a0ba9ecb0c
Replace the bare S-P-O join from articulation.realize() with the intent-differentiated surface from generate/intent_bridge.py when the bridge can produce a grounded, non-pending result. The ArticulationPlan dataclass, SentenceAssembler, turn_log, ChatResponse and all trace fields remain structurally unchanged. Only .surface is replaced. Falls back to the previous surface when the bridge returns "".
537 lines
21 KiB
Python
537 lines
21 KiB
Python
from __future__ import annotations
|
|
|
|
from dataclasses import dataclass, replace
|
|
import re
|
|
from collections.abc import Sequence
|
|
from typing import List
|
|
|
|
import numpy as np
|
|
|
|
from algebra.versor import versor_condition
|
|
from core.config import DEFAULT_CONFIG, RuntimeConfig
|
|
from core.physics.drive import DriveGradientMap, GradientField, ValueAxis
|
|
from core.physics.energy import EnergyProfile
|
|
from core.physics.exertion import CycleCost, ExertionMeter
|
|
from core.physics.identity import (
|
|
CharacterProfile,
|
|
IdentityCheck,
|
|
IdentityManifold,
|
|
IdentityScore,
|
|
TurnEvent,
|
|
)
|
|
from field.state import FieldState
|
|
from generate.articulation import ArticulationPlan, realize
|
|
from generate.dialogue import DialogueRole, classify_dialogue_blade, propose_dialogue
|
|
from generate.intent_bridge import articulate_with_intent
|
|
from generate.proposition import FrameRegistry, Proposition, propose
|
|
from generate.result import GenerationResult
|
|
from generate.stream import generate
|
|
from generate.surface import SentenceAssembler, SentencePlan, SurfaceContext
|
|
from ingest.gate import inject
|
|
from language_packs import OOVPolicy, load_mounted_packs, load_pack, load_pack_entries
|
|
from persona.motor import PersonaMotor
|
|
from session.context import SessionContext
|
|
from session.correction import CorrectionPass
|
|
from vault.decompose import default_decomposer, default_gate
|
|
|
|
_TOKEN_RE = re.compile(r"\w+", re.UNICODE)
|
|
_SEED_ALIASES = {
|
|
"logos": "\u03bb\u03cc\u03b3\u03bf\u03c2",
|
|
"dabar": "\u05d3\u05d1\u05e8",
|
|
"or": "\u05d0\u05d5\u05e8",
|
|
"phos": "\u03c6\u03c9\u03c2",
|
|
"zoe": "\u03b6\u03c9\u03ae",
|
|
"arche": "\u1f00\u03c1\u03c7\u03ae",
|
|
"aletheia": "\u1f00\u03bb\u03ae\u03b8\u03b5\u03b9\u03b1",
|
|
}
|
|
_QUESTION_WORDS = frozenset({"what", "who", "how", "why", "when", "where", "which"})
|
|
_TERMINALS = frozenset({".", "?", ";", "!"})
|
|
_UNKNOWN_DOMAIN_SURFACE = "I don't have field coordinates for that yet."
|
|
|
|
|
|
def _energy_scalar(energy_obj) -> float:
|
|
if energy_obj is None:
|
|
return 1.0
|
|
if isinstance(energy_obj, EnergyProfile):
|
|
return float(energy_obj.raw)
|
|
try:
|
|
return float(energy_obj)
|
|
except (TypeError, ValueError):
|
|
return 1.0
|
|
|
|
|
|
def _is_question_input(raw_text: str, tokens: Sequence[str]) -> bool:
|
|
if raw_text.strip().endswith("?"):
|
|
return True
|
|
return bool(tokens and tokens[0].casefold() in _QUESTION_WORDS)
|
|
|
|
|
|
def _stable_dialogue_role(role: DialogueRole, *, raw_text: str, tokens: Sequence[str]) -> DialogueRole:
|
|
if role in {"question", "refute"} and not _is_question_input(raw_text, tokens):
|
|
return "elaborate"
|
|
return role
|
|
|
|
|
|
def _terminal_for_role(role: DialogueRole, output_language: str) -> str:
|
|
if role == "question":
|
|
return ";" if output_language == "grc" else "?"
|
|
return "."
|
|
|
|
|
|
def _terminate_surface(surface: str, *, role: DialogueRole, output_language: str) -> str:
|
|
stripped = surface.strip()
|
|
if not stripped:
|
|
return stripped
|
|
if stripped[-1] in _TERMINALS:
|
|
return stripped
|
|
return f"{stripped}{_terminal_for_role(role, output_language)}"
|
|
|
|
|
|
def _prefer_prompt_anchor(
|
|
articulation: ArticulationPlan,
|
|
filtered_tokens: Sequence[str],
|
|
*,
|
|
output_language: str,
|
|
) -> ArticulationPlan:
|
|
if output_language != "en" or len(filtered_tokens) < 2:
|
|
return articulation
|
|
content_tokens = [
|
|
token
|
|
for token in filtered_tokens
|
|
if token.casefold() not in _QUESTION_WORDS and token.casefold() not in {"is", "are", "was", "were"}
|
|
]
|
|
if not content_tokens:
|
|
return articulation
|
|
anchor = content_tokens[-1]
|
|
if anchor == articulation.subject:
|
|
return articulation
|
|
return replace(
|
|
articulation,
|
|
subject=anchor,
|
|
surface=" ".join(part for part in (anchor, articulation.predicate, articulation.object) if part),
|
|
)
|
|
|
|
|
|
@dataclass
|
|
class _StubBindingFrame:
|
|
frame_id: str
|
|
coherence_magnitude: float
|
|
region_ids: frozenset
|
|
cycle_index: int
|
|
|
|
|
|
def _make_trajectory_from_result(result, turn: int):
|
|
from core.physics.reasoning import TrajectoryOperator
|
|
|
|
operator = TrajectoryOperator()
|
|
states = result.trajectory or (result.final_state,)
|
|
frames = [
|
|
_StubBindingFrame(
|
|
frame_id=f"t{turn}_s{i}",
|
|
coherence_magnitude=_energy_scalar(getattr(fs, "energy", None)),
|
|
region_ids=frozenset({str(getattr(fs, "node", 0))}),
|
|
cycle_index=turn,
|
|
)
|
|
for i, fs in enumerate(states)
|
|
]
|
|
return operator.build(frames, trajectory_id=f"turn_{turn}")
|
|
|
|
|
|
@dataclass(frozen=True, slots=True)
|
|
class ChatResponse:
|
|
surface: str
|
|
proposition: Proposition
|
|
articulation: ArticulationPlan
|
|
articulation_surface: str
|
|
dialogue_role: DialogueRole
|
|
versor_condition: float
|
|
output_language: str
|
|
frame_pack: str
|
|
walk_surface: str
|
|
salience_top_k: int | None
|
|
candidates_used: int | None
|
|
vault_hits: int
|
|
identity_score: IdentityScore | None
|
|
character_profile: CharacterProfile
|
|
flagged: bool
|
|
|
|
|
|
class ChatRuntime:
|
|
def __init__(
|
|
self,
|
|
pack_id: str | Sequence[str] | None = None,
|
|
*,
|
|
frame_pack: str | None = None,
|
|
config: RuntimeConfig = DEFAULT_CONFIG,
|
|
) -> None:
|
|
if pack_id is not None or frame_pack is not None:
|
|
pack_ids = (pack_id,) if isinstance(pack_id, str) else tuple(pack_id or config.input_packs)
|
|
resolved_config = RuntimeConfig(
|
|
input_packs=pack_ids,
|
|
output_language=config.output_language,
|
|
frame_pack=frame_pack or config.frame_pack,
|
|
max_tokens=config.max_tokens,
|
|
allow_cross_language_recall=config.allow_cross_language_recall,
|
|
allow_cross_language_generation=config.allow_cross_language_generation,
|
|
vault_reproject_interval=config.vault_reproject_interval,
|
|
use_salience=config.use_salience,
|
|
salience_top_k=config.salience_top_k,
|
|
inhibition_threshold=config.inhibition_threshold,
|
|
)
|
|
else:
|
|
resolved_config = config
|
|
pack_ids = tuple(config.input_packs)
|
|
|
|
self.config = resolved_config
|
|
manifests = []
|
|
manifolds = []
|
|
entries = []
|
|
for mounted_pack_id in pack_ids:
|
|
manifest, manifold = load_pack(mounted_pack_id)
|
|
manifests.append(manifest)
|
|
manifolds.append(manifold)
|
|
entries.extend(load_pack_entries(mounted_pack_id))
|
|
|
|
manifold = manifolds[0] if len(pack_ids) == 1 else load_mounted_packs(pack_ids)
|
|
self._manifests = tuple(manifests)
|
|
self.identity_manifold = _default_identity_manifold()
|
|
# Keep the generic runtime neutral. Identity/persona motivation belongs
|
|
# behind an explicit IdentityProfile contract, not the baseline chat path.
|
|
persona_motor = PersonaMotor.identity()
|
|
self._context = SessionContext(
|
|
manifold,
|
|
persona=persona_motor,
|
|
vault_reproject_interval=resolved_config.vault_reproject_interval,
|
|
)
|
|
self._frame_registry = FrameRegistry.from_pack(resolved_config.frame_pack, self._context.vocab)
|
|
self._surface_by_fold = {e.surface.casefold(): e.surface for e in entries}
|
|
self._surface_by_fold.update(_SEED_ALIASES)
|
|
self._pos_by_surface = {e.surface: (e.pos or e.part_of_speech or "X") for e in entries}
|
|
self.exertion_meter = ExertionMeter(capacity_ceiling=128.0)
|
|
self.drive_gradients = tuple(GradientField(axis=axis, magnitude=0.75) for axis in self.identity_manifold.value_axes)
|
|
self._drive_map = DriveGradientMap(gradients=self.drive_gradients)
|
|
self.character_profile = CharacterProfile.from_manifold(
|
|
self.identity_manifold,
|
|
drive_summaries={g.axis.name: g.magnitude for g in self.drive_gradients},
|
|
fatigue_index=0.0,
|
|
)
|
|
self._identity_check = IdentityCheck()
|
|
self.turn_log: List[TurnEvent] = []
|
|
self._correction_pass = CorrectionPass()
|
|
self._last_valence: float = 0.0
|
|
|
|
@property
|
|
def session(self) -> SessionContext:
|
|
return self._context
|
|
|
|
def _tokenize(self, text: str) -> list[str]:
|
|
return [self._surface_by_fold.get(m.group(0).casefold(), m.group(0)) for m in _TOKEN_RE.finditer(text)]
|
|
|
|
def tokenize(self, text: str) -> list[str]:
|
|
return self._tokenize(text)
|
|
|
|
def _apply_oov_policy(self, tokens: list[str]) -> list[str]:
|
|
kept: list[str] = []
|
|
for token in tokens:
|
|
try:
|
|
self._context.vocab.get_versor(token)
|
|
kept.append(token)
|
|
except KeyError:
|
|
if all(manifest.oov_policy is OOVPolicy.FAIL_CLOSED for manifest in self._manifests):
|
|
raise
|
|
if any(manifest.oov_policy is OOVPolicy.PROPOSE_VOCAB_EXPANSION for manifest in self._manifests):
|
|
raise KeyError(f"OOV token requires vocab proposal: {token}")
|
|
kept.append(token)
|
|
return kept
|
|
|
|
def _syntactic_guard(self, tokens: tuple[str, ...]) -> list[str]:
|
|
out: list[str] = []
|
|
prev_pos: str | None = None
|
|
for token in tokens:
|
|
pos = self._pos_by_surface.get(token, "X")
|
|
if pos == prev_pos:
|
|
continue
|
|
out.append(token)
|
|
prev_pos = pos
|
|
return out
|
|
|
|
def _dialogue_reference(self) -> np.ndarray | None:
|
|
blade = self._context.last_dialogue_blade
|
|
if blade is None or float(np.linalg.norm(blade)) < 1e-8:
|
|
return None
|
|
return blade
|
|
|
|
def _apply_drive_bias(self, field_state: FieldState) -> FieldState:
|
|
"""Generic runtime keeps motivation/drive disabled.
|
|
|
|
Motivation is an identity-profile concern, not a free runtime field
|
|
mutation. Keeping this a no-op preserves the neutral baseline while
|
|
generic chat closure and cognition evals are being stabilized.
|
|
"""
|
|
return field_state
|
|
|
|
def _build_surface_context(self, identity_score, current_valence: float) -> SurfaceContext:
|
|
active = self._context.referents.active_referent()
|
|
alignment = float(identity_score.alignment) if identity_score is not None else 1.0
|
|
return SurfaceContext(
|
|
active_referent_surface=active.surface if active is not None else "",
|
|
active_referent_slot=active.slot if active is not None else "neut_sg",
|
|
identity_alignment=alignment,
|
|
valence_delta=current_valence - self._last_valence,
|
|
elab_conjunction="",
|
|
)
|
|
|
|
def _stub_response(self, field_state: FieldState) -> ChatResponse:
|
|
zero = np.zeros(field_state.F.shape, dtype=np.float32)
|
|
prop = Proposition(
|
|
subject="",
|
|
predicate="",
|
|
object_=None,
|
|
surface=_UNKNOWN_DOMAIN_SURFACE,
|
|
frame_id="unknown_domain",
|
|
subject_versor=zero,
|
|
predicate_versor=zero,
|
|
object_versor=None,
|
|
relation=zero,
|
|
)
|
|
art = ArticulationPlan(
|
|
subject="",
|
|
predicate="",
|
|
object=None,
|
|
surface=_UNKNOWN_DOMAIN_SURFACE,
|
|
output_language=self.config.output_language,
|
|
frame_id="unknown_domain",
|
|
)
|
|
return ChatResponse(
|
|
surface=_UNKNOWN_DOMAIN_SURFACE,
|
|
proposition=prop,
|
|
articulation=art,
|
|
articulation_surface=_UNKNOWN_DOMAIN_SURFACE,
|
|
dialogue_role="assert",
|
|
versor_condition=versor_condition(field_state.F),
|
|
output_language=self.config.output_language,
|
|
frame_pack=self.config.frame_pack,
|
|
walk_surface=_UNKNOWN_DOMAIN_SURFACE,
|
|
salience_top_k=None,
|
|
candidates_used=None,
|
|
vault_hits=0,
|
|
identity_score=None,
|
|
character_profile=self.character_profile,
|
|
flagged=False,
|
|
)
|
|
|
|
def chat(self, text: str, max_tokens: int | None = None) -> ChatResponse:
|
|
tokens = self._tokenize(text)
|
|
filtered = self._apply_oov_policy(tokens)
|
|
if not filtered:
|
|
raise ValueError("ChatRuntime.chat() received no in-vocabulary tokens.")
|
|
|
|
probe_state = self._context.probe_ingest(filtered)
|
|
direct_hits = self._context.vault.recall(probe_state.F, top_k=3)
|
|
direct_best = max((h["score"] for h in direct_hits), default=0.0)
|
|
gate_decision = default_gate.check(
|
|
direct_best,
|
|
vault=self._context.vault,
|
|
query=probe_state.F,
|
|
decomposer=default_decomposer,
|
|
)
|
|
if gate_decision.fire:
|
|
committed = self._context.commit_ingest(filtered)
|
|
empty_result = GenerationResult(tokens=(), final_state=committed, vault_hits=0)
|
|
self._context.finalize_turn(
|
|
empty_result,
|
|
tokens_in=tuple(filtered),
|
|
input_versor=committed.F,
|
|
dialogue_role="assert",
|
|
metadata={"unknown": True, "unknown_source": gate_decision.source},
|
|
)
|
|
return self._stub_response(committed)
|
|
|
|
field_state = self._context.commit_ingest(filtered)
|
|
field_state = self._apply_drive_bias(field_state)
|
|
reference_blade = self._dialogue_reference()
|
|
base_proposition = propose(
|
|
field_state,
|
|
None,
|
|
self._context.vocab,
|
|
self._frame_registry,
|
|
output_lang=self.config.output_language,
|
|
)
|
|
dialogue_role = _stable_dialogue_role(
|
|
classify_dialogue_blade(base_proposition.relation, reference_blade),
|
|
raw_text=text,
|
|
tokens=tokens,
|
|
)
|
|
proposition = propose_dialogue(
|
|
field_state,
|
|
self._context.vault,
|
|
self._context.vocab,
|
|
self._frame_registry,
|
|
reference_blade,
|
|
output_lang=self.config.output_language,
|
|
)
|
|
articulation = realize(proposition, self._context.vocab, output_language=self.config.output_language)
|
|
articulation = _prefer_prompt_anchor(articulation, filtered, output_language=self.config.output_language)
|
|
self._context.record_dialogue(proposition)
|
|
|
|
result = generate(
|
|
field_state,
|
|
self._context.vocab,
|
|
self._context.persona,
|
|
max_tokens=self.config.max_tokens if max_tokens is None else max_tokens,
|
|
record_trajectory=True,
|
|
vault=self._context.vault,
|
|
recall_top_k=3 if self.config.allow_cross_language_recall else 0,
|
|
output_lang=self.config.output_language,
|
|
allow_cross_language_generation=self.config.allow_cross_language_generation,
|
|
use_salience=self.config.use_salience,
|
|
salience_top_k=self.config.salience_top_k,
|
|
inhibition_threshold=self.config.inhibition_threshold,
|
|
)
|
|
|
|
# --- Articulation fidelity: replace bare S-P-O join with intent-aware surface ---
|
|
# articulate_with_intent() classifies the input intent, builds a proposition
|
|
# graph grounded on the generation result's recalled tokens, and calls the
|
|
# realize_semantic() path (13-construction realizer) that was previously
|
|
# implemented but never connected to the chat hot path.
|
|
# Falls back to the existing articulation.surface when bridge returns "".
|
|
if self.config.output_language == "en":
|
|
recalled_words = tuple(
|
|
tok for tok in (result.tokens or ()) if tok and tok.isalpha()
|
|
)
|
|
intent_surface = articulate_with_intent(text, articulation, recalled_words)
|
|
if intent_surface:
|
|
articulation = replace(articulation, surface=intent_surface)
|
|
# --- end articulation fidelity fix ---
|
|
|
|
reasoning_trajectory = _make_trajectory_from_result(result, self._context.turn)
|
|
identity_score = self._identity_check.check(reasoning_trajectory, self.identity_manifold)
|
|
flagged = identity_score.flagged
|
|
cycle_cost = CycleCost(
|
|
cycle_index=self._context.turn,
|
|
attention_cost=float(result.candidates_used or 0),
|
|
inhibition_cost=float(self.config.inhibition_threshold),
|
|
digest_cost=0.0,
|
|
trajectory_cost=float(len(result.trajectory or ())),
|
|
)
|
|
self.exertion_meter.record(cycle_cost)
|
|
fatigue = self.exertion_meter.fatigue(at_cycle=self._context.turn)
|
|
self.character_profile = CharacterProfile.from_manifold(
|
|
self.identity_manifold,
|
|
drive_summaries={g.axis.name: g.magnitude * (1.0 - fatigue.value) for g in self.drive_gradients},
|
|
fatigue_index=fatigue.value,
|
|
)
|
|
|
|
self._context.finalize_turn(
|
|
result,
|
|
tokens_in=tuple(filtered),
|
|
dialogue_role=str(dialogue_role),
|
|
)
|
|
current_valence = _energy_scalar(getattr(result.final_state, "valence", None))
|
|
surface_ctx = self._build_surface_context(identity_score, current_valence)
|
|
self._last_valence = current_valence
|
|
surface = _terminate_surface(articulation.surface, role=dialogue_role, output_language=self.config.output_language)
|
|
articulation = replace(articulation, surface=surface)
|
|
sentence_plan: SentencePlan = SentenceAssembler().assemble(
|
|
articulation,
|
|
result.tokens,
|
|
role=dialogue_role,
|
|
context=surface_ctx,
|
|
)
|
|
walk_surface = sentence_plan.surface
|
|
vault_hits = int(result.vault_hits)
|
|
turn_event = TurnEvent(
|
|
turn=self._context.turn - 1,
|
|
input_tokens=tuple(filtered),
|
|
surface=surface,
|
|
walk_surface=walk_surface,
|
|
articulation_surface=articulation.surface,
|
|
dialogue_role=str(dialogue_role),
|
|
identity_score=identity_score,
|
|
cycle_cost_total=cycle_cost.total,
|
|
vault_hits=vault_hits,
|
|
versor_condition=versor_condition(result.final_state.F),
|
|
flagged=flagged,
|
|
elaboration=sentence_plan.elaboration,
|
|
)
|
|
self.turn_log.append(turn_event)
|
|
return ChatResponse(
|
|
surface=walk_surface,
|
|
proposition=proposition,
|
|
articulation=articulation,
|
|
articulation_surface=articulation.surface,
|
|
dialogue_role=dialogue_role,
|
|
versor_condition=versor_condition(result.final_state.F),
|
|
output_language=self.config.output_language,
|
|
frame_pack=self.config.frame_pack,
|
|
walk_surface=walk_surface,
|
|
salience_top_k=result.salience_top_k,
|
|
candidates_used=result.candidates_used,
|
|
vault_hits=vault_hits,
|
|
identity_score=identity_score,
|
|
character_profile=self.character_profile,
|
|
flagged=flagged,
|
|
)
|
|
|
|
def _unknown_domain_response(self, field_state: FieldState, filtered: list[str]) -> ChatResponse:
|
|
return self._stub_response(field_state)
|
|
|
|
def correct(self, text: str, target_turn: int = -1, max_tokens: int | None = None) -> ChatResponse:
|
|
tokens = self._tokenize(text)
|
|
filtered = self._apply_oov_policy(tokens)
|
|
if not filtered:
|
|
raise ValueError("correct() received no in-vocabulary tokens.")
|
|
correction_state = inject(filtered, self._context.vocab)
|
|
correction_result = self._correction_pass.apply(
|
|
self._context.graph,
|
|
correction_state.F,
|
|
from_turn=target_turn,
|
|
)
|
|
self._context.apply_corrected_outputs(correction_result.records)
|
|
regen_tokens = self._context.last_input_tokens
|
|
if not regen_tokens:
|
|
return self._stub_response(correction_state)
|
|
return self.chat(" ".join(regen_tokens), max_tokens=max_tokens)
|
|
|
|
def respond(self, text: str, max_tokens: int | None = None) -> str:
|
|
try:
|
|
return self.chat(text, max_tokens=max_tokens).surface
|
|
except ValueError:
|
|
return ""
|
|
|
|
async def achat(self, text: str, max_tokens: int | None = None) -> ChatResponse:
|
|
return self.chat(text, max_tokens=max_tokens)
|
|
|
|
async def arespond(self, text: str, max_tokens: int | None = None) -> str:
|
|
try:
|
|
return (await self.achat(text, max_tokens=max_tokens)).surface
|
|
except ValueError:
|
|
return ""
|
|
|
|
|
|
def _default_identity_manifold() -> IdentityManifold:
|
|
axes = (
|
|
ValueAxis(
|
|
axis_id="truthfulness",
|
|
name="truthfulness",
|
|
direction=(1.0, 0.0, 0.0),
|
|
theological_note="Truth is treated as a fixed value axis, not a prompt preference.",
|
|
),
|
|
ValueAxis(
|
|
axis_id="coherence",
|
|
name="coherence",
|
|
direction=(0.0, 1.0, 0.0),
|
|
theological_note="Operations must preserve field coherence under propagation.",
|
|
),
|
|
ValueAxis(
|
|
axis_id="reverence",
|
|
name="reverence",
|
|
direction=(0.0, 0.0, 1.0),
|
|
theological_note="Depth-language handling remains bounded by source structure.",
|
|
),
|
|
)
|
|
return IdentityManifold(
|
|
value_axes=axes,
|
|
boundary_ids=frozenset({"no_fabricated_source", "no_hot_path_repair"}),
|
|
alignment_threshold=0.45,
|
|
)
|