Resolves validation failures where LLM fabricated full UUIDs from 8-char
prefixes shown in context, causing 'unknown product_id' errors.
Root Cause Analysis:
- Context showed 8-char short IDs: '77cbf37c' (Phase 2 optimization)
- Function tool returned full UUIDs: '77cbf37c-3830-4927-...'
- LLM saw BOTH formats, got confused, invented UUIDs for final response
- Validators rejected fabricated UUIDs as unknown products
Solution: Consistent 8-char short_id across LLM boundary:
1. Database: New short_id column (8 chars, unique, indexed)
2. Context: Shows short_id (was: str(id)[:8])
3. Function tools: Return short_id (was: full UUID)
4. Translation layer: Expands short_id → UUID before validation
5. Database: Stores full UUIDs (no schema change for existing data)
Changes:
- Added products.short_id column with unique constraint + index
- Migration populates from UUID prefix, handles collisions via regeneration
- Product model auto-generates short_id for new products
- LLM contexts use product.short_id consistently
- Function tools return product.short_id
- Added _expand_product_id() translation layer in routines.py
- Integrated expansion in suggest_routine() and suggest_batch()
- Validators work with full UUIDs (no changes needed)
Benefits:
✅ LLM never sees full UUIDs, no format confusion
✅ Maintains Phase 2 token optimization (~85% reduction)
✅ O(1) indexed short_id lookups vs O(n) pattern matching
✅ Unique constraint prevents collisions at DB level
✅ Clean separation: 8-char for LLM, 36-char for application
From production error:
Step 1: unknown product_id 77cbf37c-3830-4927-9669-07447206689d
(LLM invented the last 28 characters)
Now resolved: LLM uses '77cbf37c' consistently, translation layer
expands to real UUID before validation.
- Add tiered context system (summary/detailed/full) to reduce token usage by 70-80%
- Replace old _build_products_context with build_products_context_summary_list (Tier 1: ~15 tokens/product vs 150)
- Optimize function tool responses: exclude INCI list by default (saves ~15KB/product)
- Reduce actives from 24 to top 5 in function tools
- Add reasoning_chain field to AICallLog model for observability
- Implement _extract_thinking_content to capture LLM reasoning (MEDIUM thinking level)
- Strengthen prompt enforcement for prohibited fields (dose, amount, quantity)
- Update get_creative_config to use MEDIUM thinking level instead of LOW
Token Savings:
- Routine suggestions: 9,613 → ~1,300 tokens (-86%)
- Batch planning: 12,580 → ~1,800 tokens (-86%)
- Function tool responses: ~15KB → ~2KB per product (-87%)
Breaks discovered in log analysis (ai_call_log.json):
- Lines 10, 27, 61, 78: LLM returned prohibited dose field
- Line 85: MAX_TOKENS failure (output truncated)
Phase 2 complete. Next: two-phase batch planning with safety verification.
Enable on-demand INCI retrieval in /routines/suggest through Gemini function calling so detailed ingredient data is fetched only when needed. Persist and normalize tool_trace data in AI logs to make function-call behavior directly inspectable via /ai-logs endpoints.
When Gemini stops generation early (e.g. due to safety filters or
thinking-model quirks), finish_reason != STOP but no exception is raised,
causing the caller to receive truncated JSON and a confusing 502 "invalid
JSON" error. Now:
- finish_reason is extracted from candidates[0] and stored in ai_call_logs
- any non-STOP finish_reason raises HTTP 502 with a clear message
- Alembic migration adds the finish_reason column to ai_call_logs
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add include_minoxidil_beard flag to SuggestRoutineRequest and SuggestBatchRequest
- Detect minoxidil products by scanning name, brand, INCI and actives; pass them
to the LLM even though they are medications
- Inject CELE UŻYTKOWNIKA context block into prompts when flag is enabled
- Add _build_objectives_context() returning empty string when flag is off
- Add call_gemini() helper that centralises Gemini API calls and logs every
request/response to a new ai_call_logs table (AICallLog model + /ai-logs router)
- Nginx: raise client_max_body_size to 16 MB for photo uploads
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Add alembic 1.14 to dependencies (uv sync → 1.18.4 installed)
- Configure alembic/env.py: loads DATABASE_URL from env, imports all
SQLModel models so metadata is fully populated for autogenerate
- Generate initial migration (c2d626a2b36c) covering all 9 tables:
products, product_inventory, medication_entries, medication_usages,
lab_results, routines, routine_steps, grooming_schedule,
skin_condition_snapshots — with all indexes and constraints
- Add ExecStartPre to innercontext.service: runs alembic upgrade head
before uvicorn starts (idempotent, safe on every restart)
- Update DEPLOYMENT.md: add migration step to backend setup and update
flow; document alembic stamp head for existing installations
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
