feat: whyrating - initial project from turbostarter boilerplate

packages/api/tests/fix-document-embeddings.ts

@@ -0,0 +1,99 @@
+/**
+ * Script to fix embeddings for a specific document with corrupted embeddings
+ * Run: pnpm with-env npx tsx packages/api/tests/fix-document-embeddings.ts
+ */
+import { generateDocumentEmbeddings } from "@turbostarter/ai/pdf/embeddings";
+import { sql } from "@turbostarter/db";
+import { pdfEmbedding } from "@turbostarter/db/schema/pdf";
+import { db } from "@turbostarter/db/server";
+
+const DOC_ID = "JnOv9Z7JK2ZWU92OrFFFuuCY1TSksaWv";
+
+async function fix() {
+  console.log(`\n=== Fixing embeddings for document: ${DOC_ID} ===\n`);
+
+  // 1. Get the document path
+  const docResult = await db.execute<{
+    id: string;
+    path: string;
+    name: string | null;
+  }>(sql`
+    SELECT id, path, name
+    FROM pdf.document
+    WHERE id = ${DOC_ID}
+  `);
+
+  const docs = Array.isArray(docResult) ? docResult : [];
+  if (docs.length === 0) {
+    console.log("❌ Document not found!");
+    process.exit(1);
+  }
+
+  const doc = docs[0]!;
+  console.log(`📄 Document: ${doc.name ?? doc.id}`);
+  console.log(`📁 Path: ${doc.path}`);
+
+  // 2. Delete existing (corrupted) embeddings
+  console.log("\n🗑️  Deleting corrupted embeddings...");
+  await db.execute(sql`
+    DELETE FROM pdf.embedding
+    WHERE document_id = ${DOC_ID}
+  `);
+  console.log("   Done.");
+
+  // 3. Regenerate embeddings from the actual PDF
+  console.log("\n📊 Generating new embeddings from PDF...");
+  try {
+    const generated = await generateDocumentEmbeddings(doc.path);
+    console.log(`   Generated ${generated.length} chunks`);
+
+    if (generated.length === 0) {
+      console.log("⚠️  No chunks generated - PDF may be empty or unreadable");
+      process.exit(1);
+    }
+
+    // Show sample of new embeddings
+    console.log("\n   Sample content:");
+    for (let i = 0; i < Math.min(3, generated.length); i++) {
+      const chunk = generated[i]!;
+      console.log(`   Page ${chunk.metadata.pageNumber}: "${chunk.content.substring(0, 60)}..."`);
+    }
+
+    // 4. Insert new embeddings
+    console.log("\n💾 Inserting new embeddings...");
+    await db
+      .insert(pdfEmbedding)
+      .values(
+        generated.map((chunk) => ({
+          content: chunk.content,
+          documentId: DOC_ID,
+          embedding: chunk.embedding,
+          pageNumber: chunk.metadata.pageNumber,
+          charStart: chunk.metadata.charStart,
+          charEnd: chunk.metadata.charEnd,
+          sectionTitle: chunk.metadata.sectionTitle,
+        })),
+      );
+
+    console.log(`   ✅ Inserted ${generated.length} embeddings`);
+
+    // 5. Verify
+    const countResult = await db.execute<{ count: number }>(sql`
+      SELECT COUNT(*)::int as count
+      FROM pdf.embedding
+      WHERE document_id = ${DOC_ID}
+    `);
+    const count = (countResult as { count: number }[])[0]?.count ?? 0;
+    console.log(`\n✅ Verification: Document now has ${count} embeddings`);
+  } catch (error) {
+    console.error("\n❌ Error generating embeddings:", error);
+    process.exit(1);
+  }
+
+  process.exit(0);
+}
+
+fix().catch((e) => {
+  console.error("Fatal error:", e);
+  process.exit(1);
+});