ContextRelevancyMetric

import { openai } from "@ai-sdk/openai";
import { ContextRelevancyMetric } from "@mastra/evals/llm";
 
// Configure the model for evaluation
const model = openai("gpt-4o-mini");
 
const metric = new ContextRelevancyMetric(model, {
  context: [
    "All data is encrypted at rest and in transit",
    "Two-factor authentication is mandatory",
    "The platform supports multiple languages",
    "Our offices are located in San Francisco",
  ],
});
 
const result = await metric.measure(
  "What are our product's security features?",
  "Our product uses encryption and requires 2FA.",
);
 
console.log(result.score); // Score from 0-1
console.log(result.info.reason); // Explanation of the relevancy assessment

import { openai } from "@ai-sdk/openai";
import { ContextRelevancyMetric } from "@mastra/evals/llm";
 
// Configure the model for evaluation
const model = openai("gpt-4o-mini");
 
const metric = new ContextRelevancyMetric(model, {
  scale: 100, // Use 0-100 scale instead of 0-1
  context: [
    "Basic plan costs $10/month",
    "Pro plan includes advanced features at $30/month",
    "Enterprise plan has custom pricing",
    "Our company was founded in 2020",
    "We have offices worldwide",
  ],
});
 
const result = await metric.measure(
  "What are our pricing plans?",
  "We offer Basic, Pro, and Enterprise plans.",
);
 
// Example output:
// {
//   score: 60,
//   info: {
//     reason: "3 out of 5 statements are relevant to pricing plans. The statements about
//           company founding and office locations are not relevant to the pricing query."
//   }
// }