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Quickstart: In-Application

Introduction​

Guardrails is a framework that validates and structures data from language models. These validations range simple checks like regex matching to more complex checks like competitor analysis. Guardrails can be used with any language model.

Installation​

Download Guardrails (required)​

First, install Guardrails for your desired language:

pip install guardrails-ai

Configure the Guardrails CLI (required)​

First, get a free auth key from Guardrails Hub. Then, configure the Guardrails CLI with the auth key.

guardrails configure

Usage​

  1. Install a guardrail from Guardrails Hub.

    guardrails hub install hub://guardrails/regex_match --quiet

  2. Create a Guard from the installed guardrail.

    # Import Guard and Validator
    from guardrails.hub import RegexMatch
    from guardrails import Guard

    # Initialize the Guard with 
    guard = Guard().use(
        RegexMatch(regex="^[A-Z][a-z]*$")
    )

    print(guard.parse("Caesar").validation_passed)  # Guardrail Passes
    print(
        guard.parse("Caesar Salad")
        .validation_passed
    )  # Guardrail Fails

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

    guardrails hub install hub://guardrails/valid_length --quiet

    Then, create a Guard from the installed guardrails.

    from guardrails.hub import RegexMatch, ValidLength
    from guardrails import Guard

    guard = Guard().use_many(
        RegexMatch(regex="^[A-Z][a-z]*$"),
        ValidLength(min=1, max=12)
    )

    print(guard.parse("Caesar").validation_passed)  # Guardrail Passes
    print(
        guard.parse("Caesar Salad")
        .validation_passed
    )  # Guardrail Fails due to regex match
    print(
        guard.parse("Caesarisagreatleader")
        .validation_passed
    )  # Guardrail Fails due to length

Structured Data Generation and Validation​

Now, let's go through an example where we ask an LLM to generate fake pet names.

  1. Create a Pydantic BaseModel that represents the structure of the output we want.
from pydantic import BaseModel, Field

class Pet(BaseModel):
    pet_type: str = Field(description="Species of pet")
    name: str = Field(description="a unique pet name")
  1. create a Guard from the Pet class. The Guard can be used to call the LLM in a manner so that the output is formatted to the Pet class. Under the hood, this is done by either of two methods:

(1) Function calling: For LLMs that support function calling, we generate structured data using the function call syntax.

(2) Prompt optimization: For LLMs that don't support function calling, we add the schema of the expected output to the prompt so that the LLM can generate structured data.

from guardrails import Guard

prompt = """
    What kind of pet should I get and what should I name it?

    ${gr.complete_json_suffix_v2}
"""
guard = Guard.for_pydantic(output_class=Pet)

res = guard(
    model="gpt-3.5-turbo",
    messages=[{
        "role": "user",
        "content": prompt
    }]
)

print(f"{res.validated_output}")

This prints:

{
    "pet_type": "dog",
    "name": "Buddy
}

Advanced installation instructions​

Install the Javascript library​

Note: The Javascript library works via an I/O bridge to run the underlying Python library. You must have Python 3.9 or greater installed on your system to use the Javascript library.

npm i @guardrails-ai/core

Install specific version​

To install a specific version in Python, run:

# pip install guardrails-ai==[version-number]

# Example:
pip install guardrails-ai==0.5.0a13

Install from GitHub​

Installing directly from GitHub is useful when a release has not yet been cut with the changes pushed to a branch that you need. Non-released versions may include breaking changes, and may not yet have full test coverage. We recommend using a released version whenever possible.

# pip install git+https://github.com/guardrails-ai/guardrails.git@[branch/commit/tag]
# Example:
pip install git+https://github.com/guardrails-ai/guardrails.git@main