85 lines
3.5 KiB
Python
85 lines
3.5 KiB
Python
from pathlib import Path
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from app.services.interviews.base import PersonaRecord, MemoryDigest
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from app.services.interviews.delphi import (
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DelphiSubagent, extract_themes, convergence_metrics,
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)
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INSTRUMENT = Path(__file__).resolve().parents[2] / "scripts" / "instruments" / "delphi_v1.yaml"
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class _Mem:
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def get_digest(self, agent_id, max_chars=2000):
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return MemoryDigest(text="x", available=True)
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class _R1LLM:
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def chat_json(self, messages, temperature=0.0, max_tokens=None, **kw):
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return {"answers": {
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"q1": "Klimawandel, Quoten, Generationswechsel",
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"q2": "MSC, Aquakultur",
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"q3": "Russland, EU-Politik",
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"q4": "Verbraucherpreise",
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}}
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class _R2LLM:
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def chat_json(self, messages, temperature=0.0, max_tokens=None, **kw):
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return {"ratings": {f"theme_{i}": {"importance": 4, "plausibility": 3} for i in range(5)}}
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class _ExtractLLM:
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def chat_json(self, messages, temperature=0.0, max_tokens=None, **kw):
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return {"themes": [
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{"theme_id": "theme_0", "label": "Klimawandel"},
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{"theme_id": "theme_1", "label": "Quoten"},
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{"theme_id": "theme_2", "label": "MSC"},
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{"theme_id": "theme_3", "label": "EU-Politik"},
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{"theme_id": "theme_4", "label": "Generationswechsel"},
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]}
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def test_delphi_round1_open():
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sub = DelphiSubagent(llm=_R1LLM(), memory=_Mem(), instrument_path=INSTRUMENT)
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persona = PersonaRecord(agent_id=2, name="A", persona="p")
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resp = sub.administer_round1(persona)
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assert resp.round == 1
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assert len(resp.answers) == 4
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def test_extract_themes_aggregates():
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from app.models.interview import DelphiOpenResponse
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r1 = [DelphiOpenResponse(agent_id=i, answers={"q1": "Klimawandel", "q2": "MSC"}) for i in range(3)]
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themes = extract_themes(r1, llm=_ExtractLLM())
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assert len(themes) == 5
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assert all("theme_id" in t for t in themes)
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def test_convergence_metrics():
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from app.models.interview import DelphiRatingResponse
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r2 = [DelphiRatingResponse(agent_id=i, round=2,
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ratings={"t1": {"importance": 3, "plausibility": 3}}) for i in range(5)]
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r3 = [DelphiRatingResponse(agent_id=i, round=3,
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ratings={"t1": {"importance": 4, "plausibility": 4}}) for i in range(5)]
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conv = convergence_metrics(r2, r3)
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assert "t1" in conv
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assert conv["t1"]["delta_iqr_importance"] is not None
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def test_delphi_r2_accepts_string_ratings():
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"""Delphi R2/R3 ratings should accept stringified importance/plausibility ints."""
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from app.services.interviews.base import PersonaRecord, MemoryDigest
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from app.services.interviews.delphi import DelphiSubagent
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from pathlib import Path as _P
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class _Mem:
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def get_digest(self, agent_id, max_chars=2000):
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return MemoryDigest(text="x", available=True)
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class _StringLLM:
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def chat_json(self, messages, temperature=0.0, max_tokens=None, **kw):
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return {"ratings": {
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"t1": {"importance": "4", "plausibility": "3"},
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"t2": {"importance": "5", "plausibility": "2"},
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}}
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inst = _P(__file__).resolve().parents[2] / "scripts" / "instruments" / "delphi_v1.yaml"
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sub = DelphiSubagent(llm=_StringLLM(), memory=_Mem(), instrument_path=inst)
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persona = PersonaRecord(agent_id=1, name="A", persona="p")
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themes = [{"theme_id": "t1", "label": "T1"}, {"theme_id": "t2", "label": "T2"}]
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resp = sub.administer_round2(persona, themes)
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assert resp.ratings["t1"]["importance"] == 4
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assert isinstance(resp.ratings["t1"]["importance"], int)
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