tomevault-io Kling AI Storage Integration
Overview
Kling AI video URLs from task_result.videos[].url are temporary CDN links that expire. You must download and store videos in your own storage. This skill covers S3, GCS, and Azure Blob.
Download from Kling CDN
import requests
import os
def download_video(video_url: str, output_dir: str = "output") -> str:
"""Download generated video from Kling CDN."""
os.makedirs(output_dir, exist_ok=True)
# Extract filename or generate one
filename = video_url.split("/")[-1].split("?")[0]
if not filename.endswith(".mp4"):
filename = f"kling_{int(time.time())}.mp4"
filepath = os.path.join(output_dir, filename)
response = requests.get(video_url, stream=True, timeout=120)
response.raise_for_status()
with open(filepath, "wb") as f:
for chunk in response.iter_content(chunk_size=8192):
f.write(chunk)
size_mb = os.path.getsize(filepath) / (1024 * 1024)
print(f"Downloaded: {filepath} ({size_mb:.1f} MB)")
return filepath
Upload to AWS S3
import boto3
def upload_to_s3(filepath: str, bucket: str, key_prefix: str = "kling-videos/") -> str:
"""Upload video to S3 and return public URL."""
s3 = boto3.client("s3")
filename = os.path.basename(filepath)
s3_key = f"{key_prefix}{filename}"
s3.upload_file(
filepath, bucket, s3_key,
ExtraArgs={"ContentType": "video/mp4", "CacheControl": "max-age=86400"}
)
url = f"https://{bucket}.s3.amazonaws.com/{s3_key}"
print(f"Uploaded to S3: {url}")
return url
# Generate signed URL for private buckets
def get_signed_url(bucket: str, key: str, expiry: int = 3600) -> str:
s3 = boto3.client("s3")
return s3.generate_presigned_url(
"get_object",
Params={"Bucket": bucket, "Key": key},
ExpiresIn=expiry,
)
Upload to Google Cloud Storage
from google.cloud import storage
def upload_to_gcs(filepath: str, bucket_name: str, prefix: str = "kling-videos/") -> str:
"""Upload video to GCS and return public URL."""
client = storage.Client()
bucket = client.bucket(bucket_name)
filename = os.path.basename(filepath)
blob = bucket.blob(f"{prefix}{filename}")
blob.upload_from_filename(filepath, content_type="video/mp4")
blob.make_public() # or use signed URLs for private access
print(f"Uploaded to GCS: {blob.public_url}")
return blob.public_url
# Signed URL for private access
def get_gcs_signed_url(bucket_name: str, blob_name: str, expiry_min: int = 60) -> str:
from datetime import timedelta
client = storage.Client()
bucket = client.bucket(bucket_name)
blob = bucket.blob(blob_name)
return blob.generate_signed_url(expiration=timedelta(minutes=expiry_min))
Upload to Azure Blob Storage
from azure.storage.blob import BlobServiceClient
def upload_to_azure(filepath: str, container: str,
connection_string: str = None) -> str:
"""Upload video to Azure Blob Storage."""
conn_str = connection_string or os.environ["AZURE_STORAGE_CONNECTION_STRING"]
client = BlobServiceClient.from_connection_string(conn_str)
filename = os.path.basename(filepath)
blob_client = client.get_blob_client(container=container, blob=f"kling-videos/{filename}")
with open(filepath, "rb") as f:
blob_client.upload_blob(f, content_type="video/mp4", overwrite=True)
url = blob_client.url
print(f"Uploaded to Azure: {url}")
return url
End-to-End Pipeline
def generate_and_store(prompt: str, bucket: str, provider: str = "s3"):
"""Generate video with Kling AI and store in cloud."""
# 1. Generate
r = requests.post(f"{BASE}/videos/text2video", headers=get_headers(), json={
"model_name": "kling-v2-master",
"prompt": prompt,
"duration": "5",
"mode": "standard",
}).json()
task_id = r["data"]["task_id"]
# 2. Poll
result = poll_task("/videos/text2video", task_id)
video_url = result["videos"][0]["url"]
# 3. Download
filepath = download_video(video_url)
# 4. Upload
if provider == "s3":
return upload_to_s3(filepath, bucket)
elif provider == "gcs":
return upload_to_gcs(filepath, bucket)
elif provider == "azure":
return upload_to_azure(filepath, bucket)
# 5. Cleanup temp file
os.remove(filepath)
Metadata Preservation
import json
def save_with_metadata(filepath: str, task_id: str, prompt: str, model: str):
"""Save video metadata alongside the file."""
meta = {
"task_id": task_id,
"prompt": prompt,
"model": model,
"generated_at": time.strftime("%Y-%m-%dT%H:%M:%SZ"),
"filename": os.path.basename(filepath),
}
meta_path = filepath.replace(".mp4", ".meta.json")
with open(meta_path, "w") as f:
json.dump(meta, f, indent=2)
return meta_path
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