core/session/context.py

119 lines
4.6 KiB
Python

"""
SessionContext — binds field, vault, vocab, and persona for one session.
One session = one field trajectory on the manifold.
The vault accumulates versors across turns.
The persona motor is fixed per session (or composable across sessions).
Generation returns GenerationResult so the evolved field state is preserved.
The assistant vault entry stores the generated final_state, not the prompt
field that entered the turn.
"""
from __future__ import annotations
import numpy as np
from algebra.backend import versor_apply
from algebra.cga import outer_product
from field.state import FieldState
from generate.dialogue import DialogueTurn
from generate.proposition import Proposition
from generate.result import GenerationResult
from generate.stream import generate
from ingest.gate import inject
from persona.motor import PersonaMotor
from vault.store import VaultStore
class SessionContext:
def __init__(self, vocab, persona=None, vault=None, vault_reproject_interval: int = 100):
self.vocab = vocab
self.persona = persona or PersonaMotor.identity()
self.vault = vault or VaultStore(reproject_interval=vault_reproject_interval)
self.state: FieldState | None = None
self.turn: int = 0
self.dialogue_history: list[DialogueTurn] = []
self.running_dialogue_blade: np.ndarray | None = None
def ingest(self, tokens: list) -> FieldState:
"""Inject a prompt into the running field. Stores the user field in vault."""
injected = inject(tokens, self.vocab)
node_idx = self.vocab.index_of(tokens[0])
if self.state is None:
self.state = FieldState(
F=injected.F,
node=node_idx,
step=injected.step,
holonomy=injected.holonomy,
energy=injected.energy,
valence=injected.valence,
)
else:
self.state = FieldState(
F=versor_apply(injected.F, self.state.F),
node=node_idx,
step=self.state.step + 1,
holonomy=injected.holonomy,
energy=injected.energy,
valence=injected.valence,
)
self.vault.store(self.state.F, {"turn": self.turn, "role": "user"})
return self.state
def record_dialogue(self, proposition: Proposition) -> DialogueTurn:
"""
Store a proposition as geometric dialogue state.
The transcript surface is deliberately not used as session memory here;
the retained object is the proposition paired with its relation blade.
"""
blade = proposition.relation
turn = DialogueTurn(proposition=proposition, outer_product_blade=blade)
self.dialogue_history.append(turn)
if self.running_dialogue_blade is None:
self.running_dialogue_blade = blade.copy()
else:
self.running_dialogue_blade = outer_product(self.running_dialogue_blade, blade)
return turn
@property
def last_dialogue_blade(self) -> np.ndarray | None:
if not self.dialogue_history:
return None
return self.dialogue_history[-1].outer_product_blade.copy()
def respond(self, max_tokens: int = 128) -> GenerationResult:
"""
Generate a response from current state and preserve the evolved field.
Returns:
GenerationResult carrying emitted tokens and final_state.
"""
assert self.state is not None, "Call ingest() before respond()."
result = generate(self.state, self.vocab, self.persona, max_tokens, vault=self.vault)
self.state = result.final_state
self.vault.store(result.final_state.F, {"turn": self.turn, "role": "assistant"})
self.turn += 1
return result
async def arespond(self, max_tokens: int = 128):
"""
Async token-yielding response path.
The generation pass still returns a GenerationResult internally so
SessionContext can store the evolved assistant final_state after
yielding the surface tokens.
"""
assert self.state is not None, "Call ingest() before arespond()."
result = generate(self.state, self.vocab, self.persona, max_tokens, vault=self.vault)
for token in result.tokens:
yield token
self.state = result.final_state
self.vault.store(result.final_state.F, {"turn": self.turn, "role": "assistant"})
self.turn += 1
def recall(self, query_tokens: list, top_k: int = 5) -> list:
"""Recall relevant past versors for a query."""
query_state = inject(query_tokens, self.vocab)
return self.vault.recall(query_state.F, top_k=top_k)