from twelvelabs import TwelveLabs
import time
# Step 1: Initialize the client
client = TwelveLabs(api_key="<YOUR_API_KEY>")
# Step 2: Upload a video or an image
asset = client.assets.create(method="url", url="<YOUR_MEDIA_URL>")  # Use direct links to raw media files. Video hosting platforms and cloud storage sharing links are not supported
# Or use method="direct" and file=open("<YOUR_FILE_PATH>", "rb") to upload a local file up to 200 MB
print(f"Asset created: {asset.id}")
# Step 3: Check the status of the asset
while True:
    status = client.assets.retrieve(asset_id=asset.id).status
    if status == "ready":
        break
    elif status == "failed":
        raise Exception("Asset processing failed")
    print(f"Status: {status}, waiting...")
    time.sleep(5)
print("Asset ready")
# Step 4: Create a knowledge store
store = client.knowledge_stores.create(name="<YOUR_KNOWLEDGE_STORE_NAME>")
print(f"Knowledge store created: {store.id}")
# Step 5: Add the asset to the knowledge store
item = client.knowledge_store_items.create(
    knowledge_store_id=store.id,
    asset_id=asset.id,
    # asset_type="image",  # Uncomment if your asset is an image (the default is video)
)
print(f"Item added: {item.id}")
# Step 6: Check the status of the knowledge store item
while True:
    status = client.knowledge_store_items.retrieve(
        knowledge_store_id=store.id, item_id=item.id
    ).status
    if status == "ready":
        break
    elif status == "failed":
        raise Exception("Indexing failed")
    print(f"Status: {status}, waiting...")
    time.sleep(10)
print("Indexing complete")
# Step 7: Generate a response
response = client.responses.create(
    knowledge_store_id=store.id,
    input=[{"type": "message", "role": "user", "content": "Give me an overview of this content - main themes, key subjects, and any patterns."}],
)
for output in response.output:
    if output.type == "message":
        for content in output.content:
            print(content.text)