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ReferenceEvalsCompleteness

CompletenessMetric

New Scorer API

We just released a new evals API called Scorers, with a more ergonomic API and more metadata stored for error analysis, and more flexibility to evaluate data structures. It’s fairly simple to migrate, but we will continue to support the existing Evals API.

The CompletenessMetric class evaluates how thoroughly an LLM’s output covers the key elements present in the input. It analyzes nouns, verbs, topics, and terms to determine coverage and provides a detailed completeness score.

Basic Usage

import { CompletenessMetric } from "@mastra/evals/nlp";
 
const metric = new CompletenessMetric();
 
const result = await metric.measure(
  "Explain how photosynthesis works in plants using sunlight, water, and carbon dioxide.",
  "Plants use sunlight to convert water and carbon dioxide into glucose through photosynthesis.",
);
 
console.log(result.score); // Coverage score from 0-1
console.log(result.info); // Object containing detailed metrics about element coverage

measure() Parameters

input:

string
The original text containing key elements to be covered

output:

string
The LLM's response to evaluate for completeness

Returns

score:

number
Completeness score (0-1) representing the proportion of input elements covered in the output

info:

object
Object containing detailed metrics about element coverage
string[]

inputElements:

string[]
Array of key elements extracted from the input
string[]

outputElements:

string[]
Array of key elements found in the output
string[]

missingElements:

string[]
Array of input elements not found in the output
object

elementCounts:

object
Count of elements in input and output

Element Extraction Details

The metric extracts and analyzes several types of elements:

  • Nouns: Key objects, concepts, and entities
  • Verbs: Actions and states (converted to infinitive form)
  • Topics: Main subjects and themes
  • Terms: Individual significant words

The extraction process includes:

  • Normalization of text (removing diacritics, converting to lowercase)
  • Splitting camelCase words
  • Handling of word boundaries
  • Special handling of short words (3 characters or less)
  • Deduplication of elements

Scoring Details

The metric evaluates completeness through linguistic element coverage analysis.

Scoring Process

  1. Extracts key elements:

    • Nouns and named entities
    • Action verbs
    • Topic-specific terms
    • Normalized word forms

  2. Calculates coverage of input elements:

    • Exact matches for short terms (≤3 chars)
    • Substantial overlap (>60%) for longer terms

Final score: (covered_elements / total_input_elements) * scale

Score interpretation

(0 to scale, default 0-1)

  • 1.0: Complete coverage - contains all input elements
  • 0.7-0.9: High coverage - includes most key elements
  • 0.4-0.6: Partial coverage - contains some key elements
  • 0.1-0.3: Low coverage - missing most key elements
  • 0.0: No coverage - output lacks all input elements

Example with Analysis

import { CompletenessMetric } from "@mastra/evals/nlp";
 
const metric = new CompletenessMetric();
 
const result = await metric.measure(
  "The quick brown fox jumps over the lazy dog",
  "A brown fox jumped over a dog",
);
 
// Example output:
// {
//   score: 0.75,
//   info: {
//     inputElements: ["quick", "brown", "fox", "jump", "lazy", "dog"],
//     outputElements: ["brown", "fox", "jump", "dog"],
//     missingElements: ["quick", "lazy"],
//     elementCounts: { input: 6, output: 4 }
//   }
// }
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