@juspay/neurolink/dist/lib/evaluation/scorers/llm/summarizationScorer.js

Universal AI Development Platform with working MCP integration, multi-provider support, voice (TTS/STT/realtime), and professional CLI. 58+ external MCP servers discoverable, multimodal file processing, RAG pipelines. Build, test, and deploy AI applicatio

/**
 * @file Summarization quality scorer
 * Evaluates the quality of AI-generated summaries
 */
import { BaseLLMScorer } from "./baseLLMScorer.js";
const SUMMARIZATION_PROMPT = `You are an expert at evaluating summary quality.

**Summary to Evaluate:**
{{response}}

**Original Content:**
{{context}}

{{#if hasQuery}}
**Summarization Request:**
{{query}}
{{/if}}

## Evaluation Criteria

1. **Accuracy**: Is the summary factually correct?
2. **Coverage**: Does it capture the key points?
3. **Conciseness**: Is it appropriately brief?
4. **Coherence**: Is it well-organized and readable?
5. **No Hallucinations**: Does it avoid adding new information?

## Output Format (JSON)

{
  "score": <0-10>,
  "accuracy": {
    "score": <0-10>,
    "errors": ["<list of factual errors>"]
  },
  "coverage": {
    "score": <0-10>,
    "keyPointsCovered": ["<covered points>"],
    "keyPointsMissed": ["<missed points>"]
  },
  "conciseness": {
    "score": <0-10>,
    "assessment": "<too long|appropriate|too short>"
  },
  "coherence": {
    "score": <0-10>,
    "issues": ["<any coherence issues>"]
  },
  "hallucinations": ["<any fabricated information>"],
  "reasoning": "<overall assessment>",
  "confidence": <0.0-1.0>
}`;
export class SummarizationScorer extends BaseLLMScorer {
    constructor(config) {
        super({
            id: "summarization",
            name: "Summarization Quality",
            description: "Evaluates the quality of AI-generated summaries",
            type: "llm",
            category: "quality",
            version: "1.0.0",
            defaultConfig: {
                enabled: true,
                threshold: 0.7,
                weight: 1.0,
                timeout: 25000,
                retries: 2,
            },
            requiredInputs: ["response", "context"],
            optionalInputs: ["query"],
        }, config);
    }
    generatePrompt(input) {
        let prompt = SUMMARIZATION_PROMPT;
        const hasQuery = !!input.query;
        prompt = this.processConditionals(prompt, { hasQuery });
        return this.substituteTemplate(prompt, {
            response: input.response,
            context: input.context?.join("\n\n") ?? "",
            query: hasQuery ? input.query : "",
        });
    }
    parseResponse(response, _input) {
        const json = this.extractJSON(response);
        if (!json) {
            const score = this.extractScoreFromText(response);
            return {
                score,
                reasoning: "Could not parse structured response",
                confidence: 0.3,
            };
        }
        const score = Math.min(10, Math.max(0, typeof json.score === "number"
            ? json.score
            : this.extractScoreFromText(response)));
        const confidence = Math.min(1, Math.max(0, typeof json.confidence === "number" ? json.confidence : 0.3));
        return {
            score,
            reasoning: typeof json.reasoning === "string"
                ? json.reasoning
                : "No reasoning provided",
            confidence,
            metadata: {
                accuracy: json.accuracy ?? null,
                coverage: json.coverage ?? null,
                conciseness: json.conciseness ?? null,
                coherence: json.coherence ?? null,
                hallucinations: json.hallucinations ?? [],
            },
        };
    }
}
export async function createSummarizationScorer(config) {
    return new SummarizationScorer(config);
}
//# sourceMappingURL=summarizationScorer.js.map