app/fix_collection.py

import asyncio
from app.services.vector_store import vector_store
from app.services.document_ingestion import initialize_knowledge_base_async

async def fix_collection():
    print("=" * 60)
    print("🔧 FIXING QDRANT COLLECTION DIMENSIONS")
    print("=" * 60)
    
    try:
        # Step 1: Delete the old collection
        print("\n1️⃣ Deleting old collection (1536 dimensions)...")
        vector_store.delete_collection()
        print("   ✓ Old collection deleted")
    except Exception as e:
        print(f"   ℹ Collection delete: {e}")
    
    try:
        # Step 2: Create new collection
        print("\n2️⃣ Creating new collection (768 dimensions for Gemini)...")
        vector_store.create_collection(force_recreate=True)
        print("   ✓ New collection created")
    except Exception as e:
        print(f"   ✗ Error creating collection: {e}")
        return
    
    try:
        # Step 3: Re-ingest all documents
        print("\n3️⃣ Re-ingesting documents with Gemini embeddings...")
        print("   (This may take a few minutes...)")
        doc_ids = await initialize_knowledge_base_async()
        print(f"   ✓ Successfully ingested {len(doc_ids)} documents")
    except Exception as e:
        print(f"   ✗ Error ingesting documents: {e}")
        import traceback
        traceback.print_exc()
        return
    
    print("\n" + "=" * 60)
    print("✅ MIGRATION COMPLETE!")
    print("=" * 60)
    print(f"✓ Collection: book_documents (768 dimensions)")
    print(f"✓ Documents: {len(doc_ids)} chunks")
    print(f"✓ Embedding model: Gemini text-embedding-004")
    print(f"✓ LLM model: Gemini 2.0 Flash")
    print("\nYou can now start your application! 🚀")
    print("=" * 60)

if __name__ == "__main__":
    asyncio.run(fix_collection())