feat: US-005 - Implement cosine similarity search module

src/lib/semantic-search.ts

@@ -0,0 +1,42 @@
+import embeddingsData from '@/data/embeddings.json'
+
+interface EmbeddingEntry {
+  id: string
+  embedding: number[]
+}
+
+interface SearchResult {
+  id: string
+  score: number
+}
+
+function cosineSimilarity(a: number[], b: number[]): number {
+  let dot = 0
+  let magA = 0
+  let magB = 0
+  for (let i = 0; i < a.length; i++) {
+    dot += a[i] * b[i]
+    magA += a[i] * a[i]
+    magB += b[i] * b[i]
+  }
+  const denom = Math.sqrt(magA) * Math.sqrt(magB)
+  return denom === 0 ? 0 : dot / denom
+}
+
+export function semanticSearch(
+  queryEmbedding: number[],
+  embeddings: EmbeddingEntry[],
+  threshold = 0.3
+): SearchResult[] {
+  return embeddings
+    .map(entry => ({
+      id: entry.id,
+      score: cosineSimilarity(queryEmbedding, entry.embedding),
+    }))
+    .filter(r => r.score >= threshold)
+    .sort((a, b) => b.score - a.score)
+}
+
+export function loadEmbeddings(): EmbeddingEntry[] {
+  return embeddingsData as EmbeddingEntry[]
+}