229 lines
7.4 KiB
Python
229 lines
7.4 KiB
Python
"""Tests for LLMRecipeGenerator — prompt builders and allergy filtering."""
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from __future__ import annotations
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import os
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from contextlib import contextmanager
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from dataclasses import dataclass
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from unittest.mock import MagicMock, patch
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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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# All LLM ingredients (butter, mushrooms, pasta) are in the pantry, so none are missing
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assert 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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# ---------------------------------------------------------------------------
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# CFOrchClient integration tests
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# ---------------------------------------------------------------------------
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@dataclass
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class _FakeAllocation:
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allocation_id: str = "alloc-test-1"
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service: str = "vllm"
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node_id: str = "node-1"
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gpu_id: int = 0
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model: str | None = "Ouro-2.6B-Thinking"
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url: str = "http://test:8000"
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started: bool = True
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warm: bool = True
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def test_recipe_gen_uses_cf_orch_when_env_set(monkeypatch):
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"""When CF_ORCH_URL is set, _call_llm uses alloc.url+/v1 as the OpenAI base_url."""
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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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fake_alloc = _FakeAllocation()
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@contextmanager
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def _fake_llm_context():
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yield fake_alloc
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captured = {}
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# Fake OpenAI that records the base_url it was constructed with
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class _FakeOpenAI:
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def __init__(self, *, base_url, api_key):
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captured["base_url"] = base_url
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msg = MagicMock()
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msg.content = "Title: Test\nIngredients: a\nDirections: do it.\nNotes: none."
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choice = MagicMock()
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choice.message = msg
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completion = MagicMock()
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completion.choices = [choice]
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self.chat = MagicMock()
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self.chat.completions = MagicMock()
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self.chat.completions.create = MagicMock(return_value=completion)
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# Patch _get_llm_context directly so no real HTTP call is made
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monkeypatch.setattr(gen, "_get_llm_context", _fake_llm_context)
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with patch("app.services.recipe.llm_recipe.OpenAI", _FakeOpenAI):
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gen._call_llm("make me a recipe")
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assert captured.get("base_url") == "http://test:8000/v1"
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def test_recipe_gen_falls_back_without_cf_orch(monkeypatch):
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"""When CF_ORCH_URL is not set, _call_llm falls back to LLMRouter."""
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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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monkeypatch.delenv("CF_ORCH_URL", raising=False)
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router_called = {}
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def _fake_complete(prompt, **_kwargs):
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router_called["prompt"] = prompt
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return "Title: Direct\nIngredients: x\nDirections: go.\nNotes: ok."
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fake_router = MagicMock()
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fake_router.complete.side_effect = _fake_complete
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# LLMRouter is imported locally inside _call_llm, so patch it at its source module.
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# new_callable=MagicMock makes the class itself a MagicMock; set return_value so
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# that LLMRouter() (instantiation) yields fake_router rather than a new MagicMock.
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with patch("circuitforge_core.llm.router.LLMRouter", new_callable=MagicMock) as mock_router_cls:
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mock_router_cls.return_value = fake_router
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gen._call_llm("direct path prompt")
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assert router_called.get("prompt") == "direct path prompt"
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