国内大模型API调用实战_volcenginesdkarkruntime

作者：从前慢现在也慢 | 2024-07-25 06:11:32

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volcenginesdkarkruntime

国内大模型API调用实战

今天我们就来具体看下具体大模型厂商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)
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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)
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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)
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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()
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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)
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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。

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