523c072818
Phase 3 — vault exact recall index: - Replace O(N) np.array_equal scan with hash-based exact-match index - Add optional max_entries with deterministic FIFO eviction - Index rebuilds on reproject for consistency Phase 4 — Rust versor_apply parity: - Fix CGA metric signature (+,+,+,+,-) and blade ordering to match Python - Implement versor_apply_closed with null-vector preservation, f64 unitize, and construction seed fallback matching Python closure semantics - Gate Rust dispatch behind CORE_BACKEND=rust; Python remains default - Add f64 geometric product for closure-path precision Phase 5 — cognitive quality pack expansion: - Expand lexicon from 55 to 70 entries (evidence, inference, procedure, verification, distinction, relation, thought, understanding, judgment, principle, order, connectives) - Improve semantic templates for cause, procedure, comparison, recall, verification intents - Expand eval cases from 20 to 45 across all categories Validation: 491 tests pass, 45 eval cases at 100% all metrics.
81 lines
2.6 KiB
Python
81 lines
2.6 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 grounded in {obj}",
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IntentTag.PROCEDURE: "first, {obj}; then, {subject} follows",
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IntentTag.COMPARISON: "{subject} and {secondary} are distinguished: {subject} {predicate_h} {secondary}",
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IntentTag.CORRECTION: "correction: {subject} {predicate_h} {obj}",
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IntentTag.RECALL: "recalling {subject}: {obj}",
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IntentTag.VERIFICATION: "{subject} is verified: {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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"is_grounded_in": "is grounded in",
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"is_distinguished_from": "is distinguished from",
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"implies": "implies",
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"entails": "entails",
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"requires": "requires",
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"verifies": "verifies",
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"evidences": "evidences",
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"orders": "orders",
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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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