pyr0ball
9371df1c95
Task 13: StyleAdapter with 5 cuisine templates (Italian, Latin, East Asian,
Eastern European, Mediterranean). Each template includes weighted method_bias
(sums to 1.0), element-filtered aromatics/depth/structure helpers, and
seasoning/finishing-fat vectors. StyleTemplate is a fully immutable frozen
dataclass with tuple fields.
Task 14: LLMRecipeGenerator for Levels 3 and 4. Level 3 builds a structured
element-scaffold prompt; Level 4 generates a minimal wildcard prompt (<1500
chars). Allergy hard-exclusion wired through RecipeRequest.allergies into
both prompt builders and the generate() call path. Parsed LLM response
(title, ingredients, directions, notes) fully propagated to RecipeSuggestion.
Task 15: User settings key-value store. Migration 012 adds user_settings
table. Store.get_setting / set_setting with upsert. GET/PUT /settings/{key}
endpoints with Pydantic SettingBody, key allowlist, get_session dependency.
RecipeEngine reads cooking_equipment from settings when hard_day_mode=True.
55 tests passing.
141 lines
4.4 KiB
Python
141 lines
4.4 KiB
Python
"""Tests for LLMRecipeGenerator — prompt builders and allergy filtering."""
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from __future__ import annotations
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import pytest
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from app.models.schemas.recipe import RecipeRequest
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from app.services.recipe.element_classifier import IngredientProfile
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def _make_store():
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"""Create a minimal in-memory Store."""
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from app.db.store import Store
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import sqlite3
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conn = sqlite3.connect(":memory:")
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conn.row_factory = sqlite3.Row
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store = Store.__new__(Store)
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store.conn = conn
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return store
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def test_build_level3_prompt_contains_element_scaffold():
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"""Level 3 prompt includes element coverage, pantry items, and constraints."""
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from app.services.recipe.llm_recipe import LLMRecipeGenerator
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store = _make_store()
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gen = LLMRecipeGenerator(store)
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req = RecipeRequest(
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pantry_items=["butter", "mushrooms"],
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level=3,
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constraints=["vegetarian"],
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)
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profiles = [
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IngredientProfile(name="butter", elements=["Richness"]),
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IngredientProfile(name="mushrooms", elements=["Depth"]),
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]
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gaps = ["Brightness", "Aroma"]
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prompt = gen.build_level3_prompt(req, profiles, gaps)
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assert "Richness" in prompt
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assert "Depth" in prompt
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assert "Brightness" in prompt
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assert "butter" in prompt
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assert "vegetarian" in prompt
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def test_build_level4_prompt_contains_pantry_and_constraints():
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"""Level 4 prompt is concise and includes key context."""
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from app.services.recipe.llm_recipe import LLMRecipeGenerator
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store = _make_store()
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gen = LLMRecipeGenerator(store)
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req = RecipeRequest(
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pantry_items=["pasta", "eggs", "mystery ingredient"],
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level=4,
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constraints=["no gluten"],
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allergies=["gluten"],
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wildcard_confirmed=True,
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)
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prompt = gen.build_level4_prompt(req)
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assert "mystery" in prompt.lower()
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assert "gluten" in prompt.lower()
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assert len(prompt) < 1500
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def test_allergy_items_excluded_from_prompt():
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"""Allergy items are listed as forbidden AND filtered from pantry shown to LLM."""
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from app.services.recipe.llm_recipe import LLMRecipeGenerator
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store = _make_store()
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gen = LLMRecipeGenerator(store)
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req = RecipeRequest(
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pantry_items=["olive oil", "peanuts", "garlic"],
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level=3,
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constraints=[],
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allergies=["peanuts"],
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)
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profiles = [
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IngredientProfile(name="olive oil", elements=["Richness"]),
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IngredientProfile(name="peanuts", elements=["Texture"]),
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IngredientProfile(name="garlic", elements=["Aroma"]),
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]
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gaps: list[str] = []
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prompt = gen.build_level3_prompt(req, profiles, gaps)
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# Check peanuts are in the exclusion section but NOT in the pantry section
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lines = prompt.split("\n")
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pantry_line = next((l for l in lines if l.startswith("Pantry")), "")
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exclusion_line = next(
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(l for l in lines if "must not" in l.lower()),
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"",
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)
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assert "peanuts" not in pantry_line.lower()
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assert "peanuts" in exclusion_line.lower()
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assert "olive oil" in prompt.lower()
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def test_generate_returns_result_when_llm_responds(monkeypatch):
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"""generate() returns RecipeResult with title when LLM returns a valid response."""
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from app.services.recipe.llm_recipe import LLMRecipeGenerator
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from app.models.schemas.recipe import RecipeResult
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store = _make_store()
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gen = LLMRecipeGenerator(store)
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canned_response = (
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"Title: Mushroom Butter Pasta\n"
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"Ingredients: butter, mushrooms, pasta\n"
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"Directions: Cook pasta. Sauté mushrooms in butter. Combine.\n"
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"Notes: Add parmesan to taste.\n"
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)
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monkeypatch.setattr(gen, "_call_llm", lambda prompt: canned_response)
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req = RecipeRequest(
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pantry_items=["butter", "mushrooms", "pasta"],
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level=3,
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constraints=["vegetarian"],
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)
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profiles = [
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IngredientProfile(name="butter", elements=["Richness"]),
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IngredientProfile(name="mushrooms", elements=["Depth"]),
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]
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gaps = ["Brightness"]
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result = gen.generate(req, profiles, gaps)
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assert isinstance(result, RecipeResult)
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assert len(result.suggestions) == 1
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suggestion = result.suggestions[0]
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assert suggestion.title == "Mushroom Butter Pasta"
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assert "butter" in suggestion.missing_ingredients
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assert len(suggestion.directions) > 0
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assert "parmesan" in suggestion.notes.lower()
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assert result.element_gaps == ["Brightness"]
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