PHASE 1 COMPLETE: mem0 + Supabase integration tested and working

✅ PHASE 1 ACHIEVEMENTS:
- Successfully migrated from Qdrant to self-hosted Supabase
- Fixed mem0 Supabase integration collection naming issues
- Resolved vector dimension mismatches (1536→768 for Ollama)
- All containers connected to localai docker network
- Comprehensive documentation updates completed

✅ TESTING COMPLETED:
- Database storage verification: Data properly stored in PostgreSQL
- Vector operations: 768-dimensional embeddings working perfectly
- Memory operations: Add, search, retrieve, delete all functional
- Multi-user support: User isolation verified
- LLM integration: Ollama qwen2.5:7b + nomic-embed-text operational
- Search functionality: Semantic search with relevance scores working

✅ INFRASTRUCTURE READY:
- Supabase PostgreSQL with pgvector: ✅ OPERATIONAL
- Neo4j graph database: ✅ READY (for Phase 2)
- Ollama LLM + embeddings: ✅ WORKING
- mem0 v0.1.115: ✅ FULLY FUNCTIONAL

PHASE 2 READY: Core memory system and API development can begin

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>

cleanup_supabase_tables.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+"""
+Clean up all Supabase vecs tables and start fresh
+"""
+
+import psycopg2
+import vecs
+
+def cleanup_all_tables():
+    """Clean up all tables in vecs schema"""
+    print("=" * 60)
+    print("SUPABASE VECS SCHEMA CLEANUP")
+    print("=" * 60)
+    
+    try:
+        # Connect to database directly
+        conn = psycopg2.connect("postgresql://supabase_admin:CzkaYmRvc26Y@localhost:5435/postgres")
+        cur = conn.cursor()
+        
+        print("🔍 Finding all tables in vecs schema...")
+        cur.execute("SELECT table_name FROM information_schema.tables WHERE table_schema = 'vecs';")
+        tables = cur.fetchall()
+        table_names = [t[0] for t in tables]
+        print(f"Found tables: {table_names}")
+        
+        if table_names:
+            print(f"\n🗑️  Dropping {len(table_names)} tables...")
+            for table_name in table_names:
+                try:
+                    cur.execute(f'DROP TABLE IF EXISTS vecs."{table_name}" CASCADE;')
+                    print(f"   ✅ Dropped: {table_name}")
+                except Exception as e:
+                    print(f"   ❌ Failed to drop {table_name}: {e}")
+            
+            # Commit the drops
+            conn.commit()
+            print("✅ All table drops committed")
+        else:
+            print("ℹ️  No tables found in vecs schema")
+        
+        # Verify cleanup
+        cur.execute("SELECT table_name FROM information_schema.tables WHERE table_schema = 'vecs';")
+        remaining_tables = cur.fetchall()
+        print(f"\n📋 Remaining tables: {[t[0] for t in remaining_tables]}")
+        
+        cur.close()
+        conn.close()
+        
+        print("\n🧪 Testing fresh vecs client connection...")
+        connection_string = "postgresql://supabase_admin:CzkaYmRvc26Y@localhost:5435/postgres"
+        db = vecs.create_client(connection_string)
+        
+        collections = db.list_collections()
+        print(f"Collections after cleanup: {[c.name for c in collections]}")
+        
+        print("\n🎯 Testing fresh collection creation...")
+        test_collection = db.get_or_create_collection(name="test_fresh_start", dimension=1536)
+        print(f"✅ Successfully created: {test_collection.name} with dimension {test_collection.dimension}")
+        
+        # Test basic operations
+        print("🧪 Testing basic vector operations...")
+        import numpy as np
+        
+        # Insert test vector
+        test_vector = np.random.random(1536).tolist()
+        test_id = "test_vector_1"
+        test_metadata = {"content": "Fresh start test", "user_id": "test"}
+        
+        test_collection.upsert([(test_id, test_vector, test_metadata)])
+        print("✅ Vector upserted successfully")
+        
+        # Search test
+        results = test_collection.query(data=test_vector, limit=1, include_metadata=True)
+        print(f"✅ Search successful, found {len(results)} results")
+        if results:
+            print(f"   Result: ID={results[0][0]}, Score={results[0][1]}")
+        
+        # Clean up test collection
+        db.delete_collection("test_fresh_start")
+        print("✅ Test collection cleaned up")
+        
+        print("\n" + "=" * 60)
+        print("🎉 CLEANUP SUCCESSFUL - VECS IS READY!")
+        print("=" * 60)
+        
+    except Exception as e:
+        print(f"❌ Cleanup failed: {str(e)}")
+        import traceback
+        traceback.print_exc()
+
+if __name__ == "__main__":
+    cleanup_all_tables()