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from twelvelabs import (
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    TwelveLabs,
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    MultiInputRequest,
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    # To combine your text with media, uncomment the next line:
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    # MultiInputMediaSource,
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)
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# 1. Initialize the client
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client = TwelveLabs(api_key="<YOUR_API_KEY>")
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# 2. Create an embedding for your query
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response = client.embed.v_2.create(
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    input_type="multi_input",
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    model_name="marengo3.5",
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    multi_input=MultiInputRequest(
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        input_text="<YOUR_TEXT>",
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        # To combine your text with media, uncomment the following lines:
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        # media_sources=[
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        #     MultiInputMediaSource(
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        #         media_type="image", # Or "video" or "audio"
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        #         asset_id="<YOUR_ASSET_ID>", # Upload your file first to create a reusable asset
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        #         # Or use url="<YOUR_MEDIA_URL>" for a direct link to a raw media file. Video hosting platforms and cloud storage sharing links are not supported
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        #     ),
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        # ],
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    ),
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)
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# 3. Process the results
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print(f"Number of embeddings: {len(response.data)}")
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for embedding_data in response.data:
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    print(f"Embedding dimensions: {len(embedding_data.embedding)}")
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    print(f"First 10 values: {embedding_data.embedding[:10]}")

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import time
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from twelvelabs import TwelveLabs, AsyncVideoInputRequest, MediaSource
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# 1. Initialize the client
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client = TwelveLabs(api_key="<YOUR_API_KEY>")
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# 2. Upload a video
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asset = client.assets.create(
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    method="url",
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    url="<YOUR_VIDEO_URL>" # Use direct links to raw media files. Video hosting platforms and cloud storage sharing links are not supported
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    # Or use method="direct" and file=open("<PATH_TO_VIDEO_FILE>", "rb") to upload a local file up to 200 MB
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)
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print(f"Created asset: id={asset.id}")
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# 3. Check the status of the asset
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print("Waiting for asset to be ready...")
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while True:
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    asset = client.assets.retrieve(asset.id)
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    if asset.status == "ready":
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        print("Asset is ready")
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        break
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    if asset.status == "failed":
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        raise RuntimeError(f"Asset processing failed: id={asset.id}")
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    time.sleep(5)
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# 4. Create an embedding task
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task = client.embed.v_2.tasks.create(
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    input_type="video",
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    model_name="marengo3.5",
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    video=AsyncVideoInputRequest(
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        media_source=MediaSource(
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            asset_id=asset.id,
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            # url="<YOUR_VIDEO_URL>", # Use direct links to raw media files. Video hosting platforms and cloud storage sharing links are not supported
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            # base_64_string="<BASE_64_ENCODED_DATA>",
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        ),
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        embedding_option=["visual", "audio"],
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    ),
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)
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print(f"Task ID: {task.id}")
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# 5. Monitor the status
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while True:
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    task = client.embed.v_2.tasks.retrieve(task_id=task.id)
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    if task.status == "ready":
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        print("Task completed")
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        break
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    elif task.status == "failed":
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        print("Task failed")
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        break
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    else:
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        print("Task still processing...")
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        time.sleep(5)
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# 6. Process the results
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print(f"Number of embeddings: {len(task.data)}")
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for embedding_data in task.data:
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    print(f"[{embedding_data.embedding_option}] {embedding_data.start_sec}s - {embedding_data.end_sec}s")
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    print(f"Embedding dimensions: {len(embedding_data.embedding)}")
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    print(f"First 10 values: {embedding_data.embedding[:10]}")