fix(scoring): use normalised key weights in _searchObjectList

src/core/index.ts

@@ -434,7 +434,15 @@ export default class Fuse<T> {
     const searcher = this._getSearcher(query)
     const requireAllTokens =
       this.options.useTokenSearch && this.options.tokenMatch === 'all'
-    const { keys, records } = this._myIndex
+    const { records } = this._myIndex
+    // Use KeyStore's normalised keys so that key.weight reflects the
+    // weight normalisation performed by KeyStore (weights sum to 1).
+    // Previously this used this._myIndex.keys whose weights are the raw
+    // user-supplied values; with extreme weights (e.g. 100) the exponent
+    // Math.pow(Number.EPSILON, weight * norm) underflows to 0, and scores
+    // become inconsistent with _searchLogical which already reads from
+    // this._keyStore.get(keyId).
+    const keys = this._keyStore.keys()
     const results: InternalResult[] | null = heap ? null : []
 
     // List is Array<Object>

test/key-weight-normalization.test.js

@@ -0,0 +1,117 @@
+// Regression tests for https://github.com/krisk/Fuse/issues/833
+//
+// _searchObjectList previously used this._myIndex.keys (raw user-specified
+// weights) instead of this._keyStore.keys() (normalised weights that sum to
+// 1). This caused two observable bugs:
+//
+//   1. For a single key, weight:N always normalises to 1.0 (only key = 100 %),
+//      so any two weight values must produce identical scores. With the raw
+//      weight in the exponent, Math.pow(Number.EPSILON, N * fieldNorm)
+//      underflows to exactly 0 for large N.
+//
+//   2. Scores from plain string queries (_searchObjectList, raw weights) differ
+//      from scores for the same search expressed as an Expression query
+//      (_searchLogical, normalised KeyStore weights).
+
+import { describe, test, expect } from 'vitest'
+
+process.env.EXTENDED_SEARCH_ENABLED = 'true'
+process.env.LOGICAL_SEARCH_ENABLED = 'true'
+
+const { default: Fuse } = await import('../src/entry')
+
+describe('key weight normalisation in _searchObjectList', () => {
+  // ── single-key weight invariance ─────────────────────────────────────────
+  //
+  // A single key always normalises to weight=1.0 regardless of its raw value,
+  // so any two raw weight values must produce the same score.
+
+  test('single key weight:1 and weight:2 produce the same score', () => {
+    const fuseW1 = new Fuse([{ text: 'hello' }], {
+      keys: [{ name: 'text', weight: 1 }],
+      includeScore: true
+    })
+    const fuseW2 = new Fuse([{ text: 'hello' }], {
+      keys: [{ name: 'text', weight: 2 }],
+      includeScore: true
+    })
+
+    const r1 = fuseW1.search('hello')
+    const r2 = fuseW2.search('hello')
+
+    expect(r1).toHaveLength(1)
+    expect(r2).toHaveLength(1)
+    // After normalisation both instances have key.weight == 1.0, so the
+    // computed score must be identical.
+    expect(r1[0].score).toBe(r2[0].score)
+  })
+
+  test('single key weight:100 does not underflow to 0', () => {
+    // Math.pow(Number.EPSILON, 100 * fieldNorm) underflows to exactly 0 when
+    // the raw weight (100) is used; the normalised weight (1.0) does not.
+    const fuseW1 = new Fuse([{ text: 'hello' }], {
+      keys: [{ name: 'text', weight: 1 }],
+      includeScore: true
+    })
+    const fuseW100 = new Fuse([{ text: 'hello' }], {
+      keys: [{ name: 'text', weight: 100 }],
+      includeScore: true
+    })
+
+    const rW1 = fuseW1.search('hello')
+    const rW100 = fuseW100.search('hello')
+
+    expect(rW1).toHaveLength(1)
+    expect(rW100).toHaveLength(1)
+
+    // weight:100 must not underflow to 0
+    expect(rW100[0].score).toBeGreaterThan(0)
+    // and must equal the weight:1 result (both normalise to 1.0)
+    expect(rW100[0].score).toBe(rW1[0].score)
+  })
+
+  // ── multi-key: proportional weight ratio is preserved ─────────────────────
+  //
+  // Scaling all weights by the same factor (e.g. ×10) must not change the
+  // result ordering because normalisation collapses the factor.
+
+  test('scaling all weights by the same factor does not change result ordering', () => {
+    const docs = [
+      { title: 'JavaScript Guide', author: 'Alice' },
+      { title: 'Python Cookbook', author: 'JavaScript Bob' }
+    ]
+
+    const fuseNorm = new Fuse(docs, {
+      keys: [
+        { name: 'title', weight: 0.7 },
+        { name: 'author', weight: 0.3 }
+      ],
+      includeScore: true,
+      threshold: 1
+    })
+    const fuseScaled = new Fuse(docs, {
+      keys: [
+        { name: 'title', weight: 7 },   // ×10 — same ratio
+        { name: 'author', weight: 3 }
+      ],
+      includeScore: true,
+      threshold: 1
+    })
+
+    const rNorm   = fuseNorm.search('javascript')
+    const rScaled = fuseScaled.search('javascript')
+
+    expect(rNorm.length).toBeGreaterThan(0)
+    expect(rScaled.length).toBeGreaterThan(0)
+
+    // Ordering must be the same after normalisation
+    const orderNorm   = rNorm.map(r => r.item.title)
+    const orderScaled = rScaled.map(r => r.item.title)
+    expect(orderNorm).toEqual(orderScaled)
+
+    // Absolute scores must also match (normalisation collapses the ×10 factor)
+    rNorm.forEach((result, i) => {
+      expect(result.score).toBeCloseTo(rScaled[i].score, 10)
+    })
+  })
+})