> For the complete documentation index, see [llms.txt](https://docs.mozilla.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.mozilla.ai/any-guardrail/quickstart.md).

# Quick Start

#### Requirements

* Python 3.11 or newer

#### Installation

You can install the bare bones library as follows (only \[`any_guardrails.guardrails.any_llm.AnyLlm`] will be available):

```bash
pip install any-guardrail
```

Or you can install it with the required dependencies for different guardrails:

```bash
pip install any-guardrail[huggingface]
```

Refer to [pyproject.toml](https://github.com/mozilla-ai/any-guardrail/blob/main/pyproject.toml) for a list of the options available.

#### Basic Usage

`AnyGuardrail` provides a seamless interface for interacting with the guardrail models. It allows you to see a list of all the supported guardrails, and to instantiate each supported guardrails. Here is a full example:

```python
from any_guardrail import AnyGuardrail, GuardrailName, GuardrailOutput

guardrail = AnyGuardrail.create(GuardrailName.DEEPSET)

result: GuardrailOutput = guardrail.validate("All smiles from me!")

assert result.valid
```

#### Discovering guardrails

Every guardrail carries static, queryable metadata (what it detects, where it runs, how it executes) that you can filter and group **without importing any model backend** — handy for building your own catalog or picking a guardrail programmatically:

```python
from any_guardrail import AnyGuardrail, BackendType, GuardrailCategory, GuardrailName

# Which guardrails detect prompt injection and run as a local encoder classifier?
names = AnyGuardrail.list_guardrails(
    category=GuardrailCategory.PROMPT_INJECTION,
    backend=BackendType.LOCAL_ENCODER,
)
assert GuardrailName.PROTECTAI in names

# Inspect one guardrail's capability metadata.
meta = AnyGuardrail.metadata(GuardrailName.LLAMA_GUARD)
assert GuardrailCategory.CONTENT_SAFETY in meta.categories

# Group the whole catalog by a dimension (e.g. for docs or a picker UI).
by_category = AnyGuardrail.group_by("category")
assert GuardrailName.DEEPSET in by_category["prompt_injection"]
```

#### Troubleshooting

Some of the models on HuggingFace require extra permissions to use. To do this, you'll need to create a HuggingFace profile and manually go through the permissions. Then, you'll need to download the HuggingFace Hub and login. One way to do this is:

```bash
pip install --upgrade huggingface_hub

hf auth login
```

More information can be found here: [HuggingFace Hub](https://huggingface.co/docs/huggingface_hub/en/quick-start#login-command)


---

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