0e6dada09b
- Remove retired HANDOFF-antigravity-2026-07-01.md stray. - Harden enrich_assessments_with_depth (remove fragile __dict__ fallback; best-effort only). - Explicit _last_node_depths in ChatRuntime; clean direct propagation from pipeline (remove hasattr/setattr dance + bare excepts). - Add clarifying propagation contract comments. - Update plan.md with cleanup section. - All targeted tests + SHA/claims checks green post-edit. - Tree clean, ready to roll. Addresses remaining MEDIUM items from code-reviewer run.
119 lines
4.4 KiB
Python
119 lines
4.4 KiB
Python
"""Pure depth canonicalization helpers for 3-lang root-aware unification.
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Extracted per strategy to enable slot-precise, node_id-keyed canonicalization
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without heuristics in callers. All functions pure, side-effect free, immutable.
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Used by derive_recognizer, recognize (for matching), and assessment enrichment.
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Connects to cognitive path: listen/comprehend (depth from packs) -> think (anti-unif canonical) -> articulate.
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Preserves exact recall, no drift repair.
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"""
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from __future__ import annotations
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from dataclasses import replace
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from typing import Any, Sequence, Tuple
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from recognition.outcome import EvidenceSpan, FeatureBundle
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# For type in tests, import GraphNode when needed
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try:
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from generate.graph_planner import GraphNode
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except ImportError:
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GraphNode = Any # type: ignore
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def canonicalize_token(token: str, node_id: str | None, depths: dict[str, dict] | None) -> str:
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"""Wrap root_normalize keyed on node_id from depths dict.
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depths: {node_id: {"language": , "root": , ...}, ...}
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If node_id in depths and lang he/grc and root, return root, else token.
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Caller must pass the token associated with that node_id.
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"""
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if not depths or not node_id:
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return token
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d = depths.get(node_id)
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if not d:
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return token
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lang = d.get("language")
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root = d.get("root")
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if lang in ("he", "grc") and root:
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return root
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return token
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def canonicalize_agent_slot(
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tokens: Sequence[str], bundle: FeatureBundle | None, depths: dict[str, dict] | None,
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*, agent_node_id: str | None = None, start_idx: int | None = None
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) -> Tuple[str, ...]:
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"""Return copy of tokens with agent slot canonicalized using EvidenceSpan.start + node_id if available.
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Single lookup by agent_node_id (node-keyed, requires explicit nid or no-op; no first-key proxy).
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If bundle use its start, else start_idx.
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Pure.
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"""
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if not depths:
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return tuple(tokens)
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start = start_idx
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if bundle and start is None:
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agent_feat = bundle.get("agent")
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if agent_feat and isinstance(agent_feat.evidence, EvidenceSpan):
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start = agent_feat.evidence.start
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if start is None or start < 0 or start >= len(tokens):
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return tuple(tokens)
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new_tokens = list(tokens)
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nid = agent_node_id
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if nid is None or nid not in depths:
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return tuple(new_tokens) # require explicit nid, no first-key proxy
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d = depths[nid]
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if d.get("language") in ("he", "grc") and d.get("root"):
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orig = new_tokens[start]
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new_tokens[start] = canonicalize_token(orig, nid, depths)
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return tuple(new_tokens)
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def build_node_depths(nodes: Sequence[Any]) -> dict[str, dict]:
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"""Lift the node_depths dict from list of GraphNode (or objects with .node_id, .language etc).
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Pure extraction of the comprehension in pipeline.
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"""
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return {
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n.node_id: {
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k: v for k, v in {
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"language": getattr(n, "language", None),
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"root": getattr(n, "root", None),
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"morphology_id": getattr(n, "morphology_id", None),
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}.items() if v is not None
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}
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for n in nodes
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if getattr(n, "language", None) or getattr(n, "root", None)
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}
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def enrich_assessments_with_depth(assessments: Tuple[Any, ...], depth: dict | None) -> Tuple[Any, ...]:
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"""Immutable enrichment of assessments with root note using dataclasses.replace.
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Returns a new tuple. Enrichment is best-effort: if an assessment does not
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support replace (e.g. non-dataclass or frozen in an incompatible way), the
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original item is kept without the depth note. No __dict__ reconstruction
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is performed (avoids producing invalid objects for slots/frozen types).
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"""
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if not depth:
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return assessments
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roots = [d.get("root") for d in depth.values() if d.get("root")]
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if not roots:
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return assessments
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note = f" [root:{roots[0]}]"
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new_ass = []
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for a in assessments:
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if hasattr(a, "explanation") and getattr(a, "runnable", False):
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try:
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new_a = replace(a, explanation=(getattr(a, "explanation", "") or "") + note)
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except Exception:
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# Best-effort only; do not synthesize copies that could violate
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# frozen/slots invariants after CGA substrate types.
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new_a = a
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new_ass.append(new_a)
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else:
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new_ass.append(a)
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return tuple(new_ass)
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