Tasks handle the uploading and processing of videos. Because these operations take some time, video uploading and processing are asynchronous.

The platform utilizes two primary types of tasks:

• Video indexing tasks: These tasks upload and index videos, making their content accesible to the Search and Analyze APIs.
• Video embedding tasks: These tasks upload and process videos, making their content accessible to the Embed API.

Video indexing tasks

A video indexing task transitions through the following stages, each representing a phase in the platform’s processing:

• validating: Ensures the video meets requirements.
• pending: Assigns a server to process the video.
• queued: Prepares the video for indexing.
• indexing: Converts the video into embeddings.
• ready: Completes the process, making the video usable.
• failed: Indicates an error occurred.

The example code below shows how you can track the progress of a task, checking its status until it’s completed:

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task = client.tasks.create(
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    index_id="<YOUR_INDEX_ID>", video_url="<YOUR_VIDEO_URL>")
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print(f"Created task: id={task.id}")
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def on_task_update(task: TasksRetrieveResponse):
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    print(f"  Status={task.status}")
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task = client.tasks.wait_for_done(task_id=task.id, callback=on_task_update)
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if task.status != "ready":
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    raise RuntimeError(f"Indexing failed with status {task.status}")
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print(
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    f"Upload complete. The unique identifier of your video is {task.video_id}.")

Related topics

• Pyton SDK Reference > Create a video indexing task
• Node.js SDK Reference > Create a video indexing task
• API Reference > Create a video indexing task

Video embedding tasks

A video embedding task progresses through the following stages:

• processing: The platform is creating the embeddings.
• ready: Processing is complete. You can now retrieve the embeddings.
• failed: The task could not be completed, and the embeddings haven’t been created.

The example code below shows how you can track the progress of a video embedding task, checking its status until it’s completed:

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task = client.embed.tasks.create(
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    model_name="Marengo-retrieval-2.7",
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    video_url="<YOUR_VIDEO_URL>",
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    video_embedding_scope=["clip", "video"]
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)
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print(f"Created video embedding task: id={task.id}")
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def on_task_update(task: TasksStatusResponse):
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    print(f"  Status={task.status}")
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status = client.embed.tasks.wait_for_done(task.id, callback=on_task_update)
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print(f"Embedding done: {status.status}")

Related topics

• Pyton SDK Reference > Create a video embedding task
• Node.js SDK Reference > Create a video embedding task
• API Reference > Create a video embedding task