コンテキスト精度

OPENAI_API_KEY=your_api_key_here

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

const context1 = [
  "Photosynthesis converts sunlight into energy.",
  "Plants use chlorophyll for photosynthesis.",
  "Photosynthesis produces oxygen as a byproduct.",
  "The process requires sunlight and chlorophyll.",
];
 
const metric1 = new ContextPrecisionMetric(openai("gpt-4o-mini"), {
  context: context1,
});
 
const query1 = "What is photosynthesis and how does it work?";
const response1 =
  "Photosynthesis is a process where plants convert sunlight into energy using chlorophyll, producing oxygen as a byproduct.";
 
console.log("Example 1 - High Precision:");
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 uses all relevant information and does not include any irrelevant information.' }

const context2 = [
  "Volcanoes are openings in the Earth's crust.",
  "Volcanoes can be active, dormant, or extinct.",
  "Hawaii has many active volcanoes.",
  "The Pacific Ring of Fire has many volcanoes.",
];
 
const metric2 = new ContextPrecisionMetric(openai("gpt-4o-mini"), {
  context: context2,
});
 
const query2 = "What are the different types of volcanoes?";
const response2 =
  "Volcanoes can be classified as active, dormant, or extinct based on their activity status.";
 
console.log("Example 2 - Mixed Precision:");
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 context uses some relevant information and includes some irrelevant information.' }

const context3 = [
  "The Nile River is in Africa.",
  "The Nile is the longest river.",
  "Ancient Egyptians used the Nile.",
  "The Nile flows north.",
];
 
const metric3 = new ContextPrecisionMetric(openai("gpt-4o-mini"), {
  context: context3,
});
 
const query3 = "Which direction does the Nile River flow?";
const response3 = "The Nile River flows northward.";
 
console.log("Example 3 - Low Precision:");
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: 0.2, reason: 'The context only has one relevant piece, which is at the end.' }