国内大模型API调用实战

大模型2026年6月7日1

国内大模型调用实战

前面我们一览了国内主要大模型厂商的API价格，今天我们就来具体看下具体API的使用。

1.API

deepseek deepseek支持requests直接调用和openai式的调用，新注册用户有500万token

import requests
import json

url = "https://api.deepseek.com/chat/completions"

payload = json.dumps({
"messages": [
    {
    "content": "You are a helpful assistant",
    "role": "system"
    },
    {
    "content": "Hi",
    "role": "user"
    }
],
"model": "deepseek-coder",
"frequency_penalty": 0,
"max_tokens": 2048,
"presence_penalty": 0,
"stop": None,
"stream": False,
"temperature": 1,
"top_p": 1,
"logprobs": False,
"top_logprobs": None
})
headers = {
'Content-Type': 'application/json',
'Accept': 'application/json',
'Authorization': 'Bearer <key>'
}
response = requests.request("POST", url, headers=headers, data=payload)

print(response.text)

from openai import OpenAI
# for backward compatibility, you can still use `https://api.deepseek.com/v1` as `base_url`.
client = OpenAI(api_key="sk-4f510fa4ca9d4ea7b56cdca99418fd9d", 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"},
],
    max_tokens=1024,
    temperature=0.7,
    stream=False
)

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

智普AI 需要安装zhipuai包，新注册用户有赠送token

pip install zhipuai

Python>=3.7

from zhipuai import ZhipuAI
client = ZhipuAI(api_key="dd") # 请填写您自己的APIKey
response = client.chat.completions.create(
model="glm-4",  # 填写需要调用的模型名称
    messages=[
        {"role": "user", "content": "你好！你叫什么名字"},
    ],
    stream=True,
    )
for chunk in response:
    print(chunk.choices[0].delta)

kimi kimi也是支持openai样式的调用，新注册用户的15元的赠送

pip install --upgrade 'openai>=1.0'

from openai import OpenAI

client = OpenAI(
    api_key = "sk-KbwNWKBaRwhkvmfIHeaV4xnlpp3haV7qLr0sSU7wIOI9ZSNh",
    base_url = "https://api.moonshot.cn/v1",
)

completion = client.chat.completions.create(
    model = "moonshot-v1-8k",
    messages = [
        {"role": "system", "content": "你是 Kimi，由 Moonshot AI 提供的人工智能助手，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。同时，你会拒绝一切涉及恐怖主义，种族歧视，黄色暴力等问题的回答。Moonshot AI 为专有名词，不可翻译成其他语言。"},
        {"role": "user", "content": "你好，我叫李雷，1+1等于多少？"}
    ],
    temperature = 0.3,
)

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

字节豆包豆包需要安装方舟包

pip install 'volcengine-python-sdk[ark]'

from volcenginesdkarkruntime import Ark

# Authentication
# 1.If you authorize your endpoint using an API key, you can set your api key to environment variable "ARK_API_KEY"
# or specify api key by Ark(api_key="${YOUR_API_KEY}").
# Note: If you use an API key, this API key will not be refreshed.
# To prevent the API from expiring and failing after some time, choose an API key with no expiration date.

# 2.If you authorize your endpoint with Volcengine Identity and Access Management（IAM), set your api key to environment variable "VOLC_ACCESSKEY", "VOLC_SECRETKEY"
# or specify ak&sk by Ark(ak="${YOUR_AK}", sk="${YOUR_SK}").
# To get your ak&sk, please refer to this document([https://www.volcengine.com/docs/6291/65568](https://www.volcengine.com/docs/6291/65568))
# For more information，please check this document（[https://www.volcengine.com/docs/82379/1263279](https://www.volcengine.com/docs/82379/1263279)）
client = Ark()

# Non-streaming:
print("----- standard request -----")
completion = client.chat.completions.create(
    model="${YOUR_ENDPOINT_ID}",
    messages = [
        {"role": "system", "content": "你是豆包，是由字节跳动开发的 AI 人工智能助手"},
        {"role": "user", "content": "常见的十字花科植物有哪些？"},
    ],
)
print(completion.choices[0].message.content)

# Streaming:
print("----- streaming request -----")
stream = client.chat.completions.create(
    model="${YOUR_ENDPOINT_ID}",
    messages = [
        {"role": "system", "content": "你是豆包，是由字节跳动开发的 AI 人工智能助手"},
        {"role": "user", "content": "常见的十字花科植物有哪些？"},
    ],
    stream=True
)
for chunk in stream:
    if not chunk.choices:
        continue
    print(chunk.choices[0].delta.content, end="")
print()

讯飞星火讯飞星火新注册用户都有免费额度，不同模型额度不要，3.5有10万token， 2.0的有200万一年token. 接口是采用websocket

pip install --upgrade spark_ai_python
python 3.8

from sparkai.llm.llm import ChatSparkLLM, ChunkPrintHandler
from sparkai.core.messages import ChatMessage

#星火认知大模型Spark3.5 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Spark3.5 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)

# TypeError: issubclass() arg 1 must be a class
python -m pip install -U pydantic spacy

通义千问

    import random
    from http import HTTPStatus
    from dashscope import Generation  # 建议dashscope SDK 的版本 >= 1.14.0


    def call_with_messages():
        messages = [{'role': 'system', 'content': 'You are a helpful assistant.'},
                    {'role': 'user', 'content': '如何做西红柿炒鸡蛋？'}]
        response = Generation.call(model="qwen-turbo",
                                messages=messages,
                                # 设置随机数种子seed，如果没有设置，则随机数种子默认为1234
                                seed=random.randint(1, 10000),
                                # 将输出设置为"message"格式
                                result_format='message')
        if response.status_code == HTTPStatus.OK:
            print(response)
        else:
            print('Request id: %s, Status code: %s, error code: %s, error message: %s' % (
                response.request_id, response.status_code,
                response.code, response.message
            ))


    if __name__ == '__main__':
        call_with_messages()

2. API使用总结

可以知道上面的API使用大同小异，有些直接兼容openai的调用格式：

需要注册获取api key，进行授权
api接口基本都支持stream流式输出
api接口都是典型chat模式，提供不同的角色信息，system，user和assistant，多轮对话在history中拼接会话信息

大家快去使用吧。

同分类下的其他文章

ollama 大模型部署

Ollama是一个集成了多种大型语言模型的工具，它支持模型的部署、运行以及API的整合和调用。Oll…

Stable Diffusion 本地安装

Stable Diffusion是Stability-AI出的文生图开源模型，DreamStudio…