Guardrails AI
Valid Address
Verifies an LLM-generated address using Google Maps' Address Validation API.
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Overview

updated 2 years
Developed by:
Guardrails AI
Date of development:
Feb 15, 2024
Validator type:
Format
Blog:
License:
Apache 2
Input/Output:
Output

Playground

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Description
Intended Use

This validator verifies whether an LLM-generated address of a place is valid using Google Maps' Address Validation API.

Requirements
  • Dependencies:

    • googlemaps
    • guardrails-ai>=0.4.0
  • API Key: Google Maps API Key

    • Steps to get the API Key:
      1. Go to the Google Cloud Console.
      2. Click on the project dropdown and create a new project.
      3. Go to the APIs & Services page.
      4. Click on the "Enable APIs and Services" button.
      5. Search for "Address Validation API" and enable it.
      6. Head over to Credentials page, click on "Create credentials" and select "API key".
      7. Copy the API key and set it as the environment variable GOOGLE_MAPS_API_KEY using the command export GOOGLE_MAPS_API_KEY=<your_api_key>.
Installation
$ guardrails hub install hub://guardrails/valid_address
Usage Examples
Validating string output via Python

In this example, we’ll use the validator to check if the output is a valid address.

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

# Use the Guard with the validator
guard = Guard().use(
    ValidAddress, on_fail="exception"
)

# Test passing response
guard.validate("1 Hacker Way, Menlo Park, CA")

try:
    # Test failing response
    guard.validate("160 Amphetheetre Pkwy")
except Exception as e:
    print(e)

Output:

Validating address: 1 Hacker Way, Menlo Park, CA
Address has no unconfirmed, inferred components or typos, returning PassResult

Validating address: 160 Amphetheetre Pkwy
Address has important components inferred, returning FailResult with fix value: 160 Amphitheatre Parkway, Mountain View, CA 94043, USA
Validation failed for field with errors: Address: '160 Amphetheetre Pkwy' has important components inferred
API Reference

__init__(self, on_fail="noop")

Initializes a new instance of the Validator class.

Parameters:

  • on_fail (str, Callable): The policy to enact when a validator fails. If str, must be one of reask, fix, filter, refrain, noop, exception or fix_reask. Otherwise, must be a function that is called when the validator fails.

validate(self, value, metadata={}) -> ValidationResult

Validates the given value using the rules defined in this validator, relying on the metadata provided to customize the validation process. This method is automatically invoked by guard.parse(...), ensuring the validation logic is applied to the input data.

Note:

  1. This method should not be called directly by the user. Instead, invoke guard.parse(...) where this method will be called internally for each associated Validator.
  2. When invoking guard.parse(...), ensure to pass the appropriate metadata dictionary that includes keys and values required by this validator. If guard is associated with multiple validators, combine all necessary metadata into a single dictionary.

Parameters:

  • value (Any): The input value to validate.
  • metadata (dict): A dictionary containing metadata required for validation. No additional metadata keys are needed for this validator.