Adriano
42a95a52b5
Dataclass per genoma agente ipotesi con campi prompt/feature/temperature/ top_p/model_tier/lookback/style + parent_ids/generation. Id sha1[:16] deterministico su contenuto canonico (feature_access ordinate, float arrotondati). to_dict/from_dict per persistenza. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
51 lines
1.6 KiB
Python
51 lines
1.6 KiB
Python
from multi_swarm.genome.hypothesis import HypothesisAgentGenome, ModelTier
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def test_genome_creation_defaults():
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g = HypothesisAgentGenome(
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system_prompt="Pensa come un fisico.",
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feature_access=["close", "volume"],
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temperature=0.9,
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top_p=0.95,
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model_tier=ModelTier.C,
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lookback_window=200,
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cognitive_style="physicist",
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)
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assert g.id is not None
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assert g.parent_ids == []
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assert g.generation == 0
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def test_genome_serialization_roundtrip():
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g = HypothesisAgentGenome(
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system_prompt="Pensa come un biologo.",
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feature_access=["close", "high", "low"],
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temperature=1.1,
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top_p=0.9,
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model_tier=ModelTier.C,
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lookback_window=300,
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cognitive_style="biologist",
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parent_ids=["abc"],
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generation=5,
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)
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payload = g.to_dict()
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g2 = HypothesisAgentGenome.from_dict(payload)
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assert g2.system_prompt == g.system_prompt
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assert g2.feature_access == g.feature_access
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assert g2.temperature == g.temperature
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assert g2.parent_ids == g.parent_ids
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assert g2.generation == g.generation
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assert g2.id == g.id
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def test_genome_id_is_deterministic_on_content():
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g1 = HypothesisAgentGenome(
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system_prompt="X", feature_access=["close"], temperature=0.5,
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top_p=0.9, model_tier=ModelTier.C, lookback_window=100, cognitive_style="x",
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)
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g2 = HypothesisAgentGenome(
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system_prompt="X", feature_access=["close"], temperature=0.5,
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top_p=0.9, model_tier=ModelTier.C, lookback_window=100, cognitive_style="x",
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)
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assert g1.id == g2.id
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