2bd70d0a9d
- close versor_apply outputs at algebra boundary - route backend versor_apply through canonical closure semantics - keep selected ChatResponse surface equal to ArticulationPlan surface - derive proposition relation from selected slots - rank proposition slots with pure CGA metric
470 lines
16 KiB
Python
470 lines
16 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass, replace
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import re
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from collections.abc import Sequence
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from typing import List
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import numpy as np
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from algebra.versor import versor_condition
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from core.config import DEFAULT_CONFIG, RuntimeConfig
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from core.physics.drive import DriveGradientMap, GradientField, ValueAxis
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from core.physics.energy import EnergyProfile
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from core.physics.exertion import CycleCost, ExertionMeter
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from core.physics.identity import (
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CharacterProfile,
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IdentityCheck,
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IdentityManifold,
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IdentityScore,
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TurnEvent,
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)
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from core.physics.reasoning import ReasoningTrajectory, TrajectoryOperator
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from field.state import FieldState
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from generate.articulation import ArticulationPlan, realize
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from generate.dialogue import DialogueRole, classify_dialogue_blade, propose_dialogue
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from generate.proposition import FrameRegistry, Proposition, propose
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from generate.stream import generate
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from generate.surface import SentenceAssembler, SentencePlan
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from language_packs import OOVPolicy, load_mounted_packs, load_pack, load_pack_entries
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from persona.motor import PersonaMotor
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from session.context import SessionContext
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_TOKEN_RE = re.compile(r"\w+", re.UNICODE)
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_SEED_ALIASES = {
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"logos": "λόγος",
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"dabar": "דבר",
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"or": "אור",
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"phos": "φῶς",
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"zoe": "ζωή",
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"arche": "ἀρχή",
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"aletheia": "ἀλήθεια",
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}
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_QUESTION_WORDS = frozenset({"what", "who", "how", "why", "when", "where", "which"})
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_TERMINALS = frozenset({".", "?", ";", "!"})
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def _energy_scalar(energy_obj) -> float:
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"""Return a plain float from a FieldState.energy value."""
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if energy_obj is None:
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return 1.0
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if isinstance(energy_obj, EnergyProfile):
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return float(energy_obj.raw)
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try:
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return float(energy_obj)
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except (TypeError, ValueError):
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return 1.0
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def _is_question_input(raw_text: str, tokens: Sequence[str]) -> bool:
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if raw_text.strip().endswith("?"):
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return True
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return bool(tokens and tokens[0].casefold() in _QUESTION_WORDS)
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def _stable_dialogue_role(role: DialogueRole, *, raw_text: str, tokens: Sequence[str]) -> DialogueRole:
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if role == "question" and not _is_question_input(raw_text, tokens):
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return "elaborate"
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return role
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def _terminal_for_role(role: DialogueRole, output_language: str) -> str:
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if role == "question":
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return ";" if output_language == "grc" else "?"
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return "."
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def _terminate_surface(surface: str, *, role: DialogueRole, output_language: str) -> str:
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stripped = surface.strip()
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if not stripped:
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return stripped
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if stripped[-1] in _TERMINALS:
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return stripped
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return f"{stripped}{_terminal_for_role(role, output_language)}"
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@dataclass
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class _StubBindingFrame:
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frame_id: str
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coherence_magnitude: float
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region_ids: frozenset
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cycle_index: int
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def _make_trajectory_from_result(
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result,
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turn: int,
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) -> ReasoningTrajectory:
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"""Build a ReasoningTrajectory from a GenerationResult for IdentityCheck."""
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operator = TrajectoryOperator()
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if result.trajectory:
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frames = [
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_StubBindingFrame(
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frame_id=f"t{turn}_s{i}",
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coherence_magnitude=_energy_scalar(getattr(fs, "energy", None)),
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region_ids=frozenset({str(getattr(fs, "node", 0))}),
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cycle_index=turn,
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)
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for i, fs in enumerate(result.trajectory)
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]
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else:
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frames = [
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_StubBindingFrame(
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frame_id=f"t{turn}_s0",
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coherence_magnitude=_energy_scalar(getattr(result.final_state, "energy", None)),
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region_ids=frozenset({str(getattr(result.final_state, "node", 0))}),
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cycle_index=turn,
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)
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]
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return operator.build(frames, trajectory_id=f"turn_{turn}")
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@dataclass(frozen=True, slots=True)
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class ChatResponse:
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surface: str
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proposition: Proposition
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articulation: ArticulationPlan
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articulation_surface: str
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dialogue_role: DialogueRole
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versor_condition: float
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output_language: str
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frame_pack: str
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walk_surface: str
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salience_top_k: int | None
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candidates_used: int | None
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vault_hits: int
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identity_score: IdentityScore | None
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character_profile: CharacterProfile
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flagged: bool
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class ChatRuntime:
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def __init__(
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self,
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pack_id: str | Sequence[str] | None = None,
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*,
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frame_pack: str | None = None,
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config: RuntimeConfig = DEFAULT_CONFIG,
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) -> None:
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if pack_id is not None or frame_pack is not None:
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pack_ids = (pack_id,) if isinstance(pack_id, str) else tuple(pack_id or config.input_packs)
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resolved_config = RuntimeConfig(
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input_packs=pack_ids,
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output_language=config.output_language,
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frame_pack=frame_pack or config.frame_pack,
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max_tokens=config.max_tokens,
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allow_cross_language_recall=config.allow_cross_language_recall,
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allow_cross_language_generation=config.allow_cross_language_generation,
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vault_reproject_interval=config.vault_reproject_interval,
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use_salience=config.use_salience,
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salience_top_k=config.salience_top_k,
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inhibition_threshold=config.inhibition_threshold,
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)
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else:
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resolved_config = config
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pack_ids = tuple(config.input_packs)
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self.config = resolved_config
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manifests = []
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manifolds = []
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entries = []
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for mounted_pack_id in pack_ids:
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manifest, manifold = load_pack(mounted_pack_id)
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manifests.append(manifest)
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manifolds.append(manifold)
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entries.extend(load_pack_entries(mounted_pack_id))
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manifold = manifolds[0] if len(pack_ids) == 1 else load_mounted_packs(pack_ids)
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self._manifests = tuple(manifests)
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self.identity_manifold = _default_identity_manifold()
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persona_motor = PersonaMotor.from_identity_manifold(self.identity_manifold)
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self._context = SessionContext(
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manifold,
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persona=persona_motor,
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vault_reproject_interval=resolved_config.vault_reproject_interval,
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)
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self._frame_registry = FrameRegistry.from_pack(
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resolved_config.frame_pack,
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self._context.vocab,
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)
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self._surface_by_fold = {e.surface.casefold(): e.surface for e in entries}
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self._surface_by_fold.update(_SEED_ALIASES)
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self._pos_by_surface = {
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e.surface: (e.pos or e.part_of_speech or "X") for e in entries
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}
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self.exertion_meter = ExertionMeter(capacity_ceiling=128.0)
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self.drive_gradients = tuple(
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GradientField(axis=axis, magnitude=0.75)
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for axis in self.identity_manifold.value_axes
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)
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self._drive_map = DriveGradientMap(gradients=self.drive_gradients)
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self.character_profile = CharacterProfile.from_manifold(
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self.identity_manifold,
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drive_summaries={
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g.axis.name: g.magnitude for g in self.drive_gradients
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},
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fatigue_index=0.0,
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)
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self._identity_check = IdentityCheck()
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self.turn_log: List[TurnEvent] = []
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@property
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def session(self) -> SessionContext:
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return self._context
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def _tokenize(self, text: str) -> list[str]:
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tokens: list[str] = []
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for match in _TOKEN_RE.finditer(text):
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raw = match.group(0)
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tokens.append(self._surface_by_fold.get(raw.casefold(), raw))
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return tokens
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def tokenize(self, text: str) -> list[str]:
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return self._tokenize(text)
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def _apply_oov_policy(self, tokens: list[str]) -> list[str]:
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kept: list[str] = []
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for token in tokens:
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try:
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self._context.vocab.get_versor(token)
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kept.append(token)
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except KeyError:
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if all(manifest.oov_policy is OOVPolicy.FAIL_CLOSED for manifest in self._manifests):
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raise
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if any(
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manifest.oov_policy is OOVPolicy.PROPOSE_VOCAB_EXPANSION
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for manifest in self._manifests
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):
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raise KeyError(f"OOV token requires vocab proposal: {token}")
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kept.append(token)
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return kept
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def _syntactic_guard(self, tokens: tuple[str, ...]) -> list[str]:
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out: list[str] = []
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prev_pos: str | None = None
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for token in tokens:
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pos = self._pos_by_surface.get(token, "X")
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if pos == prev_pos:
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continue
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out.append(token)
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prev_pos = pos
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return out
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def _dialogue_reference(self) -> np.ndarray | None:
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blade = self._context.last_dialogue_blade
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if blade is None or float(np.linalg.norm(blade)) < 1e-8:
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return None
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return blade
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def _apply_drive_bias(self, field_state: FieldState) -> FieldState:
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"""Nudge field F by the combined drive gradient before generation."""
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fatigue = self.exertion_meter.fatigue(at_cycle=self._context.turn)
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available = 1.0 - fatigue.value
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if available < 1e-4:
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return field_state
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coords = tuple(float(x) for x in field_state.F[:3])
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bias = self._drive_map.combined_bias(coords)
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if not bias or all(abs(b) < 1e-8 for b in bias):
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return field_state
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nudged_F = field_state.F.copy()
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for i, b in enumerate(bias[:3]):
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nudged_F[i] += b * available * 0.1
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return FieldState(
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F=nudged_F,
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node=field_state.node,
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step=field_state.step,
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holonomy=field_state.holonomy,
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energy=field_state.energy,
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valence=field_state.valence,
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)
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def chat(self, text: str, max_tokens: int | None = None) -> ChatResponse:
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tokens = self._tokenize(text)
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filtered = self._apply_oov_policy(tokens)
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if not filtered:
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raise ValueError("ChatRuntime.chat() received no in-vocabulary tokens.")
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field_state = self._context.ingest(filtered)
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field_state = self._apply_drive_bias(field_state)
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reference_blade = self._dialogue_reference()
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base_proposition = propose(
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field_state,
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None,
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self._context.vocab,
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self._frame_registry,
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output_lang=self.config.output_language,
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)
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dialogue_role = _stable_dialogue_role(
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classify_dialogue_blade(base_proposition.relation, reference_blade),
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raw_text=text,
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tokens=tokens,
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)
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proposition = propose_dialogue(
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field_state,
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self._context.vault,
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self._context.vocab,
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self._frame_registry,
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reference_blade,
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output_lang=self.config.output_language,
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)
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articulation = realize(
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proposition,
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self._context.vocab,
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output_language=self.config.output_language,
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)
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self._context.record_dialogue(proposition)
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result = generate(
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field_state,
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self._context.vocab,
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self._context.persona,
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max_tokens=self.config.max_tokens if max_tokens is None else max_tokens,
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record_trajectory=True,
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vault=self._context.vault,
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recall_top_k=3 if self.config.allow_cross_language_recall else 0,
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output_lang=self.config.output_language,
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allow_cross_language_generation=self.config.allow_cross_language_generation,
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use_salience=self.config.use_salience,
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salience_top_k=self.config.salience_top_k,
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inhibition_threshold=self.config.inhibition_threshold,
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)
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reasoning_trajectory = _make_trajectory_from_result(result, self._context.turn)
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identity_score = self._identity_check.check(
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reasoning_trajectory,
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self.identity_manifold,
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)
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flagged = identity_score.flagged
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cycle_cost = CycleCost(
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cycle_index=self._context.turn,
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attention_cost=float(result.candidates_used or 0),
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inhibition_cost=float(self.config.inhibition_threshold),
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digest_cost=0.0,
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trajectory_cost=float(len(result.trajectory or ())),
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)
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self.exertion_meter.record(cycle_cost)
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fatigue = self.exertion_meter.fatigue(at_cycle=self._context.turn)
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self.character_profile = CharacterProfile.from_manifold(
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self.identity_manifold,
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drive_summaries={
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g.axis.name: g.magnitude * (1.0 - fatigue.value)
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for g in self.drive_gradients
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},
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fatigue_index=fatigue.value,
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)
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self._context.state = result.final_state
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self._context.vault.store(
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result.final_state.F,
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{"turn": self._context.turn, "role": "assistant"},
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)
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self._context.turn += 1
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surface = _terminate_surface(
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articulation.surface,
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role=dialogue_role,
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output_language=self.config.output_language,
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)
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articulation = replace(articulation, surface=surface)
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sentence_plan: SentencePlan = SentenceAssembler().assemble(
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articulation,
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result.tokens,
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role=dialogue_role,
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)
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walk_surface = sentence_plan.surface
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articulation_surface = articulation.surface
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vault_hits = int(result.vault_hits)
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turn_event = TurnEvent(
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turn=self._context.turn - 1,
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input_tokens=tuple(filtered),
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surface=surface,
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walk_surface=walk_surface,
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articulation_surface=articulation_surface,
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dialogue_role=str(dialogue_role),
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identity_score=identity_score,
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cycle_cost_total=cycle_cost.total,
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vault_hits=vault_hits,
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versor_condition=versor_condition(result.final_state.F),
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flagged=flagged,
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elaboration=sentence_plan.elaboration,
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)
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self.turn_log.append(turn_event)
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return ChatResponse(
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surface=surface,
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proposition=proposition,
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articulation=articulation,
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articulation_surface=articulation_surface,
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dialogue_role=dialogue_role,
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versor_condition=versor_condition(result.final_state.F),
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output_language=self.config.output_language,
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frame_pack=self.config.frame_pack,
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walk_surface=walk_surface,
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salience_top_k=result.salience_top_k,
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candidates_used=result.candidates_used,
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vault_hits=vault_hits,
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identity_score=identity_score,
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character_profile=self.character_profile,
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flagged=flagged,
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)
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def respond(self, text: str, max_tokens: int | None = None) -> str:
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try:
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return self.chat(text, max_tokens=max_tokens).surface
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except ValueError:
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return ""
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async def achat(self, text: str, max_tokens: int | None = None) -> ChatResponse:
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"""Async equivalent of chat().
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The synchronous chat path owns ingest, drive bias, generation,
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identity telemetry, vault storage, and turn-log accounting. Reusing it
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keeps async semantics identical while avoiding an uninitialized
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SessionContext.state on the first async turn.
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"""
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return self.chat(text, max_tokens=max_tokens)
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async def arespond(self, text: str, max_tokens: int | None = None) -> str:
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"""Async equivalent of respond()."""
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try:
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return (await self.achat(text, max_tokens=max_tokens)).surface
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except ValueError:
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return ""
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def _default_identity_manifold() -> IdentityManifold:
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axes = (
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ValueAxis(
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axis_id="truthfulness",
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name="truthfulness",
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direction=(1.0, 0.0, 0.0),
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theological_note="Truth is treated as a fixed value axis, not a prompt preference.",
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),
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ValueAxis(
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axis_id="coherence",
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name="coherence",
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direction=(0.0, 1.0, 0.0),
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theological_note="Operations must preserve field coherence under propagation.",
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),
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ValueAxis(
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axis_id="reverence",
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name="reverence",
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direction=(0.0, 0.0, 1.0),
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theological_note="Depth-language handling remains bounded by source structure.",
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),
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)
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return IdentityManifold(
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value_axes=axes,
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boundary_ids=frozenset({"no_fabricated_source", "no_hot_path_repair"}),
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alignment_threshold=0.45,
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)
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