Answer relevancy scorer

The createAnswerRelevancyScorer() function accepts a single options object with the following properties:

Parameters

model:

LanguageModel

Configuration for the model used to evaluate relevancy.

options:

Record<'uncertaintyWeight' | 'scale', number>

= { uncertaintyWeight: 0.3, scale: 1 }

Configuration options for the scorer.

Record<'uncertaintyWeight' | 'scale', number>

uncertaintyWeight:

number

Weight given to 'unsure' verdicts in scoring (0-1).

scale:

number

Maximum score value.

This function returns an instance of the MastraScorer class. The .run() method accepts the same input as other scorers (see the MastraScorer reference), but the return value includes LLM-specific fields as documented below.

.run() returns

runId:

string

The id of the run (optional).

score:

number

Relevancy score (0 to scale, default 0-1)

preprocessPrompt:

string

The prompt sent to the LLM for the preprocess step (optional).

preprocessStepResult:

object

Object with extracted statements: { statements: string[] }

analyzePrompt:

string

The prompt sent to the LLM for the analyze step (optional).

analyzeStepResult:

object

Object with results: { results: Array<{ result: 'yes' | 'unsure' | 'no', reason: string }> }

generateReasonPrompt:

string

The prompt sent to the LLM for the reason step (optional).

reason:

string

Explanation of the score.

Scoring details

The scorer evaluates relevancy through query-answer alignment, considering completeness and detail level, but not factual correctness.

Scoring Process

1. Statement Preprocess:
   • Breaks output into useful statements while preserving context.
2. Relevance Analysis:
   • Each statement is evaluated as:
     • "yes": Full weight for direct matches
     • "unsure": Partial weight (default: 0.3) for approximate matches
     • "no": Zero weight for irrelevant content
3. Score Calculation:
   • ((direct + uncertainty * partial) / total_statements) * scale

Score Interpretation

A relevancy score between 0 and 1:

• 1.0: The response fully answers the query with relevant and focused information.
• 0.7 to 0.9: The response mostly answers the query but may include minor unrelated content.
• 0.4 to 0.6: The response partially answers the query, mixing relevant and unrelated information.
• 0.1 to 0.3: The response includes minimal relevant content and largely misses the intent of the query.
• 0.0: The response is entirely unrelated and doesn't answer the query.

Example

Evaluate agent responses for relevancy across different scenarios:

src/example-answer-relevancy.ts

import { runEvals } from '@mastra/core/evals'
import { createAnswerRelevancyScorer } from '@mastra/evals/scorers/prebuilt'
import { myAgent } from './agent'

const scorer = createAnswerRelevancyScorer({ model: 'openai/gpt-5.6-sol' })

const result = await runEvals({
  data: [
    {
      input: 'What are the health benefits of regular exercise?',
    },
    {
      input: 'What should a healthy breakfast include?',
    },
    {
      input: 'What are the benefits of meditation?',
    },
  ],
  scorers: [scorer],
  target: myAgent,
  onItemComplete: ({ scorerResults }) => {
    console.log({
      score: scorerResults[scorer.id].score,
      reason: scorerResults[scorer.id].reason,
    })
  },
})

console.log(result.scores)

For more details on runEvals, see the runEvals reference.

To add this scorer to an agent, see the Scorers overview guide.

Related

• Faithfulness Scorer