Azure 影片生成

LiteLLM 支援 Azure OpenAI 的影片生成模型，包括 Sora，並提供完整的端到端整合。

屬性	詳細資訊
說明	Azure OpenAI 的影片生成模型，包括 Sora-2
LiteLLM 提供者路由	azure/
支援的模型	sora-2
Cost Tracking	✅ 依時長計價（$0.10/秒）
Logging Support	✅ 完整的請求/回應記錄
Guardrails Support	✅ 內容審核與安全檢查
Proxy Server Support	✅ 與虛擬金鑰的完整代理整合
Spend Management	✅ 預算追蹤與速率限制
提供者文件連結	Azure OpenAI Video Generation ↗

快速開始

必要的 API 金鑰

import os 
os.environ["AZURE_OPENAI_API_KEY"] = "your-azure-api-key"
os.environ["AZURE_OPENAI_API_BASE"] = "https://your-resource.openai.azure.com/"

基本用法

from litellm import video_generation, video_status, video_content
import os
import time

os.environ["AZURE_OPENAI_API_KEY"] = "your-azure-api-key"
os.environ["AZURE_OPENAI_API_BASE"] = "https://your-resource.openai.azure.com/"

# Generate video
response = video_generation(
    model="azure/sora-2",
    prompt="A cat playing with a ball of yarn in a sunny garden",
    seconds="8",
    size="720x1280"
)

print(f"Video ID: {response.id}")
print(f"Initial Status: {response.status}")

# Check status until video is ready
while True:
    status_response = video_status(
        video_id=response.id
    )
    
    print(f"Current Status: {status_response.status}")
    
    if status_response.status == "completed":
        break
    elif status_response.status == "failed":
        print("Video generation failed")
        break
    
    time.sleep(10)  # Wait 10 seconds before checking again

# Download video content when ready
video_bytes = video_content(
    video_id=response.id
)

# Save to file
with open("generated_video.mp4", "wb") as f:
    f.write(video_bytes)

用法 - LiteLLM Proxy Server

以下說明如何搭配 LiteLLM Proxy Server 呼叫 Azure 影片生成模型

1. 將金鑰儲存在您的環境中

export AZURE_OPENAI_API_KEY="your-azure-api-key"
export AZURE_OPENAI_API_BASE="https://your-resource.openai.azure.com/"

2. 啟動 proxy

config.yaml

model_list:
  - model_name: azure-sora-2
    litellm_params:
      model: azure/sora-2
      api_key: os.environ/AZURE_OPENAI_API_KEY
      api_base: os.environ/AZURE_OPENAI_API_BASE

CLI

$ litellm --model azure/sora-2

# Server running on http://0.0.0.0:4000

3. 測試

Curl Request

curl --location 'http://0.0.0.0:4000/videos/generations' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer sk-1234' \
--data '{
    "model": "azure-sora-2",
    "prompt": "A cat playing with a ball of yarn in a sunny garden",
    "seconds": "8",
    "size": "720x1280"
}'

OpenAI v1.0.0+

import openai
client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

# request sent to model set on litellm proxy, `litellm --model`
response = client.videos.create(
    model="azure-sora-2",
    prompt="A cat playing with a ball of yarn in a sunny garden",
    seconds=8,
    size="720x1280"
)

print(response)

支援的模型

模型名稱
sora-2
sora-2-pro
sora-2-pro-high-res

記錄與可觀測性

請求/回應記錄

所有影片生成請求都會自動記錄以下內容：

• 請求詳細資訊：prompt、model、duration、size
• 回應詳細資訊：video ID、status、creation time
• 成本追蹤：依時長計價計算
• 效能指標：請求延遲、處理時間

記錄提供者

影片生成可與所有 LiteLLM 記錄提供者搭配使用：

• Datadog：即時監控與警示
• Helicone：請求追蹤與除錯
• LangSmith：LangChain 整合與追蹤
• 自訂 webhook：將記錄傳送到您自己的端點

範例：啟用 Datadog 記錄

general_settings:
  alerting: ["datadog"]
  datadog_api_key: os.environ/DATADOG_API_KEY

影片生成參數

• prompt（必填）：所需影片的文字描述
• model（選填）：要使用的模型，預設為 "azure/sora-2"
• seconds（選填）：影片長度（秒），例如 "8"、"16"
• size（選填）：影片尺寸，例如 "720x1280"、"1280x720"
• input_reference（選填）：供影片編輯使用的參考圖片
• user（選填）：用於追蹤的使用者識別碼

影片內容擷取

# Download video content
video_bytes = video_content(
    video_id="video_1234567890"
)

# Save to file
with open("video.mp4", "wb") as f:
    f.write(video_bytes)

完整工作流程

import litellm
import time

def generate_and_download_video(prompt):
    # Step 1: Generate video
    response = litellm.video_generation(
        prompt=prompt,
        model="azure/sora-2",
        seconds="8",
        size="720x1280"
    )
    
    video_id = response.id
    print(f"Video ID: {video_id}")
    
    # Step 2: Wait for processing (in practice, poll status)
    time.sleep(30)
    
    # Step 3: Download video
    video_bytes = litellm.video_content(
        video_id=video_id
    )
    
    # Step 4: Save to file
    with open(f"video_{video_id}.mp4", "wb") as f:
        f.write(video_bytes)
    
    return f"video_{video_id}.mp4"

# Usage
video_file = generate_and_download_video(
    "A cat playing with a ball of yarn in a sunny garden"
)

影片重混（影片編輯）

# Video editing with reference image
response = litellm.video_remix(
    video_id="video_456",
    prompt="Make the cat jump higher",
    input_reference=open("path/to/image.jpg", "rb"),  # Reference image as file object
    seconds="8"
)

print(f"Video ID: {response.id}")

錯誤處理

from litellm.exceptions import BadRequestError, AuthenticationError

try:
    response = video_generation(
        prompt="A cat playing with a ball of yarn",
        model="azure/sora-2"
    )
except AuthenticationError as e:
    print(f"Authentication failed: {e}")
except BadRequestError as e:
    print(f"Bad request: {e}")