From 914c6087bd2dcbda4bc8988c2846ecaf1b8a5bc8 Mon Sep 17 00:00:00 2001
From: Piotr Oleszczyk <piotr@oleszczyk.eu>
Date: Sun, 1 Mar 2026 22:00:48 +0100
Subject: [PATCH] fix(products): work around Gemini int-enum schema rejection
 in parse-text

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>
---
 backend/innercontext/api/products.py | 18 ++++++++++++++++--
 backend/tests/test_products.py       | 19 +++++++++++++++++++
 2 files changed, 35 insertions(+), 2 deletions(-)

diff --git a/backend/innercontext/api/products.py b/backend/innercontext/api/products.py
index 06b7aa9..bed9556 100644
--- a/backend/innercontext/api/products.py
+++ b/backend/innercontext/api/products.py
@@ -143,6 +143,19 @@ class ProductParseResponse(SQLModel):
     needle_length_mm: Optional[float] = None
 
 
+class AIActiveIngredient(ActiveIngredient):
+    # Gemini API rejects int-enum values in response_schema; override with plain int.
+    strength_level: Optional[int] = None
+    irritation_potential: Optional[int] = None
+
+
+class ProductParseLLMResponse(ProductParseResponse):
+    # Gemini response schema currently requires enum values to be strings.
+    # Strength fields are numeric in our domain (1-3), so keep them as ints here
+    # and convert via ProductParseResponse validation afterward.
+    actives: Optional[list[AIActiveIngredient]] = None
+
+
 class InventoryCreate(SQLModel):
     is_opened: bool = False
     opened_at: Optional[date] = None
@@ -373,7 +386,7 @@ def parse_product_text(data: ProductParseRequest) -> ProductParseResponse:
         config=genai_types.GenerateContentConfig(
             system_instruction=_product_parse_system_prompt(),
             response_mime_type="application/json",
-            response_schema=ProductParseResponse,
+            response_schema=ProductParseLLMResponse,
             max_output_tokens=16384,
             temperature=0.0,
         ),
@@ -387,7 +400,8 @@ def parse_product_text(data: ProductParseRequest) -> ProductParseResponse:
     except json.JSONDecodeError as e:
         raise HTTPException(status_code=502, detail=f"LLM returned invalid JSON: {e}")
     try:
-        return ProductParseResponse.model_validate(parsed)
+        llm_parsed = ProductParseLLMResponse.model_validate(parsed)
+        return ProductParseResponse.model_validate(llm_parsed.model_dump())
     except ValidationError as e:
         raise HTTPException(status_code=422, detail=e.errors())
 
diff --git a/backend/tests/test_products.py b/backend/tests/test_products.py
index 1fae538..956cc75 100644
--- a/backend/tests/test_products.py
+++ b/backend/tests/test_products.py
@@ -199,3 +199,22 @@ def test_create_inventory(client, created_product):
 def test_create_inventory_product_not_found(client):
     r = client.post(f"/products/{uuid.uuid4()}/inventory", json={})
     assert r.status_code == 404
+
+
+def test_parse_text_accepts_numeric_strength_levels(client, monkeypatch):
+    from innercontext.api import products as products_api
+
+    class _FakeResponse:
+        text = (
+            '{"name":"Test Serum","actives":[{"name":"Niacinamide","percent":10,'
+            '"functions":["niacinamide"],"strength_level":2,"irritation_potential":1}]}'
+        )
+
+    monkeypatch.setattr(products_api, "call_gemini", lambda **kwargs: _FakeResponse())
+
+    r = client.post("/products/parse-text", json={"text": "dummy input"})
+    assert r.status_code == 200
+    data = r.json()
+    assert data["name"] == "Test Serum"
+    assert data["actives"][0]["strength_level"] == 2
+    assert data["actives"][0]["irritation_potential"] == 1