Copy as Markdown[Open in ChatGPT](https://chatgpt.com/?q=Read%20https%3A%2F%2Fdocs-docusaurus.kinsta.page%2Fuser-guides%2Fai%2Fguardrails%2Ftoxicity.md%20and%20help%20me%20with%20my%20question%20about%20this%20Coralogix%20documentation%20page.)[Open in Claude](https://claude.ai/new?q=Read%20https%3A%2F%2Fdocs-docusaurus.kinsta.page%2Fuser-guides%2Fai%2Fguardrails%2Ftoxicity.md%20and%20help%20me%20with%20my%20question%20about%20this%20Coralogix%20documentation%20page.)

# Toxicity

The toxicity detection guardrail protects your LLM applications from generating or processing toxic, harmful, or offensive content such as hate speech, threats, harassment, and abusive language.

## What you need[​](#what-you-need "Direct link to What you need")

* Python 3.10 or higher.
* `cx-guardrails` installed. See [Getting Started with Guardrails](https://docs-docusaurus.kinsta.page/user-guides/ai/guardrails/getting_started/.md).
* A [Team API key](https://docs-docusaurus.kinsta.page/user-guides/account-management/api-keys/api-keys/.md) with the **AiObservability** role preset, used as `CX_GUARDRAILS_TOKEN`. The AiObservability preset includes `AI-GUARDRAILS:MANAGE` and all other permissions required to use Guardrails.
* Environment variables configured: `CX_GUARDRAILS_TOKEN`, `CX_GUARDRAILS_ENDPOINT`.
* The `AI-GUARDRAILS:MANAGE` [permission](https://docs-docusaurus.kinsta.page/user-guides/aaa/access-control/permissions/permissions-list/.md).

## Install the SDK[​](#install-the-sdk "Direct link to Install the SDK")

```
pip install cx-guardrails
```

## Set up environment variables[​](#set-up-environment-variables "Direct link to Set up environment variables")

```
export CX_GUARDRAILS_TOKEN="your-coralogix-guardrails-api-key"

export CX_GUARDRAILS_ENDPOINT="https://api.eu2.coralogix.com.coralogix.com/api/v1/guardrails/guard"

export CX_TOKEN="your-coralogix-send-your-data-key"

export CX_ENDPOINT="https://your-domain.coralogix.com"



# Optional: Application metadata for observability

export CX_APPLICATION_NAME="my-app"

export CX_SUBSYSTEM_NAME="my-subsystem"
```

## Set up observability[​](#set-up-observability "Direct link to Set up observability")

To send guardrail spans to AI Center, set up OpenTelemetry trace export. For the full overview, see [OpenTelemetry integration for AI Center](https://docs-docusaurus.kinsta.page/user-guides/ai/otel-integration/.md).

Install the OpenTelemetry packages:

```
pip install opentelemetry-sdk opentelemetry-exporter-otlp-proto-grpc
```

Export the OTLP environment variables:

```
export OTEL_EXPORTER_OTLP_ENDPOINT="https://ingress.eu2.coralogix.com:443"

export OTEL_EXPORTER_OTLP_HEADERS="Authorization=Bearer <your-api-key>"

export OTEL_SERVICE_NAME="my-ai-service"

export OTEL_RESOURCE_ATTRIBUTES="cx.application.name=my-app,cx.subsystem.name=my-subsystem"

export OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT=true

export OTEL_SEMCONV_STABILITY_OPT_IN=gen_ai_latest_experimental
```

Initialize the tracer provider in your application before any guardrail or LLM calls:

```
from opentelemetry import trace

from opentelemetry.exporter.otlp.proto.grpc.trace_exporter import OTLPSpanExporter

from opentelemetry.sdk.resources import Resource

from opentelemetry.sdk.trace import TracerProvider

from opentelemetry.sdk.trace.export import BatchSpanProcessor





def configure_otel() -> TracerProvider:

    resource = Resource.create()

    provider = TracerProvider(resource=resource)

    provider.add_span_processor(BatchSpanProcessor(OTLPSpanExporter()))

    trace.set_tracer_provider(provider)

    return provider
```

## Usage[​](#usage "Direct link to Usage")

```
import asyncio

from cx_guardrails import Guardrails, Toxicity, GuardrailsTriggered



async def main():

    guardrails = Guardrails()

    async with guardrails.guarded_session():

        try:

            await guardrails.guard_prompt(

                prompt="Hello, how can I help you today?",

                guardrails=[Toxicity()],

            )

            print("No toxicity detected")

        except GuardrailsTriggered as e:

            print(f"Toxicity detected: {e}")



asyncio.run(main())
```

## Configuration options[​](#configuration-options "Direct link to Configuration options")

### Custom threshold[​](#custom-threshold "Direct link to Custom threshold")

Adjust detection sensitivity (0.0 to 1.0, default 0.7):

```
# Lower threshold — more sensitive

await guardrails.guard_prompt(

    prompt=user_input,

    guardrails=[Toxicity(threshold=0.5)],

)



# Higher threshold — less sensitive

await guardrails.guard_prompt(

    prompt=user_input,

    guardrails=[Toxicity(threshold=0.9)],

)
```

**Threshold:** Defines the value from which a guardrail action is triggered. When the threshold is met or exceeded, the guardrail action is executed, returned through the API, and the system marks the event as an issue.

## Next steps[​](#next-steps "Direct link to Next steps")

Define domain-specific guardrail policies using natural language with [Custom guardrail policies](https://docs-docusaurus.kinsta.page/user-guides/ai/guardrails/custom_policies/.md).