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(plugin-utilities)=
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# Utility functions for plugins
LLM provides some utility functions that may be useful to plugins.
## llm.user_dir()
LLM stores various pieces of logging and configuration data in a directory on the user's machine.
On macOS this directory is `~/Library/Application Support/io.datasette.llm` , but this will differ on other operating systems.
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The `llm.user_dir()` function returns the path to this directory as a `pathlib.Path` object, after creating that directory if it does not yet exist.
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Plugins can use this to store their own data in a subdirectory of this directory.
```python
import llm
user_dir = llm.user_dir()
plugin_dir = data_path = user_dir / "my-plugin"
plugin_dir.mkdir(exist_ok=True)
data_path = plugin_dir / "plugin-data.db"
```
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## llm.ModelError
If your model encounters an error that should be reported to the user you can raise this exception. For example:
```python
import llm
raise ModelError("MPT model not installed - try running 'llm mpt30b download'")
```
This will be caught by the CLI layer and displayed to the user as an error message.
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## Response.fake()
When writing tests for a model it can be useful to generate fake response objects, for example in this test from [llm-mpt30b ](https://github.com/simonw/llm-mpt30b ):
```python
def test_build_prompt_conversation():
model = llm.get_model("mpt")
conversation = model.conversation()
conversation.responses = [
llm.Response.fake(model, "prompt 1", "system 1", "response 1"),
llm.Response.fake(model, "prompt 2", None, "response 2"),
llm.Response.fake(model, "prompt 3", None, "response 3"),
]
lines = model.build_prompt(llm.Prompt("prompt 4", model), conversation)
assert lines == [
"< |im_start|>system\system 1< |im_end|>\n",
"< |im_start|>user\nprompt 1< |im_end|>\n",
"< |im_start|>assistant\nresponse 1< |im_end|>\n",
"< |im_start|>user\nprompt 2< |im_end|>\n",
"< |im_start|>assistant\nresponse 2< |im_end|>\n",
"< |im_start|>user\nprompt 3< |im_end|>\n",
"< |im_start|>assistant\nresponse 3< |im_end|>\n",
"< |im_start|>user\nprompt 4< |im_end|>\n",
"< |im_start|>assistant\n",
]
```
The signature of `llm.Response.fake()` is:
```python
def fake(cls, model: Model, prompt: str, system: str, response: str):
```
