mitai-jinkendo/tests/backend/test_phase2_workflow_executor.py

"""
Unit Tests für workflow_executor.py (Phase 2)

Run with: PYTHONPATH=./backend pytest tests/backend/test_phase2_workflow_executor.py -v
"""
import pytest
from unittest.mock import AsyncMock, MagicMock, patch
from workflow_executor import aggregate_results
from workflow_models import NodeExecutionState, NodeStatus, NormalizedSignal, SignalStatus


# ── aggregate_results Tests ────────────────────────────────────────────────────

def test_aggregate_results_basic():
    """Test: Aggregation mit zwei executed nodes"""
    states = [
        NodeExecutionState(
            node_id="start",
            status=NodeStatus.EXECUTED,
            started_at="2026-04-03T12:00:00",
            completed_at="2026-04-03T12:00:01"
        ),
        NodeExecutionState(
            node_id="body",
            status=NodeStatus.EXECUTED,
            analysis_core="Gewichtsentwicklung positiv",
            normalized_signals=[
                NormalizedSignal(
                    question_type="relevanz",
                    raw_value="ja",
                    normalized_value="ja",
                    status=SignalStatus.VALID
                )
            ],
            started_at="2026-04-03T12:00:01",
            completed_at="2026-04-03T12:00:05"
        ),
        NodeExecutionState(
            node_id="end",
            status=NodeStatus.EXECUTED,
            started_at="2026-04-03T12:00:05",
            completed_at="2026-04-03T12:00:06"
        )
    ]

    result = aggregate_results(states)

    assert "## body" in result["combined_analysis"]
    assert "Gewichtsentwicklung" in result["combined_analysis"]
    assert result["total_nodes"] == 3
    assert result["executed_nodes"] == 3
    assert result["failed_nodes"] == 0
    assert len(result["all_signals"]) == 1
    assert result["all_signals"][0]["question_type"] == "relevanz"


def test_aggregate_results_with_failed_node():
    """Test: Aggregation mit einem fehlgeschlagenen Knoten"""
    states = [
        NodeExecutionState(
            node_id="node1",
            status=NodeStatus.EXECUTED,
            analysis_core="Success",
            started_at="2026-04-03T12:00:00",
            completed_at="2026-04-03T12:00:01"
        ),
        NodeExecutionState(
            node_id="node2",
            status=NodeStatus.FAILED,
            error="LLM timeout",
            started_at="2026-04-03T12:00:01",
            completed_at="2026-04-03T12:00:02"
        )
    ]

    result = aggregate_results(states)

    assert result["total_nodes"] == 2
    assert result["executed_nodes"] == 1
    assert result["failed_nodes"] == 1
    assert "## node1" in result["combined_analysis"]
    assert "## node2" not in result["combined_analysis"]


def test_aggregate_results_multiple_signals():
    """Test: Aggregation mit mehreren normalisierten Signalen"""
    states = [
        NodeExecutionState(
            node_id="node1",
            status=NodeStatus.EXECUTED,
            analysis_core="Analysis 1",
            normalized_signals=[
                NormalizedSignal(
                    question_type="relevanz",
                    raw_value="ja",
                    normalized_value="ja",
                    status=SignalStatus.VALID
                ),
                NormalizedSignal(
                    question_type="prioritaet",
                    raw_value="hoch",
                    normalized_value="hoch",
                    status=SignalStatus.VALID
                )
            ],
            started_at="2026-04-03T12:00:00",
            completed_at="2026-04-03T12:00:01"
        ),
        NodeExecutionState(
            node_id="node2",
            status=NodeStatus.EXECUTED,
            analysis_core="Analysis 2",
            normalized_signals=[
                NormalizedSignal(
                    question_type="selektion",
                    raw_value="nein",
                    normalized_value="nein",
                    status=SignalStatus.VALID
                )
            ],
            started_at="2026-04-03T12:00:01",
            completed_at="2026-04-03T12:00:02"
        )
    ]

    result = aggregate_results(states)

    assert len(result["all_signals"]) == 3
    assert result["all_signals"][0]["question_type"] == "relevanz"
    assert result["all_signals"][1]["question_type"] == "prioritaet"
    assert result["all_signals"][2]["question_type"] == "selektion"


def test_aggregate_results_empty():
    """Test: Aggregation mit leerer node_states Liste"""
    result = aggregate_results([])

    assert result["combined_analysis"] == ""
    assert result["all_signals"] == []
    assert result["total_nodes"] == 0
    assert result["executed_nodes"] == 0
    assert result["failed_nodes"] == 0


def test_aggregate_results_no_analysis_core():
    """Test: Aggregation mit nodes ohne analysis_core"""
    states = [
        NodeExecutionState(
            node_id="start",
            status=NodeStatus.EXECUTED,
            started_at="2026-04-03T12:00:00",
            completed_at="2026-04-03T12:00:01"
        )
    ]

    result = aggregate_results(states)

    assert result["combined_analysis"] == ""
    assert result["executed_nodes"] == 1


def test_aggregate_results_formatting():
    """Test: Formatierung der combined_analysis"""
    states = [
        NodeExecutionState(
            node_id="node1",
            status=NodeStatus.EXECUTED,
            analysis_core="First analysis",
            started_at="2026-04-03T12:00:00",
            completed_at="2026-04-03T12:00:01"
        ),
        NodeExecutionState(
            node_id="node2",
            status=NodeStatus.EXECUTED,
            analysis_core="Second analysis",
            started_at="2026-04-03T12:00:01",
            completed_at="2026-04-03T12:00:02"
        )
    ]

    result = aggregate_results(states)

    # Prüfe Format: ## node_id\nanalysis_core\n\n## node_id\nanalysis_core
    assert result["combined_analysis"].startswith("## node1\nFirst analysis")
    assert "## node2\nSecond analysis" in result["combined_analysis"]
    assert "\n\n" in result["combined_analysis"]  # Separator zwischen Knoten


# ── Integration-ähnliche Tests (ohne echte DB/LLM) ─────────────────────────────

@pytest.mark.asyncio
async def test_execute_node_start_end():
    """Test: Start/End Nodes sind No-Ops"""
    from workflow_executor import execute_node
    from workflow_models import WorkflowNode, WorkflowGraph

    start_node = WorkflowNode(id="start", type="start")
    end_node = WorkflowNode(id="end", type="end")

    context = {"variables": {}, "profile_id": "test"}
    catalog = {}
    mock_graph = WorkflowGraph(nodes=[], edges=[])  # Phase 3: graph parameter required

    async def mock_llm(prompt, model):
        return "should not be called"

    # Test start
    result = await execute_node(start_node, context, catalog, mock_graph, mock_llm)
    assert result.status == NodeStatus.EXECUTED
    assert result.analysis_core is None

    # Test end
    result = await execute_node(end_node, context, catalog, mock_graph, mock_llm)
    assert result.status == NodeStatus.EXECUTED
    assert result.analysis_core is None


@pytest.mark.asyncio
async def test_execute_node_unknown_type():
    """Test: Unbekannter Node-Typ wirft Fehler"""
    from workflow_executor import execute_node
    from workflow_models import WorkflowNode, WorkflowGraph

    # Phase 3: logic is now implemented, test with join instead
    join_node = WorkflowNode(id="join1", type="join")

    context = {"variables": {}, "profile_id": "test"}
    catalog = {}
    mock_graph = WorkflowGraph(nodes=[], edges=[])

    async def mock_llm(prompt, model):
        return ""

    result = await execute_node(join_node, context, catalog, mock_graph, mock_llm)

    # Sollte FAILED sein mit Fehlermeldung
    assert result.status == NodeStatus.FAILED
    assert "not implemented" in result.error.lower() or "phase 4" in result.error.lower()


@pytest.mark.asyncio
async def test_execute_node_analysis_simple():
    """Test: Analysis Node ohne Fragenergänzung"""
    from workflow_executor import execute_node
    from workflow_models import WorkflowNode, WorkflowGraph

    node = WorkflowNode(
        id="test_node",
        type="analysis",
        prompt_slug="test_prompt",
        question_augmentations=None
    )

    context = {"variables": {"name": "Test"}, "profile_id": "test"}
    catalog = {}
    mock_graph = WorkflowGraph(nodes=[], edges=[])

    # Mock LLM
    async def mock_llm(prompt, model):
        return "## Analyse\nTest analysis content"

    # Mock load_prompt_template
    with patch('workflow_executor.load_prompt_template') as mock_load:
        mock_load.return_value = "Test prompt for {{name}}"

        result = await execute_node(node, context, catalog, mock_graph, mock_llm)

    assert result.status == NodeStatus.EXECUTED
    assert result.analysis_core == "Test analysis content"
    assert len(result.normalized_signals) == 0  # Keine Fragen


@pytest.mark.asyncio
async def test_execute_node_analysis_with_questions():
    """Test: Analysis Node mit Fragenergänzung und Normalisierung"""
    from workflow_executor import execute_node
    from workflow_models import WorkflowNode, QuestionAugmentation, WorkflowGraph

    node = WorkflowNode(
        id="test_node",
        type="analysis",
        prompt_slug="test_prompt",
        question_augmentations=[
            QuestionAugmentation(
                id="q1",
                type="relevanz",
                question="Ist relevant?",
                answer_spectrum=["ja", "nein", "unklar"]
            )
        ]
    )

    context = {"variables": {}, "profile_id": "test"}
    catalog = {
        "relevanz": {
            "answer_spectrum": ["ja", "nein", "unklar"],
            "normalization_rules": None
        }
    }
    mock_graph = WorkflowGraph(nodes=[], edges=[])

    # Mock LLM
    async def mock_llm(prompt, model):
        # LLM antwortet mit Fragenergänzung
        return """## Analyse
Test analysis

## Entscheidungsfragen
- Relevanz: ja
"""

    # Mock load_prompt_template
    with patch('workflow_executor.load_prompt_template') as mock_load:
        mock_load.return_value = "Base prompt"

        result = await execute_node(node, context, catalog, mock_graph, mock_llm)

    assert result.status == NodeStatus.EXECUTED
    assert result.analysis_core == "Test analysis"
    assert len(result.normalized_signals) == 1
    assert result.normalized_signals[0].question_type == "relevanz"
    assert result.normalized_signals[0].normalized_value == "ja"
    assert result.normalized_signals[0].status == SignalStatus.VALID


@pytest.mark.asyncio
async def test_execute_node_hybrid_model_override():
    """
    Test: Hybrid Model - Node-spezifisches Spektrum überschreibt Catalog

    Kritischer Test für Bug-Fix: Node mit answer_spectrum ["increase", "stable", "decrease"]
    muss Catalog-Spektrum ["ja", "nein", "unklar"] überschreiben.

    Regression-Test für: https://github.com/anthropics/claude-code/issues/XXX
    """
    from workflow_executor import execute_node
    from workflow_models import WorkflowNode, QuestionAugmentation, WorkflowGraph

    # Node mit ANDEREM Spektrum als Catalog
    node = WorkflowNode(
        id="test_node",
        type="analysis",
        prompt_slug="test_prompt",
        question_augmentations=[
            QuestionAugmentation(
                id="q1",
                type="relevanz",
                question="Hat sich die Fettmasse verändert?",
                answer_spectrum=["increase", "stable", "decrease"]  # ← Node-spezifisch
            )
        ]
    )

    context = {"variables": {}, "profile_id": "test"}

    # Catalog hat ANDERES Spektrum
    catalog = {
        "relevanz": {
            "answer_spectrum": ["ja", "nein", "unklar"],  # ← Catalog-Standard
            "normalization_rules": None
        }
    }
    mock_graph = WorkflowGraph(nodes=[], edges=[])

    # Mock LLM gibt "decrease" zurück (gültig für Node, ungültig für Catalog)
    async def mock_llm(prompt, model):
        return """## Analyse
Gewicht gesunken

## Entscheidungsfragen
- Relevanz: decrease
"""

    # Mock load_prompt_template
    with patch('workflow_executor.load_prompt_template') as mock_load:
        mock_load.return_value = "Base prompt"

        result = await execute_node(node, context, catalog, mock_graph, mock_llm)

    # Assertions: "decrease" muss VALID sein (Node-Spektrum), nicht INVALID (Catalog)
    assert result.status == NodeStatus.EXECUTED
    assert len(result.normalized_signals) == 1

    signal = result.normalized_signals[0]
    assert signal.question_type == "relevanz"
    assert signal.raw_value == "decrease"
    assert signal.normalized_value == "decrease"
    assert signal.status == SignalStatus.VALID  # ← KRITISCH: Muss VALID sein, nicht INVALID!

    # Wenn dieser Test fehlschlägt, wurde der Catalog benutzt statt Node-Spektrum


if __name__ == "__main__":
    pytest.main([__file__, "-v"])