Endpoint Is Reachable - Validator Details

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Endpoint Is Reachable

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Checks if an endpoint can be reached by making a request to it.

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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 ensures that any URL generated by an LLM is an endpoint that can be reached. In order to validate this, the validator makes a request to the URL and expects a 200 response.

Requirements

Dependencies:
- requests
- guardrails-ai>=0.4.0

Installation

$ guardrails hub install hub://guardrails/endpoint_is_reachable

Usage Examples

Validating string output via Python

In this example, we apply the validator to a string URL generated by an LLM.

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


# Setup Guard
guard = Guard().use(EndpointIsReachable, on_fail="exception")

response = guard.validate("https://www.guardrailsai.com/")  # Validator passes

try:
    response = guard.validate("https://www.guardrailsai.co")  # Validator fails
except Exception as e:
    print(e)

Output:

Validation failed for field with errors: URL https://www.guardrailsai.co could not be reached

Validating JSON output via Python

In this example, we apply the validator to a URL that is a field within a Pydantic object.

# Import Guard and Validator
from pydantic import BaseModel, Field
from guardrails.hub import EndpointIsReachable
from guardrails import Guard

val = EndpointIsReachable(on_fail="exception")


# Create Pydantic BaseModel
class PaperCitations(BaseModel):
    paper_name: str
    paper_url: str = Field(description="URL at which to find paper", validators=[val])


# Create a Guard to check for valid Pydantic output
guard = Guard.from_pydantic(output_class=PaperCitations)

# Run LLM output generating JSON through guard
guard.parse(
    """
    {
        "paper_name": "Attention Is All You Need",
        "paper_url": "https://arxiv.org/abs/1706.03762"
    }
    """
)

try:
    # Run LLM output generating JSON through guard
    guard.parse(
        """
        {
            "paper_name": "Attention Is All You Need",
            "paper_url": "https://arxiv.org/abs/1706.0376234"
        }
        """
    )
except Exception as e:
    print(e)

Output:

Validation failed for field with errors: URL https://arxiv.org/abs/1706.0376234 returned status code 404

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.

__call__(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:

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.
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.