src/brainy.ts

@@ -7651,18 +7651,6 @@ export class Brainy<T = any> implements BrainyInterface<T> {
       const searchMode = params.searchMode || 'auto'
       const limit = params.limit || 10
 
-      // HYDRATE LAST (the hybrid path): its legs and its fusion rank IDS, and
-      // canonical is read at the two page exits below — never for a row the
-      // metadata filter is about to discard. This closure re-applies a hybrid
-      // row's match visibility once its entity is in hand; it is set only by
-      // the hybrid branch, so every other path hydrates unchanged.
-      let finishHybridRow: ((row: Result<T>, pending: Result<T>) => void) | undefined
-
-      // Set once the metadata block below has already ranked and CUT the page.
-      // The tail must not cut it a second time: `offset` has been consumed, and
-      // re-slicing a `limit`-long page by `offset` returns nothing at all.
-      let pagedEarly = false
-
       // Handle text-only query (user explicitly wants text search)
       if (searchMode === 'text' && params.query && params.query.trim() !== '') {
         results = await this.executeTextSearch(params.query, limit * 2)
@@ -7673,32 +7661,20 @@ export class Brainy<T = any> implements BrainyInterface<T> {
       }
       // Handle explicit hybrid or auto mode with query
       else if ((searchMode === 'auto' || searchMode === 'hybrid') && params.query && params.query.trim() !== '' && !params.vector) {
-        // Zero-config hybrid: combine text + semantic search with RRF fusion.
-        // BOTH legs are held to the metadata filter's universe: the vector leg
-        // walks it as its candidate set, and the text leg ranks inside it
-        // instead of ranking the whole store and discarding what the filter
-        // would drop. Neither leg reads canonical — the page does, once.
-        const [textScored, semanticScored] = await Promise.all([
-          this.executeTextSearchScored(params.query, limit * 2, preResolvedMetadataIds ?? undefined),
-          this.executeVectorSearchScored(params, preResolvedMetadataIds ?? undefined, preResolvedAllowedIds)
+        // Zero-config hybrid: combine text + semantic search with RRF fusion
+        const [textResults, semanticResults] = await Promise.all([
+          this.executeTextSearch(params.query, limit * 2),
+          this.executeVectorSearch(params, preResolvedMetadataIds ?? undefined, preResolvedAllowedIds)
         ])
 
         // Use user-specified alpha or auto-detect based on query length
         const alpha = params.hybridAlpha ?? this.autoAlpha(params.query)
 
-        // Tokenize query for match visibility. The word list needs the entity,
-        // so it is computed on the page, at hydration.
+        // Tokenize query for match visibility
         const queryWords = this.metadataIndex.tokenize(params.query)
-        const textResultIds = new Set(textScored.map((r) => r.id))
-        finishHybridRow = (row, pending) => {
-          row.textMatches = this.findMatchingWords(row.entity, queryWords, textResultIds)
-          row.textScore = pending.textScore
-          row.semanticScore = pending.semanticScore
-          row.matchSource = pending.matchSource
-        }
 
-        // RRF fusion combines both ranked id sets with match visibility
-        results = this.rrfFusion(textScored, semanticScored, alpha)
+        // RRF fusion combines both result sets with match visibility
+        results = await this.rrfFusion(textResults, semanticResults, alpha, queryWords)
       }
       // Handle direct vector search (no query text) - no hybrid needed
       else if (params.vector && !params.query) {
@@ -7759,13 +7735,20 @@ export class Brainy<T = any> implements BrainyInterface<T> {
             const k = offset + limit
             const order = rankIndicesByScore(results.map(r => r.score), k, true)
             results = reorderByIndices(results, order).slice(offset, k)
-            pagedEarly = true
 
-            // Batch-load entities only for the paginated results (10x faster on GCS).
-            // This is the hydrate-last seam for the deferring paths: a row that
-            // arrives as a ranked shell is rebuilt in full here — flattened
-            // fields, entity and match visibility — never `entity` alone.
-            results = await this.hydrateResultPage(results, finishHybridRow)
+            // Batch-load entities only for the paginated results (10x faster on GCS)
+            const idsToLoad = results.filter(r => !r.entity).map(r => r.id)
+            if (idsToLoad.length > 0) {
+              const entitiesMap = await this.batchGet(idsToLoad)
+              for (const result of results) {
+                if (!result.entity) {
+                  const entity = entitiesMap.get(result.id)
+                  if (entity) {
+                    result.entity = entity
+                  }
+                }
+              }
+            }
 
             // Early return if no other processing needed
             if (!params.connected && !params.fusion) {
@@ -7871,20 +7854,8 @@ export class Brainy<T = any> implements BrainyInterface<T> {
 
       const finalOffset = params.offset || 0
 
-      // Efficient pagination - only slice what we need (limit already defined
-      // above), THEN read canonical for the page. Rows that arrived hydrated
-      // pass straight through; a deferred path reads exactly these rows.
-      //
-      // A page the metadata block already cut is NOT cut again: it holds the
-      // rows at [offset, offset+limit) of the ranking, so slicing it by
-      // `offset` a second time drops the whole page. That is how
-      // `find({ query, connected, where, offset })` — the shapes that reach
-      // here after early paging, `connected` and `fusion` — answered [] for
-      // every page but the first.
-      return await this.hydrateResultPage(
-        pagedEarly ? results : results.slice(finalOffset, finalOffset + limit),
-        finishHybridRow
-      )
+      // Efficient pagination - only slice what we need (limit already defined above)
+      return results.slice(finalOffset, finalOffset + limit)
     })()
 
     // Index-integrity guard — applied ONCE here so every find() path (metadata,
@@ -12978,31 +12949,6 @@ export class Brainy<T = any> implements BrainyInterface<T> {
     }
   }
 
-  /**
-   * The text-leg twin of {@link filterIdsWithinBelted}: rank `query` INSIDE the
-   * candidate universe, through the provider's own posting-list merge so the
-   * answer can never drift from `getIdsForTextQuery`'s. A provider without the
-   * door is served by its whole-store answer intersected here — the same rows
-   * in the same order, but it pays the whole-store marshal.
-   *
-   * @param query - The text query.
-   * @param ids - The candidate universe (the metadata filter's ids).
-   * @returns `{ id, matchCount }` rows inside `ids`, ranked by match count.
-   */
-  private async textIdsWithinBelted(
-    query: string,
-    ids: readonly string[]
-  ): Promise<Array<{ id: string; matchCount: number }>> {
-    this.ensureIndexesLoaded(['metadata'])
-    const mip = this.metadataIndex as unknown as MetadataIndexProvider
-    if (typeof mip.getIdsForTextQueryWithin === 'function') {
-      return await mip.getIdsForTextQueryWithin(query, ids)
-    }
-    const within = new Set(ids)
-    const all = await this.metadataIndex.getIdsForTextQuery(query)
-    return all.filter((m) => within.has(m.id))
-  }
-
   async getIndexStatus(): Promise<{
     initialized: boolean
     /** `true` once open()'s index-build-if-needed step has run. Named for API
@@ -15895,44 +15841,6 @@ export class Brainy<T = any> implements BrainyInterface<T> {
     candidateIds?: string[],
     allowedIds?: OpaqueIdSet
   ): Promise<Result<T>[]> {
-    const scored = await this.executeVectorSearchScored(params, candidateIds, allowedIds)
-
-    // Batch-load entities for 10-50x faster cloud storage performance
-    // GCS: 10 results = 1×50ms vs 10×50ms = 500ms (10x faster)
-    const entitiesMap = await this.batchGet(scored.map((s) => s.id))
-
-    const results: Result<T>[] = []
-    for (const { id, score } of scored) {
-      const entity = entitiesMap.get(id)
-      if (entity) {
-        results.push(this.createResult(id, score, entity))
-      }
-    }
-
-    return results
-  }
-
-  /**
-   * The semantic leg WITHOUT hydration — ranked ids and their scores.
-   *
-   * The beam walk is already restricted to the candidate universe (that is what
-   * `candidateIds` / `allowedIds` are for), so the leg's cost is the walk. Its
-   * ROWS, though, are candidates for a fusion that will keep one page of them —
-   * so the hybrid path takes them unhydrated and reads exactly the page it
-   * returns. {@link executeVectorSearch} is the eager form, for the search modes
-   * whose leg output IS the answer.
-   *
-   * @param params - Find parameters (supplies the query/vector and the limit).
-   * @param candidateIds - Optional pre-resolved metadata universe (see
-   *   {@link executeVectorSearch}).
-   * @param allowedIds - Optional opaque predicate-pushdown universe.
-   * @returns Ranked `{ id, score }` rows — no entity reads.
-   */
-  private async executeVectorSearchScored(
-    params: FindParams<T>,
-    candidateIds?: string[],
-    allowedIds?: OpaqueIdSet
-  ): Promise<Array<{ id: string; score: number }>> {
     // Vector cold-read guard: before trusting a semantic/vector result, verify the
     // vector index actually SERVES a known persisted vector (one-shot per brain).
     // A pure semantic find({ query }) has no filter, so verifyMetadataLive never
@@ -15958,10 +15866,21 @@ export class Brainy<T = any> implements BrainyInterface<T> {
     // HNSW search with optional metadata-first candidate filtering
     const searchResults: [string, number][] = await this.index.search(vector, limit * 2, undefined, searchOptions)
 
-    return searchResults.map(([id, distance]) => ({
-      id,
-      score: Math.max(0, Math.min(1, 1 / (1 + distance)))
-    }))
+    // Batch-load entities for 10-50x faster cloud storage performance
+    // GCS: 10 results = 1×50ms vs 10×50ms = 500ms (10x faster)
+    const ids = searchResults.map(([id]) => id)
+    const entitiesMap = await this.batchGet(ids)
+
+    const results: Result<T>[] = []
+    for (const [id, distance] of searchResults) {
+      const entity = entitiesMap.get(id)
+      if (entity) {
+        const score = Math.max(0, Math.min(1, 1 / (1 + distance)))
+        results.push(this.createResult(id, score, entity))
+      }
+    }
+
+    return results
   }
   
   /**
@@ -16261,64 +16180,30 @@ export class Brainy<T = any> implements BrainyInterface<T> {
    * @returns Array of Results with scores based on match count
    */
   private async executeTextSearch(query: string, limit: number): Promise<Result<T>[]> {
-    const scored = await this.executeTextSearchScored(query, limit)
-    if (scored.length === 0) return []
+    const textMatches = await this.metadataIndex.getIdsForTextQuery(query)
+    if (textMatches.length === 0) return []
 
-    // Batch-load entities for the whole leg — this is the eager form, kept for
-    // the text-only search mode whose results ARE the answer.
-    const entitiesMap = await this.batchGet(scored.map((s) => s.id))
+    // Take top matches and load entities
+    const topMatches = textMatches.slice(0, limit * 2)  // Get more for filtering
+    const ids = topMatches.map(m => m.id)
+    const entitiesMap = await this.batchGet(ids)
 
+    // Create results with scores based on match count
+    const maxMatches = topMatches[0]?.matchCount || 1
     const results: Result<T>[] = []
-    for (const { id, score } of scored) {
-      const entity = entitiesMap.get(id)
+
+    for (const match of topMatches) {
+      const entity = entitiesMap.get(match.id)
       if (entity) {
-        results.push(this.createResult(id, score, entity))
+        // Normalize score to 0-1 range based on match count
+        const score = match.matchCount / maxMatches
+        results.push(this.createResult(match.id, score, entity))
       }
     }
 
     return results
   }
 
-  /**
-   * The text leg WITHOUT hydration — ranked ids and their scores.
-   *
-   * FILTER BEFORE HYDRATE: when the caller already knows the candidate
-   * universe (the metadata filter's ids in a hybrid `find({ query, where })`),
-   * it is passed here and the word index ranks INSIDE that universe. The
-   * earlier order ranked the whole store, took the top `limit * 2`, hydrated
-   * every one of them, and only then intersected with the filter — so a
-   * filtered hybrid find on a large store hydrated hundreds of rows to return
-   * a handful, and a matching row outside the store-wide text prefix was
-   * silently dropped (the same defect `find({ connected })` had before the
-   * graph-first law).
-   *
-   * The score is the match count normalized against the top row's, so a
-   * restricted call normalizes against the top row IN THE UNIVERSE — the same
-   * rule applied to the set actually being ranked.
-   *
-   * @param query - Text query to search for.
-   * @param limit - Result budget; the leg keeps `limit * 2` for the fusion.
-   * @param candidateIds - Optional candidate universe to rank inside.
-   * @returns Ranked `{ id, score }` rows — no entity reads.
-   */
-  private async executeTextSearchScored(
-    query: string,
-    limit: number,
-    candidateIds?: readonly string[]
-  ): Promise<Array<{ id: string; score: number }>> {
-    const textMatches = candidateIds
-      ? await this.textIdsWithinBelted(query, candidateIds)
-      : await this.metadataIndex.getIdsForTextQuery(query)
-    if (textMatches.length === 0) return []
-
-    // Take top matches (more than the page, for the fusion to rank)
-    const topMatches = textMatches.slice(0, limit * 2)
-
-    // Normalize score to 0-1 range based on match count
-    const maxMatches = topMatches[0]?.matchCount || 1
-    return topMatches.map((m) => ({ id: m.id, score: m.matchCount / maxMatches }))
-  }
-
   /**
    * Auto-detect optimal alpha for hybrid search
    *
@@ -16345,56 +16230,55 @@ export class Brainy<T = any> implements BrainyInterface<T> {
    *
    * Formula: score(d) = sum(1 / (k + rank(d))) for each list
    *
-   * Now includes match visibility (textScore, semanticScore, matchSource; the
-   * `textMatches` word list needs the entity and is filled at hydration).
+   * Now includes match visibility (textMatches, textScore, semanticScore, matchSource)
    *
-   * HYDRATE LAST: both legs arrive as ranked ids + scores, and the fusion ranks
-   * ids — no entity is read here. The rows it returns are ranked SHELLS; the
-   * page is cut from them and only that page is read from canonical (see
-   * {@link hydrateResultPage}). The earlier order hydrated both legs in full —
-   * hundreds of rows — to return one page of them.
-   *
-   * @param textResults - Ranked ids + scores from text search
-   * @param semanticResults - Ranked ids + scores from semantic search
+   * @param textResults - Results from text search
+   * @param semanticResults - Results from semantic search
    * @param alpha - Weight for semantic (0=text only, 1=semantic only)
+   * @param queryWords - Original query words for match tracking
    * @param k - RRF constant (default: 60, standard in literature)
-   * @returns Fused result shells sorted by combined score with match visibility
+   * @returns Fused results sorted by combined score with match visibility
    */
-  private rrfFusion(
-    textResults: ReadonlyArray<{ id: string; score: number }>,
-    semanticResults: ReadonlyArray<{ id: string; score: number }>,
+  private async rrfFusion(
+    textResults: Result<T>[],
+    semanticResults: Result<T>[],
     alpha: number,
+    queryWords: string[],
     k: number = 60
-  ): Result<T>[] {
+  ): Promise<Result<T>[]> {
     // Track scores and match details per entity
     interface MatchData {
       rrf: number
       textScore?: number
       semanticScore?: number
+      textMatches: string[]
       hasText: boolean
       hasSemantic: boolean
     }
     const matchData = new Map<string, MatchData>()
+    const entityMap = new Map<string, Entity<T>>()
 
     // Text contribution (1 - alpha weight)
     const textWeight = 1 - alpha
     textResults.forEach((r, rank) => {
       const rrfScore = textWeight * (1 / (k + rank + 1))
-      const existing = matchData.get(r.id) || { rrf: 0, hasText: false, hasSemantic: false }
+      const existing = matchData.get(r.id) || { rrf: 0, textMatches: [], hasText: false, hasSemantic: false }
       existing.rrf += rrfScore
       existing.textScore = r.score  // Original text search score (0-1)
       existing.hasText = true
       matchData.set(r.id, existing)
+      if (r.entity) entityMap.set(r.id, r.entity)
     })
 
     // Semantic contribution (alpha weight)
     semanticResults.forEach((r, rank) => {
       const rrfScore = alpha * (1 / (k + rank + 1))
-      const existing = matchData.get(r.id) || { rrf: 0, hasText: false, hasSemantic: false }
+      const existing = matchData.get(r.id) || { rrf: 0, textMatches: [], hasText: false, hasSemantic: false }
       existing.rrf += rrfScore
       existing.semanticScore = r.score  // Original semantic search score (0-1)
       existing.hasSemantic = true
       matchData.set(r.id, existing)
+      if (r.entity) entityMap.set(r.id, r.entity)
     })
 
     // Sort by fused score
@@ -16402,9 +16286,27 @@ export class Brainy<T = any> implements BrainyInterface<T> {
       .sort((a, b) => b[1].rrf - a[1].rrf)
       .map(([id, data]) => ({ id, data }))
 
-    // Create ranked shells with match visibility
+    // Build results - need to load any missing entities
+    const missingIds = sortedIds.filter(s => !entityMap.has(s.id)).map(s => s.id)
+    if (missingIds.length > 0) {
+      const loaded = await this.batchGet(missingIds)
+      for (const [id, entity] of loaded) {
+        entityMap.set(id, entity)
+      }
+    }
+
+    // Performance: Build set of text result IDs for O(1) lookup
+    // This avoids re-extracting text for entities that weren't in text results
+    const textResultIds = new Set(textResults.map(r => r.id))
+
+    // Create final results with match visibility
     const results: Result<T>[] = []
     for (const { id, data } of sortedIds) {
+      const entity = entityMap.get(id)
+      if (entity) {
+        // Find which query words matched - uses fast path if entity wasn't in text results
+        const textMatches = this.findMatchingWords(entity, queryWords, textResultIds)
+
         // Determine match source
         let matchSource: 'text' | 'semantic' | 'both'
         if (data.hasText && data.hasSemantic) {
@@ -16415,80 +16317,20 @@ export class Brainy<T = any> implements BrainyInterface<T> {
           matchSource = 'semantic'
         }
 
-      const result = this.pendingResult(id, data.rrf)
+        // Create result with match visibility
+        const result = this.createResult(id, data.rrf, entity)
+        result.textMatches = textMatches
         result.textScore = data.textScore
         result.semanticScore = data.semanticScore
         result.matchSource = matchSource
 
         results.push(result)
       }
+    }
 
     return results
   }
 
-  /**
-   * A ranked candidate whose entity has NOT been read yet.
-   *
-   * The shell carries everything the ranking tail needs — the id, the score,
-   * and the match-visibility fields — and nothing that requires canonical. It
-   * is typed `Result` so it flows through the shared dedupe / visibility /
-   * filter / rank / page tail unchanged; {@link hydrateResultPage} turns the
-   * survivors into real results before any caller sees them, and find()'s
-   * index-integrity guard drops any row that never gained an entity.
-   *
-   * @param id - The candidate's canonical id.
-   * @param score - Its rank score.
-   */
-  private pendingResult(id: string, score: number): Result<T> {
-    return { id, score } as Result<T>
-  }
-
-  /**
-   * Read canonical for exactly the rows that need it — the hydrate-last seam.
-   *
-   * Rows that already carry an entity (the eager legs: metadata, text-only,
-   * semantic-only, proximity, graph) pass through untouched, so this is a no-op
-   * for every path that has not deferred. Rows that are shells are read in ONE
-   * batch and rebuilt through {@link createResult}, so a hydrated row is
-   * indistinguishable from an eagerly-built one — same flattened fields, same
-   * `entity`, same key order — with `finish` re-applying the fields only the
-   * deferring path knows about (a hybrid row's match visibility).
-   *
-   * A shell whose id has no canonical row is dropped, exactly as the eager legs
-   * dropped it; find()'s index-integrity guard makes the same judgement on the
-   * page it returns.
-   *
-   * @param rows - The page's rows, ranked and paged already.
-   * @param finish - Applied to each rebuilt row, with its shell, after the
-   *   flattened fields are set.
-   * @returns The page with every surviving row hydrated.
-   */
-  private async hydrateResultPage(
-    rows: Result<T>[],
-    finish?: (row: Result<T>, pending: Result<T>) => void
-  ): Promise<Result<T>[]> {
-    const pendingIds: string[] = []
-    for (const row of rows) {
-      if (!row.entity) pendingIds.push(row.id)
-    }
-    if (pendingIds.length === 0) return rows
-
-    const entitiesMap = await this.batchGet(pendingIds)
-    const hydrated: Result<T>[] = []
-    for (const row of rows) {
-      if (row.entity) {
-        hydrated.push(row)
-        continue
-      }
-      const entity = entitiesMap.get(row.id)
-      if (!entity) continue
-      const filled = this.createResult(row.id, row.score, entity, row.explanation)
-      finish?.(filled, row)
-      hydrated.push(filled)
-    }
-    return hydrated
-  }
-
   /**
    * Find which query words match in an entity's text content
    *

src/plugin.ts

@@ -473,28 +473,6 @@ export interface MetadataIndexProvider {
     graphIndex: unknown
   ): Promise<{ ids: string[]; emptyAt: 'graph' | 'filter' | 'visibility' | 'none' } | null>
   getIdsForTextQuery(query: string): Promise<Array<{ id: string; matchCount: number }>>
-  /**
-   * @description OPTIONAL: score `query` over `ids` ONLY — the text-leg twin of
-   * {@link filterIdsWithin}, and the door a hybrid `find({ query, where })`
-   * walks. The metadata filter's universe is the candidate set there, so the
-   * text leg must cost O(|ids|) membership checks and marshal at most `|ids|`
-   * rows, never the whole posting list of every query word. A native index
-   * intersects its own postings with the candidate set (membership by entity
-   * int) before any string crosses the boundary; the reference index answers
-   * from its own `getIdsForTextQuery`, so the two doors can never disagree.
-   * Absent → Brainy intersects `getIdsForTextQuery`'s answer with `ids` itself
-   * (correct, and still hydrate-last, but it marshals the whole answer).
-   *
-   * The answer keeps `getIdsForTextQuery`'s contract: `{ id, matchCount }`
-   * sorted by `matchCount` descending, ties in the order the whole-store answer
-   * would have produced. Only rows in `ids` may appear.
-   * @param query - The same text query accepted by `getIdsForTextQuery`.
-   * @param ids - The candidate ids (canonical). The answer is a subset.
-   */
-  getIdsForTextQueryWithin?(
-    query: string,
-    ids: readonly string[]
-  ): Promise<Array<{ id: string; matchCount: number }>>
   getSortedIdsForFilter(filter: any, orderBy: string, order?: 'asc' | 'desc', topK?: number): Promise<string[]>
   getFilterValues(field: string): Promise<string[]>
   getFilterFields(): Promise<string[]>

src/utils/metadataIndex.ts

@@ -1509,56 +1509,11 @@ export class MetadataIndexManager implements MetadataIndexProvider {
    * @returns Array of { id, matchCount } sorted by matchCount descending
    */
   async getIdsForTextQuery(query: string): Promise<Array<{ id: string; matchCount: number }>> {
-    return this.scoreTextQuery(query)
-  }
-
-  /**
-   * Score a text query over `ids` ONLY — the reference implementation of the
-   * optional `getIdsForTextQueryWithin` door (see
-   * {@link import('../plugin.js').MetadataIndexProvider}). The hybrid
-   * `find({ query, where })` path passes the metadata filter's universe here so
-   * the text leg ranks INSIDE that universe instead of ranking the whole store
-   * and discarding the rows the filter would have dropped.
-   *
-   * It answers from the same posting-list merge as {@link getIdsForTextQuery},
-   * with the candidate membership applied as each word's postings are counted,
-   * so the two doors can never disagree: the answer is exactly the whole-store
-   * answer restricted to `ids`, in the same order.
-   *
-   * @param query - Text query to search for.
-   * @param ids - Candidate entity ids; only these may appear in the answer.
-   * @returns Array of { id, matchCount } sorted by matchCount descending.
-   */
-  async getIdsForTextQueryWithin(
-    query: string,
-    ids: readonly string[]
-  ): Promise<Array<{ id: string; matchCount: number }>> {
-    if (ids.length === 0) return []
-    return this.scoreTextQuery(query, new Set(ids))
-  }
-
-  /**
-   * The one posting-list merge behind both text doors.
-   *
-   * Each query word contributes AT MOST one match per entity (a posting list
-   * can name an id more than once), and entities are ranked by how many of the
-   * query's words they matched. `within`, when given, restricts the count to
-   * those candidates — applied during the merge, so a restricted call never
-   * materializes a whole-store match map.
-   *
-   * @param query - Text query to search for.
-   * @param within - Optional candidate universe; absent = the whole store.
-   * @returns Array of { id, matchCount } sorted by matchCount descending.
-   */
-  private async scoreTextQuery(
-    query: string,
-    within?: ReadonlySet<string>
-  ): Promise<Array<{ id: string; matchCount: number }>> {
     const queryWords = this.tokenize(query)
     if (queryWords.length === 0) return []
 
-    // Count matches per entity, one word's postings at a time.
-    const matchCounts = new Map<string, number>()
+    // Get IDs for each word hash
+    const wordIdSets: Map<string, number>[] = []
     for (const word of queryWords) {
       const wordHash = this.hashWord(word)
       let ids: string[]
@@ -1574,12 +1529,19 @@ export class MetadataIndexManager implements MetadataIndexProvider {
           throw err
         }
       }
-      // One count per (word, entity) — dedupe this word's postings first.
-      const counted = new Set<string>()
+      const idSet = new Map<string, number>()
       for (const id of ids) {
-        if (counted.has(id)) continue
-        counted.add(id)
-        if (within && !within.has(id)) continue
+        idSet.set(id, 1)
+      }
+      wordIdSets.push(idSet)
+    }
+
+    if (wordIdSets.length === 0) return []
+
+    // Count matches per entity
+    const matchCounts = new Map<string, number>()
+    for (const idSet of wordIdSets) {
+      for (const [id] of idSet) {
         matchCounts.set(id, (matchCounts.get(id) || 0) + 1)
       }
     }

tests/integration/find-hybrid-filter-before-hydrate.test.ts

@@ -1,635 +0,0 @@
-/**
- * @module tests/integration/find-hybrid-filter-before-hydrate
- * @description FILTER BEFORE HYDRATE, applied to the hybrid `find({ query })` path.
- *
- * A hybrid find fuses two legs. The semantic leg already walked only the
- * metadata filter's universe (`candidateIds` / `allowedIds`). The TEXT leg did
- * not: it ranked the WHOLE store, took the top `limit * 4`, read every one of
- * those rows from canonical, and only then intersected with the filter — so a
- * filtered hybrid find on a large store read hundreds of rows to return a
- * handful of them, and a matching row outside the store-wide text prefix was
- * silently dropped. That is the same defect `find({ connected })` carried
- * before the graph-first law, one leg over.
- *
- * Both halves are pinned here.
- *
- * THE ANSWER. Where the filter did not truncate the text leg — the universe
- * covers every text match, so both orders rank the same rows — the new
- * pipeline's answer is IDENTICAL to the old one's: same rows, same order, same
- * scores, same match visibility, same row shape. The oracle below is the
- * pre-change pipeline itself, replayed on the same brain through the same
- * doors, so the comparison is against what actually ran, not a remembered
- * expectation.
- *
- * THE CORRECTION. Where the filter DID truncate it — the query's words are
- * common outside the universe — the old order let the text leg contribute
- * nothing at all: every row it ranked was discarded by the filter, and the
- * answer came from the semantic leg alone. The new order ranks inside the
- * universe, so the text leg contributes the rows it always should have.
- *
- * THE COST. Canonical is read for exactly the page: one batch, `limit` rows,
- * never the legs. And the text leg is asked about the universe's ids only —
- * what it marshals is bounded by the universe, not by the store.
- */
-import { describe, it, expect, beforeAll, vi } from 'vitest'
-import { Brainy } from '../../src/brainy'
-import { NounType, VerbType } from '../../src/types/graphTypes'
-import { rankIndicesByScore, reorderByIndices } from '../../src/utils/resultRanking'
-import { resolveEntityId } from '../../src/utils/idNormalization'
-
-/** Embedding width of the default model — the row vectors must match it. */
-const DIM = 384
-
-/**
- * A deterministic, per-row-distinct unit vector. Distinct so the semantic leg
- * has a real ranking to produce (identical vectors would make its order a tie
- * break), deterministic so the oracle and the pipeline see the same one.
- */
-function seededVector(seed: number): number[] {
-  const v = new Array<number>(DIM)
-  for (let i = 0; i < DIM; i++) {
-    v[i] = Math.sin((i + 1) * 0.11 + seed * 0.37) * 0.5 + Math.cos((i + 1) * 0.05 + seed * 0.13) * 0.3
-  }
-  const magnitude = Math.sqrt(v.reduce((sum, x) => sum + x * x, 0))
-  return v.map((x) => x / magnitude)
-}
-
-/** The fields a caller reads off a hybrid row — the whole comparable surface. */
-function project(rows: any[]): any[] {
-  return rows.map((r) => ({
-    id: r.id,
-    score: r.score,
-    type: r.type,
-    metadata: r.metadata,
-    textMatches: r.textMatches,
-    textScore: r.textScore,
-    semanticScore: r.semanticScore,
-    matchSource: r.matchSource
-  }))
-}
-
-/**
- * The PRE-CHANGE hybrid pipeline, replayed on a live brain through the same
- * provider doors it used: whole-store text ranking with both legs hydrated in
- * full, RRF fusion, then the metadata intersection, then the page.
- *
- * Supports the shapes these pins exercise (query + where/type/excludeVFS +
- * connected + offset); `orderBy`, `fusion` and `near` are not replayed.
- */
-async function legacyHybridFind(brain: any, params: any): Promise<any[]> {
-  const index = brain.metadataIndex
-  const limit = params.limit ?? 10
-  const offset = params.offset ?? 0
-  const hasFilter = Boolean(
-    params.where || params.type || params.subtype || params.service || params.excludeVFS
-  )
-
-  let preResolvedMetadataIds: string[] | null = null
-  let preResolvedFilter: any = null
-  let graphFirstIds: string[] | null = null
-
-  if (params.connected) {
-    // find() normalizes the anchors to canonical ids before this stage runs.
-    const anchored = {
-      ...params,
-      connected: {
-        ...params.connected,
-        ...(params.connected.from && { from: resolveEntityId(params.connected.from) }),
-        ...(params.connected.to && { to: resolveEntityId(params.connected.to) })
-      }
-    }
-    graphFirstIds = await brain.resolveConnectedIds(anchored)
-    if (graphFirstIds!.length > 0 && hasFilter) {
-      preResolvedFilter = brain.buildMetadataFilter(params)
-      graphFirstIds = await brain.filterIdsWithinBelted(preResolvedFilter, graphFirstIds)
-    }
-    if (graphFirstIds!.length === 0) return []
-    preResolvedMetadataIds = graphFirstIds
-  } else if (hasFilter) {
-    preResolvedFilter = brain.buildMetadataFilter(params)
-    preResolvedMetadataIds = await brain.filterIdsBelted(preResolvedFilter)
-    if (preResolvedMetadataIds!.length === 0) return []
-  }
-
-  // Text leg — the whole store, then the top `limit * 4`, hydrated in full.
-  const allTextMatches = await index.getIdsForTextQuery(params.query)
-  const topMatches = allTextMatches.slice(0, limit * 2 * 2)
-  const maxMatches = topMatches[0]?.matchCount || 1
-  const textEntities = await brain.batchGet(topMatches.map((m: any) => m.id))
-  const textResults = topMatches
-    .filter((m: any) => textEntities.has(m.id))
-    .map((m: any) => ({ id: m.id, score: m.matchCount / maxMatches }))
-
-  // Semantic leg — the beam walk over the universe, hydrated in full.
-  const vector = await brain.embed(params.query)
-  const searchOptions = preResolvedMetadataIds ? { candidateIds: preResolvedMetadataIds } : undefined
-  const searchResults: [string, number][] = await brain.index.search(
-    vector,
-    limit * 2,
-    undefined,
-    searchOptions
-  )
-  const semanticEntities = await brain.batchGet(searchResults.map(([id]) => id))
-  const semanticResults = searchResults
-    .filter(([id]) => semanticEntities.has(id))
-    .map(([id, distance]) => ({ id, score: Math.max(0, Math.min(1, 1 / (1 + distance))) }))
-
-  // RRF fusion, with the match visibility the rows carried.
-  const alpha = params.hybridAlpha ?? brain.autoAlpha(params.query)
-  const k = 60
-  const matchData = new Map<string, any>()
-  const textWeight = 1 - alpha
-  textResults.forEach((r: any, rank: number) => {
-    const existing = matchData.get(r.id) || { rrf: 0, hasText: false, hasSemantic: false }
-    existing.rrf += textWeight * (1 / (k + rank + 1))
-    existing.textScore = r.score
-    existing.hasText = true
-    matchData.set(r.id, existing)
-  })
-  semanticResults.forEach((r: any, rank: number) => {
-    const existing = matchData.get(r.id) || { rrf: 0, hasText: false, hasSemantic: false }
-    existing.rrf += alpha * (1 / (k + rank + 1))
-    existing.semanticScore = r.score
-    existing.hasSemantic = true
-    matchData.set(r.id, existing)
-  })
-
-  const queryWords: string[] = index.tokenize(params.query)
-  const textResultIds = new Set(textResults.map((r: any) => r.id))
-  const fusedIds = Array.from(matchData.entries())
-    .sort((a, b) => b[1].rrf - a[1].rrf)
-    .map(([id, data]) => ({ id, data }))
-
-  const allEntities = await brain.batchGet(fusedIds.map((f) => f.id))
-  let rows: any[] = []
-  for (const { id, data } of fusedIds) {
-    const entity = allEntities.get(id)
-    if (!entity) continue
-    const textContent = textResultIds.has(id)
-      ? index.extractTextContent({ data: entity.data, metadata: entity.metadata }).toLowerCase()
-      : null
-    rows.push({
-      id,
-      score: data.rrf,
-      type: entity.type,
-      metadata: entity.metadata,
-      textMatches:
-        textContent === null ? [] : queryWords.filter((w) => textContent.includes(w.toLowerCase())),
-      textScore: data.textScore,
-      semanticScore: data.semanticScore,
-      matchSource: data.hasText && data.hasSemantic ? 'both' : data.hasText ? 'text' : 'semantic'
-    })
-  }
-
-  // The metadata intersection — after the legs, as it was.
-  if (preResolvedMetadataIds && preResolvedFilter) {
-    const filteredIdSet = new Set(preResolvedMetadataIds)
-    rows = rows.filter((r) => filteredIdSet.has(r.id))
-  }
-  if (graphFirstIds !== null) {
-    const neighbourSet = new Set(graphFirstIds)
-    rows = rows.filter((r) => neighbourSet.has(r.id))
-  }
-
-  // Rank to the page, then cut it.
-  const order = rankIndicesByScore(
-    rows.map((r) => r.score),
-    offset + limit,
-    true
-  )
-  return reorderByIndices(rows, order).slice(offset, offset + limit)
-}
-
-/**
- * FIXTURE A — the filter's universe covers every text match, so the two orders
- * rank exactly the same rows and the answers must be identical.
- */
-describe('hybrid find: filter before hydrate — the answer is unchanged', () => {
-  let brain: Brainy<any>
-  const QUERY = 'orbital telemetry'
-  const MATCHES = 24
-  const FILLER = 120
-  const OUTSIDE = 30
-  const VFS = 10
-  const RETRACTED = 6
-  const anchor = 'array-anchor'
-  const matchIds: string[] = []
-
-  beforeAll(async () => {
-    brain = new Brainy({ requireSubtype: false, storage: { type: 'memory' } })
-    await brain.init()
-
-    let seed = 1
-    await brain.add({
-      id: anchor,
-      data: 'ground station anchor record',
-      type: NounType.Thing,
-      metadata: { lane: 'alpha', role: 'anchor' },
-      vector: seededVector(seed++)
-    })
-
-    // Rows the query's words actually match — all inside every filter below.
-    for (let i = 0; i < MATCHES; i++) {
-      const id = `match-${i}`
-      await brain.add({
-        id,
-        data: `orbital telemetry packet ${i} recorded downlink`,
-        type: NounType.Document,
-        metadata: { lane: 'alpha', rank: i },
-        vector: seededVector(seed++)
-      })
-      matchIds.push(resolveEntityId(id))
-      await brain.relate({ from: anchor, to: id, type: VerbType.RelatedTo })
-    }
-    // Rows inside the universe that the query's words do NOT match.
-    for (let i = 0; i < FILLER; i++) {
-      await brain.add({
-        id: `filler-${i}`,
-        data: `cistern ledger entry ${i} archived`,
-        type: NounType.Document,
-        metadata: { lane: 'alpha', rank: 1000 + i },
-        vector: seededVector(seed++)
-      })
-    }
-    // Rows outside the universe.
-    for (let i = 0; i < OUTSIDE; i++) {
-      await brain.add({
-        id: `outside-${i}`,
-        data: `unrelated dossier ${i}`,
-        type: NounType.Person,
-        metadata: { lane: 'beta' },
-        vector: seededVector(seed++)
-      })
-    }
-    // VFS infrastructure rows — excluded by excludeVFS.
-    for (let i = 0; i < VFS; i++) {
-      await brain.add({
-        id: `vfs-${i}`,
-        data: `mounted path ${i}`,
-        type: NounType.Document,
-        metadata: { lane: 'alpha', vfsType: 'file' },
-        vector: seededVector(seed++)
-      })
-    }
-    // Retracted rows — excluded by a `missing` negation.
-    for (let i = 0; i < RETRACTED; i++) {
-      await brain.add({
-        id: `retracted-${i}`,
-        data: `withdrawn note ${i}`,
-        type: NounType.Document,
-        metadata: { lane: 'alpha', retracted: true },
-        vector: seededVector(seed++)
-      })
-    }
-
-    // The reference index has no opaque-set door, so the pipeline and the
-    // oracle both restrict the beam walk with the materialized candidate ids.
-    expect(typeof (brain as any).metadataIndex.getIdSetForFilter).not.toBe('function')
-  })
-
-  it('the fixture does not truncate the text leg — the universe covers every text match', async () => {
-    const index = (brain as any).metadataIndex
-    const textMatches = await index.getIdsForTextQuery(QUERY)
-    expect(textMatches).toHaveLength(MATCHES)
-    const universe = await (brain as any).filterIdsBelted({ lane: 'alpha' })
-    const inUniverse = new Set(universe)
-    for (const m of textMatches) expect(inUniverse.has(m.id)).toBe(true)
-  })
-
-  it('hybrid + where: identical rows, identical order, identical scores', async () => {
-    const params = { query: QUERY, where: { lane: 'alpha' }, limit: 8 }
-    const expected = await legacyHybridFind(brain as any, params)
-    const actual = await brain.find(params as any)
-    expect(actual.length).toBe(expected.length)
-    expect(project(actual)).toEqual(expected)
-  })
-
-  it('hybrid + where + offset: identical page two', async () => {
-    const params = { query: QUERY, where: { lane: 'alpha' }, limit: 6, offset: 6 }
-    const expected = await legacyHybridFind(brain as any, params)
-    const actual = await brain.find(params as any)
-    expect(actual.length).toBe(expected.length)
-    expect(project(actual)).toEqual(expected)
-  })
-
-  it('hybrid + type list + excludeVFS + a `missing` negation: identical', async () => {
-    const params = {
-      query: QUERY,
-      type: [NounType.Document, NounType.Person],
-      excludeVFS: true,
-      where: { lane: 'alpha', retracted: { missing: true } },
-      limit: 8
-    }
-    const expected = await legacyHybridFind(brain as any, params)
-    const actual = await brain.find(params as any)
-    expect(actual.length).toBe(expected.length)
-    expect(project(actual)).toEqual(expected)
-    for (const r of actual) {
-      expect(r.metadata.retracted).toBeUndefined()
-      expect(r.metadata.vfsType).toBeUndefined()
-    }
-  })
-
-  it('hybrid + type list + excludeVFS + a `missing` negation, offset: identical', async () => {
-    const params = {
-      query: QUERY,
-      type: [NounType.Document, NounType.Person],
-      excludeVFS: true,
-      where: { lane: 'alpha', retracted: { missing: true } },
-      limit: 5,
-      offset: 5
-    }
-    const expected = await legacyHybridFind(brain as any, params)
-    const actual = await brain.find(params as any)
-    expect(actual.length).toBe(expected.length)
-    expect(project(actual)).toEqual(expected)
-  })
-
-  it('hybrid + connected: identical, and never a non-neighbour', async () => {
-    const params = {
-      query: QUERY,
-      connected: { from: anchor, direction: 'out' as const },
-      where: { lane: 'alpha' },
-      limit: 8
-    }
-    const expected = await legacyHybridFind(brain as any, params)
-    const actual = await brain.find(params as any)
-    expect(actual.length).toBe(expected.length)
-    expect(project(actual)).toEqual(expected)
-    const neighbours = new Set(matchIds)
-    for (const r of actual) expect(neighbours.has(r.id)).toBe(true)
-  })
-
-  it('hybrid + connected + offset: page two is the page, not an empty answer', async () => {
-    const params = {
-      query: QUERY,
-      connected: { from: anchor, direction: 'out' as const },
-      where: { lane: 'alpha' },
-      limit: 5,
-      offset: 5
-    }
-    const expected = await legacyHybridFind(brain as any, params)
-    expect(expected).toHaveLength(5)
-    const actual = await brain.find(params as any)
-    expect(actual.length).toBe(expected.length)
-    expect(project(actual)).toEqual(expected)
-  })
-
-  it('hybrid + connected: paging reaches every matching neighbour exactly once', async () => {
-    const seen = new Set<string>()
-    for (let offset = 0; offset < MATCHES; offset += 6) {
-      const page = await brain.find({
-        query: QUERY,
-        connected: { from: anchor, direction: 'out' as const },
-        where: { lane: 'alpha' },
-        limit: 6,
-        offset
-      } as any)
-      for (const r of page) {
-        expect(seen.has(r.id)).toBe(false)
-        seen.add(r.id)
-      }
-    }
-    // Every row the fused candidate set holds is reachable by paging, and the
-    // neighbour set is the ceiling.
-    expect(seen.size).toBeGreaterThanOrEqual(MATCHES)
-    const neighbours = new Set(matchIds)
-    for (const id of seen) expect(neighbours.has(id)).toBe(true)
-  })
-
-  it('hybrid + fusion + offset: page two is the page', async () => {
-    const plain = await brain.find({
-      query: QUERY,
-      where: { lane: 'alpha' },
-      limit: 5,
-      offset: 5
-    } as any)
-    const fused = await brain.find({
-      query: QUERY,
-      where: { lane: 'alpha' },
-      fusion: 'weighted',
-      limit: 5,
-      offset: 5
-    } as any)
-    expect(fused).toHaveLength(plain.length)
-    expect(fused.map((r) => r.id)).toEqual(plain.map((r) => r.id))
-  })
-
-  it('a hydrated hybrid row is shaped exactly as an eagerly-built one', async () => {
-    const rows = await brain.find({ query: QUERY, where: { lane: 'alpha' }, limit: 8 } as any)
-    const row = rows[0]
-    expect(Object.keys(row)).toEqual([
-      'id',
-      'score',
-      'type',
-      'subtype',
-      'visibility',
-      'metadata',
-      'data',
-      'confidence',
-      'weight',
-      '_rev',
-      'entity',
-      'textMatches',
-      'textScore',
-      'semanticScore',
-      'matchSource'
-    ])
-    // The flattened fields are projections of the entity, as always.
-    expect(row.entity).toBeDefined()
-    expect(row.type).toBe(row.entity.type)
-    expect(row.metadata).toBe(row.entity.metadata)
-    expect(row.data).toBe(row.entity.data)
-    expect(row._rev).toBe(row.entity._rev)
-    // The match visibility survives the deferral — every leg's fields, on the
-    // rows that leg contributed, exactly as the eager pipeline set them.
-    expect(['text', 'semantic', 'both']).toContain(row.matchSource)
-    for (const r of rows) {
-      if (r.matchSource === 'semantic') {
-        expect(r.textMatches).toEqual([])
-        expect(r.textScore).toBeUndefined()
-      } else {
-        expect(r.textMatches).toEqual(['orbital', 'telemetry'])
-        expect(typeof r.textScore).toBe('number')
-      }
-      if (r.matchSource === 'text') {
-        expect(r.semanticScore).toBeUndefined()
-      } else {
-        expect(typeof r.semanticScore).toBe('number')
-      }
-    }
-  })
-
-  it('reads canonical for the page only — one batch, `limit` rows', async () => {
-    // Warm any first-read verification before the counters are read.
-    await brain.find({ query: QUERY, where: { lane: 'alpha' }, limit: 1 } as any)
-
-    const hydrate = vi.spyOn(brain as any, 'batchGet')
-    try {
-      const results = await brain.find({ query: QUERY, where: { lane: 'alpha' }, limit: 10 } as any)
-      expect(results).toHaveLength(10)
-      expect(hydrate).toHaveBeenCalledTimes(1)
-      expect((hydrate.mock.calls[0][0] as string[]).length).toBe(10)
-    } finally {
-      hydrate.mockRestore()
-    }
-  })
-
-  it('asks the text index about the universe only, never the whole store', async () => {
-    const index = (brain as any).metadataIndex
-    const wholeStore = vi.spyOn(index, 'getIdsForTextQuery')
-    const within = vi.spyOn(index, 'getIdsForTextQueryWithin')
-    try {
-      await brain.find({ query: QUERY, where: { lane: 'alpha' }, limit: 10 } as any)
-      expect(wholeStore).not.toHaveBeenCalled()
-      expect(within).toHaveBeenCalledTimes(1)
-
-      const askedIds = within.mock.calls[0][1] as string[]
-      const universe = await (brain as any).filterIdsBelted({ lane: 'alpha' })
-      expect(askedIds).toHaveLength(universe.length)
-
-      // What the text leg marshals is bounded by the universe, not the store.
-      const marshalled = (await within.mock.results[0].value) as unknown[]
-      expect(marshalled.length).toBeLessThanOrEqual(universe.length)
-      expect(marshalled).toHaveLength(MATCHES)
-    } finally {
-      wholeStore.mockRestore()
-      within.mockRestore()
-    }
-  })
-
-  it('the two text doors agree: within is the whole-store answer restricted', async () => {
-    const index = (brain as any).metadataIndex
-    const universe: string[] = await (brain as any).filterIdsBelted({
-      lane: 'alpha',
-      retracted: { missing: true }
-    })
-    const inUniverse = new Set(universe)
-    const whole = await index.getIdsForTextQuery(QUERY)
-    const within = await index.getIdsForTextQueryWithin(QUERY, universe)
-    expect(within).toEqual(whole.filter((m: any) => inUniverse.has(m.id)))
-    expect(await index.getIdsForTextQueryWithin(QUERY, [])).toEqual([])
-  })
-})
-
-/**
- * FIXTURE B — the query's words are common OUTSIDE the universe, so the old
- * order's text leg was entirely consumed by rows the filter then discarded.
- * This is the corrected behaviour, held by name.
- */
-describe('hybrid find: the text leg ranks inside the filter, not around it', () => {
-  let brain: Brainy<any>
-  const QUERY = 'orbital telemetry drift'
-  const NOISE = 150
-  const KEEP = 15
-  const keepIds: string[] = []
-
-  beforeAll(async () => {
-    brain = new Brainy({ requireSubtype: false, storage: { type: 'memory' } })
-    await brain.init()
-
-    let seed = 5000
-    // Added FIRST and matching one more query word, so they lead the
-    // store-wide text ranking outright — and none of them pass the filter.
-    for (let i = 0; i < NOISE; i++) {
-      await brain.add({
-        id: `noise-${i}`,
-        data: `orbital telemetry drift report ${i}`,
-        type: NounType.Document,
-        metadata: { lane: 'beta' },
-        vector: seededVector(seed++)
-      })
-    }
-    for (let i = 0; i < KEEP; i++) {
-      const id = `keep-${i}`
-      await brain.add({
-        id,
-        data: `orbital telemetry summary ${i}`,
-        type: NounType.Document,
-        metadata: { lane: 'alpha' },
-        vector: seededVector(seed++)
-      })
-      keepIds.push(resolveEntityId(id))
-    }
-  })
-
-  it('the old order let the filter consume the whole text leg', async () => {
-    const index = (brain as any).metadataIndex
-    const universe: string[] = await (brain as any).filterIdsBelted({ lane: 'alpha' })
-    expect(universe).toHaveLength(KEEP)
-    const inUniverse = new Set(universe)
-
-    // The store-wide prefix the old text leg took (limit 10 → limit * 4).
-    const prefix = (await index.getIdsForTextQuery(QUERY)).slice(0, 40)
-    expect(prefix).toHaveLength(40)
-    expect(prefix.filter((m: any) => inUniverse.has(m.id))).toHaveLength(0)
-
-    // Every row the old text leg ranked was then discarded by the filter, so
-    // the old answer carried NO text contribution at all — fifteen rows that
-    // match the query's words exactly, and not one of them reached the page
-    // through the text leg. What the old order returned was whatever the
-    // semantic leg alone happened to reach.
-    const legacy = await legacyHybridFind(brain as any, {
-      query: QUERY,
-      where: { lane: 'alpha' },
-      limit: 10
-    })
-    for (const r of legacy) {
-      expect(r.matchSource).toBe('semantic')
-      expect(r.textScore).toBeUndefined()
-      expect(r.textMatches).toEqual([])
-    }
-  })
-
-  it('the new order ranks the text leg inside the universe', async () => {
-    const results = await brain.find({
-      query: QUERY,
-      where: { lane: 'alpha' },
-      limit: 10
-    } as any)
-
-    expect(results).toHaveLength(10)
-    const keeps = new Set(keepIds)
-    for (const r of results) {
-      expect(keeps.has(r.id)).toBe(true)
-      expect(r.metadata.lane).toBe('alpha')
-      // The text leg is the contributor the old order threw away.
-      expect(['text', 'both']).toContain(r.matchSource)
-      expect(r.textScore).toBe(1)
-      expect(r.textMatches).toEqual(['orbital', 'telemetry'])
-    }
-  })
-
-  it('paging reaches every matching row the old order could not see', async () => {
-    const seen = new Set<string>()
-    for (let offset = 0; offset < KEEP; offset += 5) {
-      const page = await brain.find({
-        query: QUERY,
-        where: { lane: 'alpha' },
-        limit: 5,
-        offset
-      } as any)
-      expect(page).toHaveLength(5)
-      for (const r of page) {
-        expect(seen.has(r.id)).toBe(false)
-        seen.add(r.id)
-      }
-    }
-    expect(seen.size).toBe(KEEP)
-    expect([...seen].sort()).toEqual([...keepIds].sort())
-  })
-
-  it('reads canonical for the page only, on the truncating shape too', async () => {
-    await brain.find({ query: QUERY, where: { lane: 'alpha' }, limit: 1 } as any)
-
-    const hydrate = vi.spyOn(brain as any, 'batchGet')
-    try {
-      const results = await brain.find({ query: QUERY, where: { lane: 'alpha' }, limit: 10 } as any)
-      expect(results).toHaveLength(10)
-      expect(hydrate).toHaveBeenCalledTimes(1)
-      expect((hydrate.mock.calls[0][0] as string[]).length).toBe(10)
-    } finally {
-      hydrate.mockRestore()
-    }
-  })
-})