This quickstart guide provides a simplified introduction to analyzing videos to generate text using the TwelveLabs Video Understanding Platform. It includes the following:

• A basic working example
• Minimal implementation details
• Core parameters for common use cases

For a comprehensive guide, see the Analyze videos section.

Key concepts

This section explains the key concepts and terminology used in this guide:

• Asset: Your uploaded content. Once created, you can reference the same asset across multiple operations without uploading the file again.

Workflow

This guide shows how to upload your video as an asset and analyze it synchronously.

Prerequisites

• To use the platform, you need an API key:

  1

  If you don’t have an account, sign up for a free account.

  2

  Go to the API Keys page.

  3

  If you need to create a new key, select the Create API Key button. Enter a name and set the expiration period. The default is 12 months.

  4

  Select the Copy icon next to your key to copy it to your clipboard.

• Depending on the programming language you are using, install the TwelveLabs SDK by entering one of the following commands:

  PythonNode.js

  $
  pip install twelvelabs

• Your video files must meet the following requirements:

  • Upload limits: Public video URLs up to 2 GB or local video files up to 200 MB. For local files up to 2 GB, see the Upload and processing methods page.

  • Analysis method: Videos up to 1 hour. For longer videos up to 2 hours, use the asynchronous method in the complete guide.

  • Model capabilities: See the complete requirements for video files

Starter code

Copy and paste the code below, replacing the placeholders surrounded by <> with your values.

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import time
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from twelvelabs import TwelveLabs
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from twelvelabs.types import VideoContext_AssetId, AnalyzePromptV2
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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. Analyze your video
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video = VideoContext_AssetId(asset_id=asset.id)
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text_stream = client.analyze_stream(
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    model_name="pegasus1.5",
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    video=video,
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    prompt_v_2=AnalyzePromptV2(
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        input_text="<YOUR_PROMPT>",
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    ),
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)
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# 5. Process the results
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for text in text_stream:
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    if text.event_type == "text_generation":
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        print(text.text)

Code explanation