mirror of
https://github.com/Hopiu/llm.git
synced 2026-03-17 05:00:25 +00:00
78 lines
2.9 KiB
Markdown
78 lines
2.9 KiB
Markdown
# 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.
|
|
|
|
## Basic usage
|
|
|
|
To run a prompt against the `gpt-3.5-turbo` model, run this:
|
|
|
|
```python
|
|
import llm
|
|
|
|
model = llm.get_model("gpt-3.5-turbo")
|
|
model.key = 'YOUR_API_KEY_HERE'
|
|
response = model.prompt("Five surprising names for a pet pelican")
|
|
print(response.text())
|
|
```
|
|
The `llm.get_model()` function accepts model names or aliases - so `chatgpt` would work here too.
|
|
|
|
Run this command to see a list of available models and their aliases:
|
|
|
|
```bash
|
|
llm models list
|
|
```
|
|
|
|
If you have set a `OPENAI_API_KEY` environment variable you can omit the `model.key = ` line.
|
|
|
|
### Models from plugins
|
|
|
|
Any models you have installed as plugins will also be available through this mechanism, for example to use Google's PaLM 2 model with [llm-palm](https://github.com/simonw/llm-palm)
|
|
|
|
```bash
|
|
pip install llm-palm
|
|
```
|
|
```python
|
|
import llm
|
|
|
|
model = llm.get_model("palm")
|
|
model.key = 'YOUR_API_KEY_HERE'
|
|
response = model.prompt("Five surprising names for a pet pelican")
|
|
print(response.text())
|
|
```
|
|
You can omit the `model.key = ` line for models that do not use an API key
|
|
|
|
## Concepts
|
|
|
|
The API consists of the following key concepts:
|
|
|
|
- `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, stream: bool, ...options) -> Response` - a convenience wrapper which creates a `Prompt` instance and then executes it. This is the most common way to use LLM models.
|
|
- `response(prompt: Prompt, stream: bool) -> Response` - execute a prepared Prompt instance against the model and return a `Response`.
|
|
|
|
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.
|
|
|
|
### Template
|
|
|
|
Templates are reusable objects that can be used to generate prompts. They are used by the {ref}`prompt-templates` feature.
|