Workstream 1 (pack content scale-up) first load-bearing step.
Adds a new ratified content pack covering the conversational vocabulary
en_core_cognition_v1 deliberately omits — speech acts, mental states,
perception, self-reference, and discourse-object nouns. These are the
lemmas that show up in nearly every model response and that previously
fell through to the OOV invitation surface.
Pack composition (73 entries, 49 VERB + 24 NOUN):
meta.speech_act.* (20 verbs) say tell speak reply claim state
describe express name mention note
observe declare assert deny confirm
suggest propose articulate respond
meta.mental_state.* (18 verbs) know believe think suppose assume
expect hope want prefer doubt wonder
guess recognize realize consider intend
decide hold
meta.perception.* (11 verbs) see hear feel sense perceive watch
look listen find detect notice
meta.self_reference.* (10 nouns) self mind view perspective position
role agent model system speaker
meta.discourse.* (14 nouns) response reply statement fact idea
point argument proposal suggestion
case instance example kind type
Files:
language_packs/data/en_core_meta_v1/
lexicon.jsonl — 73 entries, SHA-256 checksum-sealed
manifest.json — operational_base / D0 / checksum-verified
chat/pack_resolver.py
Appended en_core_meta_v1 to DEFAULT_RESOLVABLE_PACK_IDS after
en_core_cognition_v1 so cognition lemma resolution stays first-
match-wins on any future collision (preserves cognition-lane
byte-identity invariant).
core/config.py
Added en_core_meta_v1 to RuntimeConfig.input_packs default mount.
tests/test_en_core_meta_v1_pack.py
11 contract tests: checksum-verified load, POS split, primary-
domain namespace, no-collision-with-cognition-v1 regression gate,
pack registration order, resolver routing, and cognition-lemma
resolution unchanged.
tests/test_procedure_surface.py
Swapped two test fixtures from "claim" to "hypothesis". ``claim``
is now correctly pack-resident (meta.speech_act.claim) so the
procedure composer's object-first selector picks it over the verb
— the new behavior is semantically correct. ``hypothesis`` is
genuinely OOV across all mounted packs and preserves the verb-
fallback contract these tests pin.
Authoring methodology:
Four parallel subagents authored one cluster each from a strict
exemplar + word list + forbidden-lemma list (every en_core_cognition_v1
lemma listed explicitly to prevent collision). Each subagent wrote
only its cluster JSONL; the main pass assembled, validated, computed
the SHA-256 over bytes-on-disk, and wrote the manifest.
Verification:
Full lane: 2148 passed, 2 skipped, 0 failed (+11 new tests).
Cognition eval byte-identical on both splits:
public 100 / 100 / 91.7 / 100
holdout 100 / 100 / 83.3 / 100
Live runtime probes: fresh ChatRuntime() for "What is X?" with
X ∈ {fact, doubt, statement, model, self} all emit a
pack-grounded sentence from en_core_meta_v1.
OOV path still honest for genuinely-unknown terms (e.g. hypothesis).
Scope note:
This is one pack of ~70 lemmas, not "the model now articulates
open-domain English." The architecturally-honest articulation
story still requires more pack and teaching-chain content; this
pack moves the conversational-substrate boundary forward by ~70
lemmas in one ratifiable, replay-stable step.
138 lines
5 KiB
Python
138 lines
5 KiB
Python
"""chat/pack_resolver.py — ADR-0063 cross-pack surface resolver.
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The cold-start grounding composers in :mod:`chat.pack_grounding` were
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hardcoded to a single lexicon pack (``en_core_cognition_v1``). That
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asymmetry blocked ``en_core_relations_v1`` from being mounted on the
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default runtime: mounting it would silently widen vault recall and
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intent classification without a corresponding ratified surface
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composer for kinship lemmas.
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This module supplies the missing abstraction: a *deterministic*,
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*first-match-wins* resolver that maps a lemma to ``(pack_id, semantic_domains)``
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across an ordered tuple of mounted lexicon packs. Surface composers
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consult the resolver instead of a single pack; the surface trust-boundary
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tag follows the *resolving* pack id.
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Design constraints (CLAUDE.md / Reconstruction-over-storage):
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- The resolver loads each pack lexicon at most once per process via
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:func:`functools.lru_cache`. Ratified packs are immutable, so caching
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is sound.
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- A pack that fails to load (missing file, unreadable JSON) contributes
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an empty index — callers cannot distinguish "pack absent" from "lemma
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absent in mounted packs". Both produce ``None``.
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- First-match-wins on collision. The orthogonality test in
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``tests/test_en_core_relations_v1_pack.py`` enforces zero overlap
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today; this rule documents the deterministic resolution if a future
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pack pair collides.
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- No mutation of pack data is performed anywhere in this module. All
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return types are tuples/frozensets/dicts of plain strings.
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"""
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from __future__ import annotations
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import json
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from functools import lru_cache
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from pathlib import Path
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# Default mounted lexicon-pack ids that ADR-0063 surface composers
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# consult. Order matters: earlier packs win on lemma collision. This
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# tuple is intentionally narrow — it lists only ratified ``en_core_*``
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# *content* packs. Identity/safety/ethics packs are propositional
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# overlays and never carry vocabulary; they are deliberately excluded.
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DEFAULT_RESOLVABLE_PACK_IDS: tuple[str, ...] = (
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"en_core_cognition_v1",
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"en_core_meta_v1",
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"en_core_relations_v1",
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"en_core_relations_v2",
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)
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_PACK_ROOT = Path(__file__).resolve().parent.parent / "language_packs" / "data"
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@lru_cache(maxsize=16)
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def _pack_lexicon_for(pack_id: str) -> dict[str, tuple[str, ...]]:
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"""Return ``{lemma_lower: semantic_domains}`` for *pack_id*, cached.
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Returns an empty dict if the pack's lexicon file is missing,
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unreadable, or contains no entries with ``semantic_domains``.
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Ratified packs are immutable so the cache survives the process
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lifetime.
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"""
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lexicon_path = _PACK_ROOT / pack_id / "lexicon.jsonl"
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if not lexicon_path.exists():
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return {}
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out: dict[str, tuple[str, ...]] = {}
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for line in lexicon_path.read_text(encoding="utf-8").splitlines():
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line = line.strip()
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if not line:
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continue
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try:
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entry = json.loads(line)
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except json.JSONDecodeError:
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continue
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if not isinstance(entry, dict):
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continue
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lemma = entry.get("lemma") or entry.get("surface")
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if not lemma or not isinstance(lemma, str):
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continue
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domains = entry.get("semantic_domains", ())
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if not isinstance(domains, (list, tuple)) or not domains:
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continue
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out[lemma.lower()] = tuple(str(d) for d in domains)
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return out
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def resolve_lemma(
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lemma: str,
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pack_ids: tuple[str, ...] = DEFAULT_RESOLVABLE_PACK_IDS,
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) -> tuple[str, tuple[str, ...]] | None:
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"""Return ``(pack_id, semantic_domains)`` for the first pack in
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*pack_ids* whose lexicon contains *lemma*, else ``None``.
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First-match-wins on collision. ``pack_ids`` defaults to
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:data:`DEFAULT_RESOLVABLE_PACK_IDS`.
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"""
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if not lemma or not isinstance(lemma, str):
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return None
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key = lemma.strip().lower()
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if not key:
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return None
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for pack_id in pack_ids:
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index = _pack_lexicon_for(pack_id)
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domains = index.get(key)
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if domains:
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return (pack_id, domains)
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return None
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def is_resolvable(
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lemma: str,
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pack_ids: tuple[str, ...] = DEFAULT_RESOLVABLE_PACK_IDS,
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) -> bool:
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"""Return True iff *lemma* resolves in any of *pack_ids*."""
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return resolve_lemma(lemma, pack_ids) is not None
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def mounted_lemmas(
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pack_ids: tuple[str, ...] = DEFAULT_RESOLVABLE_PACK_IDS,
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) -> frozenset[str]:
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"""Return the frozen union of lemmas across *pack_ids*.
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Used by topic extractors that iterate over user-text tokens and
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need an O(1) "is this token any mounted lemma?" check.
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"""
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out: set[str] = set()
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for pack_id in pack_ids:
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out.update(_pack_lexicon_for(pack_id).keys())
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return frozenset(out)
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def clear_resolver_cache() -> None:
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"""Drop the lru_cache for :func:`_pack_lexicon_for`.
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Test-only escape hatch: enables fixture-based pack mutation
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scenarios. Production code never calls this — ratified packs are
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immutable.
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"""
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_pack_lexicon_for.cache_clear()
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