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Unit tests for quiz and podcast artifact generators.
All external dependencies (OpenAI, ChromaDB, TTS) are mocked so these tests
run without network access or API keys.
"""
from __future__ import annotations
import json
import os
import pathlib
import sys
from unittest.mock import MagicMock, patch
import pytest
ROOT = pathlib.Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))
from src.artifacts.quiz_generator import QuizGenerator
from src.artifacts.podcast_generator import PodcastGenerator
# ── Shared fixtures ───────────────────────────────────────────────────────────
MOCK_QUIZ_LLM_RESPONSE = {
"questions": [
{
"id": 1,
"question": "What is machine learning?",
"options": [
"A) A type of computer hardware",
"B) A method for training models on data",
"C) A programming language",
"D) A database technology",
],
"correct_answer": "B",
"explanation": "Machine learning trains models on data to make predictions.",
"difficulty": "easy",
"topic": "Machine Learning Basics",
}
]
}
MOCK_PODCAST_LLM_RESPONSE = {
"segments": [
{"speaker": "Alex", "text": "Welcome to our podcast about machine learning!"},
{"speaker": "Jordan", "text": "Thanks, Alex! Machine learning is fascinating."},
{"speaker": "Alex", "text": "What is the core idea behind it?"},
{"speaker": "Jordan", "text": "The core idea is training models on data."},
]
}
MOCK_CHROMA_RESULTS = [
("chunk-1", 0.85, {"document": "Machine learning is a subset of AI.", "metadata": {}}),
("chunk-2", 0.80, {"document": "Models are trained on labelled datasets.", "metadata": {}}),
]
def _make_openai_chat_response(content_dict: dict) -> MagicMock:
"""Build a mock that mimics openai.chat.completions.create() return value."""
mock_response = MagicMock()
mock_response.choices[0].message.content = json.dumps(content_dict)
return mock_response
def _chroma_dir(tmp_path: pathlib.Path, user: str = "1", nb: str = "1") -> pathlib.Path:
"""Create and return the expected chroma directory under tmp_path."""
d = tmp_path / "data" / "users" / user / "notebooks" / nb / "chroma"
d.mkdir(parents=True, exist_ok=True)
return d
# ── QuizGenerator tests ───────────────────────────────────────────────────────
class TestQuizGenerator:
def test_generate_quiz_returns_questions(self, tmp_path):
"""Returns correct questions dict when context and LLM are available."""
_chroma_dir(tmp_path)
mock_store = MagicMock()
mock_store.query.return_value = MOCK_CHROMA_RESULTS
mock_llm_resp = _make_openai_chat_response(MOCK_QUIZ_LLM_RESPONSE)
env = {"STORAGE_BASE_DIR": str(tmp_path / "data"), "OPENAI_API_KEY": "test-key"}
with patch.dict(os.environ, env):
with patch("src.artifacts.quiz_generator.ChromaAdapter", return_value=mock_store):
with patch("src.artifacts.quiz_generator.OpenAI") as mock_openai_cls:
mock_client = MagicMock()
mock_client.chat.completions.create.return_value = mock_llm_resp
mock_openai_cls.return_value = mock_client
gen = QuizGenerator()
result = gen.generate_quiz(
user_id="1",
notebook_id="1",
num_questions=1,
difficulty="easy",
)
assert "questions" in result
assert len(result["questions"]) == 1
assert result["questions"][0]["correct_answer"] == "B"
assert result["metadata"]["difficulty"] == "easy"
assert result["metadata"]["num_questions"] == 1
def test_generate_quiz_no_chroma_dir_returns_error(self, tmp_path):
"""Returns error dict when the chroma directory does not exist."""
env = {"STORAGE_BASE_DIR": str(tmp_path / "nonexistent"), "OPENAI_API_KEY": "test-key"}
with patch.dict(os.environ, env):
with patch("src.artifacts.quiz_generator.OpenAI"):
gen = QuizGenerator()
result = gen.generate_quiz(user_id="1", notebook_id="1")
assert "error" in result
assert result["questions"] == []
def test_generate_quiz_empty_vectorstore_returns_error(self, tmp_path):
"""Returns error dict when vectorstore returns no chunks."""
_chroma_dir(tmp_path)
mock_store = MagicMock()
mock_store.query.return_value = []
env = {"STORAGE_BASE_DIR": str(tmp_path / "data"), "OPENAI_API_KEY": "test-key"}
with patch.dict(os.environ, env):
with patch("src.artifacts.quiz_generator.ChromaAdapter", return_value=mock_store):
with patch("src.artifacts.quiz_generator.OpenAI"):
gen = QuizGenerator()
result = gen.generate_quiz(user_id="1", notebook_id="1")
assert "error" in result
def test_generate_quiz_defaults_applied(self, tmp_path):
"""Default num_questions and difficulty are read from env vars."""
_chroma_dir(tmp_path)
mock_store = MagicMock()
mock_store.query.return_value = MOCK_CHROMA_RESULTS
mock_llm_resp = _make_openai_chat_response(
{"questions": [MOCK_QUIZ_LLM_RESPONSE["questions"][0]] * 3}
)
env = {
"STORAGE_BASE_DIR": str(tmp_path / "data"),
"OPENAI_API_KEY": "test-key",
"DEFAULT_QUIZ_QUESTIONS": "3",
"DEFAULT_QUIZ_DIFFICULTY": "hard",
}
with patch.dict(os.environ, env):
with patch("src.artifacts.quiz_generator.ChromaAdapter", return_value=mock_store):
with patch("src.artifacts.quiz_generator.OpenAI") as mock_openai_cls:
mock_client = MagicMock()
mock_client.chat.completions.create.return_value = mock_llm_resp
mock_openai_cls.return_value = mock_client
gen = QuizGenerator()
result = gen.generate_quiz(user_id="1", notebook_id="1")
assert result["metadata"]["num_questions"] == 3
assert result["metadata"]["difficulty"] == "hard"
def test_save_quiz_creates_markdown_file(self, tmp_path):
"""save_quiz writes a markdown file with questions and answer key."""
quiz_data = {
"questions": MOCK_QUIZ_LLM_RESPONSE["questions"],
"metadata": {"num_questions": 1, "difficulty": "easy"},
}
with patch("src.artifacts.quiz_generator.OpenAI"):
gen = QuizGenerator()
markdown = gen.format_quiz_markdown(quiz_data, title="Quiz")
saved_path = gen.save_quiz(markdown, "1", "1")
p = pathlib.Path(saved_path)
assert p.exists()
assert p.suffix == ".md"
saved = p.read_text(encoding="utf-8")
assert "## Questions" in saved
assert "## Answer Key" in saved
assert "1. **B**" in saved
def test_generate_quiz_normalizes_multiline_options(self, tmp_path):
"""Multiline option strings are normalized into labeled bullet options."""
_chroma_dir(tmp_path)
mock_store = MagicMock()
mock_store.query.return_value = MOCK_CHROMA_RESULTS
raw_payload = {
"questions": [
{
"id": 1,
"question": "What is the goal?",
"options": "A) One\nB) Two\nC) Three\nD) Four",
"correct_answer": "B) Two",
"explanation": "Two is correct.",
"topic": "Goals",
}
]
}
mock_llm_resp = _make_openai_chat_response(raw_payload)
env = {"STORAGE_BASE_DIR": str(tmp_path / "data"), "OPENAI_API_KEY": "test-key"}
with patch.dict(os.environ, env):
with patch("src.artifacts.quiz_generator.ChromaAdapter", return_value=mock_store):
with patch("src.artifacts.quiz_generator.OpenAI") as mock_openai_cls:
mock_client = MagicMock()
mock_client.chat.completions.create.return_value = mock_llm_resp
mock_openai_cls.return_value = mock_client
gen = QuizGenerator()
result = gen.generate_quiz(user_id="1", notebook_id="1", num_questions=1)
markdown = gen.format_quiz_markdown(result, title="Quiz")
assert "error" not in result
assert result["questions"][0]["options"] == ["A) One", "B) Two", "C) Three", "D) Four"]
assert "- A) One" in markdown
assert "- D) Four" in markdown
# ── PodcastGenerator tests ────────────────────────────────────────────────────
class TestPodcastGenerator:
def _make_generator(self, tmp_path: pathlib.Path, extra_env: dict | None = None):
"""Convenience: build a PodcastGenerator with EdgeTTS mocked out."""
env = {
"STORAGE_BASE_DIR": str(tmp_path / "data"),
"OPENAI_API_KEY": "test-key",
"TRANSCRIPT_LLM_PROVIDER": "openai",
"TTS_PROVIDER": "edge",
**(extra_env or {}),
}
with patch.dict(os.environ, env):
with patch("src.artifacts.tts_adapter.EdgeTTS"):
return PodcastGenerator(), env
def test_generate_podcast_returns_transcript(self, tmp_path):
"""Returns transcript list and audio_path when all mocks succeed."""
_chroma_dir(tmp_path)
mock_store = MagicMock()
mock_store.query.return_value = MOCK_CHROMA_RESULTS
mock_llm_resp = _make_openai_chat_response(MOCK_PODCAST_LLM_RESPONSE)
fake_audio = str(tmp_path / "podcast.mp3")
pathlib.Path(fake_audio).write_bytes(b"fake-audio")
env = {
"STORAGE_BASE_DIR": str(tmp_path / "data"),
"OPENAI_API_KEY": "test-key",
"TRANSCRIPT_LLM_PROVIDER": "openai",
"TTS_PROVIDER": "edge",
}
with patch.dict(os.environ, env):
with patch("src.artifacts.tts_adapter.EdgeTTS"):
with patch(
"src.artifacts.podcast_generator.ChromaAdapter", return_value=mock_store
):
with patch("src.artifacts.podcast_generator.OpenAI") as mock_openai_cls:
mock_client = MagicMock()
mock_client.chat.completions.create.return_value = mock_llm_resp
mock_openai_cls.return_value = mock_client
gen = PodcastGenerator()
with patch.object(gen, "_synthesize_segments", return_value=[fake_audio]):
with patch.object(gen, "_combine_audio", return_value=fake_audio):
result = gen.generate_podcast(
user_id="1",
notebook_id="1",
duration_target="5min",
)
assert "transcript" in result
assert len(result["transcript"]) == 4
assert result["audio_path"] == fake_audio
assert result["metadata"]["duration_target"] == "5min"
def test_generate_podcast_no_chroma_dir_returns_error(self, tmp_path):
"""Returns error dict when chroma directory does not exist."""
env = {
"STORAGE_BASE_DIR": str(tmp_path / "nonexistent"),
"OPENAI_API_KEY": "test-key",
"TRANSCRIPT_LLM_PROVIDER": "openai",
"TTS_PROVIDER": "edge",
}
with patch.dict(os.environ, env):
with patch("src.artifacts.tts_adapter.EdgeTTS"):
with patch("src.artifacts.podcast_generator.OpenAI"):
gen = PodcastGenerator()
result = gen.generate_podcast(user_id="1", notebook_id="1")
assert "error" in result
assert result["transcript"] == []
def test_generate_podcast_empty_vectorstore_returns_error(self, tmp_path):
"""Returns error dict when vectorstore has no chunks."""
_chroma_dir(tmp_path)
mock_store = MagicMock()
mock_store.query.return_value = []
env = {
"STORAGE_BASE_DIR": str(tmp_path / "data"),
"OPENAI_API_KEY": "test-key",
"TRANSCRIPT_LLM_PROVIDER": "openai",
"TTS_PROVIDER": "edge",
}
with patch.dict(os.environ, env):
with patch("src.artifacts.tts_adapter.EdgeTTS"):
with patch(
"src.artifacts.podcast_generator.ChromaAdapter", return_value=mock_store
):
with patch("src.artifacts.podcast_generator.OpenAI"):
gen = PodcastGenerator()
result = gen.generate_podcast(user_id="1", notebook_id="1")
assert "error" in result
def test_save_transcript_creates_markdown_file(self, tmp_path):
"""save_transcript writes markdown transcript at the expected path."""
podcast_data = {
"transcript": MOCK_PODCAST_LLM_RESPONSE["segments"],
"audio_path": str(tmp_path / "podcast.mp3"),
"metadata": {"duration_target": "5min"},
}
env = {
"OPENAI_API_KEY": "test-key",
"TRANSCRIPT_LLM_PROVIDER": "openai",
"TTS_PROVIDER": "edge",
}
with patch.dict(os.environ, env):
with patch("src.artifacts.tts_adapter.EdgeTTS"):
with patch("src.artifacts.podcast_generator.OpenAI"):
gen = PodcastGenerator()
saved_path = gen.save_transcript(podcast_data, "1", "1")
p = pathlib.Path(saved_path)
assert p.exists()
assert p.suffix == ".md"
saved = p.read_text(encoding="utf-8")
assert "# Podcast Transcript" in saved
assert "## Conversation" in saved
assert "**Alex:**" in saved
def test_generate_podcast_topic_focus(self, tmp_path):
"""topic_focus is passed through to metadata."""
_chroma_dir(tmp_path)
mock_store = MagicMock()
mock_store.query.return_value = MOCK_CHROMA_RESULTS
mock_llm_resp = _make_openai_chat_response(MOCK_PODCAST_LLM_RESPONSE)
env = {
"STORAGE_BASE_DIR": str(tmp_path / "data"),
"OPENAI_API_KEY": "test-key",
"TRANSCRIPT_LLM_PROVIDER": "openai",
"TTS_PROVIDER": "edge",
}
with patch.dict(os.environ, env):
with patch("src.artifacts.tts_adapter.EdgeTTS"):
with patch(
"src.artifacts.podcast_generator.ChromaAdapter", return_value=mock_store
):
with patch("src.artifacts.podcast_generator.OpenAI") as mock_openai_cls:
mock_client = MagicMock()
mock_client.chat.completions.create.return_value = mock_llm_resp
mock_openai_cls.return_value = mock_client
gen = PodcastGenerator()
with patch.object(gen, "_synthesize_segments", return_value=[]):
with patch.object(gen, "_combine_audio", return_value=""):
result = gen.generate_podcast(
user_id="1",
notebook_id="1",
topic_focus="neural networks",
)
assert result["metadata"]["topic_focus"] == "neural networks"
def test_generate_podcast_when_tts_fails_returns_error_with_transcript(self, tmp_path):
"""If TTS produces no audio segments, generator returns an explicit error."""
_chroma_dir(tmp_path)
mock_store = MagicMock()
mock_store.query.return_value = MOCK_CHROMA_RESULTS
mock_llm_resp = _make_openai_chat_response(MOCK_PODCAST_LLM_RESPONSE)
env = {
"STORAGE_BASE_DIR": str(tmp_path / "data"),
"OPENAI_API_KEY": "test-key",
"TRANSCRIPT_LLM_PROVIDER": "openai",
"TTS_PROVIDER": "edge",
}
with patch.dict(os.environ, env):
with patch("src.artifacts.tts_adapter.EdgeTTS"):
with patch(
"src.artifacts.podcast_generator.ChromaAdapter", return_value=mock_store
):
with patch("src.artifacts.podcast_generator.OpenAI") as mock_openai_cls:
mock_client = MagicMock()
mock_client.chat.completions.create.return_value = mock_llm_resp
mock_openai_cls.return_value = mock_client
gen = PodcastGenerator()
with patch.object(gen, "_synthesize_segments", return_value=[]):
result = gen.generate_podcast(user_id="1", notebook_id="1")
assert "error" in result
assert "audio synthesis failed" in str(result["error"]).lower()
assert isinstance(result.get("transcript"), list)
assert len(result["transcript"]) > 0
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