Import source: app/backend/src/lib/embeddings

Import the existing local project snapshot. This commit organizes existing files; it does not represent newly developed functionality.

app/backend/src/lib/embeddings.ts

@@ -0,0 +1,47 @@
+import { prisma } from '../db.js';
+
+// Simulated embedding generation for Phase 14 demo
+// In production, this would call OpenAI text-embedding-3-small or similar
+export async function generateEmbedding(text: string): Promise<number[]> {
+  // Deterministic-ish random for demo purposes based on text length
+  const vector = new Array(768).fill(0).map((_, i) => Math.sin(text.length * i));
+  // Normalize vector
+  const magnitude = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0));
+  return vector.map(val => val / magnitude);
+}
+
+export async function storeArticleEmbedding(articleId: string, text: string) {
+  const vector = await generateEmbedding(text);
+  
+  // Format for pgvector: '[0.1, 0.2, ...]'
+  const vectorString = `[${vector.join(',')}]`;
+
+  await prisma.$executeRaw`
+    INSERT INTO "ArticleEmbedding" ("id", "articleId", "embedding", "updatedAt")
+    VALUES (gen_random_uuid(), ${articleId}, ${vectorString}::vector, NOW())
+    ON CONFLICT ("articleId") DO UPDATE SET
+      "embedding" = EXCLUDED."embedding",
+      "updatedAt" = NOW();
+  `;
+}
+
+export async function semanticSearch(query: string, limit = 5) {
+  const queryVector = await generateEmbedding(query);
+  const vectorString = `[${queryVector.join(',')}]`;
+
+  // Cosine distance operator <=> in pgvector
+  const results = await prisma.$queryRaw<Array<{ id: string; articleId: string; distance: number; title: string; slug: string }>>`
+    SELECT 
+      e."id", 
+      e."articleId", 
+      (e."embedding" <=> ${vectorString}::vector) as distance,
+      a."title",
+      a."slug"
+    FROM "ArticleEmbedding" e
+    JOIN "Article" a ON e."articleId" = a."id"
+    ORDER BY distance ASC
+    LIMIT ${limit};
+  `;
+
+  return results;
+}