Metadata-Version: 2.4
Name: any-guardrail
Version: 0.3.2
License: Apache-2.0
Project-URL: Documentation, https://mozilla-ai.github.io/any-guardrail/
Project-URL: Issues, https://github.com/mozilla-ai/any-guardrail/issues
Project-URL: Source, https://github.com/mozilla-ai/any-guardrail
Requires-Python: >=3.11
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: any-llm-sdk>=1.7.0
Requires-Dist: pydantic
Provides-Extra: all
Requires-Dist: any-guardrail[azure-content-safety,flowjudge,huggingface]; extra == "all"
Provides-Extra: flowjudge
Requires-Dist: flow-judge[hf]>=0.1.2; extra == "flowjudge"
Provides-Extra: huggingface
Requires-Dist: huggingface-hub>=0.33.4; extra == "huggingface"
Requires-Dist: transformers>=4.53.2; extra == "huggingface"
Requires-Dist: torch>=2.7.1; extra == "huggingface"
Requires-Dist: hf-xet>=1.1.5; extra == "huggingface"
Provides-Extra: azure-content-safety
Requires-Dist: azure-ai-contentsafety>=1.0.0; extra == "azure-content-safety"
Requires-Dist: pathvalidate>=3.3.1; extra == "azure-content-safety"
Dynamic: license-file

<p align="center">
  <picture>
    <img src="docs/images/any-guardrail-favicon.png" width="20%" alt="Project logo"/>
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<div align="center">

# any-guardrail

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A single interface to use different guardrail models.

</div>


`any-guardrail` provides a unified interface for AI safety guardrails, for example, letting you detect toxic content, jailbreak attempts, and other risks in LLM inputs and outputs. Switch between different guardrail providers, both encoder-based (discriminative) and decoder-based (generative) models like Llama Guard and ShieldGemma, without changing your code.

Some guardrails are extremely customizable, which `any-guardrail` fully exposes. See the complete list of supported providers and customization examples in our [docs](https://mozilla-ai.github.io/any-guardrail/).

## Why any-guardrail?

- **Unified API**: Switch between evergrowing list of guardrail providers
- **Production-ready**: Built for real-world LLM applications
- **Flexible**: Use encoder-based (fast) or decoder-based (customizable) models

## Quickstart

### Requirements

- Python 3.11 or newer

### Installation

Install with `pip`:

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

### 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 guardrail. Here is a full example:

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

# Initialize guardrail
guardrail = AnyGuardrail.create(GuardrailName.DEEPSET)

# Validate input before sending to your LLM
result: GuardrailOutput = guardrail.validate("How do I hack into a system?")

if not result.valid:
    print(f"Blocked: {result.explanation}")
else:
    # Safe to proceed with LLM call
    response = your_llm(user_input)
```

## Documentation
Full guides at [docs link](https://mozilla-ai.github.io/any-guardrail/)

## 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)

## Contributing to `any-guardrail`

The guardrail space is ever growing. If there is a guardrail that you'd like us to support, please see our [CONTRIBUTING.md](CONTRIBUTING.md) for details.
