LlamaFactory

Minimax Client使用

2024-08-20

chatcompletion_pro api ​

标准请求 ​

python
import requests

group_id = "请填写您的group_id"
api_key = "请填写您的api_key"

url = "https://api.minimax.chat/v1/text/chatcompletion_pro?GroupId=" + group_id
headers = {"Content-Type": "application/json", "Authorization": "Bearer " + api_key}

payload = {
    "model": "abab6.5s-chat",
    "tokens_to_generate": 2048,
    "temperature": 0.1,
    "top_p": 0.9,
    "stream": False,
    "reply_constraints": {"sender_type": "BOT", "sender_name": "MM智能助理"},
    "sample_messages": [],
    "plugins": [],
    "messages": [
        {
            "sender_type": "USER",
            "sender_name": "小明",
            "text": "帮我用英文翻译下面这句话：我是谁",
        }
    ],
    "bot_setting": [
        {
            "bot_name": "MM智能助理",
            "content": "MM智能助理是一款由MiniMax自研的，没有调用其他产品的接口的大型语言模型。MiniMax是一家中国科技公司，一直致力于进行大模型相关的研究。",
        }
    ],
}

response = requests.post(url, headers=headers, json=payload)

print(response.status_code)
print(response.text)

流式请求 ​

python
import requests

group_id = "请填写您的group_id"
api_key = "请填写您的api_key"

url = "https://api.minimax.chat/v1/text/chatcompletion_pro?GroupId=" + group_id
headers = {"Content-Type": "application/json", "Authorization": "Bearer " + api_key}

payload = {
    "model": "abab6.5s-chat",
    "tokens_to_generate": 2048,
    "temperature": 0.1,
    "top_p": 0.9,
    "stream": True,
    "reply_constraints": {"sender_type": "BOT", "sender_name": "MM智能助理"},
    "sample_messages": [],
    "plugins": [],
    "messages": [
        {
            "sender_type": "USER",
            "sender_name": "小明",
            "text": "帮我用英文翻译下面这句话：我是谁",
        }
    ],
    "bot_setting": [
        {
            "bot_name": "MM智能助理",
            "content": "MM智能助理是一款由MiniMax自研的，没有调用其他产品的接口的大型语言模型。MiniMax是一家中国科技公司，一直致力于进行大模型相关的研究。",
        }
    ],
}

response = requests.post(url, headers=headers, json=payload, stream=True)


# 处理响应流
for line in response.iter_lines():
    if line:
        # 处理接收到的数据
        print(line.decode("utf-8"))

chatcompletion_v2 api ​

标准请求 ​

python
import requests
import json

url = "https://api.minimax.chat/v1/text/chatcompletion_v2"
api_key = "请填写您的api_key"

payload = json.dumps(
    {
        "model": "替换成具体的模型名，例如：abab6.5s-chat",
        "messages": [
            {
                "role": "system",
                "name": "MM智能助理",  # 选填字段
                "content": "MM智能助理是一款由MiniMax自研的，没有调用其他产品的接口的大型语言模型。MiniMax是一家中国科技公司，一直致力于进行大模型相关的研究。",
            },
            {
                "role": "user",
                "name": "用户",  # 选填字段
                "content": "你会按照以下要求回复我的内容：“根据我给出的多段信息分别判断信息文本内容表达了哪类情绪，并给出判断的理由，判断理由务必精简、准确。”我的内容是：“1、学习三星堆正确拍照姿势，留下难忘回忆！2、太可爱啦！换了个小猫图标！3、当代社畜分类图鉴，你是哪种？”",
            },
        ],
        "tool_choice": "none",
        "stream": False,
        "max_tokens": 2048,
        "temperature": 0.1,
        "top_p": 0.9,
    }
)


headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}

response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)

流式请求 ​

python
import requests
import json

url = "https://api.minimax.chat/v1/text/chatcompletion_v2"
api_key = "请填写您的api_key"

payload = json.dumps(
    {
        "model": "替换成具体的模型名，例如：abab6.5s-chat",
        "messages": [
            {
                "role": "system",
                "name": "MM智能助理",  # 选填字段
                "content": "MM智能助理是一款由MiniMax自研的，没有调用其他产品的接口的大型语言模型。MiniMax是一家中国科技公司，一直致力于进行大模型相关的研究。",
            },
            {
                "role": "user",
                "name": "用户",  # 选填字段
                "content": "你会按照以下要求回复我的内容：“根据我给出的多段信息分别判断信息文本内容表达了哪类情绪，并给出判断的理由，判断理由务必精简、准确。”我的内容是：“1、学习三星堆正确拍照姿势，留下难忘回忆！2、太可爱啦！换了个小猫图标！3、当代社畜分类图鉴，你是哪种？”",
            },
        ],
        "tool_choice": "none",
        "stream": True,
        "max_tokens": 2048,
        "temperature": 0.1,
        "top_p": 0.9,
    }
)


headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}

response = requests.post(url, headers=headers, data=payload, stream=True)

# 处理响应流
for line in response.iter_lines():
    if line:
        # 处理接收到的数据
        print(line.decode("utf-8"))

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