116 lines
3.8 KiB
Python
116 lines
3.8 KiB
Python
"""employment_agent parse_employment_response — linkedin_url is an agent key.
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parse_employment_response returns a DICT. These tests used to unpack it
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positionally, which silently read dict KEYS instead of values and asserted
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against whatever the last key happened to be.
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"""
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from __future__ import annotations
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from employment_agent.decorators import parse_employment_response
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from employment_agent.prompt import EDUCATION, NO_COMPANY, NO_LINKEDIN
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def test_parses_linkedin_url_key_separately():
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# The resume must actually contain the slug: _clean_linkedin keeps a URL
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# only when the CV evidences it, so a resume that never mentions LinkedIn
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# correctly yields None however confident the model was.
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fields = parse_employment_response(
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{
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"current_employment": "Acme",
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"education": "BS CS",
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"current_title": "Engineer",
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"linkedin_url": "https://www.linkedin.com/in/jane-doe",
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},
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"Acme BS CS Engineer https://www.linkedin.com/in/jane-doe",
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)
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assert fields["current_employment"] == "Acme"
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assert fields["education"] == "BS CS"
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assert fields["current_title"] == "Engineer"
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assert fields["linkedin_url"] == "https://www.linkedin.com/in/jane-doe"
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def test_url_absent_from_the_resume_is_not_trusted():
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fields = parse_employment_response(
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{
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"current_employment": "Acme",
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"education": "BS CS",
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"current_title": "Engineer",
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"linkedin_url": "https://www.linkedin.com/in/jane-doe",
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},
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"Acme BS CS Engineer",
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)
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assert fields["linkedin_url"] is None
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def test_sentinel_and_non_linkedin_are_dropped():
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sentinel = parse_employment_response(
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{
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"current_employment": NO_COMPANY,
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"education": EDUCATION,
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"current_title": "x",
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"linkedin_url": NO_LINKEDIN,
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},
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"",
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)
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assert sentinel["linkedin_url"] is None
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github = parse_employment_response(
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{
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"current_employment": NO_COMPANY,
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"education": EDUCATION,
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"current_title": "x",
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"linkedin_url": "https://github.com/jane",
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},
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"",
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)
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assert github["linkedin_url"] is None
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def test_adds_scheme_and_rejects_company_page():
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bare = parse_employment_response(
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{
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"current_employment": NO_COMPANY,
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"education": EDUCATION,
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"current_title": "x",
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"linkedin_url": "www.linkedin.com/in/jane-doe",
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},
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"",
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)
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assert bare["linkedin_url"] == "https://www.linkedin.com/in/jane-doe"
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company_page = parse_employment_response(
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{
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"current_employment": NO_COMPANY,
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"education": EDUCATION,
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"current_title": "x",
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"linkedin_url": "https://www.linkedin.com/company/acme",
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},
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"",
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)
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assert company_page["linkedin_url"] is None
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def test_city_prompt_asks_openai_for_a_proper_city_name():
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from employment_agent.prompt import city_list_prompt, prompt
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text = prompt()
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assert "Karachi(Malir)" in text
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assert 'JSON city must be "Karachi"' in text
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assert "Return ONE proper city name only" in text
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listed = city_list_prompt()
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assert "Karachi(Malir)" in listed
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assert '"cities"' in listed
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def test_parse_normalized_cities_keeps_agent_names():
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from employment_agent.execute_agent import parse_normalized_cities
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assert parse_normalized_cities(
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{"cities": ["Karachi", "Karachi", "Lahore", "no city mentioned"]},
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fallback=["Karachi(Malir)"],
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) == ["Karachi", "Lahore"]
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def test_parse_normalized_cities_falls_back_when_the_model_shape_is_wrong():
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from employment_agent.execute_agent import parse_normalized_cities
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assert parse_normalized_cities({"oops": True}, fallback=["Karachi(Malir)"]) == ["Karachi(Malir)"]
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