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.
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
"""Deterministic surface templates for rhetorical moves.
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Each template is a format string keyed by RhetoricalMove. Slots:
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{subject} — primary subject from the articulation step
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{predicate} — semantic predicate (e.g. "is_defined_as", "contrasts_with")
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{obj} — object slot from the graph node (may be "<pending>")
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Templates are intentionally simple. The goal is structural correctness,
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not fluency — fluency comes in a later phase when the generation stream
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consumes these as constraints rather than final output.
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"""
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from __future__ import annotations
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from generate.graph_planner import RhetoricalMove
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_PREDICATE_DISPLAY: 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_DISPLAY.get(predicate, predicate.replace("_", " "))
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_MOVE_TEMPLATES: dict[RhetoricalMove, str] = {
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RhetoricalMove.ASSERT: "{subject} {predicate_h} {obj}",
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RhetoricalMove.ELABORATE: "furthermore, {subject} {predicate_h} {obj}",
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RhetoricalMove.CONTRAST: "in contrast, {subject} {predicate_h} {obj}",
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RhetoricalMove.SEQUENCE: "next, {subject} {predicate_h} {obj}",
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RhetoricalMove.CORRECT: "correction: {subject} {predicate_h} {obj}",
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}
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def render_step(
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move: RhetoricalMove,
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subject: str,
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predicate: str,
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obj: str,
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) -> str:
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"""Render a single articulation step into a surface fragment."""
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template = _MOVE_TEMPLATES[move]
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predicate_h = _humanize_predicate(predicate)
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obj_display = obj if obj != "<pending>" 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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)
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