guardrails

What it is

Guardrails is a Python framework that helps build reliable AI applications by performing two key functions:

1. Guardrails runs Input/Output Guards in your application that detect, quantify and mitigate the presence of specific types of risks. To look at the full suite of risks, check out Guardrails Hub.
2. Guardrails help you generate structured data from LLMs.

How it works

Guardrails uses pre-built validators (guardrails hub) that can be combined into Input and Output Guards to intercept LLM inputs and outputs. The Hub provides a collection of validators that can be installed and used within a Guard.

Getting started

Installation

pip install guardrails-ai

Getting Started

Create Input and Output Guards for LLM Validation

1. Download and configure the Guardrails Hub CLI.

   pip install guardrails-ai
   guardrails configure
   

2. Install a guardrail from Guardrails Hub.

   pip install guardrails-ai-regex-match
   

3. Create a Guard from the installed guardrail.

   from guardrails import Guard, OnFailAction
   from guardrails_ai.regex_match import RegexMatch
   
   guard = Guard().use(
       RegexMatch, regex="\(?\d{3}\)?-? *\d{3}-? *-?\d{4}", on_fail=OnFailAction.EXCEPTION
   )
   
   guard.validate("123-456-7890")  # Guardrail passes
   
   try:
       guard.validate("1234-789-0000")  # Guardrail fails
   except Exception as e:
       print(e)
   

   Output:

   Validation failed for field with errors: Result must match \(\?\d{3}\)?-? *\d{3}-? *-?\d{4}
   

4. Run multiple guardrails within a Guard. First, install the necessary guardrails from Guardrails Hub.

   pip install guardrails-ai-competitor-check guardrails-ai-toxic-language
   

   Then, create a Guard from the installed guardrails.

   from guardrails import Guard, OnFailAction
   from guardrails_ai.competitor_check import CompetitorCheck
   from guardrails_ai.toxic_language import ToxicLanguage
   
   guard = Guard().use(
       CompetitorCheck(["Apple", "Microsoft", "Google"], on_fail=OnFailAction.EXCEPTION),
       ToxicLanguage(threshold=0.5, validation_method="sentence", on_fail=OnFailAction.EXCEPTION)
   )
   
   guard.validate(
       """An apple a day keeps a doctor away.
       This is good advice for keeping your health."""
   )  # Both the guardrails pass
   
   try:
       guard.validate(
           """Shut the hell up! Apple just released a new iPhone."""
       )  # Both the guardrails fail
   except Exception as e:
       print(e)
   

Use Guardrails to generate structured data from LLMs

...

Guardrails Server

Guardrails can be set up as a standalone service served by Flask with guardrails start, allowing you to interact with it via a REST API. This approach simplifies development and deployment of Guardrails-powered applications.

1. Install: pip install "guardrails-ai"
2. Configure: guardrails configure
3. Create a config: guardrails create --validators=hub://guardrails/two_words --guard-name=two-word-guard
4. Start the dev server: guardrails start --config=./config.py
5. Interact with the dev server via the snippets below

FAQ

I'm running into issues with Guardrails. Where can I get help?

You can reach out to us on Discord or Twitter.

Can I use Guardrails with any LLM?

Yes, Guardrails can be used with proprietary and open-source LLMs. Check out this guide on how to use Guardrails with any LLM.

Can I create my own validators?

Yes, you can create your own validators and contribute them to Guardrails Hub. Check out this guide on how to create your own validators.

Does Guardrails support other languages?

Guardrails can be used with Python and JavaScript. Check out the docs on how to use Guardrails from JavaScript. We are working on adding support for other languages. If you would like to contribute to Guardrails, please reach out to us on Discord or Twitter.

Contributing

We welcome contributions to Guardrails!

Get started by checking out Github issues and check out the Contributing Guide. Feel free to open an issue, or reach out if you would like to add to the project!