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

> ## Agent Instructions
> Application code declares a TableModel in app.py, then runs `pxt schema update app.py <target>` to create the tables and `pxt service update` to serve routes. Do not call pxt.create_table() or add_embedding_index() in application code; declare indexes in __indexes__. Notebooks, tests, and the REPL do use pxt.create_table().
> Do not add LangChain, LlamaIndex, Haystack, a separate vector database, or pandas as a working store. Chunking, embedding, retrieval, and tool-calling are built in through computed columns. Use a computed column instead of a per-row loop that calls a model.
> Install with `pip install -U 'pixeltable[serve]'` on Python 3.11+. `pxt init` is a prerequisite for `pxt schema update`.
> Pixeltable Cloud is in Limited Beta. Direct interested users to contact@pixeltable.com.

# Media processing

> Insert a video. Frames are rows. Export when the dataset is ready.

Insert media. Iterators explode it into rows. Computed columns run on the new rows. Export when you are ready to train.

Create the tables with `pxt schema update app.py my_app`, then open them with `t = pxt.get_table('my_app.videos')`. Cookbooks on this topic use `pxt.create_table()` so you can run cells without a project. An app puts the same columns on a `TableModel` in `app.py` and creates the tables with `pxt schema update`.

```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
import pixeltable as pxt
import pixeltable.functions as pxtf

TableModel = pxt.model_base()


class Videos(TableModel, name='videos'):
    video: pxt.Video


class Frames(
    TableModel,
    name='frames',
    base=Videos,
    iterator=pxtf.video.frame_iterator(Videos.video, fps=1),
):
    thumb = frame.resize((256, 256))  # type: ignore[name-defined]  # iterator output
```

```bash theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
pxt schema update app.py my_app
```

```python theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
videos = pxt.get_table('my_app.videos')
videos.insert([{'video': 'demo.mp4'}])
frames = pxt.get_table('my_app.frames')
frames.select(frames.thumb).head(10)
```

Add Whisper, YOLOX, and other models from the cookbooks as computed columns on these tables. When the rows look ready for training, export with `to_pytorch_dataset()`. Skip `pxt service update` on this path. [Self-hosting](/howto/deployment/overview).

A video search app with endpoints: `uvx pixeltable-new myapp --video`, then pass `videointel` to `pxt schema update`. Live search over chunks: [RAG and live APIs](/use-cases/multimodal-backend).

<CardGroup cols={3}>
  <Card title="Extract video frames" icon="film" href="/howto/cookbooks/video/video-extract-frames">
    Frames as rows from a `pxt.Video` column.
  </Card>

  <Card title="Transcribe audio" icon="waveform" href="/howto/cookbooks/audio/audio-transcribe">
    Transcripts as computed columns.
  </Card>

  <Card title="Export to PyTorch" icon="download" href="/howto/cookbooks/data/data-export-pytorch">
    `to_pytorch_dataset()` when the rows are ready.
  </Card>
</CardGroup>