> ## Documentation Index
> Fetch the complete documentation index at: https://docs.perceptron.inc/llms.txt
> Use this file to discover all available pages before exploring further.

# Video Q&A

> Ask grounded questions about a short video and get reasoned natural-language answers.

<Card icon="play" title="Run in Colab" href="https://colab.research.google.com/github/perceptron-ai-inc/perceptron/blob/main/cookbook/recipes/capabilities/perceptron-mk1/video-qa.ipynb">
  Step through this example interactively
</Card>

The `question()` helper accepts a `video()` node alongside a natural-language prompt and returns a textual answer. Combine with `reasoning=True` for step-by-step analysis of long-horizon episodes — assembly walkthroughs, training videos, customer-session recordings, and other content where the answer depends on watching what happens over time.

## Basic usage

```python theme={null}
from perceptron import question, video

result = question(
    video(video_path),         # str: Local path or URL to MP4 or WebM
    "What happens in this video?",  # str: Natural-language question
    reasoning=True,            # bool: enable reasoning and include chain-of-thought
)

print(result.reasoning)  # Model's chain-of-thought when reasoning=True
print(result.text)       # The answer
```

**Parameters:**

| Parameter       | Type        | Default  | Description                                                                        |
| --------------- | ----------- | -------- | ---------------------------------------------------------------------------------- |
| `media_obj`     | `VideoNode` | -        | Wrap your MP4 or WebM (URL or local file path) with `video()`                      |
| `question_text` | `str`       | -        | The question to ask about the video                                                |
| `reasoning`     | `bool`      | `False`  | Set `True` to enable reasoning and include the model's chain-of-thought            |
| `expects`       | `str`       | `"text"` | Output structure for the SDK (`"text"`, `"clip"`, `"point"`, `"box"`, `"polygon"`) |

**Returns:**

`PerceiveResult` object:

* `text` (`str`): The answer to your question.
* `reasoning` (`str | None`): The model's chain-of-thought when `reasoning=True`.
* `clips`, `points`, `boxes`, `polygons` (`list | None`): Populated when the corresponding `expects` is requested.

## Example: Robot assembly walkthrough

In this example we download a short robot-assembly clip, ask Perceptron Mk1 to identify the overall goal and the sub-goals it observes, and let it think through the episode before answering.

```python theme={null}
from pathlib import Path
from urllib.request import urlretrieve

from perceptron import configure, question, video

configure(
    provider="perceptron",
    model="perceptron-mk1",
    api_key="YOUR_API_KEY",
)

# Download reference video
VIDEO_URL = "https://raw.githubusercontent.com/perceptron-ai-inc/perceptron/main/cookbook/_shared/assets/capabilities/video-qa/robot_assembly.mp4"
VIDEO_PATH = Path("robot_assembly.mp4")

if not VIDEO_PATH.exists():
    urlretrieve(VIDEO_URL, VIDEO_PATH)

# Ask the question with reasoning enabled
result = question(
    video(str(VIDEO_PATH)),
    "Watch this episode and state the overall goal and sub-goals.",
    reasoning=True,
)

print("--- Reasoning ---")
print(result.reasoning or "(none)")
print("\n--- Answer ---")
print(result.text)
```

## Best practices

* **Reach for `expects="clip"` when you need timestamps**: If the answer needs to point at *when* something happens in the video, switch to the [Video Clipping](/perceptron-mk1/capabilities/video-clipping) workflow instead.

<Note>
  Run through the full Jupyter notebook [here](https://github.com/perceptron-ai-inc/perceptron/blob/main/cookbook/recipes/capabilities/perceptron-mk1/video-qa.ipynb). Reach out to [Perceptron support](mailto:support@perceptron.inc) if you have questions.
</Note>