scai/dist/fileRules/scoreFiles.js

> AI-powered CLI tool for commit messages **and** pull request reviews — using local models.

import { stringSimilarity } from 'string-similarity-js';
export function scoreFiles(query, embedding, candidates) {
    const terms = query.toLowerCase().split(/\s+/);
    const bm25Min = Math.min(...candidates.map(r => r.bm25Score));
    const bm25Max = Math.max(...candidates.map(r => r.bm25Score));
    return candidates.map(result => {
        let finalScore = 0;
        let sim = 0;
        const path = result.path.toLowerCase();
        const filename = result.filename.toLowerCase();
        const summary = (result.summary || '').toLowerCase();
        // 🎯 Normalize BM25
        const normalizedBm25 = 1 - ((result.bm25Score - bm25Min) / (bm25Max - bm25Min + 1e-5));
        // 🧠 Embedding similarity
        if (result.embedding) {
            try {
                const vector = JSON.parse(result.embedding);
                sim = cosineSimilarity(embedding, vector);
            }
            catch {
                sim = 0;
            }
        }
        // 🧩 Match ratio
        let matchRatio = 0;
        let matchedTerms = 0;
        for (const term of terms) {
            if (path.includes(term) || summary.includes(term))
                matchedTerms++;
        }
        matchRatio = matchedTerms / terms.length;
        const termBoost = matchRatio >= 1 ? 1.0 : matchRatio >= 0.5 ? 0.5 : 0;
        // 🪜 Path heuristics
        const isHtml = path.endsWith('.html');
        const isSrc = path.includes('/src/') || path.includes('/controls/');
        const isDoc = path.includes('/docs/') || path.includes('/mvndist/');
        const isExactMatch = filename === `${terms[0]}.js`;
        let weight = 1;
        if (isHtml)
            weight *= 0.85;
        if (isDoc)
            weight *= 0.8;
        if (isSrc)
            weight *= 1.2;
        if (isExactMatch)
            weight *= 1.5;
        // 🧪 Fuzzy score
        const fuzzyScore = stringSimilarity(query, `${path} ${summary}`);
        // 🧮 Final composite score
        finalScore =
            (0.4 * normalizedBm25) +
                (0.4 * sim) +
                (0.2 * termBoost) +
                (fuzzyScore * 0.5); // scale fuzzy match moderately
        finalScore *= weight;
        return {
            id: result.id,
            path: result.path,
            summary: result.summary,
            score: finalScore,
            sim,
            bm25: result.bm25Score,
        };
    }).sort((a, b) => b.score - a.score);
}
function cosineSimilarity(a, b) {
    const dot = a.reduce((sum, ai, i) => sum + ai * b[i], 0);
    const magA = Math.sqrt(a.reduce((sum, ai) => sum + ai * ai, 0));
    const magB = Math.sqrt(b.reduce((sum, bi) => sum + bi * bi, 0));
    return dot / (magA * magB);
}