feat: array-unnest groupBy for aggregates + batch-embed entity extraction

- groupBy now supports { field, unnest: true }: an entity contributes once per
  distinct element of an array field (tag frequency / faceted counts). Duplicate
  elements on one entity count once; an empty/missing array joins no group. The
  incremental add/update/delete paths fan out across the unnested groups.
- extractEntities/extractConcepts batch-embed the unique candidate spans in one
  embedBatch call instead of one embed() per candidate (N sequential model calls);
  falls back to per-candidate embedding if the batch fails. No behavior change.

src/neural/SmartExtractor.ts

@@ -34,6 +34,7 @@
 
 import type { Brainy } from '../brainy.js'
 import type { NounType } from '../types/graphTypes.js'
+import type { Vector } from '../coreTypes.js'
 import { ExactMatchSignal } from './signals/ExactMatchSignal.js'
 import { PatternSignal } from './signals/PatternSignal.js'
 import { EmbeddingSignal } from './signals/EmbeddingSignal.js'
@@ -211,6 +212,8 @@ export class SmartExtractor {
       formatContext?: FormatContext
       allTerms?: string[]
       metadata?: any
+      /** Pre-computed candidate embedding (from a batch embed) — forwarded to EmbeddingSignal. */
+      vector?: Vector
     },
     minConfidence?: number
   ): Promise<ExtractionResult | null> {
@@ -243,7 +246,8 @@ export class SmartExtractor {
       const enrichedContext = {
         definition: context?.definition,
         allTerms: [...(context?.allTerms || []), ...formatHints],
-        metadata: context?.metadata
+        metadata: context?.metadata,
+        vector: context?.vector
       }
 
       // Execute all signals in parallel

src/neural/entityExtractor.ts

@@ -137,7 +137,27 @@ export class NeuralEntityExtractor {
     
     // Step 1: Extract potential entities using patterns
     const candidates = await this.extractCandidates(text)
-    
+
+    // Step 1b: Batch-embed the unique candidate texts once, instead of one brain.embed()
+    // per candidate inside EmbeddingSignal (N sequential model calls). The vectors are
+    // forwarded into the signal; if the batch fails, the signal falls back to per-candidate
+    // embedding, so this is a pure performance optimization with no behavior change.
+    const vectorByText = new Map<string, Vector>()
+    if (useNeuralMatching) {
+      const uniqueTexts = [...new Set(candidates.map(c => c.text))]
+      if (uniqueTexts.length > 0) {
+        try {
+          const vectors = await this.brain.embedBatch(uniqueTexts)
+          uniqueTexts.forEach((t, i) => {
+            const v = vectors[i]
+            if (v) vectorByText.set(t, v)
+          })
+        } catch {
+          // Leave the map empty — EmbeddingSignal will embed per-candidate as before.
+        }
+      }
+    }
+
     // Step 2: Classify each candidate using SmartExtractor
     for (const candidate of candidates) {
       // Use SmartExtractor for unified neural + rule-based classification.
@@ -146,7 +166,8 @@ export class NeuralEntityExtractor {
       // option had no loosening effect).
       const classification = await this.smartExtractor.extract(candidate.text, {
         definition: candidate.context,
-        allTerms: [candidate.text, candidate.context]
+        allTerms: [candidate.text, candidate.context],
+        vector: vectorByText.get(candidate.text)
       }, minConfidence)
 
       // SmartExtractor already gates at minConfidence; this guards against null only.

src/neural/signals/EmbeddingSignal.ts

@@ -151,6 +151,8 @@ export class EmbeddingSignal {
       definition?: string
       allTerms?: string[]
       metadata?: any
+      /** Pre-computed candidate embedding (from a batch embed) — skips the per-candidate embed. */
+      vector?: Vector
     }
   ): Promise<TypeSignal | null> {
     this.stats.calls++
@@ -167,8 +169,8 @@ export class EmbeddingSignal {
     }
 
     try {
-      // Embed candidate once (efficiency!)
-      const vector = await this.embedWithTimeout(candidate)
+      // Use the pre-computed vector when the caller batch-embedded; otherwise embed once here.
+      const vector = context?.vector ?? await this.embedWithTimeout(candidate)
 
       // Check all three sources in parallel
       const [typeMatch, graphMatch, historyMatch] = await Promise.all([