core/generate/dialogue.py
Shay 5feedcebd9 feat(persistence): Shape B+ Phase C — SessionContext.snapshot/restore (full lived state)
Composes the FieldState (A) and VaultStore (B) codecs with new codecs for
SessionGraph/TurnNode, ReferentRegistry/ReferentEntry, Proposition, and
DialogueTurn into SessionContext.snapshot()/restore() — the complete lived
session state that must survive reboot for resume-as-same-life.

- session/graph.py: TurnNode + SessionGraph to_dict/from_dict (versors bit-exact).
- session/referents.py: ReferentEntry + ReferentRegistry, preserving the
  _slots<->_history object aliasing via slot->history-index (update_turn_versor
  relies on `is` identity).
- generate/proposition.py + generate/dialogue.py: Proposition + DialogueTurn
  codecs (relation_norm is derived in __post_init__, not persisted).
- vault/store.py: complete the metadata codec — vault metadata can hold a
  Proposition ({"kind":"proposition",...} from generate/proposition.py), tagged
  on encode and reconstructed on decode (lazy import, cycle-free). This closes a
  gap Phase B assumed away ("metadata is primitives only"); surfaced by the
  Phase C JSON-safe integration test.
- session/context.py: snapshot()/restore(). vocab/persona are NOT serialized
  (shared, supplied at restore); restore() mutates self by design (a load).

Exit gate: a real 4-turn session, snapshotted and restored into a fresh context,
is field-equal — field bit-exact, vault recall identical, graph/referents/
dialogue preserved (incl. the referent aliasing). 9 new tests; INV-02 +
session-coherence regression green (68 passed).

Part of the A->E Shape B+ scope (Phase C).
2026-06-05 12:13:46 -07:00

133 lines
4.6 KiB
Python

"""
Dialogue move selection from proposition relation blades.
The proposition layer constructs X_prompt ^ X_field. Dialogue reads that
grade-2 relation against the prior relation blade and chooses the next move
kind: assertion, elaboration, question/contrast, or refutation.
"""
from __future__ import annotations
from dataclasses import dataclass, field
from typing import Literal
import numpy as np
from algebra.cga import cga_inner, outer_product
from core.array_codec import decode_array, encode_array
from field.state import FieldState
from generate.proposition import FrameRegistry, Proposition, propose
DialogueRole = Literal["assert", "elaborate", "question", "refute"]
_PARALLEL_THRESHOLD = 0.35
_ANTI_PARALLEL_THRESHOLD = -0.35
_ORTHOGONAL_THRESHOLD = 0.20
@dataclass(frozen=True, slots=True)
class DialogueTurn:
proposition: Proposition
outer_product_blade: np.ndarray = field(repr=False)
def __post_init__(self) -> None:
blade = np.asarray(self.outer_product_blade, dtype=np.float32).copy()
object.__setattr__(self, "outer_product_blade", blade)
def to_dict(self) -> dict:
return {
"proposition": self.proposition.to_dict(),
"outer_product_blade": encode_array(self.outer_product_blade),
}
@classmethod
def from_dict(cls, payload: dict) -> "DialogueTurn":
return cls(
proposition=Proposition.from_dict(payload["proposition"]),
outer_product_blade=decode_array(payload["outer_product_blade"]),
)
def blade_alignment(blade: np.ndarray, reference: np.ndarray) -> float:
"""
Return signed orientation of two relation blades.
Positive values mean same plane orientation, near-zero values mean
orthogonal relation, and negative values mean the same relation reversed.
The scalar is derived from CORE's algebraic inner product, not an external
approximate metric.
"""
blade = np.asarray(blade, dtype=np.float32)
reference = np.asarray(reference, dtype=np.float32)
blade_norm = abs(cga_inner(blade, blade)) ** 0.5
reference_norm = abs(cga_inner(reference, reference)) ** 0.5
if blade_norm < 1e-8 or reference_norm < 1e-8:
return 0.0
return float(-cga_inner(blade, reference) / (blade_norm * reference_norm))
def classify_dialogue_blade(
blade: np.ndarray,
reference_blade: np.ndarray | None = None,
) -> DialogueRole:
"""
Classify a relation blade as the next dialogue move.
With no prior relation the engine has no contrastive plane yet, so the
first move is an assertion. After that:
same orientation -> elaboration
near-orthogonal -> question/contrast
reversed orientation -> refutation
"""
if reference_blade is None:
return "assert"
alignment = blade_alignment(blade, reference_blade)
if alignment >= _PARALLEL_THRESHOLD:
return "elaborate"
if alignment <= _ANTI_PARALLEL_THRESHOLD:
return "refute"
if abs(alignment) <= _ORTHOGONAL_THRESHOLD:
return "question"
return "question"
def trajectory_blade(blades: tuple[np.ndarray, ...] | list[np.ndarray]) -> np.ndarray | None:
"""Fold a dialogue path into the running outer product of its blades."""
if not blades:
return None
running = np.asarray(blades[0], dtype=np.float32).copy()
for blade in blades[1:]:
running = outer_product(running, blade)
return running
def propose_dialogue(
field_state: FieldState,
vault,
vocab,
frame_registry: FrameRegistry,
reference_blade: np.ndarray | None = None,
output_lang: str | None = None,
) -> Proposition:
"""
Generate a proposition through a dialogue-role constrained frame choice.
"""
base = propose(field_state, None, vocab, frame_registry, output_lang=output_lang)
role = classify_dialogue_blade(base.relation, reference_blade)
frame = frame_registry.select_dialogue(base.relation, role)
role_registry = FrameRegistry((frame,))
proposition = propose(field_state, vault, vocab, role_registry, output_lang=output_lang)
if reference_blade is not None and blade_alignment(proposition.relation, reference_blade) < 0.0:
proposition = Proposition(
subject=proposition.subject,
predicate=proposition.predicate,
object_=proposition.object_,
surface=proposition.surface,
frame_id=proposition.frame_id,
subject_versor=proposition.subject_versor,
predicate_versor=proposition.predicate_versor,
object_versor=proposition.object_versor,
relation=-proposition.relation,
)
return proposition