a7febd48ef
- add intent-aware semantic templates for seed-pack relation predicates - add semantic realization path for ArticulationTarget outputs - wire semantic realization into CognitiveTurnPipeline results without changing ChatRuntime.chat - expand cognition CLI suite coverage for semantic realizer integration - add focused tests for deterministic semantic surfaces and response contract stability
73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
"""Intent-aware semantic templates for the realizer.
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Maps (IntentTag, relation_predicate) pairs to deterministic surface
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templates that use the seed pack's relation predicates (defines, means,
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grounds, supports, contrasts_with, corrects).
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Design constraints:
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- No LLM fallback
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- No random template selection
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- Deterministic: same (intent, predicate, subject, object) -> same surface
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- Uses seed pack vocabulary directly
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"""
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from __future__ import annotations
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from generate.intent import IntentTag
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_INTENT_TEMPLATES: dict[IntentTag, str] = {
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IntentTag.DEFINITION: "{subject} is defined as {obj}",
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IntentTag.CAUSE: "{subject} is caused by {obj}",
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IntentTag.PROCEDURE: "{subject} has the following steps: {obj}",
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IntentTag.COMPARISON: "{subject} and {secondary} are contrasted by {predicate_h}",
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IntentTag.CORRECTION: "correction: {subject} {predicate_h} {obj}",
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IntentTag.RECALL: "{subject} recalls {obj}",
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IntentTag.VERIFICATION: "{subject} is verified as {obj}",
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IntentTag.UNKNOWN: "{subject} {predicate_h} {obj}",
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}
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_PREDICATE_HUMANIZE: dict[str, str] = {
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"is_defined_as": "is defined as",
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"is_caused_by": "is caused by",
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"has_steps": "has the following steps",
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"contrasts_with": "contrasts with",
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"corrects": "corrects",
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"recalls": "recalls",
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"is_verified_as": "is verified as",
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"addresses": "addresses",
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"defines": "defines",
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"means": "means",
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"grounds": "grounds",
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"supports": "supports",
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"causes": "causes",
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"reveals": "reveals",
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"precedes": "precedes",
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"follows": "follows",
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"belongs_to": "belongs to",
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"answers": "answers",
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}
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def humanize_predicate(predicate: str) -> str:
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return _PREDICATE_HUMANIZE.get(predicate, predicate.replace("_", " "))
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def render_semantic(
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intent: IntentTag,
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subject: str,
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predicate: str,
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obj: str,
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secondary: str | None = None,
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) -> str:
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"""Render a semantic surface from intent, subject, predicate, and object."""
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template = _INTENT_TEMPLATES.get(intent, _INTENT_TEMPLATES[IntentTag.UNKNOWN])
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predicate_h = humanize_predicate(predicate)
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obj_display = obj if obj not in ("<pending>", "<prior>") else "..."
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return template.format(
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subject=subject,
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predicate_h=predicate_h,
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obj=obj_display,
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secondary=secondary or obj_display,
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)
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