最新大模型项目实战大全，（非常详细）从零基础入门到精通，收藏这篇就够了-优快云博客

本文链接：https://blog.youkuaiyun.com/Libra1313/article/details/148157463

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)

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()