> ## 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 puts a TableModel in app.py, then runs `pxt schema update app.py my_app` to create the tables and `pxt service update app.py my_app` to start HTTP. The last argument can be a Cloud URL such as `pxt://org:db`. Do not call pxt.create_table() or add_embedding_index() in application code; put 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.
# ResultSet
> 
# class pixeltable.ResultSet
A dataset obtained by executing a [`Query`](./query). Returned by
[`Query.collect()`](./query#method-collect), [`Query.head()`](./query#method-head),
[`Query.tail()`](./query#method-tail), and the equivalent methods on class [`Table`](./table).
A `ResultSet` is structured as a table with rows (indexed by integers) and columns (indexed by strings).
The column names correspond to the expressions in the query's select list. The values in a `ResultSet` can
be accessed in various ways:
* `len(result)` returns the number of rows
* `result[i]` returns the `i`th row as a `dict` mapping column names to values
* `result['col']` returns a `list` of all values in the column named `'col'`
* `result[i, 'col']` returns the specific value in the `i`th row and column `'col'`
`ResultSet` implements the Sequence protocol, so it can be iterated over and converted to other sequence
types in the usual fashion; for example:
* `for row in result` (iterates over rows)
* `list(result)` (converts to a list of rows)
## method to\_pandas()
```python Signature theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
to_pandas() -> pd.DataFrame
```
Convert the `ResultSet` to a Pandas `DataFrame`.
**Returns:**
* `pd.DataFrame`: A `DataFrame` with one column per column in the `ResultSet`.
## method to\_pydantic()
```python Signature theme={"theme":{"light":"light-plus","dark":"dark-plus"}}
to_pydantic(model: type[BaseModelT]) -> Iterator[BaseModelT]
```
Convert the `ResultSet` to Pydantic model instances.
**Parameters:**
* **`model`** (`type[BaseModelT]`): A Pydantic model class.
**Returns:**
* `Iterator[BaseModelT]`: An iterator over Pydantic model instances, one for each row in the result set.
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