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Answer Similarity Scorer

The createAnswerSimilarityScorer() function creates a scorer that evaluates how similar an agent's output is to a ground truth answer. This scorer is specifically designed for CI/CD testing scenarios where you have expected answers and want to ensure consistency over time.

For usage examples, see the Answer Similarity Examples.

Parameters​

model:

LanguageModel
The language model used to evaluate semantic similarity between outputs and ground truth.

options:

AnswerSimilarityOptions
Configuration options for the scorer.

AnswerSimilarityOptions​

requireGroundTruth:

boolean
= true
Whether to require ground truth for evaluation. If false, missing ground truth returns score 0.

semanticThreshold:

number
= 0.8
Weight for semantic matches vs exact matches (0-1).

exactMatchBonus:

number
= 0.2
Additional score bonus for exact matches (0-1).

missingPenalty:

number
= 0.15
Penalty per missing key concept from ground truth.

contradictionPenalty:

number
= 1.0
Penalty for contradictory information. High value ensures wrong answers score near 0.

extraInfoPenalty:

number
= 0.05
Mild penalty for extra information not present in ground truth (capped at 0.2).

scale:

number
= 1
Score scaling factor.

This function returns an instance of the MastraScorer class. The .run() method accepts the same input as other scorers (see the MastraScorer reference), but requires ground truth to be provided in the run object.

.run() Returns​

runId:

string
The id of the run (optional).

score:

number
Similarity score between 0-1 (or 0-scale if custom scale used). Higher scores indicate better similarity to ground truth.

reason:

string
Human-readable explanation of the score with actionable feedback.

preprocessStepResult:

object
Extracted semantic units from output and ground truth.

analyzeStepResult:

object
Detailed analysis of matches, contradictions, and extra information.

preprocessPrompt:

string
The prompt used for semantic unit extraction.

analyzePrompt:

string
The prompt used for similarity analysis.

generateReasonPrompt:

string
The prompt used for generating the explanation.

Usage with runExperiment​

This scorer is designed for use with runExperiment for CI/CD testing:

import { runExperiment } from "@mastra/core/scores";
import { createAnswerSimilarityScorer } from "@mastra/evals/scorers/llm";

const scorer = createAnswerSimilarityScorer({ model });

await runExperiment({
  data: [
    {
      input: "What is the capital of France?",
      groundTruth: "Paris is the capital of France",
    },
  ],
  scorers: [scorer],
  target: myAgent,
  onItemComplete: ({ scorerResults }) => {
    // Assert similarity score meets threshold
    expect(scorerResults["Answer Similarity Scorer"].score).toBeGreaterThan(
      0.8,
    );
  },
});

Key Features​

  • Semantic Analysis: Uses LLM to extract and compare semantic units rather than simple string matching
  • Contradiction Detection: Identifies factually incorrect information and scores it near 0
  • Flexible Matching: Supports exact, semantic, partial, and missing match types
  • CI/CD Ready: Designed for automated testing with ground truth comparison
  • Actionable Feedback: Provides specific explanations of what matched and what needs improvement

Scoring Algorithm​

The scorer uses a multi-step process:

  1. Extract: Breaks down output and ground truth into semantic units
  2. Analyze: Compares units and identifies matches, contradictions, and gaps
  3. Score: Calculates weighted similarity with penalties for contradictions
  4. Reason: Generates human-readable explanation

Score calculation: max(0, base_score - contradiction_penalty - missing_penalty - extra_info_penalty) × scale