Phase 9. Goals form a mind-scoped tree with kind, priority, progress and blockers; their deterministic relevance to a candidate feeds the goal_relevance attention feature and a goal monitor raises stalled goals and observations that touch one. Drives are a closed set of eight scalars with operator-configured ranges and personality-derived baselines; there is no self-preservation, replication or acquisition drive and no way to add one at runtime. Affect is ten scalars moved by an explicit rule table over events and decaying towards baseline; both modulate attention weights each cycle and the effective weights are recorded with every cycle and shown beside the configured ones. Predictions carry a window and machine-checkable criteria (event terms occurring or staying absent, a belief reaching a confidence, or operator judgement). A resolver job judges them deterministically, keeps an append-only outcome history for calibration (accuracy, Brier score, confidence buckets, overconfidence flag), emits prediction_error events scaled by how confident the miss was, and feeds the outcome back into the linked belief as evidence. Causal hypotheses derive predictions automatically. Curiosity turns misses, contradictions and unexplained observations into questions that compete for attention and become investigation goals when admitted. Brain gains after-cycle hooks and goal/motivation state. API, UI (goals, prediction dashboard, cognitive state panel, effective weights) and CLI (goal, prediction, state). Demo scenario 4 verified end to end.
717 lines
24 KiB
Go
717 lines
24 KiB
Go
package brain
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import (
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"context"
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"encoding/json"
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"errors"
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"fmt"
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"log/slog"
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"strings"
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"time"
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"github.com/jackc/pgx/v5"
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"github.com/jackc/pgx/v5/pgxpool"
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"github.com/andydixon/noema/internal/attention"
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evstore "github.com/andydixon/noema/internal/events"
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"github.com/andydixon/noema/internal/minds"
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"github.com/andydixon/noema/internal/motivation"
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"github.com/andydixon/noema/internal/observability"
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"github.com/andydixon/noema/internal/scheduler"
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"github.com/andydixon/noema/internal/storage"
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"github.com/andydixon/noema/internal/workspace"
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"github.com/andydixon/noema/pkg/events"
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"github.com/andydixon/noema/pkg/ids"
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)
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// cursorConsumer names the brain's durable read position.
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const cursorConsumer = "brain"
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// settle is how old an event must be before the brain reads it, so that a
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// concurrently committing transaction with a lower sequence number is not
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// skipped forever (see ADR-0002 and PLAN risk R3).
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const settle = 250 * time.Millisecond
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// processTimeout bounds one process's Observe call.
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const processTimeout = 15 * time.Second
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// Cycle is the working context of one cognitive cycle, handed to handlers.
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type Cycle struct {
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Runtime *Runtime
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State *State
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Tx pgx.Tx
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pending []events.New
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emitted []events.Event
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}
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// Emit queues an event for the cycle's transaction. Correlation is the cycle
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// id unless the caller set one.
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func (c *Cycle) Emit(n events.New) {
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n.MindID = c.State.Mind.ID
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if n.CorrelationID.IsZero() {
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n.CorrelationID = c.State.CycleID
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}
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if n.OccurredAt.IsZero() {
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n.OccurredAt = c.State.Now
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}
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if n.Metadata == nil {
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n.Metadata = map[string]any{}
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}
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n.Metadata["cycle"] = c.State.Cycle
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c.pending = append(c.pending, n)
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}
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// EmitNow appends one event immediately inside the cycle transaction and
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// returns it, for handlers that need the event id for provenance. It is still
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// published only after commit.
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func (c *Cycle) EmitNow(ctx context.Context, n events.New) (events.Event, error) {
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if err := c.flush(ctx); err != nil {
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return events.Event{}, err
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}
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n.MindID = c.State.Mind.ID
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if n.CorrelationID.IsZero() {
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n.CorrelationID = c.State.CycleID
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}
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if n.OccurredAt.IsZero() {
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n.OccurredAt = c.State.Now
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}
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if n.Metadata == nil {
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n.Metadata = map[string]any{}
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}
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n.Metadata["cycle"] = c.State.Cycle
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evs, err := c.Runtime.events.AppendIn(ctx, c.Tx, n)
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if err != nil {
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return events.Event{}, err
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}
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c.emitted = append(c.emitted, evs...)
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return evs[0], nil
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}
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// flush writes queued events inside the transaction and returns them.
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func (c *Cycle) flush(ctx context.Context) error {
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if len(c.pending) == 0 {
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return nil
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}
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evs, err := c.Runtime.events.AppendIn(ctx, c.Tx, c.pending...)
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if err != nil {
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return err
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}
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c.emitted = append(c.emitted, evs...)
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c.pending = nil
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return nil
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}
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// Runtime is the cognitive loop of one mind.
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type Runtime struct {
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mind minds.Mind
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db *pgxpool.Pool
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events *evstore.Store
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minds *minds.Store
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registry *Registry
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pool *scheduler.Pool
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log *slog.Logger
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metrics *observability.Metrics
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services Services
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wake chan struct{}
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commands chan command
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snaps chan chan workspace.State
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state workspace.State
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loops *loopDetector
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budget *cycleBudget
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now func() time.Time
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}
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type command struct {
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kind string // cycle | pause | resume | reset
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args map[string]bool
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reply chan error
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}
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// Config groups runtime dependencies.
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type Config struct {
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DB *pgxpool.Pool
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Events *evstore.Store
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Minds *minds.Store
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Registry *Registry
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Pool *scheduler.Pool
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Log *slog.Logger
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Metrics *observability.Metrics
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Services Services
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}
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// NewRuntime creates a runtime for a mind and loads its persisted state.
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func NewRuntime(ctx context.Context, cfg Config, m minds.Mind) (*Runtime, error) {
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r := &Runtime{mind: m, db: cfg.DB, events: cfg.Events, minds: cfg.Minds, registry: cfg.Registry, pool: cfg.Pool,
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log: cfg.Log.With("mind", m.Slug), metrics: cfg.Metrics, services: cfg.Services,
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wake: make(chan struct{}, 1), commands: make(chan command), snaps: make(chan chan workspace.State), now: time.Now}
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r.loops = newLoopDetector(m.Config.LoopDetectWindow, m.Config.LoopDetectThreshold)
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r.budget = newCycleBudget(m.Config.MaxCyclesPerMinute)
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if err := r.loadState(ctx); err != nil {
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return nil, err
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}
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return r, nil
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}
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// persisted wraps the workspace state with runtime bookkeeping.
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type persisted struct {
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workspace.State
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LoopRing []string `json:"loop_ring,omitempty"`
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}
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func (r *Runtime) loadState(ctx context.Context) error {
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var raw []byte
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err := r.db.QueryRow(ctx, `SELECT state FROM workspace_states WHERE mind_id=$1`, r.mind.ID).Scan(&raw)
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if errors.Is(err, pgx.ErrNoRows) {
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r.state = workspace.State{Mode: string(ModeAwake)}
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return nil
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}
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if err != nil {
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return err
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}
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var p persisted
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if err := json.Unmarshal(raw, &p); err != nil {
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return fmt.Errorf("corrupt workspace state: %w", err)
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}
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r.state = p.State
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r.loops.restore(p.LoopRing)
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if r.state.Mode == "" {
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r.state.Mode = string(ModeAwake)
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}
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return nil
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}
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func (r *Runtime) saveState(ctx context.Context, q storage.Querier) error {
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raw, _ := json.Marshal(persisted{State: r.state, LoopRing: r.loops.snapshot()})
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_, err := q.Exec(ctx, `INSERT INTO workspace_states (mind_id, state) VALUES ($1,$2) ON CONFLICT (mind_id) DO UPDATE SET state=excluded.state, updated_at=now()`, r.mind.ID, raw)
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return err
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}
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// Wake asks the runtime to run a cycle soon (debounced by CycleMinInterval).
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func (r *Runtime) Wake() {
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select {
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case r.wake <- struct{}{}:
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default:
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}
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}
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// Command sends an operator command and waits for the result.
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func (r *Runtime) Command(ctx context.Context, kind string, args map[string]bool) error {
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reply := make(chan error, 1)
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select {
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case r.commands <- command{kind: kind, args: args, reply: reply}:
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case <-ctx.Done():
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return ctx.Err()
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}
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select {
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case err := <-reply:
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return err
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case <-ctx.Done():
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return ctx.Err()
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}
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}
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// Snapshot returns a copy of the current workspace state, taken on the loop
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// goroutine so no lock is needed.
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func (r *Runtime) Snapshot(ctx context.Context) (workspace.State, error) {
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reply := make(chan workspace.State, 1)
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select {
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case r.snaps <- reply:
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case <-ctx.Done():
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return workspace.State{}, ctx.Err()
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}
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select {
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case st := <-reply:
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return st, nil
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case <-ctx.Done():
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return workspace.State{}, ctx.Err()
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}
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}
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// Run is the loop. It returns when ctx ends.
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func (r *Runtime) Run(ctx context.Context) error {
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r.log.Info("mind runtime started", "mode", r.state.Mode, "cycle", r.state.Cycle)
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idle := time.NewTimer(r.mind.Config.IdleCycleInterval.D())
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defer idle.Stop()
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var debounce *time.Timer
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var debounceC <-chan time.Time
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for {
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select {
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case <-ctx.Done():
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return nil
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case <-r.wake:
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if debounce == nil {
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debounce = time.NewTimer(r.mind.Config.CycleMinInterval.D() + settle)
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debounceC = debounce.C
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}
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case <-debounceC:
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debounce, debounceC = nil, nil
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r.runCycle(ctx, "stimulus")
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idle.Reset(r.mind.Config.IdleCycleInterval.D())
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case <-idle.C:
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r.runCycle(ctx, "idle")
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idle.Reset(r.mind.Config.IdleCycleInterval.D())
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case cmd := <-r.commands:
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cmd.reply <- r.handleCommand(ctx, cmd)
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case reply := <-r.snaps:
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reply <- r.copyState()
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}
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}
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}
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func (r *Runtime) handleCommand(ctx context.Context, cmd command) error {
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switch cmd.kind {
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case "cycle":
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if Mode(r.state.Mode) == ModePaused {
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return errors.New("mind is paused")
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}
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r.runCycle(ctx, "operator")
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return nil
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case "pause":
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return r.setMode(ctx, ModePaused, "operator paused cognition")
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case "resume":
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if Mode(r.state.Mode) != ModePaused {
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return nil
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}
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return r.setMode(ctx, ModeAwake, "operator resumed cognition")
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case "reset":
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return r.reset(ctx, cmd.args)
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case "reload":
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m, err := r.minds.Get(ctx, r.mind.ID)
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if err != nil {
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return err
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}
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r.mind = m
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r.loops = newLoopDetector(m.Config.LoopDetectWindow, m.Config.LoopDetectThreshold)
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r.budget = newCycleBudget(m.Config.MaxCyclesPerMinute)
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return nil
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}
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return fmt.Errorf("unknown command %q", cmd.kind)
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}
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func (r *Runtime) setMode(ctx context.Context, to Mode, why string) error {
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from := r.state.Mode
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if from == string(to) {
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return nil
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}
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r.state.Mode = string(to)
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return storage.Tx(ctx, r.db, func(tx pgx.Tx) error {
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if _, err := r.events.AppendIn(ctx, tx, events.New{MindID: r.mind.ID, Type: events.ModeChanged, Source: "brain", Summary: fmt.Sprintf("mode %s → %s: %s", from, to, why),
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Salience: 0.3, Confidence: 1, Metadata: map[string]any{"from": from, "to": to}, Payload: map[string]any{"from": from, "to": to, "reason": why}}); err != nil {
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return err
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}
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return r.saveState(ctx, tx)
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})
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}
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// reset clears transient state on operator request (spec §3). It never touches
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// events, memories or beliefs.
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func (r *Runtime) reset(ctx context.Context, what map[string]bool) error {
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var parts []string
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if what["workspace"] {
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r.state.Workspace = nil
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parts = append(parts, "workspace")
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}
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if what["working_memory"] {
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r.state.WorkingMemory = nil
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parts = append(parts, "working memory")
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}
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if what["loops"] {
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r.loops = newLoopDetector(r.mind.Config.LoopDetectWindow, r.mind.Config.LoopDetectThreshold)
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parts = append(parts, "loop detector")
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}
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if len(parts) == 0 {
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return nil
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}
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return storage.Tx(ctx, r.db, func(tx pgx.Tx) error {
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if _, err := r.events.AppendIn(ctx, tx, events.New{MindID: r.mind.ID, Type: events.OperatorAction, Source: "operator", Summary: "Operator reset transient state: " + strings.Join(parts, ", "),
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Salience: 0.5, Confidence: 1, Metadata: map[string]any{"action": "reset"}, Payload: map[string]any{"reset": parts}}); err != nil {
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return err
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}
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return r.saveState(ctx, tx)
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})
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}
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// runCycle executes one cycle. Errors are logged, never fatal: a broken
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// process or malformed event must not stop the mind (spec §46).
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func (r *Runtime) runCycle(ctx context.Context, trigger string) {
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if Mode(r.state.Mode) == ModePaused {
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return
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}
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if !r.budget.allow(r.now()) {
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r.log.Warn("cycle rate limit reached; skipping", "trigger", trigger)
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return
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}
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start := r.now()
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cctx, cancel := context.WithTimeout(ctx, 60*time.Second)
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defer cancel()
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var emitted []events.Event
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before := r.copyState()
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err := storage.Tx(cctx, r.db, func(tx pgx.Tx) error {
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var err error
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emitted, err = r.cycle(cctx, tx, trigger, start)
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return err
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})
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if err != nil {
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r.state = before // the transaction rolled back; forget in-memory changes too
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r.log.Error("cycle failed", "trigger", trigger, "err", err)
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return
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}
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r.events.Publish(emitted...) // only after commit, so subscribers never see phantom events
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}
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func (r *Runtime) cycle(ctx context.Context, tx pgx.Tx, trigger string, start time.Time) ([]events.Event, error) {
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cursor, err := r.events.Cursor(ctx, tx, r.mind.ID, cursorConsumer)
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if err != nil {
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return nil, err
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}
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// Read settled events after the cursor. The query is done on the pool (not
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// the tx) so a long transaction does not hold a snapshot; ordering by seq.
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newEvents, err := r.events.Query(ctx, evstore.Filter{MindID: r.mind.ID, AfterSeq: cursor, Ascending: true, Limit: 200})
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if err != nil {
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return nil, err
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}
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// Recorded-at settle: Query filters by occurred_at, so also drop events
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// recorded too recently (occurred_at may be backdated by integrations).
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filtered := newEvents[:0]
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for _, ev := range newEvents {
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if start.Sub(ev.RecordedAt) >= settle {
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filtered = append(filtered, ev)
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}
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}
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newEvents = filtered
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stimulus := false
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for _, ev := range newEvents {
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if !strings.HasPrefix(ev.Source, "brain") {
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stimulus = true
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break
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}
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}
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if trigger == "stimulus" && !stimulus {
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// Woken by our own output or nothing has settled yet: consume and wait.
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if len(newEvents) > 0 {
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if err := r.events.SetCursor(ctx, tx, r.mind.ID, cursorConsumer, newEvents[len(newEvents)-1].Seq); err != nil {
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return nil, err
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}
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}
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return nil, nil
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}
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if trigger == "idle" && !stimulus && len(r.state.Workspace) == 0 {
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// Nothing to think about: just move to idle mode.
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if Mode(r.state.Mode) != ModeIdle && r.state.LastStimulus.Add(5*time.Minute).Before(start) {
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r.state.Mode = string(ModeIdle)
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}
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return nil, r.saveState(ctx, tx)
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}
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cycleID := ids.New()
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if stimulus {
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r.state.LastStimulus = start
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if Mode(r.state.Mode) == ModeIdle {
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r.state.Mode = string(ModeAwake)
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}
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}
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sm, _ := r.minds.GetSelfModel(ctx, r.mind.ID)
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st := &State{Mind: r.mind, SelfModel: sm, Mode: Mode(r.state.Mode), Cycle: r.state.Cycle + 1, CycleID: cycleID, Now: start, NewEvents: newEvents, Workspace: r.state, Services: r.services}
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if r.services.Goals != nil {
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st.Goals, _ = r.services.Goals.Active(ctx, r.mind.ID)
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}
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st.Weights = r.mind.Config.AttentionWeights
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if r.services.Motivation != nil {
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if ms, err := r.services.Motivation.Load(ctx, tx, r.mind); err == nil {
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motivation.Decay(&ms, motivation.Baselines(r.mind.Personality), start.Sub(ms.Affect.UpdatedAt))
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st.Motiv = ms
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st.Weights = motivation.Modulate(r.mind.Config.AttentionWeights, ms)
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}
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}
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c := &Cycle{Runtime: r, State: st, Tx: tx}
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// 1. Ask every process, concurrently and bounded.
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cands := r.observe(ctx, st)
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if len(cands) == 0 && len(r.state.Workspace) == 0 {
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// Nothing proposed and nothing held: consume the events without
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// counting a cycle, so self-generated events cannot keep the loop busy.
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if len(newEvents) > 0 {
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if err := r.events.SetCursor(ctx, tx, r.mind.ID, cursorConsumer, newEvents[len(newEvents)-1].Seq); err != nil {
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return nil, err
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}
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}
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return nil, r.saveState(ctx, tx)
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}
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r.state.Cycle++
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// 2. Score deterministically with the cycle's effective weights; suppress loops.
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weights := st.Weights
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var scored []attention.Scored
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byID := map[ids.ID]*Candidate{}
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suppressed := map[ids.ID]string{}
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for i := range cands {
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cand := &cands[i]
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cand.ID = ids.New()
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byID[cand.ID] = cand
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b := attention.Score(cand.Features, weights)
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fp := fingerprint(*cand)
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if r.loops.looping(fp) {
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suppressed[cand.ID] = fp
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b.Total = 0
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if r.loops.shouldWarn(fp) {
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c.Emit(events.New{Type: events.Metacognition, Source: "brain.loop-detector", Summary: "suppressed recurring thought without progress: " + cand.Summary,
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Salience: 0.6, Confidence: 0.9, Metadata: map[string]any{"fingerprint": fp, "check": "loop"}, Payload: map[string]any{"fingerprint": fp, "kind": cand.Kind, "subject": cand.Subject}})
|
|
}
|
|
}
|
|
scored = append(scored, attention.Scored{ID: string(cand.ID), Breakdown: b})
|
|
}
|
|
attention.Rank(scored)
|
|
if r.metrics != nil {
|
|
r.metrics.Candidates.Add(float64(len(scored)))
|
|
}
|
|
|
|
// 3. Decay, then admit.
|
|
prevTop, _ := r.state.Top()
|
|
r.state.Decay(r.mind.Config.WorkingMemoryDecay)
|
|
var thoughts []workspace.Thought
|
|
for _, sc := range scored {
|
|
cand := byID[ids.ID(sc.ID)]
|
|
if _, isSuppressed := suppressed[cand.ID]; isSuppressed {
|
|
continue
|
|
}
|
|
thoughts = append(thoughts, workspace.Thought{ID: cand.ID, Process: cand.Process, Kind: cand.Kind, Subject: cand.Subject, Summary: cand.Summary, Content: cand.Content,
|
|
Score: sc.Breakdown.Total, Breakdown: sc.Breakdown, Evidence: cand.Evidence, Confidence: cand.Confidence})
|
|
}
|
|
outcomes, displaced := r.state.Admit(thoughts, r.mind.Config.WorkspaceCapacity, r.state.Cycle, start)
|
|
outcomeByID := map[ids.ID]workspace.Outcome{}
|
|
for _, o := range outcomes {
|
|
outcomeByID[o.ID] = o
|
|
}
|
|
|
|
// 4. Record every candidate with its arithmetic and fate.
|
|
admittedThoughts := map[ids.ID]workspace.Thought{}
|
|
for _, t := range r.state.Workspace {
|
|
if t.AdmittedCycle == r.state.Cycle {
|
|
admittedThoughts[t.ID] = t
|
|
}
|
|
}
|
|
for _, sc := range scored {
|
|
cand := byID[ids.ID(sc.ID)]
|
|
o, has := outcomeByID[cand.ID]
|
|
fate := "rejected"
|
|
reason := o.Reason
|
|
if fp, s := suppressed[cand.ID]; s {
|
|
fate, reason = "suppressed", "loop detector: "+fp
|
|
} else if has && o.Admitted {
|
|
fate = "admitted"
|
|
}
|
|
var causation ids.ID
|
|
if len(cand.SourceEvents) > 0 {
|
|
causation = cand.SourceEvents[0]
|
|
}
|
|
c.Emit(events.New{Type: events.ThoughtCandidate, Source: "brain." + cand.Process, Summary: fmt.Sprintf("[%s %.2f] %s", fate, sc.Breakdown.Total, cand.Summary),
|
|
Salience: sc.Breakdown.Total, Confidence: cand.Confidence, CausationID: causation, ParentIDs: cand.SourceEvents, Evidence: cand.Evidence,
|
|
Metadata: map[string]any{"candidate_id": string(cand.ID), "process": cand.Process, "kind": cand.Kind, "subject": cand.Subject, "fate": fate, "rank": o.Rank},
|
|
Payload: map[string]any{"candidate_id": cand.ID, "kind": cand.Kind, "subject": cand.Subject, "features": cand.Features, "breakdown": sc.Breakdown, "fate": fate, "reason": reason, "rank": o.Rank, "content": cand.Content}})
|
|
}
|
|
if err := c.flush(ctx); err != nil {
|
|
return nil, err
|
|
}
|
|
candidateEvent := map[ids.ID]ids.ID{}
|
|
for _, ev := range c.emitted {
|
|
if ev.Type == events.ThoughtCandidate {
|
|
if s, ok := ev.Metadata["candidate_id"].(string); ok {
|
|
candidateEvent[ids.ID(s)] = ev.ID
|
|
}
|
|
}
|
|
}
|
|
|
|
// 5. thought_selected for admissions, then broadcast.
|
|
for i := range r.state.Workspace {
|
|
t := &r.state.Workspace[i]
|
|
if t.AdmittedCycle != r.state.Cycle {
|
|
continue
|
|
}
|
|
cand := byID[t.ID]
|
|
var parents []ids.ID
|
|
if pe, ok := candidateEvent[t.ID]; ok {
|
|
parents = append(parents, pe)
|
|
}
|
|
if cand != nil {
|
|
parents = append(parents, cand.SourceEvents...)
|
|
}
|
|
c.Emit(events.New{Type: events.ThoughtSelected, Source: "brain.workspace", Summary: t.Summary, Salience: t.Score, Confidence: t.Confidence,
|
|
CausationID: firstOr(parents, ids.Nil), ParentIDs: parents, Evidence: t.Evidence,
|
|
Metadata: map[string]any{"candidate_id": string(t.ID), "process": t.Process, "kind": t.Kind, "subject": t.Subject, "rank": outcomeByID[t.ID].Rank},
|
|
Payload: map[string]any{"thought": t, "why": fmt.Sprintf("rank %d of %d with score %.2f; %s", outcomeByID[t.ID].Rank, len(scored), t.Score, outcomeByID[t.ID].Reason)}})
|
|
if r.metrics != nil {
|
|
r.metrics.Admissions.Inc()
|
|
}
|
|
}
|
|
if err := c.flush(ctx); err != nil {
|
|
return nil, err
|
|
}
|
|
for _, ev := range c.emitted {
|
|
if ev.Type == events.ThoughtSelected {
|
|
if s, ok := ev.Metadata["candidate_id"].(string); ok {
|
|
for i := range r.state.Workspace {
|
|
if string(r.state.Workspace[i].ID) == s {
|
|
r.state.Workspace[i].EventID = ev.ID
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
// 6. Executive: dispatch admitted thoughts to handlers (sequential, ordered by rank).
|
|
for _, t := range r.state.Workspace {
|
|
if t.AdmittedCycle != r.state.Cycle {
|
|
continue
|
|
}
|
|
h, ok := r.registry.handlers[t.Kind]
|
|
if !ok {
|
|
h = defaultHandler
|
|
}
|
|
if err := h(ctx, c, t); err != nil {
|
|
r.log.Warn("handler failed", "kind", t.Kind, "err", err)
|
|
c.Emit(events.New{Type: events.Metacognition, Source: "brain.executive", Summary: "handler for " + t.Kind + " failed: " + err.Error(), Salience: 0.4, Confidence: 1,
|
|
Metadata: map[string]any{"check": "handler_error", "kind": t.Kind}})
|
|
}
|
|
r.loops.record(fingerprint(Candidate{Process: t.Process, Kind: t.Kind, Subject: t.Subject}))
|
|
}
|
|
for _, h := range r.registry.hooks {
|
|
if err := h.AfterCycle(ctx, c, c.emitted); err != nil {
|
|
r.log.Warn("hook failed", "hook", h.Name(), "err", err)
|
|
}
|
|
}
|
|
pruned := r.state.Prune(0.05)
|
|
|
|
// 7. Broadcast the resulting workspace and note attention shifts.
|
|
if newTop, ok := r.state.Top(); ok && (prevTop.ID == ids.Nil || prevTop.Subject != newTop.Subject || prevTop.Kind != newTop.Kind) {
|
|
c.Emit(events.New{Type: events.AttentionShift, Source: "brain.workspace", Summary: "attention now on: " + newTop.Summary, Salience: newTop.Score, Confidence: newTop.Confidence,
|
|
CausationID: newTop.EventID, Metadata: map[string]any{"from": prevTop.Subject, "to": newTop.Subject}, Payload: map[string]any{"from": prevTop.Summary, "to": newTop.Summary}})
|
|
}
|
|
if len(scored) > 0 || len(displaced) > 0 || pruned > 0 {
|
|
c.Emit(events.New{Type: events.WorkspaceBroadcast, Source: "brain.workspace", Summary: fmt.Sprintf("workspace: %d thoughts, %d in working memory", len(r.state.Workspace), len(r.state.WorkingMemory)),
|
|
Salience: 0.2, Confidence: 1, Metadata: map[string]any{"thoughts": len(r.state.Workspace), "working_memory": len(r.state.WorkingMemory)},
|
|
Payload: map[string]any{"workspace": r.state.Workspace, "working_memory": r.state.WorkingMemory, "displaced": displaced, "pruned": pruned}})
|
|
c.Emit(events.New{Type: events.CycleCompleted, Source: "brain", Summary: fmt.Sprintf("cycle %d (%s): %d events, %d candidates, %d admitted, %d suppressed in %dms", r.state.Cycle, trigger, len(newEvents), len(scored), len(admittedThoughts), len(suppressed), time.Since(start).Milliseconds()),
|
|
Salience: 0.1, Confidence: 1, Metadata: map[string]any{"trigger": trigger, "candidates": len(scored), "admitted": len(admittedThoughts), "suppressed": len(suppressed), "duration_ms": time.Since(start).Milliseconds()},
|
|
Payload: map[string]any{"effective_weights": st.Weights}})
|
|
}
|
|
if err := c.flush(ctx); err != nil {
|
|
return nil, err
|
|
}
|
|
|
|
// 8. Persist and advance the cursor to the last *read* event.
|
|
r.state.LastCycleID, r.state.LastCycleAt = cycleID, start
|
|
r.state.EffectiveWeights = st.Weights
|
|
if len(newEvents) > 0 {
|
|
if err := r.events.SetCursor(ctx, tx, r.mind.ID, cursorConsumer, newEvents[len(newEvents)-1].Seq); err != nil {
|
|
return nil, err
|
|
}
|
|
}
|
|
if err := r.saveState(ctx, tx); err != nil {
|
|
return nil, err
|
|
}
|
|
return c.emitted, nil
|
|
}
|
|
|
|
// copyState deep-copies slices so callers cannot alias loop-owned state.
|
|
func (r *Runtime) copyState() workspace.State {
|
|
st := r.state
|
|
st.Workspace = append([]workspace.Thought{}, r.state.Workspace...)
|
|
st.WorkingMemory = append([]workspace.Item{}, r.state.WorkingMemory...)
|
|
return st
|
|
}
|
|
|
|
func firstOr(xs []ids.ID, d ids.ID) ids.ID {
|
|
if len(xs) > 0 {
|
|
return xs[0]
|
|
}
|
|
return d
|
|
}
|
|
|
|
// observe runs all processes through the bounded pool with per-process
|
|
// timeouts and isolation. A failing process yields no candidates.
|
|
func (r *Runtime) observe(ctx context.Context, st *State) []Candidate {
|
|
procs := r.registry.Processes()
|
|
results := make([][]Candidate, len(procs))
|
|
tasks := make([]func(context.Context) error, len(procs))
|
|
for i, p := range procs {
|
|
tasks[i] = func(ctx context.Context) error {
|
|
pctx, cancel := context.WithTimeout(ctx, processTimeout)
|
|
defer cancel()
|
|
out, err := p.Observe(pctx, st)
|
|
if err != nil {
|
|
r.log.Warn("process failed", "process", p.Name(), "err", err)
|
|
return nil
|
|
}
|
|
for j := range out {
|
|
out[j].Process = p.Name()
|
|
out[j].Features = out[j].Features.Clamp()
|
|
if out[j].Kind == "" {
|
|
out[j].Kind = "note"
|
|
}
|
|
}
|
|
results[i] = out
|
|
return nil
|
|
}
|
|
}
|
|
scheduler.Parallel(ctx, r.pool, tasks) //nolint:errcheck // per-task errors already logged
|
|
var all []Candidate
|
|
for _, rs := range results {
|
|
all = append(all, rs...)
|
|
}
|
|
return all
|
|
}
|
|
|
|
// Workspace returns a copy of the live workspace state for handlers.
|
|
func (r *Runtime) Workspace() workspace.State { return r.copyState() }
|
|
|
|
// Events exposes the event store to handlers.
|
|
func (r *Runtime) Events() *evstore.Store { return r.events }
|
|
|
|
// Hold places an item in working memory using the mind's configured capacity.
|
|
// It must only be called from handlers (on the loop goroutine).
|
|
func (r *Runtime) Hold(it workspace.Item) {
|
|
r.state.Hold(it, r.mind.Config.WorkingMemoryCapacity, r.state.Cycle, r.now())
|
|
}
|
|
|
|
// defaultHandler places an admitted thought into working memory.
|
|
func defaultHandler(_ context.Context, c *Cycle, t workspace.Thought) error {
|
|
key := t.Kind + ":" + t.Subject
|
|
if t.Subject == "" {
|
|
key = t.Kind + ":" + string(t.ID)
|
|
}
|
|
ref, _ := t.Content["ref"].(string)
|
|
var src ids.ID
|
|
if s, ok := t.Content["source_event"].(string); ok {
|
|
src = ids.ID(s)
|
|
}
|
|
c.Runtime.state.Hold(workspace.Item{Key: key, Content: t.Summary, Kind: t.Kind, SourceEvent: src, Ref: ref}, c.Runtime.mind.Config.WorkingMemoryCapacity, c.State.Cycle, c.State.Now)
|
|
return nil
|
|
}
|
|
|
|
// cycleBudget is a per-minute token bucket for cycles.
|
|
type cycleBudget struct {
|
|
perMinute int
|
|
tokens float64
|
|
last time.Time
|
|
}
|
|
|
|
func newCycleBudget(perMinute int) *cycleBudget {
|
|
return &cycleBudget{perMinute: perMinute, tokens: float64(perMinute)}
|
|
}
|
|
|
|
func (b *cycleBudget) allow(now time.Time) bool {
|
|
if !b.last.IsZero() {
|
|
b.tokens += now.Sub(b.last).Minutes() * float64(b.perMinute)
|
|
}
|
|
b.last = now
|
|
if b.tokens > float64(b.perMinute) {
|
|
b.tokens = float64(b.perMinute)
|
|
}
|
|
if b.tokens < 1 {
|
|
return false
|
|
}
|
|
b.tokens--
|
|
return true
|
|
}
|