Kimi K2.7 Code API by Moonshot
moonshotai/kimi-k2.7-code
Kimi-k2.7-code
Powerful coding model for programming, debugging, and AI developer workflows.
Powerful coding model for programming, debugging, and AI developer workflows.
import os
from openai import OpenAI
# Vision Understanding Example
# Image: Use base64 encoding (data:image/png;base64,...)
client = OpenAI(
api_key=os.getenv("ATLASCLOUD_API_KEY"),
base_url="https://api.atlascloud.ai/v1"
)
response = client.chat.completions.create(
model="moonshotai/kimi-k2.7-code",
messages=[
{
"role": "user",
"content": [
{
"type": "image_url",
"image_url": {
"url": "data:image/png;base64,<BASE64_IMAGE_DATA>"
}
},
{
"type": "text",
"text": "Please describe the content of this image"
}
]
}
],
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": "moonshotai/kimi-k2.7-code",
"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 400+ 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 400+ 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"
}
}
}
}