ExamplesEvalsHallucination

Hallucination

This example demonstrates how to use Mastra’s Hallucination metric to evaluate whether responses contradict information provided in the context.

Overview

The example shows how to:

  1. Configure the Hallucination metric
  2. Evaluate factual contradictions
  3. Analyze hallucination scores
  4. Handle different accuracy levels

Setup

Environment Setup

Make sure to set up your environment variables:

.env
OPENAI_API_KEY=your_api_key_here

Dependencies

Import the necessary dependencies:

src/index.ts
import { openai } from '@ai-sdk/openai';
import { HallucinationMetric } from '@mastra/evals/llm';

Example Usage

No Hallucination Example

Evaluate a response that matches context exactly:

src/index.ts
const context1 = [
  'The iPhone was first released in 2007.',
  'Steve Jobs unveiled it at Macworld.',
  'The original model had a 3.5-inch screen.',
];
 
const metric1 = new HallucinationMetric(openai('gpt-4o-mini'), {
  context: context1,
});
 
const query1 = 'When was the first iPhone released?';
const response1 = 'The iPhone was first released in 2007, when Steve Jobs unveiled it at Macworld. The original iPhone featured a 3.5-inch screen.';
 
console.log('Example 1 - No Hallucination:');
console.log('Context:', context1);
console.log('Query:', query1);
console.log('Response:', response1);
 
const result1 = await metric1.measure(query1, response1);
console.log('Metric Result:', {
  score: result1.score,
  reason: result1.info.reason,
});
// Example Output:
// Metric Result: { score: 0, reason: 'The response matches the context exactly.' }

Mixed Hallucination Example

Evaluate a response that contradicts some facts:

src/index.ts
const context2 = [
  'The first Star Wars movie was released in 1977.',
  'It was directed by George Lucas.',
  'The film earned $775 million worldwide.',
  'The movie was filmed in Tunisia and England.',
];
 
const metric2 = new HallucinationMetric(openai('gpt-4o-mini'), {
  context: context2,
});
 
const query2 = 'Tell me about the first Star Wars movie.';
const response2 = 'The first Star Wars movie came out in 1977 and was directed by George Lucas. It made over $1 billion at the box office and was filmed entirely in California.';
 
console.log('Example 2 - Mixed Hallucination:');
console.log('Context:', context2);
console.log('Query:', query2);
console.log('Response:', response2);
 
const result2 = await metric2.measure(query2, response2);
console.log('Metric Result:', {
  score: result2.score,
  reason: result2.info.reason,
});
// Example Output:
// Metric Result: { score: 0.5, reason: 'The response contradicts some facts in the context.' }

Complete Hallucination Example

Evaluate a response that contradicts all facts:

src/index.ts
const context3 = [
  'The Wright brothers made their first flight in 1903.',
  'The flight lasted 12 seconds.',
  'It covered a distance of 120 feet.',
];
 
const metric3 = new HallucinationMetric(openai('gpt-4o-mini'), {
  context: context3,
});
 
const query3 = 'When did the Wright brothers first fly?';
const response3 = 'The Wright brothers achieved their historic first flight in 1908. The flight lasted about 2 minutes and covered nearly a mile.';
 
console.log('Example 3 - Complete Hallucination:');
console.log('Context:', context3);
console.log('Query:', query3);
console.log('Response:', response3);
 
const result3 = await metric3.measure(query3, response3);
console.log('Metric Result:', {
  score: result3.score,
  reason: result3.info.reason,
});
// Example Output:
// Metric Result: { score: 1, reason: 'The response completely contradicts the context.' }

Understanding the Results

The metric provides:

  1. A hallucination score between 0 and 1:

    • 0.0: No hallucination - no contradictions with context
    • 0.3-0.4: Low hallucination - few contradictions
    • 0.5-0.6: Mixed hallucination - some contradictions
    • 0.7-0.8: High hallucination - many contradictions
    • 0.9-1.0: Complete hallucination - contradicts all context
  2. Detailed reason for the score, including analysis of:

    • Statement verification
    • Contradictions found
    • Factual accuracy
    • Overall hallucination level





View Example on GitHub