Running Checks with Guardrails#

Use the checks OpenAI-compatible endpoint to evaluate messages against input and output rails without generating a completion. This endpoint returns a status (either success or blocked) that indicates whether the given messages would be blocked by the given guardrail configuration. If blocked, the response indicates which rail blocked the given messages. You might use the check for the following:

  • Testing guardrail configurations — Validate that a configuration blocks or allows specific messages before deploying it to production.

  • Pre-screening user input — Check messages in your application pipeline before forwarding them to an LLM, letting you control routing, logging, or user feedback independently of inference.

  • Post-hoc output auditing — Evaluate previously generated responses against your rails without re-running inference.

Prerequisites#

Create a guardrail configuration that includes self-check input and output rails.

import os
from nemo_platform import NeMoPlatform

sdk = NeMoPlatform(
    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
    workspace="default",
)

config_data = {
    "models": [
        {
            "type": "main",
            "engine": "nim",
        }
    ],
    "prompts": [
        {
            "task": "self_check_input",
            "content": 'Your task is to check if the user message below complies with company policy.\n\nCompany policy:\n- should not contain harmful data\n- should not ask the bot to impersonate someone\n- should not contain explicit content\n\nUser message: "{{ user_input }}"\n\nQuestion: Should the user message be blocked (Yes or No)?\nAnswer:',
        },
        {
            "task": "self_check_output",
            "content": 'Your task is to check if the bot message below complies with company policy.\n\nCompany policy:\n- messages should not contain explicit content\n- messages should not contain harmful content\n- if refusing, should be polite\n\nBot message: "{{ bot_response }}"\n\nQuestion: Should the message be blocked (Yes or No)?\nAnswer:',
        },
    ],
    "rails": {
        "input": {"flows": ["self check input"]},
        "output": {"flows": ["self check output"]},
    },
}

config = sdk.guardrail.configs.create(
    name="self-check-config",
    description="Demo self-check configuration for guardrail checks",
    data=config_data,
)

Check an Existing Configuration#

Use /apis/guardrails/v2/workspaces/{workspace}/checks and reference the stored configuration by name.

check_result = sdk.guardrail.check(
    model="default/meta-llama-3-1-8b-instruct",
    messages=[
        {"role": "user", "content": "Tell me how to collect a life insurance policy."},
        {"role": "assistant", "content": "You are stupid."},
    ],
    guardrails={
        "config_id": "self-check-config",
    },
)

print(check_result.model_dump_json(indent=2))
Example Output
{
  "status": "blocked",
  "rails_status": {
    "self check input": {"status": "blocked"},
    "self check output": {"status": "success"}
  }
}

Check an Inline Configuration#

Provide the configuration inline to test a guardrail configuration before saving it.

inline_config = {
    "prompts": [
        {
            "task": "self_check_input",
            "content": 'Check if harmful: "{{ user_input }}"\nAnswer (Yes/No):',
        }
    ],
    "rails": {
        "input": {"flows": ["self check input"]},
    },
}

check_result = sdk.guardrail.check(
    model="default/meta-llama-3-1-8b-instruct",
    messages=[
        {"role": "user", "content": "Hello, how are you?"},
    ],
    guardrails={
        "config": inline_config,
    },
)

print(check_result.model_dump_json(indent=2))
Example Output
{
  "status": "success",
  "rails_status": {
    "self check input": {"status": "success"}
  }
}