7e3ba093c4
✅ 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>
92 lines
3.4 KiB
Python
92 lines
3.4 KiB
Python
#!/usr/bin/env python3
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"""
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Clean up all Supabase vecs tables and start fresh
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"""
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import psycopg2
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import vecs
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def cleanup_all_tables():
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"""Clean up all tables in vecs schema"""
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print("=" * 60)
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print("SUPABASE VECS SCHEMA CLEANUP")
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print("=" * 60)
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try:
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# Connect to database directly
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conn = psycopg2.connect("postgresql://supabase_admin:CzkaYmRvc26Y@localhost:5435/postgres")
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cur = conn.cursor()
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print("🔍 Finding all tables in vecs schema...")
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cur.execute("SELECT table_name FROM information_schema.tables WHERE table_schema = 'vecs';")
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tables = cur.fetchall()
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table_names = [t[0] for t in tables]
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print(f"Found tables: {table_names}")
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if table_names:
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print(f"\n🗑️ Dropping {len(table_names)} tables...")
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for table_name in table_names:
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try:
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cur.execute(f'DROP TABLE IF EXISTS vecs."{table_name}" CASCADE;')
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print(f" ✅ Dropped: {table_name}")
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except Exception as e:
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print(f" ❌ Failed to drop {table_name}: {e}")
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# Commit the drops
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conn.commit()
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print("✅ All table drops committed")
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else:
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print("ℹ️ No tables found in vecs schema")
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# Verify cleanup
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cur.execute("SELECT table_name FROM information_schema.tables WHERE table_schema = 'vecs';")
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remaining_tables = cur.fetchall()
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print(f"\n📋 Remaining tables: {[t[0] for t in remaining_tables]}")
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cur.close()
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conn.close()
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print("\n🧪 Testing fresh vecs client connection...")
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connection_string = "postgresql://supabase_admin:CzkaYmRvc26Y@localhost:5435/postgres"
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db = vecs.create_client(connection_string)
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collections = db.list_collections()
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print(f"Collections after cleanup: {[c.name for c in collections]}")
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print("\n🎯 Testing fresh collection creation...")
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test_collection = db.get_or_create_collection(name="test_fresh_start", dimension=1536)
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print(f"✅ Successfully created: {test_collection.name} with dimension {test_collection.dimension}")
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# Test basic operations
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print("🧪 Testing basic vector operations...")
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import numpy as np
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# Insert test vector
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test_vector = np.random.random(1536).tolist()
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test_id = "test_vector_1"
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test_metadata = {"content": "Fresh start test", "user_id": "test"}
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test_collection.upsert([(test_id, test_vector, test_metadata)])
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print("✅ Vector upserted successfully")
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# Search test
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results = test_collection.query(data=test_vector, limit=1, include_metadata=True)
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print(f"✅ Search successful, found {len(results)} results")
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if results:
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print(f" Result: ID={results[0][0]}, Score={results[0][1]}")
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# Clean up test collection
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db.delete_collection("test_fresh_start")
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print("✅ Test collection cleaned up")
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print("\n" + "=" * 60)
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print("🎉 CLEANUP SUCCESSFUL - VECS IS READY!")
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print("=" * 60)
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except Exception as e:
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print(f"❌ Cleanup failed: {str(e)}")
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import traceback
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traceback.print_exc()
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if __name__ == "__main__":
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cleanup_all_tables() |