Lars
ca562b7130
Backend: - question_augmenter.py (290 Zeilen): Hybrid-Modell für Fragenergänzungen * merge_question_augmentations(): Knotengebundene Fragen überschreiben Prompt-Defaults * augment_prompt_with_questions(): Markdown-formatierte Fragenergänzung * parse_question_augmentations_from_jsonb(): JSONB → QuestionAugmentation[] - result_container_parser.py (250 Zeilen): Markdown-Sektionen-Parsing * parse_result_container(): Extrahiert Analysekern, Entscheidungsanteil, Begründungsanker * validate_decision_signal(): Normalisierung gegen answer_spectrum * Fallback-Parsing bei unstrukturierten Antworten - routers/workflow_questions.py (236 Zeilen): CRUD für workflow_question_catalog * GET /api/workflow/questions (mit active_only Filter) * POST/PUT/DELETE (Admin only, Soft Delete) - prompt_executor.py: Integration in execute_base_prompt() * Fragenergänzung vor LLM-Call (wenn node_questions oder catalog vorhanden) * Result-Container-Parsing nach LLM-Response - main.py: Router-Registrierung (workflow_questions) Tests: - test_phase1_question_augmenter.py (8 Tests): Hybrid-Modell, Formatierung, JSONB-Parsing - test_phase1_result_container_parser.py (17 Tests): Sektion-Extraktion, Decision-Parsing, Validierung Alle 25 Unit-Tests bestanden. version: 0.9j (backend) module: workflow 0.2.0 Konzept: .claude/task/Workflow_engine_prompting_engine/konzept_workflow_engine_konsolidated.md (Phase 1)
136 lines
4.0 KiB
Python
136 lines
4.0 KiB
Python
"""
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Unit Tests für question_augmenter.py (Phase 1)
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Run with: PYTHONPATH=./backend pytest tests/backend/test_phase1_question_augmenter.py -v
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"""
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import pytest
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from workflow_models import QuestionAugmentation
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from question_augmenter import (
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augment_prompt_with_questions,
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merge_question_augmentations,
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format_question_list,
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parse_question_augmentations_from_jsonb
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)
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def test_format_question_list():
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"""Test: Formatierung der Fragenliste"""
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questions = [
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QuestionAugmentation(
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id="q1",
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type="relevanz",
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question="Ist relevant?",
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answer_spectrum=["ja", "nein", "unklar"]
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),
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QuestionAugmentation(
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id="q2",
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type="prioritaet",
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question="Wie hoch?",
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answer_spectrum=["hoch", "mittel", "niedrig", "unklar"]
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)
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]
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result = format_question_list(questions)
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assert "Relevanz" in result
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assert "[ja/nein/unklar]" in result
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assert "Prioritaet" in result # Lowercase wird capitalized
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assert "[hoch/mittel/niedrig/unklar]" in result
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def test_augment_prompt_with_questions():
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"""Test: Prompt-Erweiterung mit Fragenergänzungen"""
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base_prompt = "Analysiere die Körperdaten."
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questions = [
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QuestionAugmentation(
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id="q1",
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type="relevanz",
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question="Ist relevant?",
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answer_spectrum=["ja", "nein", "unklar"]
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)
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]
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augmented = augment_prompt_with_questions(base_prompt, questions)
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assert "Analysiere die Körperdaten." in augmented
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assert "## Analyse" in augmented
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assert "## Entscheidungsfragen" in augmented
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assert "Relevanz" in augmented
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assert "[ja/nein/unklar]" in augmented
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def test_merge_question_augmentations_node_priority():
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"""Test: Knotengebundene Fragen haben Vorrang (Hybridmodell)"""
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node_questions = [
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QuestionAugmentation(id="q1", type="relevanz", question="Q1", answer_spectrum=["ja", "nein"])
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]
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prompt_questions = [
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QuestionAugmentation(id="q2", type="prioritaet", question="Q2", answer_spectrum=["hoch", "niedrig"])
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]
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result = merge_question_augmentations(node_questions, prompt_questions)
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# Knotengebundene haben Vorrang
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assert len(result) == 1
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assert result[0].type == "relevanz"
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def test_merge_question_augmentations_prompt_fallback():
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"""Test: Prompt-Defaults werden verwendet wenn Knoten leer"""
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node_questions = None
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prompt_questions = [
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QuestionAugmentation(id="q2", type="prioritaet", question="Q2", answer_spectrum=["hoch", "niedrig"])
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]
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result = merge_question_augmentations(node_questions, prompt_questions)
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# Prompt-Defaults werden verwendet
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assert len(result) == 1
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assert result[0].type == "prioritaet"
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def test_merge_question_augmentations_empty():
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"""Test: Leere Liste wenn weder Knoten noch Prompt Fragen haben"""
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result = merge_question_augmentations(None, None)
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assert result == []
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def test_parse_question_augmentations_from_jsonb():
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"""Test: Parsing aus JSONB-Format"""
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jsonb_data = [
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{
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"id": "q1",
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"type": "relevanz",
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"question": "Ist relevant?",
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"answer_spectrum": ["ja", "nein", "unklar"]
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},
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{
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"id": "q2",
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"type": "prioritaet",
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"question": "Wie hoch?",
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"answer_spectrum": ["hoch", "mittel", "niedrig"]
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}
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]
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result = parse_question_augmentations_from_jsonb(jsonb_data)
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assert len(result) == 2
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assert result[0].type == "relevanz"
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assert result[1].type == "prioritaet"
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def test_parse_question_augmentations_empty_jsonb():
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"""Test: Leere Liste bei None JSONB"""
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result = parse_question_augmentations_from_jsonb(None)
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assert result == []
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def test_parse_question_augmentations_invalid_jsonb():
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"""Test: ValueError bei ungültigem JSONB"""
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with pytest.raises(ValueError, match="muss ein Array sein"):
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parse_question_augmentations_from_jsonb({"invalid": "format"})
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if __name__ == "__main__":
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pytest.main([__file__, "-v"])
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