c1e723f185
- Add LexicalResolution dataclass + resolve_entry() in chat/pack_resolver.py
that returns language, root, morphology_id, gloss, semantic_domains from
he/grc/en packs (lru-cached, first-match, full depth support).
- Extend GraphNode (generate/graph_planner.py) with optional language/root/
morphology_id fields (defaults preserve all call sites). Update as_dict()
to include them conditionally. ground_graph() now propagates depth.
- Generalize enrichment in core/cognition/pipeline.py:
- Per-subject resolution map using depth packs.
- Enrich all matching nodes before ground (subject→node map).
- Pass depth alongside recalled_words to ground_graph().
- Consume depth on articulation side:
- realize_semantic() and render_semantic() now accept/use language+root
for etymological/Logos framing on Hebrew/Greek nodes (e.g. "אמת (Hebrew
root: א-מ-ן) is defined as..."). English unchanged.
- Enrich oov_geometric_context with node_depths for future geometric
anti-unification using roots.
- Extend recognition/connector.py to forward depth from EpistemicNode
paths into GraphNode.
- Add full Hebrew turn test under realizer_grounded_authority flag.
- Update related tests (semantic realizer, OOV context, surface resolution).
- Cleaned legacy type() hack immediately on discovery (hard-stop rule).
All targeted tests green (52+ in slices), broad relevant suite 581 passed.
Invariants preserved: versor only at owned boundaries, exact recall,
immutable updates, no new legacy parsers. 3 pillars upheld.
Work continues tomorrow from this checkpoint.
76 lines
2.4 KiB
Python
76 lines
2.4 KiB
Python
"""Connector: EpistemicNode → GraphNode — ADR-0144.
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Maps an admitted EpistemicNode's FeatureBundle to a generation-side
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GraphNode so the recognition path can feed the articulation planner.
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The v1 mapping covers has-relation feature bundles (agent, relation,
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count, unit). New proposition types extend the mapping here; unknown
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feature layouts raise ValueError so gaps surface explicitly rather than
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silently defaulting.
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"""
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from __future__ import annotations
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from generate.graph_planner import GraphNode
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from generate.intent import IntentTag
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from recognition.carrier import EpistemicNode
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from recognition.outcome import EVIDENCED
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def epistemic_node_to_graph_node(
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node: EpistemicNode,
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*,
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source_intent: IntentTag,
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node_id: str | None = None,
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language: str | None = None,
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root: str | None = None,
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morphology_id: str | None = None,
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) -> GraphNode:
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"""Derive a generation-side GraphNode from an admitted EpistemicNode.
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Raises ``ValueError`` if ``node.recognition_outcome.state != EVIDENCED``.
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Feature-bundle → GraphNode mapping (v1, has-relation propositions):
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subject ← bundle["agent"].value
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predicate ← bundle["relation"].value
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obj ← "{count.value} {unit.value}"
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Optional depth params allow 3-lang (Hebrew/Greek) morphology/root info
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from recognition to flow into the shared PropositionGraph for
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comprehension/articulation/reasoning.
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"""
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outcome = node.recognition_outcome
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if outcome.state != EVIDENCED:
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raise ValueError(
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f"Cannot derive GraphNode from non-EVIDENCED EpistemicNode: "
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f"state={outcome.state!r}"
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)
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bundle = outcome.proposition
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assert bundle is not None # invariant: EVIDENCED → proposition not None
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agent = bundle.get("agent")
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relation = bundle.get("relation")
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count = bundle.get("count")
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unit = bundle.get("unit")
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subject = str(agent.value) if agent is not None else "<unknown-agent>"
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predicate = str(relation.value) if relation is not None else "has"
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obj = (
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f"{count.value} {unit.value}"
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if count is not None and unit is not None
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else "<pending>"
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)
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return GraphNode(
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node_id=node_id or node.node_id,
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subject=subject,
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predicate=predicate,
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obj=obj,
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source_intent=source_intent,
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language=language,
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root=root,
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morphology_id=morphology_id,
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)
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__all__ = ["epistemic_node_to_graph_node"]
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