c9a644e496
Adds referent tracking, session graph traversal, unknown-domain gating, correction propagation, compositional surface assembly, and regression coverage. Follow-up fixes included before merge: - split probe/commit/finalize turn flow so unknown-domain checks run before current-query vault writes - record real input tokens and input versors for sync and async session paths - return true graph distances from backward walks and consume them in correction decay - synchronize corrected graph outputs into vault-backed recall and live referent state - regenerate correction responses from corrected context rather than correction text - keep coreference pronouns lowercase in question bodies - centralize elaboration-string construction to avoid plan/surface drift - add targeted dialogue fluency regression tests
266 lines
11 KiB
Python
266 lines
11 KiB
Python
"""
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SessionContext — binds field, vault, vocab, persona, referents, and graph.
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The ingest path is split into a non-mutating probe and a committing ingest so
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runtime gates can inspect the candidate field before durable vault writes. All
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response paths finalize through one graph/vault/session-state method.
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"""
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from __future__ import annotations
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import numpy as np
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from algebra.backend import cga_inner, versor_apply
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from algebra.cga import outer_product
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from field.state import FieldState
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from generate.dialogue import DialogueTurn
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from generate.proposition import Proposition
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from generate.result import GenerationResult
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from generate.stream import generate
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from ingest.gate import inject
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from persona.motor import PersonaMotor
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from session.graph import SessionGraph
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from session.referents import ReferentRegistry
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from vault.store import VaultStore
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class SessionContext:
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def __init__(self, vocab, persona=None, vault=None, vault_reproject_interval: int = 100):
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self.vocab = vocab
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self.persona = persona or PersonaMotor.identity()
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self.vault = vault or VaultStore(reproject_interval=vault_reproject_interval)
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self.state: FieldState | None = None
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self.turn: int = 0
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self.graph: SessionGraph = SessionGraph()
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self.referents: ReferentRegistry = ReferentRegistry()
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self.running_dialogue_blade: np.ndarray | None = None
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self._last_response_tokens: tuple[str, ...] | None = None
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self._anchor_field: np.ndarray | None = None
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self._dialogue_history_compat: list[DialogueTurn] = []
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self._last_input_tokens: tuple[str, ...] = ()
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self._last_resolved_input_tokens: tuple[str, ...] = ()
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self._last_input_versor: np.ndarray | None = None
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@property
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def dialogue_history(self) -> list[DialogueTurn]:
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return self._dialogue_history_compat
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@property
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def last_input_tokens(self) -> tuple[str, ...]:
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return self._last_input_tokens
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@property
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def last_resolved_input_tokens(self) -> tuple[str, ...]:
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return self._last_resolved_input_tokens
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def _field_from_tokens(self, tokens: list[str], *, resolve_referents: bool) -> tuple[FieldState, list[str]]:
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resolved_tokens = self.referents.resolve(tokens) if resolve_referents else list(tokens)
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injected = inject(resolved_tokens, self.vocab)
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anchor_token = resolved_tokens[0] if resolved_tokens else (tokens[0] if tokens else "")
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try:
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node_idx = self.vocab.index_of(anchor_token)
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except (KeyError, IndexError):
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node_idx = self.vocab.index_of(tokens[0]) if tokens else 0
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if self.state is None:
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candidate = FieldState(
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F=injected.F,
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node=node_idx,
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step=injected.step,
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holonomy=injected.holonomy,
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energy=injected.energy,
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valence=injected.valence,
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)
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else:
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candidate = FieldState(
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F=versor_apply(injected.F, self.state.F),
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node=node_idx,
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step=self.state.step + 1,
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holonomy=injected.holonomy,
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energy=injected.energy,
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valence=injected.valence,
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)
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return candidate, resolved_tokens
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def probe_ingest(self, tokens: list[str]) -> FieldState:
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"""Build the candidate ingest field without mutating state or vault."""
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snapshot_sources = self.referents.consumed_turns()
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snapshot_slots = self.referents.consumed_slots()
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candidate, _ = self._field_from_tokens(tokens, resolve_referents=True)
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# Restore consumed metadata because probe must not define graph edges.
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self.referents._last_resolved_sources = snapshot_sources # internal rollback by design
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self.referents._last_resolved_slots = snapshot_slots
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return candidate
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def commit_ingest(self, tokens: list[str]) -> FieldState:
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"""Resolve, inject, mutate live state, and store the user field."""
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field_state, resolved_tokens = self._field_from_tokens(tokens, resolve_referents=True)
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self.state = field_state
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if self._anchor_field is None:
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self._anchor_field = field_state.F.copy()
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self._last_input_tokens = tuple(tokens)
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self._last_resolved_input_tokens = tuple(resolved_tokens)
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self._last_input_versor = field_state.F.copy()
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self.vault.store(field_state.F, {"turn": self.turn, "role": "user"})
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return field_state
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def ingest(self, tokens: list[str]) -> FieldState:
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"""Backward-compatible committing ingest."""
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return self.commit_ingest(tokens)
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def record_dialogue(self, proposition: Proposition) -> DialogueTurn:
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from generate.dialogue import DialogueTurn as _DT
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blade = proposition.relation
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turn = _DT(proposition=proposition, outer_product_blade=blade)
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self._dialogue_history_compat.append(turn)
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if self.running_dialogue_blade is None:
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self.running_dialogue_blade = blade.copy()
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else:
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self.running_dialogue_blade = outer_product(self.running_dialogue_blade, blade)
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return turn
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@property
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def last_dialogue_blade(self) -> np.ndarray | None:
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if not self._dialogue_history_compat:
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return None
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return self._dialogue_history_compat[-1].outer_product_blade.copy()
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def _register_result_referent(self, result: GenerationResult) -> None:
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if not result.tokens:
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return
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versors: dict[str, np.ndarray] = {}
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for tok in result.tokens:
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try:
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versors[tok] = self.vocab.get_versor(tok)
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except KeyError:
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pass
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self.referents.register_from_tokens(result.tokens, versors, turn=self.turn)
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def finalize_turn(
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self,
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result: GenerationResult,
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*,
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tokens_in: tuple[str, ...] | None = None,
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dialogue_role: str = "assert",
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input_versor: np.ndarray | None = None,
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metadata: dict | None = None,
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) -> None:
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"""Finalize assistant output into referents, graph, vault, and state."""
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if self.state is None and input_versor is None:
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raise AssertionError("Call ingest() before finalize_turn().")
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input_F = (
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np.asarray(input_versor, dtype=np.float32).copy()
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if input_versor is not None
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else (self._last_input_versor.copy() if self._last_input_versor is not None else self.state.F.copy())
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)
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turn_tokens = tuple(tokens_in if tokens_in is not None else self._last_input_tokens)
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backward_edges = self.referents.consumed_turns()
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active_slots = self.referents.active_slots()
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self._register_result_referent(result)
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# Include any newly registered output referent in the turn metadata.
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active_slots = self.referents.active_slots() | active_slots
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self.graph.add_turn(
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turn_idx=self.turn,
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input_versor=input_F,
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output_versor=result.final_state.F,
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tokens_in=turn_tokens,
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tokens_out=tuple(result.tokens or []),
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dialogue_role=dialogue_role,
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referent_slots=active_slots,
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backward_edges=backward_edges,
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)
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self.state = result.final_state
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payload = {"turn": self.turn, "role": "assistant"}
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if metadata:
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payload.update(metadata)
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self.vault.store(result.final_state.F, payload)
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self.turn += 1
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self._last_response_tokens = result.tokens
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def apply_corrected_outputs(self, records) -> None:
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"""Synchronize corrected graph records into live session recall surfaces."""
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for record in records:
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self.vault.store(
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record.new_versor,
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{"turn": record.turn_idx, "role": "assistant", "corrected": True},
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)
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self.referents.update_turn_versor(record.turn_idx, record.new_versor)
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if records:
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last = max(records, key=lambda r: r.turn_idx)
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if self.state is not None:
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self.state = FieldState(
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F=last.new_versor,
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node=self.state.node,
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step=self.state.step,
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holonomy=self.state.holonomy,
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energy=self.state.energy,
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valence=self.state.valence,
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)
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def respond(self, max_tokens: int = 128) -> GenerationResult:
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assert self.state is not None, "Call ingest() before respond()."
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input_versor = self._last_input_versor.copy() if self._last_input_versor is not None else self.state.F.copy()
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result = generate(self.state, self.vocab, self.persona, max_tokens, vault=self.vault)
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if self._last_response_tokens is not None and result.tokens == self._last_response_tokens and result.tokens:
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try:
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pivot_node = self.vocab.index_of(result.tokens[0])
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except KeyError:
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pivot_node = self.state.node
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if pivot_node != self.state.node:
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pivot = FieldState(
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F=self.state.F,
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node=pivot_node,
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step=self.state.step,
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holonomy=self.state.holonomy,
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energy=self.state.energy,
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valence=self.state.valence,
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)
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result = generate(pivot, self.vocab, self.persona, max_tokens, vault=self.vault)
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result = self._orient_result_to_anchor(result)
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self.finalize_turn(result, input_versor=input_versor, dialogue_role="assert")
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return result
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def _orient_result_to_anchor(self, result: GenerationResult) -> GenerationResult:
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final_state = result.final_state
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coherence_anchor = self._anchor_field if self._anchor_field is not None else (self.state.F if self.state is not None else None)
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if coherence_anchor is None:
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return result
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cga_score = cga_inner(final_state.F, coherence_anchor)
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euclidean_score = float(np.dot(final_state.F, coherence_anchor))
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if cga_score < 0.0 or euclidean_score < 0.0:
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final_state = FieldState(
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F=-final_state.F,
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node=final_state.node,
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step=final_state.step,
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holonomy=final_state.holonomy,
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energy=final_state.energy,
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valence=final_state.valence,
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)
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return GenerationResult(
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tokens=result.tokens,
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final_state=final_state,
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trajectory=result.trajectory,
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salience_top_k=result.salience_top_k,
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candidates_used=result.candidates_used,
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vault_hits=result.vault_hits,
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identity_score=result.identity_score,
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)
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return result
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async def arespond(self, max_tokens: int = 128):
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assert self.state is not None, "Call ingest() before arespond()."
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input_versor = self._last_input_versor.copy() if self._last_input_versor is not None else self.state.F.copy()
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result = self._orient_result_to_anchor(
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generate(self.state, self.vocab, self.persona, max_tokens, vault=self.vault)
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)
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for token in result.tokens:
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yield token
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self.finalize_turn(result, input_versor=input_versor, dialogue_role="assert")
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def recall(self, query_tokens: list, top_k: int = 5) -> list:
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query_state = inject(query_tokens, self.vocab)
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return self.vault.recall(query_state.F, top_k=top_k)
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