tests/test_answer_generator.py

from warbler_cda.answer_generator import AnswerGenerator


def test_generate_answer_prefers_available_provider():
    generator = AnswerGenerator({"enabled": True})
    results = [
        {
            "id": "doc-1",
            "content": "Courage is acting despite fear rather than waiting for fear to disappear.",
            "relevance_score": 0.9,
            "metadata": {"pack": "warbler-pack-core"},
        }
    ]

    generator._try_openai_generation = lambda prompt: None
    generator._try_local_generation = lambda prompt: "Courage means acting despite fear. Sources: [1]"

    answer = generator.generate_answer("What is courage?", results)

    assert answer.answer.startswith("Courage means acting despite fear")
    assert answer.provider == generator.model_name
    assert answer.used_fallback is False
    assert answer.citations[0]["pack"] == "warbler-pack-core"


def test_generate_answer_uses_extractive_fallback_when_models_unavailable():
    generator = AnswerGenerator({"enabled": True})
    results = [
        {
            "id": "doc-1",
            "content": "Resilience grows through repeated recovery after setbacks.",
            "relevance_score": 0.85,
            "metadata": {"pack": "warbler-pack-core"},
        }
    ]

    generator._try_openai_generation = lambda prompt: None
    generator._try_local_generation = lambda prompt: None

    answer = generator.generate_answer("How does resilience grow?", results)

    assert "Resilience grows through repeated recovery" in answer.answer
    assert answer.provider == "extractive"
    assert answer.used_fallback is True


def test_generate_answer_handles_missing_results():
    generator = AnswerGenerator({"enabled": True})

    answer = generator.generate_answer("What now?", [])

    assert "could not find enough relevant context" in answer.answer.lower()
    assert answer.citations == []


def test_extractive_fallback_uses_query_relevant_sentence_not_document_header():
    generator = AnswerGenerator({"enabled": True})
    results = [
        {
            "id": "doc-1",
            "content": (
                "Drizzt ship tutorial version 1.5. "
                "Milkshape 3d (ms3d) is an excellent 3d modeling program used for finishing touches. "
                "Export your ship as a quake 3 file and import it into ms3d."
            ),
            "relevance_score": 0.9,
            "metadata": {"pack": "synthesis-session"},
        }
    ]

    generator._try_openai_generation = lambda prompt: None
    generator._try_local_generation = lambda prompt: None

    answer = generator.generate_answer("what is milkshape?", results)

    assert "Milkshape 3d" in answer.answer
    assert "Drizzt ship tutorial version 1.5" not in answer.answer