コンテキスト位置

OPENAI_API_KEY=your_api_key_here

import { openai } from "@ai-sdk/openai";
import { ContextPositionMetric } from "@mastra/evals/llm";

const context1 = [
  "The capital of France is Paris.",
  "Paris has been the capital since 508 CE.",
  "Paris serves as France's political center.",
  "The capital city hosts the French government.",
];
 
const metric1 = new ContextPositionMetric(openai("gpt-4o-mini"), {
  context: context1,
});
 
const query1 = "What is the capital of France?";
const response1 = "The capital of France is Paris.";
 
console.log("Example 1 - High Position Adherence:");
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: 1, reason: 'The context is in the correct sequential order.' }

const context2 = [
  "Elephants are herbivores.",
  "Adult elephants can weigh up to 13,000 pounds.",
  "Elephants are the largest land animals.",
  "Elephants eat plants and grass.",
];
 
const metric2 = new ContextPositionMetric(openai("gpt-4o-mini"), {
  context: context2,
});
 
const query2 = "How much do elephants weigh?";
const response2 =
  "Adult elephants can weigh up to 13,000 pounds, making them the largest land animals.";
 
console.log("Example 2 - Mixed Position Adherence:");
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: 0.4, reason: 'The context includes relevant information and irrelevant information and is not in the correct sequential order.' }

const context3 = [
  "Rainbows appear in the sky.",
  "Rainbows have different colors.",
  "Rainbows are curved in shape.",
  "Rainbows form when sunlight hits water droplets.",
];
 
const metric3 = new ContextPositionMetric(openai("gpt-4o-mini"), {
  context: context3,
});
 
const query3 = "How do rainbows form?";
const response3 =
  "Rainbows are created when sunlight interacts with water droplets in the air.";
 
console.log("Example 3 - Low Position Adherence:");
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: 0.12, reason: 'The context includes some relevant information, but most of the relevant information is at the end.' }