wehub-resource-sync
117 lines
4.0 KiB
Python
117 lines
4.0 KiB
Python
from contextlib import contextmanager
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from unittest.mock import AsyncMock, patch
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import pytest
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from pydantic import ValidationError
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from app.services.interview_prep import generate_interview_prep
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SAMPLE_RESUME = {
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"personalInfo": {"name": "Jane Doe"},
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"summary": "Backend engineer",
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"workExperience": [],
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"education": [],
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"personalProjects": [],
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"additional": {"technicalSkills": ["Python", "FastAPI"]},
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}
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def _valid_payload():
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return {
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"role_fit_analysis": ["Python API experience is relevant."],
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"resume_questions": [
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{
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"question": "How did you build the API?",
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"focus_area": "Backend APIs",
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"suggested_answer_points": ["Use resume-grounded API details."],
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}
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],
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"project_follow_ups": [],
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"skill_gaps": [
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{
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"skill": "Kubernetes",
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"why_it_matters": "The JD mentions deployment.",
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"preparation_suggestion": "Review basics without claiming experience.",
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}
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],
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"talking_points": ["Connect FastAPI work to the role."],
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}
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@contextmanager
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def _patched_llm_token_helpers(max_tokens: int = 4096):
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config = object()
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with patch(
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"app.services.interview_prep.get_llm_config",
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return_value=config,
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) as mock_get_llm_config, patch(
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"app.services.interview_prep.get_model_name",
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return_value="openai/small-output-model",
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) as mock_get_model_name, patch(
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"app.services.interview_prep.get_safe_max_tokens",
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return_value=max_tokens,
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) as mock_get_safe_max_tokens:
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yield mock_get_llm_config, mock_get_model_name, mock_get_safe_max_tokens
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@pytest.mark.asyncio
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async def test_generate_interview_prep_validates_successful_json():
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with patch(
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"app.services.interview_prep.complete_json",
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new_callable=AsyncMock,
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) as mock_complete, _patched_llm_token_helpers() as token_helpers:
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mock_complete.return_value = _valid_payload()
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result = await generate_interview_prep(SAMPLE_RESUME, "Need FastAPI", "en")
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mock_get_llm_config, mock_get_model_name, mock_get_safe_max_tokens = token_helpers
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assert result.role_fit_analysis == ["Python API experience is relevant."]
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mock_complete.assert_awaited_once()
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mock_get_llm_config.assert_called_once_with()
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mock_get_model_name.assert_called_once_with(mock_get_llm_config.return_value)
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mock_get_safe_max_tokens.assert_called_once_with(
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"openai/small-output-model",
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requested=8192,
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)
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assert mock_complete.await_args.kwargs["max_tokens"] == 4096
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assert mock_complete.await_args.kwargs["schema_type"] == "interview_prep"
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@pytest.mark.asyncio
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async def test_generate_interview_prep_bounds_prompt_inputs():
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with patch(
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"app.services.interview_prep.complete_json",
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new_callable=AsyncMock,
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) as mock_complete, _patched_llm_token_helpers():
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mock_complete.return_value = _valid_payload()
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large_resume = {
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**SAMPLE_RESUME,
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"summary": "Backend engineer " + ("with API delivery evidence. " * 3000),
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}
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await generate_interview_prep(
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large_resume,
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"Need FastAPI. " + ("Detailed requirement. " * 1500),
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"en",
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)
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prompt = mock_complete.await_args.kwargs["prompt"]
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assert len(prompt) < 50_000
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assert "Content truncated for prompt length" in prompt
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assert "do not infer or invent omitted details" in prompt
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@pytest.mark.asyncio
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async def test_generate_interview_prep_rejects_malformed_llm_json():
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with patch(
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"app.services.interview_prep.complete_json",
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new_callable=AsyncMock,
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) as mock_complete, _patched_llm_token_helpers():
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mock_complete.return_value = {
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"role_fit_analysis": ["Only one required key is present."]
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}
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with pytest.raises(ValidationError):
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await generate_interview_prep(SAMPLE_RESUME, "Need FastAPI", "en")
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