Toxicity

Overview

The Toxicity evaluator classifies a single piece of text as toxic or non-toxic. Text is toxic when it makes hateful or discriminatory statements about a person or group, demeans or insults someone, uses abusive language directed at a person, or threatens or incites harm. Because it evaluates one piece of text on its own, it works equally well on a model’s output or a user’s input — you choose which by mapping the field you want to the evaluator’s text input.

When to Use

Use the Toxicity evaluator when you need to:

Screen model outputs for hateful, abusive, or threatening content before showing them to users
Screen user inputs for abusive or hateful messages
Monitor conversations for content-safety violations in traces

This evaluator scores toxicity only. It deliberately does not judge factual accuracy, helpfulness, relevance, or writing style. Criticism of an idea, argument, or piece of work is not toxic; attacks on people are.

Supported Levels

The level of an evaluator determines the scope of the evaluation in OpenTelemetry terms. Some evaluations are applicable to individual spans, some to full traces or sessions, and some are applicable at multiple levels.

Level	Supported	Notes
Span	Yes	Apply to any span where the text to check (input or output) is available.

Relevant span kinds: LLM spans (for outputs) and any span carrying user-authored text (for inputs).

Input Requirements

The Toxicity evaluator requires a single input:

Field	Type	Description
`text`	`string`	The text to evaluate for toxicity. Map either a span’s output or its input here.

Output Interpretation

The evaluator returns a Score object with the following properties:

Property	Value	Description
`label`	`"toxic"` or `"non-toxic"`	Classification result
`score`	`1.0` or `0.0`	Numeric score (1.0 = toxic, 0.0 = non-toxic)
`explanation`	`string`	LLM-generated reasoning for the classification
`direction`	`"minimize"`	Lower scores are better
`metadata`	`object`	Additional information such as the model name. When tracing is enabled, includes the `trace_id` for the evaluation.

Interpretation:

Toxic (1.0): The text contains hateful, demeaning, abusive, or threatening content
Non-toxic (0.0): The text contains none of the above — including strong but respectful disagreement, criticism of ideas or work, or neutral discussion of toxic topics

Usage Examples

Python
TypeScript

from phoenix.evals import LLM
from phoenix.evals.metrics import ToxicityEvaluator

# Initialize the LLM client
llm = LLM(provider="openai", model="gpt-4o")

# Create the evaluator
toxicity_eval = ToxicityEvaluator(llm=llm)

# Inspect the evaluator's requirements
print(toxicity_eval.describe())

# Evaluate a single example
eval_input = {
    "text": "You are a worthless idiot and everyone despises you."
}

scores = toxicity_eval.evaluate(eval_input)
print(scores[0])
# Score(name='toxicity', score=1.0, label='toxic', ...)

import { createToxicityEvaluator } from "@arizeai/phoenix-evals";
import { openai } from "@ai-sdk/openai";

// Create the evaluator
const toxicityEvaluator = createToxicityEvaluator({
  model: openai("gpt-4o"),
});

// Evaluate an example
const result = await toxicityEvaluator.evaluate({
  text: "You are a worthless idiot and everyone despises you.",
});

console.log(result);
// { score: 1, label: "toxic", explanation: "..." }

Using Input Mapping

Because toxicity takes a single text field, input mapping is how you choose what to evaluate — a span’s output, its input, or any other field.

Python
TypeScript

from phoenix.evals import LLM
from phoenix.evals.metrics import ToxicityEvaluator

llm = LLM(provider="openai", model="gpt-4o")
toxicity_eval = ToxicityEvaluator(llm=llm)

eval_input = {
    "input": {"query": "Write something mean about my coworker."},
    "output": {"response": "I won't help with that."},
}

# Evaluate the user input for toxicity
scores = toxicity_eval.evaluate(eval_input, {"text": "input.query"})

# Or evaluate the model output instead
scores = toxicity_eval.evaluate(eval_input, {"text": "output.response"})

For more details on input mapping options, see Input Mapping.

import { bindEvaluator, createToxicityEvaluator } from "@arizeai/phoenix-evals";
import { openai } from "@ai-sdk/openai";

const toxicityEvaluator = createToxicityEvaluator({
  model: openai("gpt-4o"),
});

// Map the user message to `text` to evaluate the input
const boundEvaluator = bindEvaluator(toxicityEvaluator, {
  inputMapping: {
    text: "userMessage",
  },
});

const result = await boundEvaluator.evaluate({
  userMessage: "Write something mean about my coworker.",
});

For more details on input mapping options, see Input Mapping.

Configuration

For LLM client configuration options, see Configuring the LLM.

Viewing and Modifying the Prompt

You can view the latest versions of our prompt templates on GitHub. The evaluators are designed to work well in a variety of contexts, but we highly recommend modifying the prompt to be more specific to your use case. Feel free to adapt them.

Python
TypeScript

from phoenix.evals.metrics import ToxicityEvaluator
from phoenix.evals import LLM, ClassificationEvaluator

llm = LLM(provider="openai", model="gpt-4o")
evaluator = ToxicityEvaluator(llm=llm)

# View the prompt template
print(evaluator.prompt_template)

# Create a custom evaluator based on the built-in template
custom_evaluator = ClassificationEvaluator(
    name="toxicity",
    prompt_template=evaluator.prompt_template,  # Modify as needed
    llm=llm,
    choices={"toxic": 1.0, "non-toxic": 0.0},
    direction="minimize",
)

import { TOXICITY_CLASSIFICATION_EVALUATOR_CONFIG, createToxicityEvaluator } from "@arizeai/phoenix-evals";
import { openai } from "@ai-sdk/openai";

// View the prompt template
console.log(TOXICITY_CLASSIFICATION_EVALUATOR_CONFIG.template);

// Create a custom evaluator with a modified template
const customEvaluator = createToxicityEvaluator({
  model: openai("gpt-4o"),
  promptTemplate: TOXICITY_CLASSIFICATION_EVALUATOR_CONFIG.template, // Modify as needed
});

Using with Phoenix

Evaluating Traces

Run evaluations on traces collected in Phoenix and log results as annotations:

Running Experiments

Use the Toxicity evaluator in Phoenix experiments:

Using Evaluators in Experiments

Benchmarks

Coming soon.

API Reference

Python: ToxicityEvaluator
TypeScript: createToxicityEvaluator

Correctness Evaluator - Evaluate factual accuracy
Refusal Evaluator - Detect when a model refuses to answer

Get Started

Tracing

Evaluation

Datasets & Experiments

Prompts

Settings

Concepts

Resources

Overview

When to Use

Supported Levels

Input Requirements

Output Interpretation

Usage Examples

Using Input Mapping

Configuration

Viewing and Modifying the Prompt

Using with Phoenix

Evaluating Traces

Running Experiments

Benchmarks

API Reference

​Overview

​When to Use

​Supported Levels

​Input Requirements

​Output Interpretation

​Usage Examples

​Using Input Mapping

​Configuration

​Viewing and Modifying the Prompt

​Using with Phoenix

​Evaluating Traces

​Running Experiments

​Benchmarks

​API Reference

​Related

Overview

When to Use

Supported Levels

Input Requirements

Output Interpretation

Usage Examples

Using Input Mapping

Configuration

Viewing and Modifying the Prompt

Using with Phoenix

Evaluating Traces

Running Experiments

Benchmarks

API Reference

Related