Moonshot AI
總覽
| 屬性 | 詳細資訊 |
|---|---|
| 說明 | Moonshot AI 提供大型語言模型，包括 moonshot-v1 系列和 kimi 模型。 |
| LiteLLM 上的提供者路由 | moonshot/ |
| 提供者文件連結 | Moonshot AI ↗ |
| Base URL | https://api.moonshot.ai/ |
| 支援的操作 | /chat/completions |
我們支援所有 Moonshot AI 模型，只要在送出 completion 請求時將 moonshot/ 設為前綴即可
必要變數
Environment Variables
os.environ["MOONSHOT_API_KEY"] = "" # your Moonshot AI API key
注意：
Moonshot AI 提供兩個不同的 API 端點：全球端點與中國專用端點。
- 全球 API Base URL:
https://api.moonshot.ai/v1（這是目前實作的端點） - 中國 API Base URL:
https://api.moonshot.cn/v1
您可以用以下方式覆寫 base url：
os.environ["MOONSHOT_API_BASE"] = "https://api.moonshot.cn/v1"
使用方式 - LiteLLM Python SDK
非串流
Moonshot Non-streaming Completion
import os
import litellm
from litellm import completion
os.environ["MOONSHOT_API_KEY"] = "" # your Moonshot AI API key
messages = [{"content": "Hello, how are you?", "role": "user"}]
# Moonshot call
response = completion(
model="moonshot/moonshot-v1-8k",
messages=messages
)
print(response)
串流
Moonshot Streaming Completion
import os
import litellm
from litellm import completion
os.environ["MOONSHOT_API_KEY"] = "" # your Moonshot AI API key
messages = [{"content": "Hello, how are you?", "role": "user"}]
# Moonshot call with streaming
response = completion(
model="moonshot/moonshot-v1-8k",
messages=messages,
stream=True
)
for chunk in response:
print(chunk)
使用方式 - LiteLLM Proxy
將以下內容加入您的 LiteLLM Proxy 設定檔：
config.yaml
model_list:
- model_name: moonshot-v1-8k
litellm_params:
model: moonshot/moonshot-v1-8k
api_key: os.environ/MOONSHOT_API_KEY
- model_name: moonshot-v1-32k
litellm_params:
model: moonshot/moonshot-v1-32k
api_key: os.environ/MOONSHOT_API_KEY
- model_name: moonshot-v1-128k
litellm_params:
model: moonshot/moonshot-v1-128k
api_key: os.environ/MOONSHOT_API_KEY
啟動您的 LiteLLM Proxy 伺服器：
Start LiteLLM Proxy
litellm --config config.yaml
# RUNNING on http://0.0.0.0:4000
- OpenAI SDK
- LiteLLM SDK
- cURL
Moonshot via Proxy - Non-streaming
from openai import OpenAI
# Initialize client with your proxy URL
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-proxy-api-key" # Your proxy API key
)
# Non-streaming response
response = client.chat.completions.create(
model="moonshot-v1-8k",
messages=[{"role": "user", "content": "hello from litellm"}]
)
print(response.choices[0].message.content)
Moonshot via Proxy - Streaming
from openai import OpenAI
# Initialize client with your proxy URL
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-proxy-api-key" # Your proxy API key
)
# Streaming response
response = client.chat.completions.create(
model="moonshot-v1-8k",
messages=[{"role": "user", "content": "hello from litellm"}],
stream=True
)
for chunk in response:
if chunk.choices[0].delta.content is not None:
print(chunk.choices[0].delta.content, end="")
Moonshot via Proxy - LiteLLM SDK
import litellm
# Configure LiteLLM to use your proxy
response = litellm.completion(
model="litellm_proxy/moonshot-v1-8k",
messages=[{"role": "user", "content": "hello from litellm"}],
api_base="http://localhost:4000",
api_key="your-proxy-api-key"
)
print(response.choices[0].message.content)
Moonshot via Proxy - LiteLLM SDK Streaming
import litellm
# Configure LiteLLM to use your proxy with streaming
response = litellm.completion(
model="litellm_proxy/moonshot-v1-8k",
messages=[{"role": "user", "content": "hello from litellm"}],
api_base="http://localhost:4000",
api_key="your-proxy-api-key",
stream=True
)
for chunk in response:
if hasattr(chunk.choices[0], 'delta') and chunk.choices[0].delta.content is not None:
print(chunk.choices[0].delta.content, end="")
Moonshot via Proxy - cURL
curl http://localhost:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer your-proxy-api-key" \
-d '{
"model": "moonshot-v1-8k",
"messages": [{"role": "user", "content": "hello from litellm"}]
}'
Moonshot via Proxy - cURL Streaming
curl http://localhost:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer your-proxy-api-key" \
-d '{
"model": "moonshot-v1-8k",
"messages": [{"role": "user", "content": "hello from litellm"}],
"stream": true
}'
如需更詳細的 LiteLLM Proxy 使用資訊，請參閱 LiteLLM Proxy 文件。
圖片 / 視覺支援
Moonshot 視覺模型（kimi-k2.5、kimi-latest、moonshot-v1-*-vision-preview 等）接受標準 OpenAI content array，並支援 image_url blocks。
LiteLLM 會自動偵測您的訊息何時包含圖片，並保留 content array，讓圖片 payload 能送達 Moonshot API。對於純文字請求，content 會依 Moonshot 文字模型的需求攤平成一般字串。
Moonshot Vision Example
import os
import litellm
os.environ["MOONSHOT_API_KEY"] = ""
response = litellm.completion(
model="moonshot/kimi-k2.5",
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": "What is in this image?"},
{
"type": "image_url",
"image_url": {"url": "https://example.com/image.png"},
},
],
}
],
)
print(response.choices[0].message.content)
Moonshot AI 限制與 LiteLLM 處理方式
LiteLLM 會自動處理以下 Moonshot AI 限制，以提供無縫的 OpenAI 相容性：
Temperature 範圍限制
限制：Moonshot AI 只支援 temperature 範圍 [0, 1]（相較於 OpenAI 的 [0, 2]）
LiteLLM 處理方式：自動將任何大於 1 的 temperature 限制為 1
Temperature + 多重輸出限制
限制：如果 temperature < 0.3 且 n > 1，Moonshot AI 會拋出例外
LiteLLM 處理方式：在偵測到此條件時，自動將 temperature 設為 0.3
不支援 Tool Choice「Required」
限制：Moonshot AI 不支援 tool_choice="required"
LiteLLM 處理方式：透過以下方式轉換：
- 新增訊息："Please select a tool to handle the current issue."
- 從請求中移除
tool_choice參數