aadaf11612
Add salience and attention operators, wire salience-gated candidate selection into generation, expose vault/salience trace telemetry, and add tests proving non-placeholder salience behavior.
37 lines
1.4 KiB
Python
37 lines
1.4 KiB
Python
"""
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GenerationResult — the complete output of one generation pass.
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Generate() must return the evolved field state, not only surface tokens.
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The field state after generation is semantically different from the
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field state before generation; discarding it means the vault stores
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the prompt field, not the assistant response field.
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Contracts:
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tokens — the decoded token sequence in emission order
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final_state — FieldState after the last propagation step
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trajectory — optional ordered list of intermediate FieldStates;
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None unless the caller explicitly requests it (expensive)
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from field.state import FieldState
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@dataclass(frozen=True, slots=True)
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class GenerationResult:
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tokens: tuple # decoded token sequence, immutable
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final_state: FieldState
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trajectory: tuple | None = None # (FieldState, ...) or None
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salience_top_k: int | None = None
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candidates_used: int | None = None
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def __post_init__(self) -> None:
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# Coerce list inputs to tuple for immutability.
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object.__setattr__(self, "tokens", tuple(self.tokens))
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if self.trajectory is not None:
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object.__setattr__(self, "trajectory", tuple(self.trajectory))
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def text(self, sep: str = " ") -> str:
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"""Join tokens into a string for display."""
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return sep.join(self.tokens)
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