feat(aep-v2): restore composants V2 manquants sur main (NavMapV2 + HashtagFilter + GraphView + FicheFamilleModal + types + css + server)

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

server/utils/vectorSearch.ts

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+/**
+ * Recherche vectorielle sur les embeddings V2
+ * Cosine similarity + top-K
+ *
+ * Utilisé par : server/api/chatbot-v2.post.ts
+ * Données     : server/data/embeddings-v2.json (généré par scripts/vectorize-v2.js)
+ */
+
+import { readFileSync, existsSync } from 'fs'
+import { fileURLToPath } from 'url'
+import { resolve, dirname } from 'path'
+
+// ── Types ──────────────────────────────────────────────────────────────────────
+
+export interface EmbeddingEntry {
+  fiche_id: string
+  nom: string
+  famille: number
+  hashtags: string[]
+  embedding: number[]
+  text_preview: string
+}
+
+export interface SearchResult {
+  fiche_id: string
+  nom: string
+  famille: number
+  hashtags: string[]
+  score: number
+  text_preview: string
+}
+
+// ── Cosine similarity ──────────────────────────────────────────────────────────
+
+export function cosineSimilarity(a: number[], b: number[]): number {
+  if (a.length !== b.length) return 0
+  let dot = 0, normA = 0, normB = 0
+  for (let i = 0; i < a.length; i++) {
+    dot += a[i] * b[i]
+    normA += a[i] * a[i]
+    normB += b[i] * b[i]
+  }
+  const denom = Math.sqrt(normA) * Math.sqrt(normB)
+  return denom === 0 ? 0 : dot / denom
+}
+
+// ── Top-K search ───────────────────────────────────────────────────────────────
+
+export function topKSearch(
+  embeddings: EmbeddingEntry[],
+  queryEmbedding: number[],
+  k: number = 5
+): SearchResult[] {
+  return embeddings
+    .map(e => ({
+      fiche_id: e.fiche_id,
+      nom: e.nom,
+      famille: e.famille,
+      hashtags: e.hashtags,
+      score: cosineSimilarity(e.embedding, queryEmbedding),
+      text_preview: e.text_preview
+    }))
+    .sort((a, b) => b.score - a.score)
+    .slice(0, k)
+}
+
+// ── Chargement lazy des embeddings (cache module-level) ────────────────────────
+
+let _embeddingsV2: EmbeddingEntry[] | null = null
+
+export function loadEmbeddingsV2(): EmbeddingEntry[] {
+  if (_embeddingsV2 !== null) return _embeddingsV2
+
+  try {
+    // Résolution du chemin depuis server/utils/ vers server/data/
+    const currentDir = dirname(fileURLToPath(import.meta.url))
+    const embPath = resolve(currentDir, '..', 'data', 'embeddings-v2.json')
+
+    if (!existsSync(embPath)) {
+      console.warn('[vectorSearch] embeddings-v2.json absent - V2 vector search désactivé')
+      console.warn('[vectorSearch] Lancer : MISTRAL_API_KEY=xxx node scripts/vectorize-v2.js')
+      _embeddingsV2 = []
+      return []
+    }
+
+    const raw = readFileSync(embPath, 'utf-8')
+    const data = JSON.parse(raw)
+    _embeddingsV2 = data.embeddings ?? []
+    console.log(`[vectorSearch] ${_embeddingsV2!.length} embeddings V2 chargés (${data.meta?.model ?? 'unknown'})`)
+    return _embeddingsV2!
+  } catch (e: any) {
+    console.warn('[vectorSearch] Erreur chargement embeddings-v2.json :', e?.message ?? e)
+    _embeddingsV2 = []
+    return []
+  }
+}