fe4cc2cd1f
ADR-0066 P3.1 + P3.2. Conversation now reads as a thread: turns
carry structured summaries of their predecessors and (optionally)
prefix new pack/teaching surfaces with deterministic backreferences.
P3.1 — chat/thread_context.py.
TurnSummary(turn_index, intent_tag_name, subject, grounding_source,
chain_id, corpus_id) — frozen, structured-fields-only.
ThreadContext — bounded FIFO (default MAX_THREAD_TURNS=8) with
snapshot(), recent_for_subject(), recent_subjects(), clear().
recent_for_subject() excludes ungrounded tiers (oov/partial/none)
by default — those are not strong-enough anchors.
ChatRuntime.thread_context is owned at construction.
_push_thread_summary runs at end-of-turn on BOTH stub and walk
paths. Teaching-grounded turns carry chain_id + corpus_id so
downstream composers (P3.2) can detect same-chain reference.
Cold-start intent classification now runs unconditionally (was:
gated on sink attachment) so thread context captures subject
regardless of sink state.
P3.2 — chat/anaphora.py.
thread_anaphora_prefix(ctx, subject, intent_name, source) returns
a deterministic prefix when:
- current turn is pack/teaching tier
- a prior pack/teaching turn on the same subject exists
- the prior intent differs from the current intent
Format (structural-fields-only — no prose):
"(Recalling turn N: chain <chain_id>.) " # prior was teaching
"(Recalling turn N: <subject> grounded pack.) " # prior was pack
Opt-in via RuntimeConfig.thread_anaphora=False. Default off keeps
every existing surface byte-identical.
Live verification (with thread_anaphora=True + seeded context):
> What is light? # following a "Why does light exist?" teaching turn
[pack] (Recalling turn 0: chain cause_light_reveals_truth.)
light — pack-grounded (en_core_cognition_v1): cognition.illumination;
logos.core; perception.clarity. No session evidence yet.
32 new tests passed. Curated lanes green. Cognition eval
byte-identical to pre-ADR baseline.
1484 lines
66 KiB
Python
1484 lines
66 KiB
Python
from __future__ import annotations
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from dataclasses import dataclass, replace
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import hashlib
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import json
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import re
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from collections.abc import Sequence
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from typing import Any, List
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import numpy as np
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from algebra.versor import versor_condition
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from chat.pack_grounding import (
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pack_grounded_surface,
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pack_grounded_comparison_surface,
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pack_grounded_correction_surface,
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pack_grounded_procedure_surface,
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PACK_ID as _COGNITION_PACK_ID,
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)
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from chat.teaching_grounding import (
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teaching_grounded_surface,
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teaching_grounded_surface_composed,
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TEACHING_CORPUS_ID as _TEACHING_CORPUS_ID,
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)
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from chat.refusal import (
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build_hedge_prefix,
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build_refusal_surface,
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inject_hedge,
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should_inject_hedge,
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)
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from chat.telemetry import (
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TurnEventSink,
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format_correction_event_jsonl,
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format_turn_event_jsonl,
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)
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from chat.verdicts import TurnVerdicts
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from teaching.discovery import (
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extract_discovery_candidates,
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format_candidate_jsonl,
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)
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from teaching.discovery_sink import DiscoveryCandidateSink
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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.ethics.check import EthicsCheck, EthicsContext
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from packs.ethics.loader import (
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DEFAULT_ETHICS_PACK as _DEFAULT_ETHICS_PACK,
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EthicsPackError,
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load_ethics_pack,
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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, SafetyContext
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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.graph_constraint import build_graph_constraint
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from generate.intent_bridge import articulate_with_intent, build_graph_from_input
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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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@dataclass(frozen=True, slots=True)
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class _FieldStateWithVersor:
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"""Adapter exposing ``versor_condition`` for SafetyContext.
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``FieldState`` itself does not carry a precomputed
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``versor_condition`` attribute; it is computed on demand from
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``versor_condition(state.F)``. The SafetyCheck predicate for
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``preserve_versor_closure`` reads ``ctx.field_state.versor_condition``
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via ``getattr``. This adapter exposes the precomputed value so the
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predicate is runtime-checkable each turn.
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"""
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versor_condition: float
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def _hash_identity_manifold(manifold) -> str:
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"""Deterministic SHA-256 of the load-bearing identity-manifold fields.
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ADR-0035 — feeds the ``no_identity_override`` predicate in
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:class:`SafetyCheck`. The runtime never mutates ``identity_manifold``
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after composition, so before- and after-turn hashes are equal by
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construction; an unequal hash would indicate the predicate's exact
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failure mode.
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"""
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payload = {
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"value_axes": [
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{
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"axis_id": axis.axis_id,
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"name": axis.name,
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"direction": list(axis.direction),
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"weight": axis.weight,
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}
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for axis in manifold.value_axes
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],
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"boundary_ids": sorted(manifold.boundary_ids),
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"alignment_threshold": manifold.alignment_threshold,
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}
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blob = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode("utf-8")
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return hashlib.sha256(blob).hexdigest()
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def _surface_contains_hedge(surface: str, manifold) -> bool:
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"""Detect whether the realized surface emitted a hedge phrase.
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Compares case-insensitively against the manifold's preferred hedge
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phrases (ADR-0028). False when surface is empty. Coarse but
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deterministic: the predicate downstream is observational, so
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occasional false negatives are surfaced as
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``acknowledge_uncertainty`` violations in audit and corrected by
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refining hedge detection, not by silently passing.
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"""
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if not surface:
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return False
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prefs = getattr(manifold, "surface_preferences", None)
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if prefs is None:
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return False
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candidates: list[str] = []
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for field_name in (
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"preferred_hedge_strong",
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"preferred_hedge_soft",
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"preferred_qualifier",
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):
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value = getattr(prefs, field_name, "")
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if value:
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candidates.append(value)
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for _, hedge in getattr(prefs, "axis_hedges", ()) or ():
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for sub in ("strong", "soft", "qualifier"):
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value = getattr(hedge, sub, "")
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if value:
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candidates.append(value)
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surface_fold = surface.casefold()
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return any(c.casefold() in surface_fold for c in candidates if c)
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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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# ADR-0035 — verdicts surfaced from SafetyCheck and EthicsCheck.
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# ``None`` only on stub/refusal paths that bypass the turn loop.
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safety_verdict: object = None
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ethics_verdict: object = None
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# ADR-0039 — unified TurnVerdicts bundle carrying identity / safety
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# / ethics verdicts and the two remediation flags
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# (refusal_emitted, hedge_injected). Typed as ``object`` to avoid
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# coupling at module-resolution time; downcast at use site.
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verdicts: object = None
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# ADR-0048 / ADR-0050 / ADR-0052 — provenance tag for the surface's
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# grounding. One of:
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# "vault" — answer drawn from session vault evidence (main path).
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# "pack" — answer drawn from the ratified language pack
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# (cold-start DEFINITION/RECALL/COMPARISON on pack-known
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# lemmas — ADR-0048 / ADR-0050).
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# "teaching" — answer drawn from a reviewed teaching-chain corpus
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# (cold-start CAUSE/VERIFICATION — ADR-0052).
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# "none" — universal "insufficient grounding" disclosure on stub.
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# The string is preserved verbatim in TurnEvent for downstream audit.
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grounding_source: str = "none"
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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
|
|
# 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)
|
|
# ADR-0029: safety pack is always loaded; its boundary_ids are
|
|
# unioned into the runtime manifold. Identity packs may add
|
|
# boundaries but cannot remove safety boundaries. Failure to
|
|
# load the safety pack is fail-closed; SafetyPackError propagates
|
|
# and prevents runtime startup.
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self.safety_pack = load_safety_pack()
|
|
# ADR-0033 — ethics pack composes alongside identity + safety.
|
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# Swappable like identity; falls back to the default pack on
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|
# load failure rather than refusing startup (safety is the
|
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# fail-closed layer, not ethics).
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ethics_pack_id = resolved_config.ethics_pack or _DEFAULT_ETHICS_PACK
|
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try:
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self.ethics_pack = load_ethics_pack(ethics_pack_id)
|
|
except EthicsPackError:
|
|
if ethics_pack_id == _DEFAULT_ETHICS_PACK:
|
|
raise
|
|
self.ethics_pack = load_ethics_pack(_DEFAULT_ETHICS_PACK)
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ethics_pack_id = _DEFAULT_ETHICS_PACK
|
|
self.ethics_pack_id = ethics_pack_id
|
|
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
|
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| self.safety_pack.boundary_ids
|
|
| self.ethics_pack.commitment_ids
|
|
),
|
|
alignment_threshold=identity_manifold.alignment_threshold,
|
|
surface_preferences=identity_manifold.surface_preferences,
|
|
)
|
|
self.identity_pack_id = identity_pack_id
|
|
# 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(
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|
manifold,
|
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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()
|
|
# ADR-0032 — structural safety surface. Observational at v1:
|
|
# ChatRuntime exposes ``safety_check`` for callers (audit /
|
|
# logging / future enforcement), but does not auto-invoke it in
|
|
# the turn loop. Wiring violations into refusal paths is a
|
|
# future ADR.
|
|
self.safety_check = SafetyCheck()
|
|
# ADR-0034 — structural ethics surface, sibling to SafetyCheck.
|
|
self.ethics_check = EthicsCheck()
|
|
# ADR-0035 — auto-invoke both checks at end-of-turn. The
|
|
# manifold is constructed once and never mutated, so the
|
|
# pre-turn hash is a stable property of this runtime instance.
|
|
# ``last_refusal_was_typed`` defaults True (no untyped refusals
|
|
# observed); turn-loop bookkeeping flips this on typed-refusal
|
|
# paths so the predicate has live evidence.
|
|
self._identity_manifold_hash: str = _hash_identity_manifold(
|
|
self.identity_manifold,
|
|
)
|
|
self._last_refusal_was_typed: bool = True
|
|
self.turn_log: List[TurnEvent] = []
|
|
# P3.1 — session-thread state for downstream anaphora /
|
|
# NARRATIVE composers. Bounded recency window of structured
|
|
# TurnSummary records. Data layer only at P3.1 — no surface
|
|
# emission consults this until P3.2 (anaphora composer) opt-in
|
|
# flag flips on.
|
|
from chat.thread_context import ThreadContext
|
|
self.thread_context = ThreadContext()
|
|
# ADR-0040 — opt-in structured-logging sink. Default None
|
|
# preserves prior behavior; callers attach via
|
|
# ``attach_telemetry_sink``. ``_telemetry_include_content``
|
|
# gates surface / token emission per the redact-by-default
|
|
# trust boundary.
|
|
self._telemetry_sink: TurnEventSink | None = None
|
|
self._telemetry_include_content: bool = False
|
|
# ADR-0055 Phase B — opt-in DiscoveryCandidate sink. Default
|
|
# None preserves prior behavior; callers attach via
|
|
# ``attach_discovery_sink``. Candidates are *evidence*, never
|
|
# mutate the corpus or runtime state.
|
|
self._discovery_sink: DiscoveryCandidateSink | None = None
|
|
# Phase 2.3 — opt-in OOV candidate sink. Default None preserves
|
|
# prior behavior; callers attach via ``attach_oov_sink``.
|
|
# Candidates are evidence; ratified-pack-mutation is the only
|
|
# path an OOV promotion becomes a real pack change.
|
|
self._oov_sink: Any = None
|
|
# ADR-0056 Phase C1 — opt-in contemplation pass that enriches
|
|
# each emitted DiscoveryCandidate with polarity / claim_domain /
|
|
# evidence / sub_questions before the sink writes the JSONL
|
|
# line. Default False preserves prior behavior (Phase B raw
|
|
# candidates). Toggling on does NOT mutate the corpus; the
|
|
# loop is read-only over pack + corpus + (optional) vault.
|
|
self._contemplate_discoveries: bool = False
|
|
self._correction_pass = CorrectionPass()
|
|
self._last_valence: float = 0.0
|
|
|
|
@property
|
|
def session(self) -> SessionContext:
|
|
return self._context
|
|
|
|
def attach_telemetry_sink(
|
|
self,
|
|
sink: TurnEventSink | None,
|
|
*,
|
|
include_content: bool = False,
|
|
) -> None:
|
|
"""ADR-0040 — attach a structured-logging sink.
|
|
|
|
After each turn (main or stub path), the runtime serialises
|
|
the appended ``TurnEvent`` as one JSONL line and calls
|
|
``sink.emit(line)``. Passing ``None`` detaches.
|
|
|
|
``include_content`` opts surface text and input tokens into
|
|
the emitted record. Default ``False`` preserves the
|
|
redact-by-default trust boundary (CLAUDE.md): audit pipelines
|
|
get counts, ids, and flags without raw user content.
|
|
"""
|
|
self._telemetry_sink = sink
|
|
self._telemetry_include_content = bool(include_content)
|
|
|
|
def attach_oov_sink(self, sink: Any) -> None:
|
|
"""Phase 2.3 — attach an OOV candidate sink.
|
|
|
|
After each turn whose surface fired the P2.1 OOV invitation
|
|
(``grounding_source="oov"``), the runtime emits one
|
|
:class:`teaching.oov_sink.OOVCandidate` JSONL line to the
|
|
attached sink. Passing ``None`` detaches.
|
|
|
|
Candidates are evidence: emission never mutates any pack.
|
|
The ratified pack-mutation path (ADR-0027 +
|
|
:mod:`teaching.proposals`) is the only way an OOV promotion
|
|
becomes a real pack change.
|
|
"""
|
|
self._oov_sink = sink
|
|
|
|
def attach_discovery_sink(
|
|
self,
|
|
sink: DiscoveryCandidateSink | None,
|
|
) -> None:
|
|
"""ADR-0055 Phase B — attach a DiscoveryCandidate sink.
|
|
|
|
After each turn, the runtime extracts zero-or-more candidates
|
|
from the most recent ``TurnEvent`` (deterministic rule firing
|
|
on the audit trail) and forwards each as one JSONL line.
|
|
Passing ``None`` detaches.
|
|
|
|
Candidates are **evidence**: emission never mutates the
|
|
active teaching corpus. Phase C's ``TeachingChainProposal``
|
|
is the only path to corpus extension and runs through
|
|
review + replay.
|
|
"""
|
|
self._discovery_sink = sink
|
|
|
|
def attach_contemplation(self, *, enabled: bool = True) -> None:
|
|
"""ADR-0056 Phase C1 — opt-in inline contemplation.
|
|
|
|
When enabled, each emitted ``DiscoveryCandidate`` is passed
|
|
through ``teaching.contemplation.contemplate`` before the
|
|
sink writes the JSONL line. The sink therefore receives an
|
|
*enriched* candidate (polarity / claim_domain / evidence /
|
|
sub_questions populated) instead of the Phase B raw record.
|
|
|
|
Read-only over pack + corpus. No corpus mutation, no clock-
|
|
time read, no LLM step. Requires ``attach_discovery_sink``
|
|
to have been called first — without a sink there is nowhere
|
|
to emit, so contemplation would do hidden work.
|
|
"""
|
|
self._contemplate_discoveries = bool(enabled)
|
|
|
|
def _push_thread_summary(
|
|
self,
|
|
*,
|
|
turn_event: TurnEvent,
|
|
intent_tag: Any,
|
|
intent_subject: str | None,
|
|
grounding_source: str | None,
|
|
surface: str | None = None,
|
|
) -> None:
|
|
"""P3.1 — append one :class:`TurnSummary` to the bounded
|
|
session-thread context. Called at end-of-turn from both the
|
|
stub path (cold start / pack / teaching / OOV / partial)
|
|
and the walk path (vault).
|
|
|
|
For teaching-grounded turns the chain_id + corpus_id are
|
|
recovered from the most-recently-loaded aggregated chain
|
|
index (deterministic O(1) lookup since the index is lru-
|
|
cached). For non-teaching turns those fields stay None.
|
|
|
|
Pure data layer: this method does NOT consult the surface,
|
|
does NOT mutate any composer state, and does NOT call any
|
|
LLM. Push runs unconditionally — anaphora consumers are
|
|
opt-in elsewhere.
|
|
"""
|
|
from chat.thread_context import TurnSummary
|
|
|
|
turn_index = len(self.turn_log) - 1 # turn_log was just appended
|
|
# Normalise the intent tag name; ``None`` (walk path) projects
|
|
# to the empty string so the recency lookup can still ignore
|
|
# mismatched intents without raising.
|
|
if intent_tag is not None and hasattr(intent_tag, "name"):
|
|
intent_name = str(intent_tag.name).lower()
|
|
else:
|
|
intent_name = ""
|
|
subject = (intent_subject or "").strip().lower()
|
|
source = (grounding_source or "none").lower()
|
|
|
|
# Recover chain_id + corpus_id for teaching-grounded turns so
|
|
# the anaphora composer can detect "same chain" vs "same
|
|
# subject, different chain".
|
|
chain_id: str | None = None
|
|
corpus_id: str | None = None
|
|
if source == "teaching" and subject and intent_name in {"cause", "verification"}:
|
|
from chat.teaching_grounding import _all_chains_index
|
|
chain = _all_chains_index().get((subject, intent_name))
|
|
if chain is not None:
|
|
chain_id = chain.chain_id
|
|
corpus_id = chain.corpus_id
|
|
# ``surface`` is accepted so future extensions can hash it,
|
|
# but P3.1 intentionally does not retain the text.
|
|
_ = surface
|
|
|
|
self.thread_context.push(
|
|
TurnSummary(
|
|
turn_index=turn_index,
|
|
intent_tag_name=intent_name,
|
|
subject=subject,
|
|
grounding_source=source,
|
|
chain_id=chain_id,
|
|
corpus_id=corpus_id,
|
|
)
|
|
)
|
|
|
|
def _emit_oov_candidate(
|
|
self,
|
|
*,
|
|
turn_event: TurnEvent,
|
|
intent_tag: Any,
|
|
token: str | None,
|
|
) -> None:
|
|
"""P2.3 — emit one OOVCandidate per OOV-grounded turn.
|
|
|
|
No-op unless ``attach_oov_sink`` was called. The token is
|
|
already safe-displayed at the surface composer; persistence
|
|
carries the same sanitised form.
|
|
"""
|
|
sink = self._oov_sink
|
|
if sink is None or not token:
|
|
return
|
|
# Local imports — keep OOV machinery out of the runtime
|
|
# hot-path import graph for callers that never opt in.
|
|
from teaching.oov_sink import (
|
|
OOVCandidate,
|
|
format_oov_candidate_jsonl,
|
|
hash_oov_candidate_id,
|
|
)
|
|
from generate.intent import IntentTag
|
|
|
|
if intent_tag is None or not isinstance(intent_tag, IntentTag):
|
|
return
|
|
intent_name = intent_tag.name.lower()
|
|
# Pull trace hash from the turn event when present so the
|
|
# candidate_id replays deterministically.
|
|
trace_hash = getattr(turn_event, "trace_hash", "") or ""
|
|
boundary_clean = (
|
|
not getattr(turn_event, "refusal_emitted", False)
|
|
and not getattr(turn_event, "hedge_injected", False)
|
|
)
|
|
cleaned_token = (token or "").strip().lower()
|
|
if not cleaned_token:
|
|
return
|
|
candidate_id = hash_oov_candidate_id(cleaned_token, intent_name, trace_hash)
|
|
candidate = OOVCandidate(
|
|
candidate_id=candidate_id,
|
|
token=cleaned_token,
|
|
intent=intent_name, # type: ignore[arg-type]
|
|
trigger="unresolved_subject",
|
|
source_turn_trace=trace_hash,
|
|
boundary_clean=boundary_clean,
|
|
)
|
|
sink.emit(format_oov_candidate_jsonl(candidate))
|
|
|
|
def _emit_discovery_candidates(
|
|
self,
|
|
*,
|
|
turn_event: TurnEvent,
|
|
intent_tag: Any,
|
|
intent_subject: str | None,
|
|
grounding_source: str | None,
|
|
) -> None:
|
|
sink = self._discovery_sink
|
|
if sink is None:
|
|
return
|
|
candidates = extract_discovery_candidates(
|
|
turn_event,
|
|
intent_tag,
|
|
intent_subject,
|
|
grounding_source=grounding_source,
|
|
)
|
|
if self._contemplate_discoveries and candidates:
|
|
# Local import — keeps the contemplation module out of
|
|
# the runtime hot-path import graph for callers that
|
|
# never opt in.
|
|
from teaching.contemplation import contemplate
|
|
candidates = tuple(contemplate(c) for c in candidates)
|
|
for candidate in candidates:
|
|
sink.emit(format_candidate_jsonl(candidate))
|
|
|
|
def _emit_turn_event(self, event: TurnEvent) -> None:
|
|
"""Internal — emit one serialised line for the current event.
|
|
|
|
Called after every ``turn_log.append``. No-op when no sink
|
|
is attached. Sink errors are intentionally NOT swallowed:
|
|
a broken telemetry path should surface, not silently drop
|
|
audit signal.
|
|
"""
|
|
sink = self._telemetry_sink
|
|
if sink is None:
|
|
return
|
|
line = format_turn_event_jsonl(
|
|
event,
|
|
safety_pack_id=self.safety_pack.pack_id,
|
|
ethics_pack_id=self.ethics_pack_id,
|
|
identity_pack_id=self.identity_pack_id,
|
|
include_content=self._telemetry_include_content,
|
|
)
|
|
sink.emit(line)
|
|
|
|
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
|
|
deviation_axes = (
|
|
frozenset(identity_score.deviation_axes)
|
|
if identity_score is not None
|
|
else frozenset()
|
|
)
|
|
prefs = self.identity_manifold.surface_preferences
|
|
# ADR-0031 — flatten the manifold's axis_hedges (tuple of
|
|
# (axis_id, AxisHedge)) into the wire-format quadruples that
|
|
# SurfaceContext carries. Order is preserved (loader emits in
|
|
# lex order); _axis_specific_phrase relies on this.
|
|
axis_hedges = tuple(
|
|
(axis_id, hedge.strong, hedge.soft, hedge.qualifier)
|
|
for axis_id, hedge in prefs.axis_hedges
|
|
)
|
|
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="",
|
|
hedge_threshold_strong=prefs.hedge_threshold_strong,
|
|
hedge_threshold_soft=prefs.hedge_threshold_soft,
|
|
preferred_hedge_strong=prefs.preferred_hedge_strong,
|
|
preferred_hedge_soft=prefs.preferred_hedge_soft,
|
|
claim_strength=prefs.claim_strength,
|
|
qualified_band_high=prefs.qualified_band_high,
|
|
preferred_qualifier=prefs.preferred_qualifier,
|
|
deviation_axes=deviation_axes,
|
|
axis_hedges=axis_hedges,
|
|
)
|
|
|
|
def _maybe_pack_grounded_surface(
|
|
self, text: str, gate_source: str
|
|
) -> tuple[str, str] | None:
|
|
"""Return ``(surface, grounding_source)`` or ``None``.
|
|
|
|
ADR-0048 / ADR-0050 / ADR-0052 — three reviewed sources of
|
|
cold-start grounding share this dispatcher:
|
|
|
|
- DEFINITION / RECALL → pack-grounded surface (ADR-0048)
|
|
- COMPARISON → pack-grounded surface (ADR-0050)
|
|
- CAUSE / VERIFICATION → teaching-grounded surface (ADR-0052)
|
|
|
|
Engagement conditions common to all three branches:
|
|
|
|
- the gate fired because the session vault is empty,
|
|
- ``config.output_language == "en"``,
|
|
- the classified intent has a clean subject lemma.
|
|
|
|
Returns ``None`` when no branch applies and the caller falls
|
|
through to the universal "insufficient grounding" disclosure.
|
|
|
|
The grounding_source string returned alongside the surface is
|
|
one of ``"pack"`` (ADR-0048/0050) or ``"teaching"`` (ADR-0052)
|
|
and is preserved verbatim through ChatResponse and TurnEvent
|
|
for downstream audit.
|
|
"""
|
|
if gate_source != "empty_vault":
|
|
return None
|
|
if self.config.output_language != "en":
|
|
return None
|
|
from generate.intent import IntentTag # local to avoid coupling at import time
|
|
from generate.intent_bridge import classify_intent_from_input
|
|
intent = classify_intent_from_input(text)
|
|
# ADR-0050 — COMPARISON path: deterministic side-by-side surface
|
|
# composed from both lemmas' pack semantic_domains. Engages only
|
|
# when both subject and secondary_subject are pack lemmas.
|
|
if intent.tag is IntentTag.COMPARISON:
|
|
# The intent classifier may retain terminal punctuation on
|
|
# secondary_subject when it falls at the end of the prompt
|
|
# ("Compare A and B."). Strip terminal sentence punctuation
|
|
# so the resolver can find the underlying lemma. This is
|
|
# a normalization at the runtime boundary, not in the
|
|
# classifier itself, to keep the classifier's verbatim
|
|
# extraction available to other consumers.
|
|
lemma_a = (intent.subject or "").strip().rstrip(".,?!;:")
|
|
lemma_b = (intent.secondary_subject or "").strip().rstrip(".,?!;:")
|
|
if lemma_a and lemma_b:
|
|
surface = pack_grounded_comparison_surface(lemma_a, lemma_b)
|
|
if surface is not None:
|
|
return (surface, "pack")
|
|
# P2.2 — Partial-grounding tier. When exactly one of
|
|
# the two compared lemmas is pack-resident, emit a
|
|
# hedged surface that grounds the known side and
|
|
# explicitly disclaims the OOV side. Better than
|
|
# falling through to OOV invitation (which would name
|
|
# only one token while ignoring the other's actual
|
|
# grounding).
|
|
from chat.partial_surface import partial_comparison_surface
|
|
partial = partial_comparison_surface(lemma_a, lemma_b)
|
|
if partial is not None:
|
|
return (partial[0], "partial")
|
|
# ADR-0052 — teaching-grounded CAUSE / VERIFICATION. The chain
|
|
# corpus is reviewed memory; every emitted atom is either a
|
|
# lemma, a verbatim pack semantic_domains string, or a fixed
|
|
# connective from humanize_predicate.
|
|
# P3.3 — NARRATIVE: "Tell me about X" / "Describe X".
|
|
# Multi-clause composer aggregates every reviewed chain
|
|
# rooted on X across all registered teaching corpora.
|
|
if intent.tag is IntentTag.NARRATIVE:
|
|
lemma = (intent.subject or "").strip()
|
|
if lemma:
|
|
from chat.narrative_surface import narrative_grounded_surface
|
|
surface = narrative_grounded_surface(lemma)
|
|
if surface is not None:
|
|
return (surface, "teaching")
|
|
# P3.4 — EXAMPLE: "Give me an example of X". Reverse-chain
|
|
# composer surfaces chains where X is the OBJECT. Same
|
|
# aggregated corpus index as NARRATIVE; inverts the access
|
|
# pattern.
|
|
if intent.tag is IntentTag.EXAMPLE:
|
|
lemma = (intent.subject or "").strip()
|
|
if lemma:
|
|
from chat.example_surface import example_grounded_surface
|
|
surface = example_grounded_surface(lemma)
|
|
if surface is not None:
|
|
return (surface, "teaching")
|
|
if intent.tag in (IntentTag.CAUSE, IntentTag.VERIFICATION):
|
|
lemma = (intent.subject or "").strip()
|
|
if lemma:
|
|
# ADR-0062 — when ``composed_surface`` is enabled, the
|
|
# teaching-grounded composer extends the single-chain
|
|
# surface with a follow-up chain whose subject equals
|
|
# the initial chain's object. Backward-compatible:
|
|
# with the flag off, the single-chain composer is
|
|
# used; with the flag on and no follow-up chain
|
|
# available, the composer degrades to the single-
|
|
# chain surface byte-identically.
|
|
if self.config.composed_surface:
|
|
surface = teaching_grounded_surface_composed(lemma, intent.tag)
|
|
else:
|
|
surface = teaching_grounded_surface(lemma, intent.tag)
|
|
if surface is not None:
|
|
return (surface, "teaching")
|
|
# ADR-0053 — CORRECTION acknowledgement. Cold-start CORRECTION
|
|
# has no prior session turn to apply to; emit a pack-grounded
|
|
# surface that acknowledges the correction was received and
|
|
# states the missing-prior-turn constraint explicitly. The
|
|
# post-correction reviewed-teaching path (``teaching/correction.py``)
|
|
# engages only once a prior turn exists in the session.
|
|
if intent.tag is IntentTag.CORRECTION:
|
|
# ADR-0060 — pass the raw text so the acknowledgement can
|
|
# weave the corrected claim's first pack-resident topical
|
|
# lemma into the surface. Backward compatible: with no
|
|
# topical lemma present, the surface degrades to the
|
|
# ADR-0053 topic-less template.
|
|
surface = pack_grounded_correction_surface(text)
|
|
if surface is not None:
|
|
return (surface, "pack")
|
|
# ADR-0061 — PROCEDURE pack-grounded surface. Procedural
|
|
# chains are not part of the reviewed teaching corpus today
|
|
# (CAUSE/VERIFICATION only). Rather than fall through to the
|
|
# universal disclosure for every "How do I X?" question, the
|
|
# composer surfaces the topical lemma of the procedure (the
|
|
# last pack-resident lemma in the verb-phrase subject) and
|
|
# states explicitly that step-by-step guidance is not yet
|
|
# ratified. Honest, deterministic, pack-grounded.
|
|
if intent.tag is IntentTag.PROCEDURE:
|
|
subject_text = (intent.subject or "").strip()
|
|
if subject_text:
|
|
surface = pack_grounded_procedure_surface(subject_text)
|
|
if surface is not None:
|
|
return (surface, "pack")
|
|
if intent.tag in (IntentTag.DEFINITION, IntentTag.RECALL):
|
|
lemma = (intent.subject or "").strip()
|
|
if not lemma:
|
|
return None
|
|
surface = pack_grounded_surface(lemma)
|
|
if surface is not None:
|
|
return (surface, "pack")
|
|
# P2.1 — OOV "teach me" surface. If the classified intent is
|
|
# one of the surface-supported shapes AND the subject lemma
|
|
# does not resolve in any mounted lexicon pack, emit a
|
|
# deterministic learning-invitation surface tagged
|
|
# ``grounding_source="oov"`` instead of falling through to
|
|
# the universal disclosure. Converts the OOV cliff into a
|
|
# gradient that names the unknown token + points the operator
|
|
# at the reviewed pack-mutation path.
|
|
oov_lemma = (intent.subject or "").strip()
|
|
if oov_lemma:
|
|
from chat.oov_surface import oov_learning_invitation_surface
|
|
oov_surface = oov_learning_invitation_surface(oov_lemma, intent.tag)
|
|
if oov_surface is not None:
|
|
return (oov_surface, "oov")
|
|
return None
|
|
|
|
def _stub_response(
|
|
self,
|
|
field_state: FieldState,
|
|
*,
|
|
tokens: tuple[str, ...] = (),
|
|
pack_grounded_surface: str | None = None,
|
|
grounded_source_tag: str = "pack",
|
|
discovery_intent_tag: Any = None,
|
|
discovery_intent_subject: str | None = None,
|
|
) -> 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",
|
|
)
|
|
# ADR-0035 — stub responses are exactly the ungrounded path that
|
|
# triggers ``disclose_limitations``. Surfacing verdicts here
|
|
# keeps the audit contract uniform: every ChatResponse carries
|
|
# a SafetyVerdict and EthicsVerdict.
|
|
safety_ctx = SafetyContext(
|
|
field_state=_FieldStateWithVersor(
|
|
versor_condition=float(versor_condition(field_state.F)),
|
|
),
|
|
last_refusal_was_typed=self._last_refusal_was_typed,
|
|
identity_manifold_hash_before=self._identity_manifold_hash,
|
|
identity_manifold_hash_after=_hash_identity_manifold(self.identity_manifold),
|
|
)
|
|
safety_verdict = self.safety_check.check(safety_ctx, self.safety_pack)
|
|
ethics_ctx = EthicsContext(
|
|
alignment_score=0.0,
|
|
hedge_threshold_soft=float(
|
|
self.identity_manifold.surface_preferences.hedge_threshold_soft
|
|
),
|
|
hedge_emitted=False,
|
|
grounded_in_evidence=False,
|
|
disclosure_emitted=True,
|
|
)
|
|
ethics_verdict = self.ethics_check.check(ethics_ctx, self.ethics_pack)
|
|
# ADR-0036 — typed refusal also applies on the stub path. When
|
|
# a runtime-checkable safety boundary is violated even on the
|
|
# ungrounded surface (e.g. versor-closure failure), replace the
|
|
# user-facing ``surface`` with the deterministic typed refusal.
|
|
refusal_surface = build_refusal_surface(
|
|
safety_verdict, ethics_verdict, self.ethics_pack,
|
|
)
|
|
refusal_emitted = refusal_surface is not None
|
|
if refusal_emitted:
|
|
response_surface = refusal_surface
|
|
self._last_refusal_was_typed = True
|
|
elif pack_grounded_surface is not None:
|
|
# ADR-0048 — pack-grounded surface for cold-start DEFINITION /
|
|
# RECALL on a known pack lemma. Safety/ethics refusal still
|
|
# take priority above this branch; the pack surface only
|
|
# replaces the universal "insufficient grounding" disclosure
|
|
# when no refusal applies.
|
|
response_surface = pack_grounded_surface
|
|
# P3.2 — opt-in thread anaphora prefix. Engages only when
|
|
# the current turn AND a recent turn (same subject) are
|
|
# both pack/teaching grounded. Default-off so pre-P3.2
|
|
# surfaces stay byte-identical; turning it on prepends a
|
|
# deterministic backreference referencing the prior turn
|
|
# by turn-index + chain_id (no prose generation).
|
|
if (
|
|
self.config.thread_anaphora
|
|
and grounded_source_tag in {"pack", "teaching"}
|
|
and discovery_intent_subject
|
|
and discovery_intent_tag is not None
|
|
):
|
|
from chat.anaphora import thread_anaphora_prefix
|
|
prefix = thread_anaphora_prefix(
|
|
self.thread_context,
|
|
discovery_intent_subject,
|
|
discovery_intent_tag.name.lower(),
|
|
grounded_source_tag,
|
|
)
|
|
if prefix is not None:
|
|
response_surface = prefix + response_surface
|
|
else:
|
|
response_surface = _UNKNOWN_DOMAIN_SURFACE
|
|
# ADR-0048 — grounding provenance recorded for both ChatResponse
|
|
# and TurnEvent. ``"pack"`` only when we actually emit the
|
|
# pack-grounded surface (refusal does not override the source —
|
|
# refusal is a remediation tier, not a grounding source).
|
|
if pack_grounded_surface is not None and not refusal_emitted:
|
|
# ADR-0052 — preserve provenance: pack-grounded surfaces tag
|
|
# ``"pack"``, teaching-grounded surfaces tag ``"teaching"``.
|
|
grounding_source = grounded_source_tag
|
|
else:
|
|
grounding_source = "none"
|
|
# ADR-0038 — hedge injection does NOT run on the stub path
|
|
# (the unknown-domain marker is already a disclosure surface;
|
|
# prepending a hedge would be a confused double-disclosure).
|
|
# ``hedge_injected`` is therefore always False on stub paths.
|
|
verdicts_bundle = TurnVerdicts(
|
|
identity_score=None,
|
|
safety_verdict=safety_verdict,
|
|
ethics_verdict=ethics_verdict,
|
|
refusal_emitted=refusal_emitted,
|
|
hedge_injected=False,
|
|
)
|
|
# ADR-0039 — emit a TurnEvent on stub paths too so ``turn_log``
|
|
# covers the entire turn stream for audit consumers. Only
|
|
# append when invoked from a real turn (``tokens`` is
|
|
# non-empty); defensive call sites that pass no tokens
|
|
# preserve the prior bypass-turn_log behavior.
|
|
if tokens:
|
|
stub_event = TurnEvent(
|
|
turn=max(self._context.turn - 1, 0),
|
|
input_tokens=tokens,
|
|
surface=response_surface,
|
|
walk_surface=_UNKNOWN_DOMAIN_SURFACE,
|
|
articulation_surface=_UNKNOWN_DOMAIN_SURFACE,
|
|
dialogue_role="assert",
|
|
identity_score=None,
|
|
cycle_cost_total=0.0,
|
|
vault_hits=0,
|
|
versor_condition=float(versor_condition(field_state.F)),
|
|
flagged=False,
|
|
elaboration=None,
|
|
safety_verdict=safety_verdict,
|
|
ethics_verdict=ethics_verdict,
|
|
verdicts=verdicts_bundle,
|
|
grounding_source=grounding_source,
|
|
)
|
|
self.turn_log.append(stub_event)
|
|
self._emit_turn_event(stub_event)
|
|
# ADR-0055 Phase B — opt-in discovery candidate emission.
|
|
# Only meaningful when the caller threads classified
|
|
# intent forward (gate-fire / fall-through site). Pure
|
|
# rule firing on the just-appended TurnEvent.
|
|
if discovery_intent_tag is not None:
|
|
self._emit_discovery_candidates(
|
|
turn_event=stub_event,
|
|
intent_tag=discovery_intent_tag,
|
|
intent_subject=discovery_intent_subject,
|
|
grounding_source=grounding_source,
|
|
)
|
|
# P2.3 — emit OOV candidate when the surface fired the
|
|
# OOV invitation. Only when an operator has attached
|
|
# a sink — otherwise this is a no-op.
|
|
if grounding_source == "oov":
|
|
self._emit_oov_candidate(
|
|
turn_event=stub_event,
|
|
intent_tag=discovery_intent_tag,
|
|
token=discovery_intent_subject,
|
|
)
|
|
# P3.1 — push session-thread summary. Data layer only;
|
|
# downstream composers (P3.2 anaphora) consult this.
|
|
self._push_thread_summary(
|
|
turn_event=stub_event,
|
|
intent_tag=discovery_intent_tag,
|
|
intent_subject=discovery_intent_subject,
|
|
grounding_source=grounding_source,
|
|
surface=response_surface,
|
|
)
|
|
return ChatResponse(
|
|
surface=response_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,
|
|
safety_verdict=safety_verdict,
|
|
ethics_verdict=ethics_verdict,
|
|
verdicts=verdicts_bundle,
|
|
grounding_source=grounding_source,
|
|
)
|
|
|
|
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)
|
|
# INV-24 recall role: RECOGNITION. Feeds UnknownDomainGate — asks
|
|
# "have we seen anything like this before?", not "what is admissible
|
|
# evidence?". Session-tier SPECULATIVE memory must count here, so
|
|
# no min_status filter is applied.
|
|
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)
|
|
# ADR-0048 — pack-grounded fallback for cold-start DEFINITION /
|
|
# RECALL on a known pack lemma. Only engages when the gate
|
|
# fired because the session vault is empty (``empty_vault``)
|
|
# AND the classified intent is DEFINITION or RECALL AND the
|
|
# intent's subject lemma is in the ratified cognition pack.
|
|
# Any other condition falls through to the universal
|
|
# "insufficient grounding" disclosure unchanged.
|
|
pack_result = self._maybe_pack_grounded_surface(
|
|
text, gate_decision.source
|
|
)
|
|
if pack_result is None:
|
|
pack_surface = None
|
|
pack_source_tag = "none"
|
|
else:
|
|
pack_surface, pack_source_tag = pack_result
|
|
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,
|
|
"grounding_source": pack_source_tag if pack_surface else "none",
|
|
},
|
|
)
|
|
# ADR-0055 Phase B — thread classified intent forward only
|
|
# when a sink is attached. Discovery emission is opt-in;
|
|
# the deterministic classification used here is the same
|
|
# call ``_maybe_pack_grounded_surface`` already ran for the
|
|
# empty-vault English path, so behaviour is identical when
|
|
# no sink is attached.
|
|
discovery_intent_tag = None
|
|
discovery_intent_subject: str | None = None
|
|
# Classify intent up-front whenever the gate fired on an
|
|
# empty vault. P2.3 needs it for OOV sink emission,
|
|
# ADR-0055 Phase B needs it for discovery sink emission,
|
|
# and P3.1 needs it for session-thread context — the
|
|
# classifier is cheap and deterministic, so always run
|
|
# it on the cold-start English path. Sinks themselves
|
|
# remain opt-in (no-op without ``attach_*_sink``).
|
|
if (
|
|
gate_decision.source == "empty_vault"
|
|
and self.config.output_language == "en"
|
|
):
|
|
from generate.intent_bridge import classify_intent_from_input
|
|
_intent = classify_intent_from_input(text)
|
|
discovery_intent_tag = _intent.tag
|
|
discovery_intent_subject = _intent.subject
|
|
return self._stub_response(
|
|
committed,
|
|
tokens=tuple(filtered),
|
|
pack_grounded_surface=pack_surface,
|
|
grounded_source_tag=pack_source_tag,
|
|
discovery_intent_tag=discovery_intent_tag,
|
|
discovery_intent_subject=discovery_intent_subject,
|
|
)
|
|
|
|
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)
|
|
|
|
# ADR-0046 / ADR-0047 — Forward graph constraint.
|
|
# Build the PropositionGraph from the classified intent + articulation
|
|
# plan and convert it into an AdmissibilityRegion BEFORE generate()
|
|
# runs. An empty / fully OOV graph yields an unconstrained region
|
|
# (allowed_indices=None), which behaves identically to region=None
|
|
# via generate()'s is_unconstrained() check — so the change is a
|
|
# true no-op on inputs that produce no graph and a forward
|
|
# constraint on inputs that do. Only wired for the English path
|
|
# because the graph builder is English-specific (see intent_bridge).
|
|
forward_region = None
|
|
if self.config.forward_graph_constraint and self.config.output_language == "en":
|
|
pre_gen_graph = build_graph_from_input(text, articulation)
|
|
forward_region = build_graph_constraint(pre_gen_graph, self._context.vocab)
|
|
|
|
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,
|
|
region=forward_region,
|
|
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)
|
|
# ADR-0035 — auto-invoke safety + ethics surfaces. Observational
|
|
# at v1; verdicts are attached to TurnEvent and ChatResponse for
|
|
# audit but do not gate behavior. Refusal/re-articulation
|
|
# wiring is a future ADR.
|
|
is_grounded = walk_surface != _UNKNOWN_DOMAIN_SURFACE
|
|
hedge_emitted = _surface_contains_hedge(walk_surface, self.identity_manifold)
|
|
safety_ctx = SafetyContext(
|
|
field_state=_FieldStateWithVersor(
|
|
versor_condition=float(versor_condition(result.final_state.F)),
|
|
),
|
|
last_refusal_was_typed=self._last_refusal_was_typed,
|
|
identity_manifold_hash_before=self._identity_manifold_hash,
|
|
identity_manifold_hash_after=_hash_identity_manifold(self.identity_manifold),
|
|
)
|
|
safety_verdict = self.safety_check.check(safety_ctx, self.safety_pack)
|
|
ethics_ctx = EthicsContext(
|
|
alignment_score=float(getattr(identity_score, "alignment", 0.0)),
|
|
hedge_threshold_soft=float(
|
|
self.identity_manifold.surface_preferences.hedge_threshold_soft
|
|
),
|
|
hedge_emitted=hedge_emitted,
|
|
grounded_in_evidence=is_grounded,
|
|
disclosure_emitted=not is_grounded,
|
|
)
|
|
ethics_verdict = self.ethics_check.check(ethics_ctx, self.ethics_pack)
|
|
# ADR-0036 — safety-only typed refusal. A runtime-checkable
|
|
# SafetyVerdict violation replaces the user-facing ``surface``
|
|
# with a deterministic typed refusal string. ``walk_surface``
|
|
# and ``articulation_surface`` retain the original token-walk /
|
|
# realizer evidence for audit (per the runtime surface
|
|
# contract in CLAUDE.md). Ethics violations remain audit-only.
|
|
refusal_surface = build_refusal_surface(
|
|
safety_verdict, ethics_verdict, self.ethics_pack,
|
|
)
|
|
refusal_emitted = refusal_surface is not None
|
|
hedge_injected = False
|
|
if refusal_emitted:
|
|
response_surface = refusal_surface
|
|
self._last_refusal_was_typed = True
|
|
else:
|
|
response_surface = walk_surface
|
|
# ADR-0038 — hedge injection. When an ethics commitment in
|
|
# ``ethics_pack.hedge_commitments`` fires runtime-checkable
|
|
# and the manifold has a hedge phrase configured, prepend
|
|
# the hedge to the user-facing surface. Mutually exclusive
|
|
# with refusal at the pack-schema level; this branch only
|
|
# runs when refusal did not fire. ``walk_surface`` and
|
|
# ``articulation_surface`` are preserved unchanged for
|
|
# audit (same discipline as ADR-0036).
|
|
if should_inject_hedge(ethics_verdict, self.ethics_pack):
|
|
hedge_prefix = build_hedge_prefix(self.identity_manifold)
|
|
before = response_surface
|
|
response_surface = inject_hedge(response_surface, hedge_prefix)
|
|
hedge_injected = response_surface != before
|
|
# ADR-0039 — unified TurnVerdicts bundle attached to both
|
|
# ChatResponse and TurnEvent. Audit consumers read the bundle
|
|
# instead of correlating individual fields.
|
|
verdicts_bundle = TurnVerdicts(
|
|
identity_score=identity_score,
|
|
safety_verdict=safety_verdict,
|
|
ethics_verdict=ethics_verdict,
|
|
refusal_emitted=refusal_emitted,
|
|
hedge_injected=hedge_injected,
|
|
)
|
|
turn_event = TurnEvent(
|
|
turn=self._context.turn - 1,
|
|
input_tokens=tuple(filtered),
|
|
surface=response_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,
|
|
safety_verdict=safety_verdict,
|
|
ethics_verdict=ethics_verdict,
|
|
verdicts=verdicts_bundle,
|
|
grounding_source="vault",
|
|
)
|
|
self.turn_log.append(turn_event)
|
|
self._emit_turn_event(turn_event)
|
|
# P3.1 — push session-thread summary for the walk path.
|
|
# Subject is taken from the articulation (deterministic;
|
|
# matches what the surface foregrounded).
|
|
self._push_thread_summary(
|
|
turn_event=turn_event,
|
|
intent_tag=None,
|
|
intent_subject=articulation.subject,
|
|
grounding_source="vault",
|
|
surface=response_surface,
|
|
)
|
|
return ChatResponse(
|
|
surface=response_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,
|
|
safety_verdict=safety_verdict,
|
|
ethics_verdict=ethics_verdict,
|
|
verdicts=verdicts_bundle,
|
|
grounding_source="vault",
|
|
)
|
|
|
|
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)
|
|
# ADR-0059 — emit a correction event before the regen turn so
|
|
# audit consumers can pair the backward perturbation with the
|
|
# forward turn event it produces. No-op when no sink attached.
|
|
self._emit_correction_event(correction_result, target_turn=target_turn)
|
|
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 _emit_correction_event(
|
|
self, correction_result, *, target_turn: int,
|
|
) -> None:
|
|
"""ADR-0059 — emit one JSONL correction event to the telemetry sink.
|
|
|
|
Mirrors ``_emit_turn_event``: no-op when no sink is attached;
|
|
sink errors are intentionally NOT swallowed so a misconfigured
|
|
durable sink surfaces loudly rather than silently dropping
|
|
audit evidence.
|
|
"""
|
|
sink = self._telemetry_sink
|
|
if sink is None:
|
|
return
|
|
line = format_correction_event_jsonl(
|
|
correction_result,
|
|
target_turn=target_turn,
|
|
identity_pack_id=self.identity_pack_id,
|
|
safety_pack_id=getattr(self.safety_pack, "pack_id", ""),
|
|
ethics_pack_id=self.ethics_pack_id,
|
|
)
|
|
sink.emit(line)
|
|
|
|
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``.
|