CLAUDE CODE PROGRESS LOG - CleanKitchens 2.0
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Last Updated: 2025-08-14 22:40 UTC

PROJECT STATUS: Setting up free vectorization for 295k Chicago historical records

COMPLETED:
✓ Backed up old site to ck-aug-backup/
✓ Installed Python environment at ~/cleankitchens-env/
✓ Set up Weaviate Docker container (port 8080)
✓ Created Weaviate schema with 38 fields
✓ Downloaded Chicago historical CSV (326MB, 295k records)
✓ Created test scripts in ~/cleankitchens/scripts/
✓ Tested Chicago API access - working
✓ Generated sample article for Maison Parisienne
✓ Added API keys to ~/.env (ANTHROPIC and OPENAI)

CURRENT TASK: 
IMPORTING 295k Chicago records with FREE vectorization - IN PROGRESS

LATEST UPDATE (22:47 UTC):
✓ Installed Sentence Transformers (CPU version for cost efficiency)
✓ Created import_with_vectors.py script that:
  - Uses all-MiniLM-L6-v2 model (runs locally, no API costs)
  - Converts each record to key:value pairs
  - Generates 384-dimension vectors for semantic search
  - Stores in Weaviate "RawInspection" collection
  - Includes progress bar and batch processing
  - Tests semantic search after import
✓ Test import successful - 10 records imported with FREE vectors
✓ Semantic search working - 384-dimension vectors
🚀 PRODUCTION IMPORT RUNNING (23:01 UTC)
✅ Database cleaned - all collections deleted
✅ Uploading to temp collection first (no vectors yet)
📊 Progress: ~35,400/295,254 records uploaded
⏱️ Processing speed: ~450 records/second
🔒 Duplicate prevention: Active (checking each ID)

Next steps (automatic):
   4. ✅ Add vectors AFTER verification
   5. Move to production collection
   6. Delete temp collection
   
Log: /var/www/twin-digital-media/public_html/_sites/cleankitchens/production/logs/clean_import.log

FILES CREATED TODAY:
- ~/cleankitchens/scripts/test_chicago_api.py - Tests API access
- ~/cleankitchens/scripts/import_chicago_csv.py - CSV importer (partially complete)
- ~/cleankitchens/scripts/test_vectorization.py - Vector test
- ~/cleankitchens/data/chicago_historical.csv - 295k records downloaded
- ~/setup_weaviate_schema.py - Schema setup script
- ~/.env - API keys configured

NEXT STEPS:
1. Install Sentence Transformers for free vectorization
2. Update Weaviate to use local transformer model
3. Import all 295k Chicago records as key:value pairs
4. Vectorize everything for pattern detection
5. Test semantic search across dataset

STRATEGY:
- Use free Sentence Transformers for ALL raw inspection data (295k records)
- Use Claude Haiku ($0.0016/article) for article generation
- Use OpenAI embeddings only for final published articles
- This gives pattern detection on all data for $0

WEAVIATE STATUS:
- Running on Docker container ID: 19605b88e08d
- URL: http://localhost:8080
- Schema: Article collection with 38 properties
- Vectorizer: Currently set to none (updating to sentence-transformers)

COST ANALYSIS:
- Raw data vectorization: $0 (Sentence Transformers)
- Article generation: ~$0.0016 per article (Claude Haiku)
- Article vectorization: ~$0.00002 per article (OpenAI)
- Total for 180k violations: ~$286 if all generated

CURRENT WORKING DIRECTORY: ~/cleankitchens/