When products are loaded from PostgreSQL, JSON columns (effect_profile,
context_rules) are deserialized as plain dicts, not Pydantic models.
The build_product_context_summary function was accessing these fields
as object attributes (.safe_with_compromised_barrier) which caused:
AttributeError: 'dict' object has no attribute 'safe_with_compromised_barrier'
Fix: Add isinstance(dict) checks like build_product_context_detailed already does.
Handle both dict (from DB) and object (from Pydantic) cases.
Traceback from production:
File "llm_context.py", line 91, in build_product_context_summary
if product.context_rules.safe_with_compromised_barrier:
AttributeError: 'dict' object has no attribute...
- 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.
Two bugs in /routines/suggest where the LLM could override hard constraints:
1. Products with min_interval_hours (e.g. retinol at 72h) were passed to
the LLM even if used too recently. The LLM reasoned away the constraint
in at least one observed case. Fix: added _filter_products_by_interval()
which removes ineligible products before the prompt is built, so they
don't appear in AVAILABLE PRODUCTS at all.
2. Minoxidil was included in the available products list regardless of the
include_minoxidil_beard flag. Only the objectives context was gated,
leaving the product visible to the LLM which would include it based on
recent usage history. Fix: added include_minoxidil param to
_get_available_products() and threaded it through suggest_routine and
suggest_batch.
Also refactored _build_products_context() to accept a pre-supplied
products list instead of calling _get_available_products() internally,
ensuring the tool handler and context text always use the same filtered set.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Keep /products/suggest lean by exposing product UUIDs and fetching INCI, safety rules, actives, and usage notes on demand through Gemini function tools. Add conservative fallback behavior for tool roundtrip limits and expand helper tests to cover tool wiring and payload handlers.
Keep the /routines/suggest base context lean by sending only active names and fetching detailed safety, actives, usage notes, and INCI on demand. Add a conservative fallback when tool roundtrip limits are hit to preserve safe outputs instead of failing the request.
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.
Expose leave-on behavior, contraindications, safety alerts, and compact usage notes in AVAILABLE PRODUCTS so Gemini can make safer routine decisions with real-world product constraints.
Introduces `get_extraction_config` and `get_creative_config` to standardize Gemini API calls.
* Defines explicit config profiles with appropriate `temperature` and `thinking_level` for Gemini 3 Flash.
* Extraction tasks use minimal thinking and temp=0.0 to reduce latency and token usage.
* Creative tasks use low thinking, temp=0.4, and top_p=0.8 to balance naturalness and safety.
* Applies these helpers across products, routines, and skincare endpoints.
* Also updates default model to `gemini-3-flash-preview`.
- Add POST /api/products/suggest endpoint that analyzes skin condition
and inventory to suggest product types (e.g., 'Salicylic Acid 2% Masque')
- Add MCP tool get_shopping_suggestions() for MCP clients
- Add 'Suggest' button to Products page in frontend
- Add /products/suggest page with suggestion cards
- Include product type, key ingredients, target concerns, why_needed,
recommended_time, and frequency in suggestions
- Fix stock logic: sealed products now count as available inventory
- Add legend to clarify ✓ (in stock) vs ✗ (not in stock) markers
- Remove _build_inventory_context; fold pao_months into DOSTĘPNE PRODUKTY entries
- Remove "Otwarte równolegle" duplicate section from prompt
- Rename OSTATNIE RUTYNY (7 dni) → OSTATNIE RUTYNY
- Add _build_day_context and SuggestRoutineRequest.leaving_home (optional bool)
- System prompt: replace unconditional PAO rule with conditional; add SPF factor
selection logic based on KONTEKST DNIA leaving_home value
- Frontend: leaving_home checkbox (AM only) + i18n keys pl/en
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Gemini API rejects int-valued enums (StrengthLevel) in response_schema,
raising a validation error before any request is sent. Fix by introducing
AIActiveIngredient (inherits ActiveIngredient, overrides strength_level and
irritation_potential as Optional[int]) and ProductParseLLMResponse used only
as the Gemini schema. The two-step validation converts ints back to StrengthLevel
via Pydantic coercion. Adds a test covering the numeric strength level path.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
gemini-flash-latest resolves to gemini-3-flash-preview which uses
thinking_level instead of the legacy thinking_budget (mixing both
returns HTTP 400). Use LOW to reduce thinking overhead while keeping
basic reasoning, replacing the now-incompatible thinking_budget=0.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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>
