Issue 1: Forecasting demand insights not triggered in demo workflow
- Created internal ML endpoint: /forecasting/internal/ml/generate-demand-insights
- Added trigger_demand_insights_internal() to ForecastServiceClient
- Integrated forecasting insights into demo session post-clone workflow
- Now triggers 4 AI insight types: price, safety stock, yield, + demand
Issue 2: RabbitMQ client cleanup error in procurement service
- Fixed: rabbitmq_client.close() → rabbitmq_client.disconnect()
- Added proper cleanup in exception handler
- Error: "'RabbitMQClient' object has no attribute 'close'"
Files modified:
- services/forecasting/app/api/ml_insights.py (new internal_router)
- services/forecasting/app/main.py (register internal router)
- shared/clients/forecast_client.py (new trigger method)
- services/demo_session/app/services/clone_orchestrator.py (+ demand insights)
- services/procurement/app/api/internal_demo.py (fix disconnect)
Expected impact:
- Demo sessions will now generate demand forecasting insights
- No more RabbitMQ cleanup errors in logs
- AI insights count should increase from 1 to 2-3 per session
🤖 Generated with Claude Code
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Fixed two critical issues preventing forecast data from being cloned:
1. **Missing batch_name field**: The fixture uses `batch_id` but the
PredictionBatch model requires `batch_name` (NOT NULL constraint).
Added field mapping to handle batch_id -> batch_name conversion.
2. **UUID type mismatch**: The fixture's `product_id` is a string but
the Forecast model expects `inventory_product_id` as UUID type.
Added conversion from string to UUID.
3. **Field mappings added**:
- batch_id -> batch_name
- total_forecasts -> total_products
- created_at -> requested_at (fallback)
- Calculated completed_products from status
These fixes enable the forecasting service to successfully clone all
28 forecasts from the fixture file, unlocking demand forecasting
AI insights in demo sessions.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Root Causes Fixed:
1. BatchForecastResponse schema mismatch in forecasting service
- Changed 'batch_id' to 'id' (required field name)
- Changed 'products_processed' to 'total_products'
- Changed 'success' to 'status' with "completed" value
- Changed 'message' to 'error_message'
- Added all required fields: batch_name, completed_products, failed_products,
requested_at, completed_at, processing_time_ms, forecasts
- This was causing "11 validation errors for BatchForecastResponse"
which made the forecast service return None, triggering saga failure
2. Missing pandas dependency in orchestrator service
- Added pandas==2.2.2 and numpy==1.26.4 to requirements.txt
- Fixes "No module named 'pandas'" warning when loading AI enhancement
These issues prevented the orchestrator from completing Step 3 (generate_forecasts)
in the daily workflow, causing the entire saga to fail and compensate.
