Contextual Recall

This example demonstrates how to use Mastra’s Contextual Recall metric to evaluate how effectively responses incorporate information from provided context.

Overview

The example shows how to:

Configure the Contextual Recall metric
Evaluate context incorporation
Analyze recall scores
Handle different recall 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 { ContextualRecallMetric } from '@mastra/evals/llm';

Example Usage

High Recall Example

Evaluate a response that includes all context information:

src/index.ts

const context1 = [
  'Product features include cloud sync.',
  'Offline mode is available.',
  'Supports multiple devices.',
];
 
const metric1 = new ContextualRecallMetric(openai('gpt-4o-mini'), {
  context: context1,
});
 
const query1 = 'What are the key features of the product?';
const response1 = 'The product features cloud synchronization, offline mode support, and the ability to work across multiple devices.';
 
console.log('Example 1 - High Recall:');
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: 'All elements of the output are supported by the context.' }

Mixed Recall Example

Evaluate a response that includes some context information:

src/index.ts

const context2 = [
  'Python is a high-level programming language.',
  'Python emphasizes code readability.',
  'Python supports multiple programming paradigms.',
  'Python is widely used in data science.',
];
 
const metric2 = new ContextualRecallMetric(openai('gpt-4o-mini'), {
  context: context2,
});
 
const query2 = 'What are Python\'s key characteristics?';
const response2 = 'Python is a high-level programming language. It is also a type of snake.';
 
console.log('Example 2 - Mixed Recall:');
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: 'Only half of the output is supported by the context.' }

Low Recall Example

Evaluate a response that misses most context information:

src/index.ts

const context3 = [
  'The solar system has eight planets.',
  'Mercury is closest to the Sun.',
  'Venus is the hottest planet.',
  'Mars is called the Red Planet.',
];
 
const metric3 = new ContextualRecallMetric(openai('gpt-4o-mini'), {
  context: context3,
});
 
const query3 = 'Tell me about the solar system.';
const response3 = 'Jupiter is the largest planet in the solar system.';
 
console.log('Example 3 - Low Recall:');
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, reason: 'None of the output is supported by the context.' }

Understanding the Results

The metric provides:

A recall score between 0 and 1:
- 1.0: Perfect recall - all context information used
- 0.7-0.9: High recall - most context information used
- 0.4-0.6: Mixed recall - some context information used
- 0.1-0.3: Low recall - little context information used
- 0.0: No recall - no context information used
Detailed reason for the score, including analysis of:
- Information incorporation
- Missing context
- Response completeness
- Overall recall quality

View Example on GitHub

Context Relevancy Custom Eval