# Python API

LLM provides a Python API for executing prompts, in addition to the command-line interface.

Understanding this API is also important for writing plugins.

The API consists of the following key classes:

- `Model` - represents a language model against which prompts can be executed
- `Prompt` - a prompt that can be prepared and then executed against a model
- `Response` - the response executing a prompt against a model
- `Template` - a reusable template for generating prompts

## Prompt

A prompt object represents all of the information needed to be passed to the LLM. This could be a single prompt string, but it might also include a separate system prompt, various settings (for temperature etc) or even a JSON array of previous messages.

## Model

The `Model` class is an abstract base class that needs to be subclassed to provide a concrete implementation. Different LLMs will use different implementations of this class.

Model instances provide the following methods:

- `prompt(prompt: str, ...options) -> Prompt` - a convenience wrapper which creates a `Prompt` instance and then executes it. This is the most common way to use LLM models.
- `stream(prompt: str) -> Response` - a convenient wrapper for `.execute(..., stream=True)`.
- `execute(prompt: Prompt, stream: bool) -> Response` - execute a prepared Prompt instance against the model and return a `Response`, streaming or not-streaming.

Models usually return subclasses of `Response` that are specific to that model.

## Response

The response from an LLM. This could encapusulate a string of text, but for streaming APIs this class will be iterable, with each iteration yielding a short string of text as it is generated.

Calling `.text()` will return the full text of the response, waiting for the stream to stop executing if necessary.

The `.debug()` method, once the stream has finished, will return a dictionary of debug information about the response. This varies between different models.

## Template

Templates are reusable objects that can be used to generate prompts. They are used  by the {ref}`prompt-templates` feature.