GLM 5.2 API by ZHIPUAI
zai-org/glm-5.2
Glm-5.2
Agent-oriented model built for complex reasoning, tool use, and autonomous task execution.
Agent-oriented model built for complex reasoning, tool use, and autonomous task execution.
Join the Discord community for the latest model updates, prompts, and support.
import os
from openai import OpenAI
client = OpenAI(
api_key=os.getenv("ATLASCLOUD_API_KEY"),
base_url="https://api.atlascloud.ai/v1"
)
response = client.chat.completions.create(
model="zai-org/glm-5.2",
messages=[
{
"role": "user",
"content": "hello"
}
],
max_tokens=1024,
temperature=0.7
)
print(response.choices[0].message.content)安裝所需的相依套件。
pip install requests所有 API 請求都需要透過 API Key 進行認證。您可以在 Atlas Cloud 控制台取得 API Key。
export ATLASCLOUD_API_KEY="your-api-key-here"import os
API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {API_KEY}"
}切勿在客戶端程式碼或公開儲存庫中暴露您的 API Key。請使用環境變數或後端代理。
import requests
url = "https://api.atlascloud.ai/v1/chat/completions"
headers = {
"Content-Type": "application/json",
"Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
"model": "your-model",
"messages": [{"role": "user", "content": "Hello"}],
"max_tokens": 1024
}
response = requests.post(url, headers=headers, json=data)
print(response.json())以下參數在請求主體中被接受。
{
"model": "zai-org/glm-5.2",
"messages": [
{
"role": "user",
"content": "Hello"
}
],
"max_tokens": 1024,
"temperature": 0.7,
"stream": false
}API 傳回相容 ChatCompletion 的回應格式。
{
"id": "chatcmpl-abc123",
"object": "chat.completion",
"created": 1700000000,
"model": "model-name",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "Hello! How can I assist you today?"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 10,
"completion_tokens": 20,
"total_tokens": 30
}
}Atlas Cloud Skills 將 300+ AI 模型直接整合到您的 AI 程式碼助手中。一條命令安裝，即可用自然語言生成圖片、影片，以及與 LLM 對話。
npx skills add AtlasCloudAI/atlas-cloud-skills從 Atlas Cloud 控制台取得 API Key，並將其設定為環境變數。
export ATLASCLOUD_API_KEY="your-api-key-here"安裝完成後，您可以在 AI 助手中使用自然語言存取所有 Atlas Cloud 模型。
Atlas Cloud MCP Server 透過 Model Context Protocol 將您的 IDE 與 300+ AI 模型連接。支援任何相容 MCP 的客戶端。
npx -y atlascloud-mcp將以下設定新增到您的 IDE 的 MCP 設定檔中。
{
"mcpServers": {
"atlascloud": {
"command": "npx",
"args": [
"-y",
"atlascloud-mcp"
],
"env": {
"ATLASCLOUD_API_KEY": "your-api-key-here"
}
}
}
}