Andrew Simonson
434df718d7
Constraint resolver: aggregate mass/footprint across a combo instead of pairwise-only checks, treat medium/atmosphere as agreement not supply/demand, reduce multi-provider checks by best/sum instead of AND-ing every provider, fail closed on unrecognized mutex values, add a propulsion-viability (thrust-to-weight) rule. Seed data updated to match (nuclear/solar-sail footprint floors, water-medium exclusions, explicit ground/gravity providers). Domain metric units were stored globally per metric name instead of per-domain, silently corrupting cost_efficiency for every domain but the first one seeded — fixed with a schema migration. Stub estimator's cost_efficiency/safety/availability/reliability were a backwards formula and flat constants; replaced with heuristics grounded in each entity's thrust_profile/energy_form/infrastructure. LLM estimate_physics() now receives each metric's unit and expected range instead of a bare name, fixing wildly miscalibrated estimates traced back to the prompt's own hardcoded example anchoring the model to the wrong order of magnitude. Sharpened the safety-estimation and plausibility-review prompts. Deduped provider parsing logic into llm/parsing.py. Web pipeline form can now pick an LLM provider per run instead of only via server env var. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
33 lines
1.0 KiB
Python
33 lines
1.0 KiB
Python
"""Tests for shared LLM response-parsing logic."""
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from __future__ import annotations
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from physcom.llm.parsing import parse_metric_json, parse_verdict
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from physcom.models.domain import MetricBound
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def _bounds():
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return [
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MetricBound("power_density", weight=0.5, norm_min=1, norm_max=2000, unit="W/kg"),
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MetricBound("safety", weight=0.5, norm_min=0.0, norm_max=1.0, unit="0-1"),
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]
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def test_parse_metric_json_strips_fences():
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text = '```json\n{"power_density": 500.0, "safety": 0.7}\n```'
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result = parse_metric_json(text, _bounds())
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assert result == {"power_density": 500.0, "safety": 0.7}
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def test_parse_metric_json_falls_back_to_range_midpoint_on_invalid():
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result = parse_metric_json("not json", _bounds())
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assert result == {"power_density": 1000.5, "safety": 0.5}
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def test_parse_verdict_plausible():
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assert parse_verdict("blah blah\nVERDICT: PLAUSIBLE") is True
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def test_parse_verdict_implausible():
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assert parse_verdict("blah blah\nVERDICT: IMPLAUSIBLE") is False
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