GuardrailsAI Integration#

NeMo Guardrails provides out-of-the-box support for GuardrailsAI validators, enabling comprehensive input and output validation using a rich ecosystem of community-built validators. GuardrailsAI offers validators for content safety, PII detection, toxic language filtering, jailbreak detection, topic restriction, and much more.

The integration provides access to both built-in validators and the entire Guardrails Hub ecosystem, allowing you to dynamically load and configure validators for your specific use cases.

Setup#

To use GuardrailsAI validators, you need to install the guardrails-ai package:

pip install guardrails-ai

You may also need to install specific validators from the Guardrails Hub:

guardrails hub install guardrails/toxic_language
guardrails hub install guardrails/detect_jailbreak
guardrails hub install guardrails/guardrails_pii

Usage#

The GuardrailsAI integration uses a flexible configuration system that allows you to define validators with their parameters and metadata, then reference them in your input and output rails.

Configuration Structure#

Add GuardrailsAI validators to your config.yml:

rails:
  config:
    guardrails_ai:
      validators:
        - name: toxic_language
          parameters:
            threshold: 0.5
            validation_method: "sentence"
          metadata: {}
        - name: guardrails_pii
          parameters:
            entities: ["phone_number", "email", "ssn"]
          metadata: {}
        - name: competitor_check
          parameters:
            competitors: ["Apple", "Google", "Microsoft"]
          metadata: {}

Input Rails#

To use GuardrailsAI validators for input validation:

rails:
  input:
    flows:
      - guardrailsai check input $validator="guardrails_pii"
      - guardrailsai check input $validator="competitor_check"

Output Rails#

To use GuardrailsAI validators for output validation:

rails:
  output:
    flows:
      - guardrailsai check output $validator="toxic_language"
      - guardrailsai check output $validator="restricttotopic"

Built-in Validators#

The integration includes support for the following validators that are pre-registered in the NeMo Guardrails validator registry. For detailed parameter specifications and usage examples, refer to the official GuardrailsAI Hub documentation for each validator:

competitor_check - hub://guardrails/competitor_check
detect_jailbreak - hub://guardrails/detect_jailbreak
guardrails_pii - hub://guardrails/guardrails_pii
one_line - hub://guardrails/one_line
provenance_llm - hub://guardrails/provenance_llm
regex_match - hub://guardrails/regex_match
restricttotopic - hub://tryolabs/restricttotopic
toxic_language - hub://guardrails/toxic_language
valid_json - hub://guardrails/valid_json
valid_length - hub://guardrails/valid_length

Complete Example#

Here’s a comprehensive example configuration:

models:
  - type: main
    engine: openai
    model: gpt-4

rails:
  config:
    guardrails_ai:
      validators:
        - name: toxic_language
          parameters:
            threshold: 0.5
            validation_method: "sentence"
          metadata: {}
        - name: guardrails_pii
          parameters:
            entities: ["phone_number", "email", "ssn", "credit_card"]
          metadata: {}
        - name: competitor_check
          parameters:
            competitors: ["Apple", "Google", "Microsoft", "Amazon"]
          metadata: {}
        - name: restricttotopic
          parameters:
            valid_topics: ["technology", "science", "education"]
          metadata: {}
        - name: valid_length
          parameters:
            min: 10
            max: 500
          metadata: {}

  input:
    flows:
      - guardrailsai check input $validator="guardrails_pii"
      - guardrailsai check input $validator="competitor_check"

  output:
    flows:
      - guardrailsai check output $validator="toxic_language"
      - guardrailsai check output $validator="restricttotopic"
      - guardrailsai check output $validator="valid_length"

Custom Validators from Guardrails Hub#

You can use any validator from the Guardrails Hub by specifying its hub path:

rails:
  config:
    guardrails_ai:
      validators:
        - name: custom_validator_name
          parameters:
            # Custom parameters specific to the validator
          metadata: {}

The integration will automatically fetch validator information from the hub if it’s not in the built-in registry.

Performance Considerations#

Validators are cached to improve performance on repeated use
Guard instances are reused when the same validator is called with identical parameters
Consider the latency impact when chaining multiple validators

For a complete working example, see the GuardrailsAI example configuration.