Context Relevance Scorer

The createContextRelevanceScorerLLM() function creates a scorer that evaluates how relevant and useful provided context was for generating agent responses. It uses weighted relevance levels and applies penalties for unused high-relevance context and missing information.

It is especially useful for these use cases:

Content Generation Evaluation

Best for evaluating context quality in:

• Chat systems where context usage matters
• RAG pipelines needing nuanced relevance assessment
• Systems where missing context affects quality

Context Selection Optimization

Use when optimizing for:

• Comprehensive context coverage
• Effective context utilization
• Identifying context gaps

Parameters

model:

MastraModelConfig

The language model to use for evaluating context relevance

options:

ContextRelevanceOptions

Configuration options for the scorer

Note: Either context or contextExtractor must be provided. If both are provided, contextExtractor takes precedence.

.run() Returns

score:

number

Weighted relevance score between 0 and scale (default 0-1)

reason:

string

Human-readable explanation of the context relevance evaluation

Scoring Details

Weighted Relevance Scoring

Context Relevance uses a sophisticated scoring algorithm that considers:

1. Relevance Levels: Each context piece is classified with weighted values:

   • high = 1.0 (directly addresses the query)
   • medium = 0.7 (supporting information)
   • low = 0.3 (tangentially related)
   • none = 0.0 (completely irrelevant)

2. Usage Detection: Tracks whether relevant context was actually used in the response

3. Penalties Applied (configurable via penalties options):

   • Unused High-Relevance: unusedHighRelevanceContext penalty per unused high-relevance context (default: 0.1)
   • Missing Context: Up to maxMissingContextPenalty for identified missing information (default: 0.5)

Scoring Formula

Base Score = Σ(relevance_weights) / (num_contexts × 1.0)
Usage Penalty = count(unused_high_relevance) × unusedHighRelevanceContext
Missing Penalty = min(count(missing_context) × missingContextPerItem, maxMissingContextPenalty)

Final Score = max(0, Base Score - Usage Penalty - Missing Penalty) × scale

Default Values:

• unusedHighRelevanceContext = 0.1 (10% penalty per unused high-relevance context)
• missingContextPerItem = 0.15 (15% penalty per missing context item)
• maxMissingContextPenalty = 0.5 (maximum 50% penalty for missing context)
• scale = 1

Score interpretation

• 0.9-1.0: Excellent - all context highly relevant and used
• 0.7-0.8: Good - mostly relevant with minor gaps
• 0.4-0.6: Mixed - significant irrelevant or unused context
• 0.2-0.3: Poor - mostly irrelevant context
• 0.0-0.1: Very poor - no relevant context found

Reason analysis

The reason field provides insights on:

• Relevance level of each context piece (high/medium/low/none)
• Which context was actually used in the response
• Penalties applied for unused high-relevance context (configurable via unusedHighRelevanceContext)
• Missing context that would have improved the response (penalized via missingContextPerItem up to maxMissingContextPenalty)

Optimization strategies

Use results to improve your system:

• Filter irrelevant context: Remove low/none relevance pieces before processing
• Ensure context usage: Make sure high-relevance context is incorporated
• Fill context gaps: Add missing information identified by the scorer
• Balance context size: Find optimal amount of context for best relevance
• Tune penalty sensitivity: Adjust unusedHighRelevanceContext, missingContextPerItem, and maxMissingContextPenalty based on your application's tolerance for unused or missing context

Difference from Context Precision

Aspect	Context Relevance	Context Precision
Algorithm	Weighted levels with penalties	Mean Average Precision (MAP)
Relevance	Multiple levels (high/medium/low/none)	Binary (yes/no)
Position	Not considered	Critical (rewards early placement)
Usage	Tracks and penalizes unused context	Not considered
Missing	Identifies and penalizes gaps	Not evaluated

Scorer configuration

Custom penalty configuration

Control how penalties are applied for unused and missing context:

import { createContextRelevanceScorerLLM } from "@mastra/evals";

// Stricter penalty configuration
const strictScorer = createContextRelevanceScorerLLM({
  model: "openai/gpt-4o-mini",
  options: {
    context: [
      "Einstein won the Nobel Prize for photoelectric effect",
      "He developed the theory of relativity",
      "Einstein was born in Germany",
    ],
    penalties: {
      unusedHighRelevanceContext: 0.2, // 20% penalty per unused high-relevance context
      missingContextPerItem: 0.25, // 25% penalty per missing context item
      maxMissingContextPenalty: 0.6, // Maximum 60% penalty for missing context
    },
    scale: 1,
  },
});

// Lenient penalty configuration
const lenientScorer = createContextRelevanceScorerLLM({
  model: "openai/gpt-4o-mini",
  options: {
    context: [
      "Einstein won the Nobel Prize for photoelectric effect",
      "He developed the theory of relativity",
      "Einstein was born in Germany",
    ],
    penalties: {
      unusedHighRelevanceContext: 0.05, // 5% penalty per unused high-relevance context
      missingContextPerItem: 0.1, // 10% penalty per missing context item
      maxMissingContextPenalty: 0.3, // Maximum 30% penalty for missing context
    },
    scale: 1,
  },
});

const testRun = {
  input: {
    inputMessages: [
      {
        id: "1",
        role: "user",
        content: "What did Einstein achieve in physics?",
      },
    ],
  },
  output: [
    {
      id: "2",
      role: "assistant",
      content:
        "Einstein won the Nobel Prize for his work on the photoelectric effect.",
    },
  ],
};

const strictResult = await strictScorer.run(testRun);
const lenientResult = await lenientScorer.run(testRun);

console.log("Strict penalties:", strictResult.score); // Lower score due to unused context
console.log("Lenient penalties:", lenientResult.score); // Higher score, less penalty

Dynamic Context Extraction

const scorer = createContextRelevanceScorerLLM({
  model: "openai/gpt-4o",
  options: {
    contextExtractor: (input, output) => {
      // Extract context based on the query
      const userQuery = input?.inputMessages?.[0]?.content || "";
      if (userQuery.includes("Einstein")) {
        return [
          "Einstein won the Nobel Prize for the photoelectric effect",
          "He developed the theory of relativity",
        ];
      }
      return ["General physics information"];
    },
    penalties: {
      unusedHighRelevanceContext: 0.15,
    },
  },
});

Custom scale factor

const scorer = createContextRelevanceScorerLLM({
  model: "openai/gpt-4o-mini",
  options: {
    context: ["Relevant information...", "Supporting details..."],
    scale: 100, // Scale scores from 0-100 instead of 0-1
  },
});

// Result will be scaled: score: 85 instead of 0.85

Combining multiple context sources

const scorer = createContextRelevanceScorerLLM({
  model: "openai/gpt-4o-mini",
  options: {
    contextExtractor: (input, output) => {
      const query = input?.inputMessages?.[0]?.content || "";

      // Combine from multiple sources
      const kbContext = knowledgeBase.search(query);
      const docContext = documentStore.retrieve(query);
      const cacheContext = contextCache.get(query);

      return [...kbContext, ...docContext, ...cacheContext];
    },
    scale: 1,
  },
});

Examples

High relevance example

This example shows excellent context relevance where all context directly supports the response:

import { createContextRelevanceScorerLLM } from "@mastra/evals";

const scorer = createContextRelevanceScorerLLM({
  model: "openai/gpt-4o-mini",
  options: {
    context: [
      "Einstein won the Nobel Prize for his discovery of the photoelectric effect in 1921.",
      "He published his theory of special relativity in 1905.",
      "His general relativity theory, published in 1915, revolutionized our understanding of gravity.",
    ],
    scale: 1,
  },
});

const result = await scorer.run({
  input: {
    inputMessages: [
      {
        id: "1",
        role: "user",
        content: "What were Einstein's major scientific achievements?",
      },
    ],
  },
  output: [
    {
      id: "2",
      role: "assistant",
      content:
        "Einstein's major achievements include the Nobel Prize for the photoelectric effect, special relativity in 1905, and general relativity in 1915.",
    },
  ],
});

console.log(result);
// Output:
// {
//   score: 1.0,
//   reason: "The score is 1.0 because all context pieces are highly relevant to Einstein's achievements and were effectively used in generating the comprehensive response."
// }

Mixed relevance example

This example shows moderate relevance with some context being irrelevant or unused:

import { createContextRelevanceScorerLLM } from "@mastra/evals";

const scorer = createContextRelevanceScorerLLM({
  model: "openai/gpt-4o-mini",
  options: {
    context: [
      "Solar eclipses occur when the Moon blocks the Sun.",
      "The Moon moves between the Earth and Sun during eclipses.",
      "The Moon is visible at night.",
      "Stars twinkle due to atmospheric interference.",
      "Total eclipses can last up to 7.5 minutes.",
    ],
    scale: 1,
  },
});

const result = await scorer.run({
  input: {
    inputMessages: [
      {
        id: "1",
        role: "user",
        content: "What causes solar eclipses?",
      },
    ],
  },
  output: [
    {
      id: "2",
      role: "assistant",
      content:
        "Solar eclipses happen when the Moon moves between Earth and the Sun, blocking sunlight.",
    },
  ],
});

console.log(result);
// Output with default penalties:
// {
//   score: 0.64,
//   reason: "The score is 0.64 because contexts 1 and 2 are highly relevant and used, context 5 is relevant but unused (10% penalty), while contexts 3 and 4 are irrelevant."
// }

// With custom penalty configuration
const customScorer = createContextRelevanceScorerLLM({
  model: "openai/gpt-4o-mini",
  options: {
    context: [
      "Solar eclipses occur when the Moon blocks the Sun.",
      "The Moon moves between the Earth and Sun during eclipses.",
      "The Moon is visible at night.",
      "Stars twinkle due to atmospheric interference.",
      "Total eclipses can last up to 7.5 minutes.",
    ],
    penalties: {
      unusedHighRelevanceContext: 0.05, // Lower penalty for unused context
      missingContextPerItem: 0.1,
      maxMissingContextPenalty: 0.3,
    },
  },
});

const customResult = await customScorer.run({
  input: {
    inputMessages: [
      { id: "1", role: "user", content: "What causes solar eclipses?" },
    ],
  },
  output: [
    {
      id: "2",
      role: "assistant",
      content:
        "Solar eclipses happen when the Moon moves between Earth and the Sun, blocking sunlight.",
    },
  ],
});

console.log(customResult);
// Output with lenient penalties:
// {
//   score: 0.69, // Higher score due to reduced penalty for unused context
//   reason: "The score is 0.69 because contexts 1 and 2 are highly relevant and used, context 5 is relevant but unused (5% penalty), while contexts 3 and 4 are irrelevant."
// }

Low relevance example

This example shows poor context relevance with mostly irrelevant information:

import { createContextRelevanceScorerLLM } from "@mastra/evals";

const scorer = createContextRelevanceScorerLLM({
  model: "openai/gpt-4o-mini",
  options: {
    context: [
      "The Great Barrier Reef is located in Australia.",
      "Coral reefs need warm water to survive.",
      "Many fish species live in coral reefs.",
      "Australia has six states and two territories.",
      "The capital of Australia is Canberra.",
    ],
    scale: 1,
  },
});

const result = await scorer.run({
  input: {
    inputMessages: [
      {
        id: "1",
        role: "user",
        content: "What is the capital of Australia?",
      },
    ],
  },
  output: [
    {
      id: "2",
      role: "assistant",
      content: "The capital of Australia is Canberra.",
    },
  ],
});

console.log(result);
// Output:
// {
//   score: 0.26,
//   reason: "The score is 0.26 because only context 5 is relevant to the query about Australia's capital, while the other contexts about reefs are completely irrelevant."
// }

Dynamic context extraction

Extract context dynamically based on the run input:

import { createContextRelevanceScorerLLM } from "@mastra/evals";

const scorer = createContextRelevanceScorerLLM({
  model: "openai/gpt-4o-mini",
  options: {
    contextExtractor: (input, output) => {
      // Extract query from input
      const query = input?.inputMessages?.[0]?.content || "";

      // Dynamically retrieve context based on query
      if (query.toLowerCase().includes("einstein")) {
        return [
          "Einstein developed E=mc²",
          "He won the Nobel Prize in 1921",
          "His theories revolutionized physics",
        ];
      }

      if (query.toLowerCase().includes("climate")) {
        return [
          "Global temperatures are rising",
          "CO2 levels affect climate",
          "Renewable energy reduces emissions",
        ];
      }

      return ["General knowledge base entry"];
    },
    penalties: {
      unusedHighRelevanceContext: 0.15, // 15% penalty for unused relevant context
      missingContextPerItem: 0.2, // 20% penalty per missing context item
      maxMissingContextPenalty: 0.4, // Cap at 40% total missing context penalty
    },
    scale: 1,
  },
});

RAG system integration

Integrate with RAG pipelines to evaluate retrieved context:

import { createContextRelevanceScorerLLM } from "@mastra/evals";

const scorer = createContextRelevanceScorerLLM({
  model: "openai/gpt-4o-mini",
  options: {
    contextExtractor: (input, output) => {
      // Extract from RAG retrieval results
      const ragResults = input.metadata?.ragResults || [];

      // Return the text content of retrieved documents
      return ragResults
        .filter((doc) => doc.relevanceScore > 0.5)
        .map((doc) => doc.content);
    },
    penalties: {
      unusedHighRelevanceContext: 0.12, // Moderate penalty for unused RAG context
      missingContextPerItem: 0.18, // Higher penalty for missing information in RAG
      maxMissingContextPenalty: 0.45, // Slightly higher cap for RAG systems
    },
    scale: 1,
  },
});

// Evaluate RAG system performance
const evaluateRAG = async (testCases) => {
  const results = [];

  for (const testCase of testCases) {
    const score = await scorer.run(testCase);
    results.push({
      query: testCase.input.inputMessages[0].content,
      relevanceScore: score.score,
      feedback: score.reason,
      unusedContext: score.reason.includes("unused"),
      missingContext: score.reason.includes("missing"),
    });
  }

  return results;
};

Comparison with Context Precision

Choose the right scorer for your needs:

Use Case	Context Relevance	Context Precision
RAG evaluation	When usage matters	When ranking matters
Context quality	Nuanced levels	Binary relevance
Missing detection	✓ Identifies gaps	✗ Not evaluated
Usage tracking	✓ Tracks utilization	✗ Not considered
Position sensitivity	✗ Position agnostic	✓ Rewards early placement

Related

• Context Precision Scorer - Evaluates context ranking using MAP
• Faithfulness Scorer - Measures answer groundedness in context
• Custom Scorers - Creating your own evaluation metrics