ContextPrecisionMetric

import { openai } from "@ai-sdk/openai";
import { ContextPrecisionMetric } from "@mastra/evals/llm";
 
// Configure the model for evaluation
const model = openai("gpt-4o-mini");
 
const metric = new ContextPrecisionMetric(model, {
  context: [
    "Photosynthesis is a biological process used by plants to create energy from sunlight.",
    "Plants need water and nutrients from the soil to grow.",
    "The process of photosynthesis produces oxygen as a byproduct.",
  ],
});
 
const result = await metric.measure(
  "What is photosynthesis?",
  "Photosynthesis is the process by which plants convert sunlight into energy.",
);
 
console.log(result.score); // Precision score from 0-1
console.log(result.info.reason); // Explanation of the score

import { openai } from "@ai-sdk/openai";
import { ContextPrecisionMetric } from "@mastra/evals/llm";
 
// Configure the model for evaluation
const model = openai("gpt-4o-mini");
 
const metric = new ContextPrecisionMetric(model, {
  context: [
    "Exercise strengthens the heart and improves blood circulation.",
    "A balanced diet is important for health.",
    "Regular physical activity reduces stress and anxiety.",
    "Exercise equipment can be expensive.",
  ],
});
 
const result = await metric.measure(
  "What are the benefits of exercise?",
  "Regular exercise improves cardiovascular health and mental wellbeing.",
);
 
// Example output:
// {
//   score: 0.75,
//   info: {
//     reason: "The score is 0.75 because the first and third contexts are highly relevant
//           to the benefits mentioned in the output, while the second and fourth contexts
//           are not directly related to exercise benefits. The relevant contexts are well-positioned
//           at the beginning and middle of the sequence."
//   }
// }