6f67e9a616
Closes the 'boundaries are checked at scattered call sites' gap noted
in ADR-0029. Adds a centralized observational surface parallel in
shape to IdentityCheck — produces a verdict, does not refuse. Wiring
verdicts into refusal paths is a future ADR.
Shape (parallel to IdentityCheck, different in mechanism):
SafetyContext — duck-typed input bag (field_state, citations,
refusal-was-typed flag, identity manifold hashes
before/after). Every field optional with safe
defaults; absence of evidence is not evidence of
violation.
SafetyCheckResult — per-boundary: boundary_id, upheld, reason,
runtime_checkable, evidence tuple.
SafetyVerdict — aggregate: pack_id, results (lex order on
boundary_id), upheld, violated_boundaries,
runtime_checkable_count.
SafetyCheck — registry of predicates; check(ctx, pack) returns
SafetyVerdict. register(boundary_id, predicate)
adds custom predicates.
Five default predicates for v1 boundaries:
preserve_versor_closure runtime_checkable=True field.versor_condition < 1e-6
no_fabricated_source runtime_checkable=True* cited ⊆ allowed
no_silent_correction runtime_checkable=True last refusal was typed
no_identity_override runtime_checkable=True* hash before == hash after
no_hot_path_repair runtime_checkable=FALSE code-path; static-analysis
*Conditional on the caller supplying the necessary fields.
The honest answer on no_hot_path_repair: it is a code-path boundary
enforced by static analysis + code review. Runtime cannot judge it.
A predicate that silently reported upheld=True would be a small lie —
exactly the kind of thing CLAUDE.md forbids. SafetyCheck reports
runtime_checkable=False with a clear reason so auditors see the truth.
ChatRuntime integration:
ChatRuntime.__init__ now constructs self.safety_check = SafetyCheck()
alongside self._identity_check. Turn loop does NOT auto-invoke at
v1 — operators and future ADRs decide when/where to call it.
Files:
packs/safety/check.py new — SafetyCheck + value types +
default predicates
packs/safety/__init__.py re-exports the new public surface
chat/runtime.py constructs self.safety_check
tests/test_safety_check.py new — 20 tests covering each
default predicate (positive +
negative), unknown-boundary
fallback, custom registration,
defensive boundary-id rebinding,
verdict aggregation, ChatRuntime
integration
docs/decisions/ADR-0032-safety-check-surface.md Accepted
docs/safety_packs.md §SafetyCheck section added,
known-limit #1 struck through
memory/safety-pack.md refreshed; new follow-up about
turn-loop auto-invocation
Suite status (all green):
cognition 121, teaching 17, runtime 19, formation 182, smoke 67
identity / safety / surface divergence suites: 108 tests passing
(was 88 before this ADR; +20 safety-check tests)
Scope limits (documented):
- No auto-invocation in the turn loop.
- No refusal wiring on violation.
- No refactoring of existing scattered enforcement sites.
- Defensive boundary-id rebinding masks predicate bugs; debug-mode
surfacing is a future enhancement.
586 lines
24 KiB
Python
586 lines
24 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, DEFAULT_IDENTITY_PACK, RuntimeConfig
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from core.physics.drive import DriveGradientMap, GradientField
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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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IdentityScore,
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TurnEvent,
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)
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from packs.identity.loader import load_identity_manifold
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from packs.safety.check import SafetyCheck
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from packs.safety.loader import load_safety_pack
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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.intent_bridge import articulate_with_intent
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from generate.proposition import FrameRegistry, Proposition, propose
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from generate.result import GenerationResult
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from generate.stream import generate
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from generate.surface import SentenceAssembler, SentencePlan, SurfaceContext
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from ingest.gate import inject
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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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from session.correction import CorrectionPass
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from vault.decompose import default_decomposer, default_gate
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_TOKEN_RE = re.compile(r"\w+", re.UNICODE)
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_SEED_ALIASES = {
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"logos": "\u03bb\u03cc\u03b3\u03bf\u03c2",
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"dabar": "\u05d3\u05d1\u05e8",
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"or": "\u05d0\u05d5\u05e8",
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"phos": "\u03c6\u03c9\u03c2",
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"zoe": "\u03b6\u03c9\u03ae",
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"arche": "\u1f00\u03c1\u03c7\u03ae",
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"aletheia": "\u1f00\u03bb\u03ae\u03b8\u03b5\u03b9\u03b1",
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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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_UNKNOWN_DOMAIN_SURFACE = "I don't know — insufficient grounding for that yet."
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def _energy_scalar(energy_obj) -> float:
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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 in {"question", "refute"} 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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def _prefer_prompt_anchor(
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articulation: ArticulationPlan,
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filtered_tokens: Sequence[str],
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*,
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output_language: str,
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) -> ArticulationPlan:
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if output_language != "en" or len(filtered_tokens) < 2:
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return articulation
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content_tokens = [
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token
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for token in filtered_tokens
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if token.casefold() not in _QUESTION_WORDS and token.casefold() not in {"is", "are", "was", "were"}
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]
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if not content_tokens:
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return articulation
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anchor = content_tokens[-1]
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if anchor == articulation.subject:
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return articulation
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return replace(
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articulation,
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subject=anchor,
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surface=" ".join(part for part in (anchor, articulation.predicate, articulation.object) if part),
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)
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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(result, turn: int):
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from core.physics.reasoning import TrajectoryOperator
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operator = TrajectoryOperator()
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states = result.trajectory or (result.final_state,)
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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(states)
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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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# ADR-0023 §2 — per-transition admissibility evidence and region
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# provenance flag. An empty tuple is the contract for "no
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# admissibility was checked this turn" (cold start, refusal, stub).
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admissibility_trace: tuple = ()
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region_was_unconstrained: bool = True
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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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inner_loop_admissibility=config.inner_loop_admissibility,
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admissibility_threshold=config.admissibility_threshold,
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admissibility_mode=config.admissibility_mode,
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admissibility_margin=config.admissibility_margin,
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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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identity_pack_id = resolved_config.identity_pack or DEFAULT_IDENTITY_PACK
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# ADR-0027 Phase 5 complete: v1 packs are ratified. Loader defaults
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# to production mode (require_ratified=None -> require unless
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# CORE_ALLOW_UNRATIFIED_IDENTITY=1).
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identity_manifold = load_identity_manifold(identity_pack_id)
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# ADR-0029: safety pack is always loaded; its boundary_ids are
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# unioned into the runtime manifold. Identity packs may add
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# boundaries but cannot remove safety boundaries. Failure to
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# load the safety pack is fail-closed; SafetyPackError propagates
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# and prevents runtime startup.
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self.safety_pack = load_safety_pack()
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self.identity_manifold = type(identity_manifold)(
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value_axes=identity_manifold.value_axes,
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boundary_ids=(
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identity_manifold.boundary_ids | self.safety_pack.boundary_ids
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),
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alignment_threshold=identity_manifold.alignment_threshold,
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surface_preferences=identity_manifold.surface_preferences,
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)
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self.identity_pack_id = identity_pack_id
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# Keep the generic runtime neutral. Identity/persona motivation belongs
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# behind an explicit IdentityProfile contract, not the baseline chat path.
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persona_motor = PersonaMotor.identity()
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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(resolved_config.frame_pack, self._context.vocab)
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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 = {e.surface: (e.pos or e.part_of_speech or "X") for e in entries}
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self.exertion_meter = ExertionMeter(capacity_ceiling=128.0)
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self.drive_gradients = tuple(GradientField(axis=axis, magnitude=0.75) for axis in self.identity_manifold.value_axes)
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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={g.axis.name: g.magnitude for g in self.drive_gradients},
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fatigue_index=0.0,
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)
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self._identity_check = IdentityCheck()
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# ADR-0032 — structural safety surface. Observational at v1:
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# ChatRuntime exposes ``safety_check`` for callers (audit /
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# logging / future enforcement), but does not auto-invoke it in
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# the turn loop. Wiring violations into refusal paths is a
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# future ADR.
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self.safety_check = SafetyCheck()
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self.turn_log: List[TurnEvent] = []
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self._correction_pass = CorrectionPass()
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self._last_valence: float = 0.0
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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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return [self._surface_by_fold.get(m.group(0).casefold(), m.group(0)) for m in _TOKEN_RE.finditer(text)]
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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(manifest.oov_policy is OOVPolicy.PROPOSE_VOCAB_EXPANSION for manifest in self._manifests):
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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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"""Generic runtime keeps motivation/drive disabled.
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Motivation is an identity-profile concern, not a free runtime field
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mutation. Keeping this a no-op preserves the neutral baseline while
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generic chat closure and cognition evals are being stabilized.
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"""
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return field_state
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def _build_surface_context(self, identity_score, current_valence: float) -> SurfaceContext:
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active = self._context.referents.active_referent()
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alignment = float(identity_score.alignment) if identity_score is not None else 1.0
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deviation_axes = (
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frozenset(identity_score.deviation_axes)
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if identity_score is not None
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else frozenset()
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)
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prefs = self.identity_manifold.surface_preferences
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# ADR-0031 — flatten the manifold's axis_hedges (tuple of
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# (axis_id, AxisHedge)) into the wire-format quadruples that
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# SurfaceContext carries. Order is preserved (loader emits in
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# lex order); _axis_specific_phrase relies on this.
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axis_hedges = tuple(
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(axis_id, hedge.strong, hedge.soft, hedge.qualifier)
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for axis_id, hedge in prefs.axis_hedges
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)
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return SurfaceContext(
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active_referent_surface=active.surface if active is not None else "",
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active_referent_slot=active.slot if active is not None else "neut_sg",
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identity_alignment=alignment,
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valence_delta=current_valence - self._last_valence,
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elab_conjunction="",
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hedge_threshold_strong=prefs.hedge_threshold_strong,
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hedge_threshold_soft=prefs.hedge_threshold_soft,
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preferred_hedge_strong=prefs.preferred_hedge_strong,
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preferred_hedge_soft=prefs.preferred_hedge_soft,
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claim_strength=prefs.claim_strength,
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qualified_band_high=prefs.qualified_band_high,
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preferred_qualifier=prefs.preferred_qualifier,
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deviation_axes=deviation_axes,
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axis_hedges=axis_hedges,
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)
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def _stub_response(self, field_state: FieldState) -> ChatResponse:
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zero = np.zeros(field_state.F.shape, dtype=np.float32)
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prop = Proposition(
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subject="",
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predicate="",
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object_=None,
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surface=_UNKNOWN_DOMAIN_SURFACE,
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frame_id="unknown_domain",
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subject_versor=zero,
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predicate_versor=zero,
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object_versor=None,
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relation=zero,
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)
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art = ArticulationPlan(
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subject="",
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predicate="",
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object=None,
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surface=_UNKNOWN_DOMAIN_SURFACE,
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output_language=self.config.output_language,
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frame_id="unknown_domain",
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)
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return ChatResponse(
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surface=_UNKNOWN_DOMAIN_SURFACE,
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proposition=prop,
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articulation=art,
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articulation_surface=_UNKNOWN_DOMAIN_SURFACE,
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dialogue_role="assert",
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versor_condition=versor_condition(field_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=_UNKNOWN_DOMAIN_SURFACE,
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salience_top_k=None,
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candidates_used=None,
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vault_hits=0,
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identity_score=None,
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character_profile=self.character_profile,
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flagged=False,
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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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probe_state = self._context.probe_ingest(filtered)
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# INV-24 recall role: RECOGNITION. Feeds UnknownDomainGate — asks
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# "have we seen anything like this before?", not "what is admissible
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# evidence?". Session-tier SPECULATIVE memory must count here, so
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# no min_status filter is applied.
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direct_hits = self._context.vault.recall(probe_state.F, top_k=3)
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direct_best = max((h["score"] for h in direct_hits), default=0.0)
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gate_decision = default_gate.check(
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direct_best,
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vault=self._context.vault,
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query=probe_state.F,
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decomposer=default_decomposer,
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)
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if gate_decision.fire:
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committed = self._context.commit_ingest(filtered)
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empty_result = GenerationResult(tokens=(), final_state=committed, vault_hits=0)
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self._context.finalize_turn(
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empty_result,
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tokens_in=tuple(filtered),
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input_versor=committed.F,
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dialogue_role="assert",
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metadata={"unknown": True, "unknown_source": gate_decision.source},
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)
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return self._stub_response(committed)
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|
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,
|
|
inner_loop_admissibility=self.config.inner_loop_admissibility,
|
|
admissibility_threshold=self.config.admissibility_threshold,
|
|
admissibility_mode=self.config.admissibility_mode,
|
|
admissibility_margin=self.config.admissibility_margin,
|
|
)
|
|
|
|
# --- 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,
|
|
admissibility_trace=result.admissibility_trace,
|
|
region_was_unconstrained=result.region_was_unconstrained,
|
|
)
|
|
|
|
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 ""
|
|
|
|
|
|
# The previous ``_default_identity_manifold()`` constructor was removed as
|
|
# part of ADR-0027. Identity is now loaded from a pack at runtime via
|
|
# ``packs.identity.loader.load_identity_manifold`` using
|
|
# ``RuntimeConfig.identity_pack`` (default ``DEFAULT_IDENTITY_PACK``).
|
|
# The previously-hardcoded three axes (truthfulness / coherence /
|
|
# reverence) live in ``packs/identity/default_general_v1.json``.
|