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Integrationstest: Simuliere den kompletten AIRR-Pipeline-Durchlauf
mit normalize_output -> _validateOutput -> AIAnalysisRecord.create
"""
from src.research_assistant.models import AIAnalysisRecord, ResearchPacket
# 1. ResearchPacket erstellen (minimal)
packet = ResearchPacket(
experiment_id='EXP-TEST-NORMALIZE-0001',
research_question={},
hypotheses=[],
claims=[],
manifest={},
data={},
evidence=[],
literature_sources=[],
protocol={},
known_limitations=[],
previous_analyses=[],
provenance={
'git_commit': 'test',
'git_dirty': 'False',
'source_freeze_sha': 'test',
'configuration_path': '/dev/null',
'configuration_sha256': 'test',
'experiment_manifest_digest': 'test',
'data_ids': 'TEST',
'evid_ids': 'TEST',
'protocol_id': 'TEST',
'protocol_digest': 'test',
'data_digest': 'test',
'evid_digests': '[]',
},
)
model_info = {
'provider': 'integration-test',
'model': 'test-model',
'model_digest': 'test',
'quantization': 'unknown',
'context_length': 'unknown',
'temperature': 0.0,
'top_p': 1.0,
'seed': 'test',
'backend_version': 'test',
}
# 2. Simuliere Modell-Ausgaben, die Felder auslassen
test_cases = [
('nur assessment', {'assessment': 'Das Experiment zeigt klare Ergebnisse.'}),
('nur observations', {'observations': ['Spike rate: 10 Hz']}),
('leeres Dict', {}),
('assessment + confidence aber keine observations', {'assessment': 'Test', 'confidence': 0.85}),
('assessment als None', {'assessment': None, 'observations': None, 'confidence': '0.85'}),
]
all_ok = True
for name, output in test_cases:
try:
record = AIAnalysisRecord.create(
role='scientific_writer',
model=model_info,
packet=packet,
output=output,
prompt='Test prompt',
)
print(f'PASS {name}: AIAR={record.analysis_id}')
print(f' assessment={str(record.output["assessment"])[:60]}...')
print(f' observations={record.output["observations"]}')
print(f' confidence={record.output["confidence"]}')
except Exception as e:
print(f'FAIL {name}: {e}')
all_ok = False
print()
print('--- Test: scientific_analyst mit effect_direction ---')
try:
record = AIAnalysisRecord.create(
role='scientific_analyst',
model=model_info,
packet=packet,
output={'assessment': 'Analyse'},
prompt='Test',
)
print(f'PASS analyst: effect_direction={record.output["effect_direction"]}')
except Exception as e:
print(f'FAIL analyst: {e}')
all_ok = False
print()
print('--- Test: critical_reviewer mit fehlenden Feldern ---')
try:
record = AIAnalysisRecord.create(
role='critical_reviewer',
model=model_info,
packet=packet,
output={'assessment': 'Review', 'observations': 'keine liste'},
prompt='Test',
)
print(f'PASS reviewer: observations={record.output["observations"]}')
except Exception as e:
print(f'FAIL reviewer: {e}')
all_ok = False
print()
if all_ok:
print('ALLE INTEGRATIONSTESTS BESTANDEN')
else:
print('EINIGE TESTS FEHLGESCHLAGEN')
exit(1)
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