxxxxxxxxxxxx',
    )
 
    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content":f"{query}",
            }
        ],
        model="gpt-3.5-turbo",
    )
    return chat_completion.choices[0].message.content
print(chatgpt_api('你是谁'))

2. chatglm

import requests
 
def chatglm_api(query):
    url = "https://api.siliconflow.cn/v1/chat/completions"
 
    payload = {
        "model": "THUDM/glm-4-9b-chat",
        "messages": [
            {
                "role": "user",
                "content": f"{query}"
            }
        ]
    }
    headers = {
        "accept": "application/json",
        "content-type": "application/json",
        "authorization": "Bearer sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
    }
 
    response = requests.post(url, json=payload, headers=headers)
    return eval(response.text)['choices']
 
print(chatglm_api('你是谁啊'))

3. Qwen

import requests
 
def qwen_api(query):
    url = "https://api.siliconflow.cn/v1/chat/completions"
 
    payload = {
        "model": "Qwen/Qwen2-72B-Instruct",
        "messages": [
            {
                "role": "user",
                "content": f"{query}"
            }
        ]
    }
    headers = {
        "accept": "application/json",
        "content-type": "application/json",
        "authorization": "Bearer sk-xxxxxxxxxxxxxxxxxxx"
    }
 
    response = requests.post(url, json=payload, headers=headers)
    return eval(response.text)['choices']
 
print(chatglm_api('你是谁啊'))

4. Baichuan

def baichuan_api(query):
    api_key = 'sk-xxxxxxxxxxxxxxxxxxxxxxxxx'
    # 设置请求的URL、头部信息和请求体
    url = "https://api.baichuan-ai.com/v1/chat/completions"
    headers = {
        "Content-Type": "application/json",
        "Authorization": f"Bearer {api_key}"
    }
 
    data = {
        "model": "Baichuan2-Turbo",
        "messages": [
            {
                "role": "user",
                "content": f"{query}"
            }
        ],
        "temperature": 0.3,
        "top_p": 0.85,
        "max_tokens": 2048,
        "with_search_enhance": True,
        "knowledge_base": {
            "ids": []
        },
        "stream": False
    }
 
    # 发送POST请求
    response = requests.post(url, headers=headers, json=data)
    # 检查请求是否成功
    if response.status_code == 200:
        # 输出响应内容
        return response.json()
    else:
        # 输出错误信息
        print(f"Error: {response.status_code} - {response.text}")
        return None

5. 01

import requests
 
def onezero_api(query):
    url = "https://api.siliconflow.cn/v1/chat/completions"
 
    payload = {
        "model": "01-ai/Yi-1.5-34B-Chat-16K",
        "messages": [
            {
                "role": "user",
                "content": f"{query}"
            }
        ]
    }
    headers = {
        "accept": "application/json",
        "content-type": "application/json",
        "authorization": "Bearer sk-xxxxxxxxxxxxxxxxxxx"
    }
 
    response = requests.post(url, json=payload, headers=headers)
    return eval(response.text)['choices']
 
print(chatglm_api('你是谁啊'))

6. 星火认知

pip install --upgrade spark_ai_python
 
from sparkai.llm.llm import ChatSparkLLM, ChunkPrintHandler
from sparkai.core.messages import ChatMessage
 
#星火认知大模型Spark Max的URL值，其他版本大模型URL值请前往文档（https://www.xfyun.cn/doc/spark/Web.html）查看
SPARKAI_URL = 'wss://spark-api.xf-yun.com/v3.5/chat'
#星火认知大模型调用秘钥信息，请前往讯飞开放平台控制台（https://console.xfyun.cn/services/bm35）查看
SPARKAI_APP_ID = ''
SPARKAI_API_SECRET = ''
SPARKAI_API_KEY = ''
#星火认知大模型Spark Max的domain值，其他版本大模型domain值请前往文档（https://www.xfyun.cn/doc/spark/Web.html）查看
SPARKAI_DOMAIN = 'generalv3.5'
 
if __name__ == '__main__':
    spark = ChatSparkLLM(
        spark_api_url=SPARKAI_URL,
        spark_app_id=SPARKAI_APP_ID,
        spark_api_key=SPARKAI_API_KEY,
        spark_api_secret=SPARKAI_API_SECRET,
        spark_llm_domain=SPARKAI_DOMAIN,
        streaming=False,
    )
    messages = [ChatMessage(
        role="user",
        content='你好呀'
    )]
    handler = ChunkPrintHandler()
    a = spark.generate([messages], callbacks=[handler])
    print(a)

7. Deepseek

import requests
 
def seepseek_api(query):
    url = "https://api.siliconflow.cn/v1/chat/completions"
 
    payload = {
        "model": "deepseek-ai/DeepSeek-V2-Chat",
        "messages": [
            {
                "role": "user",
                "content": f"{query}"
            }
        ]
    }
    headers = {
        "accept": "application/json",
        "content-type": "application/json",
        "authorization": "Bearer sk-xxxxxxxxxxxxxxxxxxx"
    }
 
    response = requests.post(url, json=payload, headers=headers)
    return eval(response.text)['choices']
 
print(chatglm_api('你是谁啊'))