Add local AI and Qdrant vector backends

scripts/rebuild-embeddings.ts

@@ -0,0 +1,71 @@
+import { getSQLiteClient } from '@/services/database/sqlite-client';
+import { NodeEmbedder } from '@/services/typescript/embed-nodes';
+import { UniversalEmbedder } from '@/services/typescript/embed-universal';
+
+async function maybeRecreateQdrantCollections() {
+  if (process.env.VECTOR_BACKEND !== 'qdrant' || process.env.QDRANT_RECREATE_COLLECTIONS !== 'true') {
+    return;
+  }
+
+  const baseUrl = (process.env.QDRANT_URL || 'http://localhost:6333').replace(/\/+$/, '');
+  const headers = process.env.QDRANT_API_KEY ? { 'api-key': process.env.QDRANT_API_KEY } : undefined;
+  const collections = [
+    process.env.QDRANT_CHUNKS_COLLECTION || 'rah_chunks',
+    process.env.QDRANT_NODES_COLLECTION || 'rah_nodes',
+  ];
+
+  for (const collection of collections) {
+    const response = await fetch(`${baseUrl}/collections/${encodeURIComponent(collection)}`, {
+      method: 'DELETE',
+      headers,
+    });
+    if (!response.ok && response.status !== 404) {
+      const detail = await response.text().catch(() => response.statusText);
+      throw new Error(`Failed to delete Qdrant collection ${collection}: ${detail}`);
+    }
+    console.log(`[rebuild-embeddings] Recreated Qdrant collection on next upsert: ${collection}`);
+  }
+}
+
+async function main() {
+  const sqlite = getSQLiteClient();
+  await maybeRecreateQdrantCollections();
+  const nodeRows = sqlite.query<{ id: number; source?: string | null }>(`
+    SELECT id, source
+    FROM nodes
+    ORDER BY id
+  `).rows;
+
+  console.log(`[rebuild-embeddings] Rebuilding node embeddings for ${nodeRows.length} nodes`);
+  const nodeEmbedder = new NodeEmbedder();
+  try {
+    await nodeEmbedder.embedNodes({ forceReEmbed: true, verbose: true });
+  } finally {
+    nodeEmbedder.close();
+  }
+
+  const sourceRows = nodeRows.filter((node) => typeof node.source === 'string' && node.source.trim().length > 0);
+  console.log(`[rebuild-embeddings] Rebuilding chunk embeddings for ${sourceRows.length} nodes with source text`);
+
+  const chunkEmbedder = new UniversalEmbedder();
+  try {
+    let processed = 0;
+    for (const node of sourceRows) {
+      await chunkEmbedder.processNode({ nodeId: node.id, verbose: false });
+      processed += 1;
+      if (processed % 10 === 0 || processed === sourceRows.length) {
+        console.log(`[rebuild-embeddings] Chunked ${processed}/${sourceRows.length}`);
+      }
+    }
+  } finally {
+    chunkEmbedder.close();
+  }
+
+  sqlite.markEmbeddingProfileCurrent();
+  console.log('[rebuild-embeddings] Active embedding/vector profile recorded.');
+}
+
+main().catch((error) => {
+  console.error(error);
+  process.exit(1);
+});