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.
190 lines
8.5 KiB
Go
190 lines
8.5 KiB
Go
package cognition
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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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"strings"
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"time"
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"github.com/andydixon/noema/internal/beliefs"
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"github.com/andydixon/noema/internal/brain"
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"github.com/andydixon/noema/internal/conversation"
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"github.com/andydixon/noema/internal/memory"
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"github.com/andydixon/noema/internal/predictions"
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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/concepts"
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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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// Handlers are the executive actions for admitted thought kinds.
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type Handlers struct {
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Memory *memory.Store
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Beliefs *beliefs.Store
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Conversations *conversation.Store
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}
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// Register installs the handlers on a registry.
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func (h Handlers) Register(r *brain.Registry) {
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r.Handle("attend", h.attend)
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r.Handle("retrieve", h.retrieve)
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r.Handle("hypothesis", h.hypothesis)
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r.Handle("perceive", h.perceive)
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r.Handle("goal", h.goal)
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r.Handle("question", h.question)
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}
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// emitter adapts the cycle to the beliefs store's Emitter.
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func emitter(c *brain.Cycle) beliefs.Emitter {
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return func(ctx context.Context, _ storage.Querier, n events.New) (events.Event, error) {
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return c.EmitNow(ctx, n)
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}
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}
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// hypothesis turns an admitted hypothesis candidate into a belief whose
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// confidence is computed from the cited observations. Temporal co-occurrence
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// is weak evidence, so weights are small and the result stays a hypothesis
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// until independent support arrives.
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func (h Handlers) hypothesis(ctx context.Context, c *brain.Cycle, t workspace.Thought) error {
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if h.Beliefs == nil {
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return nil
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}
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prop, _ := t.Content["proposition"].(string)
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if prop == "" {
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prop = strings.TrimPrefix(t.Summary, "hypothesis: ")
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}
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var ev []beliefs.EvidenceInput
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for _, ref := range t.Evidence {
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if ref.Kind != "event" {
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continue
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}
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cred := 0.6
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if e, err := c.Runtime.Events().Get(ctx, ids.ID(ref.ID)); err == nil {
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var p events.ObservationPayload
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if json.Unmarshal(e.Payload, &p) == nil && p.Trust.Valid() {
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cred = p.Trust.Credibility()
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}
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}
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ev = append(ev, beliefs.EvidenceInput{Direction: "for", Kind: "event", RefID: ref.ID, Source: "co-occurrence:" + ref.ID, Weight: 0.15, Credibility: cred, Note: "temporal co-occurrence (" + ref.Note + ")"})
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}
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res, err := h.Beliefs.Create(ctx, c.Tx, emitter(c), beliefs.New{MindID: c.State.Mind.ID, Proposition: prop, Epistemic: concepts.Hypothesis, Source: "brain.associate",
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Evidence: ev, Alternatives: []string{"coincidence: the two events are unrelated", "a third factor caused both"}, OriginEvent: t.EventID, Causation: t.EventID, Correlation: c.State.CycleID,
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Detail: map[string]any{"cause_event": t.Content["cause_event"], "effect_event": t.Content["effect_event"], "gap_seconds": t.Content["gap_seconds"]}})
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if err != nil {
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if errors.Is(err, beliefs.ErrBlocked) {
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c.Emit(events.New{Type: events.Metacognition, Source: "brain.executive", Summary: "hypothesis blocked by operator correction: " + prop, Salience: 0.3, Confidence: 1, Metadata: map[string]any{"check": "do_not_infer"}})
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return nil
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}
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return err
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}
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c.Runtime.Hold(workspace.Item{Key: "belief:" + string(res.Belief.ID), Content: "hypothesis (" + fmt.Sprintf("%.2f", res.Belief.Confidence) + "): " + prop, Kind: "thought", Ref: string(res.Belief.ID), SourceEvent: t.EventID})
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// A causal hypothesis implies a checkable expectation: the effect recurs
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// after the cause. Its confidence is the belief's, so a miss will lower it.
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if c.State.Services.Predictions != nil {
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effect, _ := t.Content["effect_event"].(string)
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if ev, err := c.Runtime.Events().Get(ctx, ids.ID(effect)); err == nil {
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terms := distinctiveTerms(ev.Summary, 3)
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if len(terms) > 0 {
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p, err := c.State.Services.Predictions.Create(ctx, c.Tx, predictions.New{MindID: c.State.Mind.ID, Statement: "something like \"" + truncate(ev.Summary, 80) + "\" will recur within 24 hours if the cause is real",
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Subject: subjectKey(ev.Summary), Confidence: res.Belief.Confidence, WindowEnd: c.State.Now.Add(24 * time.Hour),
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Criteria: predictions.Criteria{Kind: "event_match", Expect: "occurs", Terms: terms, MinTerms: min(2, len(terms))}, Source: "brain.associate", OriginEvent: res.Events[0].ID, BeliefID: res.Belief.ID})
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if err == nil {
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c.Emit(events.New{Type: events.PredictionCreated, Source: "brain.associate", Summary: fmt.Sprintf("predicted (%.2f): %s", p.Confidence, p.Statement), Salience: 0.3, Confidence: p.Confidence, CausationID: res.Events[0].ID,
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Evidence: []events.EvidenceRef{{Kind: "prediction", ID: string(p.ID)}, {Kind: "belief", ID: string(res.Belief.ID)}}, Metadata: map[string]any{"prediction_id": string(p.ID), "belief_id": string(res.Belief.ID), "window_end": p.WindowEnd}, Payload: p})
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}
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}
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}
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}
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return nil
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}
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// distinctiveTerms picks the longest tokens of a summary as match terms.
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func distinctiveTerms(text string, n int) []string {
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toks := tokeniseWS(text)
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var out []string
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for _, t := range toks {
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if len(t) >= 5 {
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out = append(out, t)
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}
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if len(out) == n {
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break
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}
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}
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return out
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}
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// attend encodes the attended event as a memory and holds it in working
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// memory. Only attended events become episodes: attention gates encoding.
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func (h Handlers) attend(ctx context.Context, c *brain.Cycle, t workspace.Thought) error {
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evID, _ := t.Content["source_event"].(string)
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var mem memory.Memory
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if h.Memory != nil && evID != "" {
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ev, err := c.Runtime.Events().Get(ctx, ids.ID(evID))
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if err == nil && ev.Metadata["conversation_id"] != nil {
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err = errSkipEncode // the perceive handler encodes conversational episodes
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}
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if err == nil {
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n := memory.New{MindID: c.State.Mind.ID, Kind: concepts.Episodic, Epistemic: concepts.Observation, Content: ev.Summary, Source: ev.Source,
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Trust: events.TrustLow, Confidence: ev.Confidence, Importance: t.Score, Salience: ev.Salience, Valence: ev.Valence, Arousal: ev.Arousal, OccurredAt: ev.OccurredAt, OriginEvent: ev.ID,
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Detail: map[string]any{"event_type": ev.Type, "participants": []string{}}}
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switch ev.Type {
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case events.Observation:
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var p events.ObservationPayload
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if json.Unmarshal(ev.Payload, &p) == nil {
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if p.Text != "" {
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n.Content = p.Text
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}
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n.Trust = p.Trust
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n.Detail["channel"], n.Detail["reference"], n.Detail["method"] = p.Channel, p.Reference, p.Method
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if p.Actor != "" {
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n.Detail["participants"] = []string{p.Actor}
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}
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}
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case events.OperatorAction:
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n.Kind, n.Trust, n.Importance = concepts.Autobiographical, events.TrustOperator, max(t.Score, 0.6)
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n.Content = "An operator changed me: " + ev.Summary
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default:
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n.Trust = events.TrustSystem
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}
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mem, err = h.Memory.Create(ctx, c.Tx, n)
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if err != nil {
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return fmt.Errorf("encode memory: %w", err)
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}
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c.Emit(events.New{Type: events.MemoryCreated, Source: "brain.memory", Summary: fmt.Sprintf("encoded %s memory: %s", mem.Kind, truncate(mem.Content, 120)),
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Salience: 0.3, Confidence: mem.Confidence, CausationID: t.EventID, ParentIDs: []ids.ID{t.EventID, ev.ID},
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Evidence: []events.EvidenceRef{{Kind: "memory", ID: string(mem.ID)}, {Kind: "event", ID: string(ev.ID), Note: "source"}},
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Metadata: map[string]any{"memory_id": mem.ID, "kind": mem.Kind}, Payload: map[string]any{"memory_id": mem.ID, "kind": mem.Kind, "importance": mem.Importance, "trust": mem.Trust}})
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}
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}
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item := workspace.Item{Key: "attend:" + t.Subject, Content: t.Summary, Kind: "observation", SourceEvent: ids.ID(evID)}
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if !mem.ID.IsZero() {
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item.Ref = string(mem.ID)
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}
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c.Runtime.Hold(item)
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return nil
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}
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// retrieve reinforces a recalled memory, records the retrieval with its
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// scoring components, and holds it in working memory.
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func (h Handlers) retrieve(ctx context.Context, c *brain.Cycle, t workspace.Thought) error {
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ref, _ := t.Content["ref"].(string)
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if h.Memory == nil || ref == "" {
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return nil
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}
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mid := ids.ID(ref)
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if err := h.Memory.Reinforce(ctx, c.Tx, c.State.Mind.ID, mid); err != nil {
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return err
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}
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src, _ := t.Content["source_event"].(string)
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c.Emit(events.New{Type: events.MemoryRetrieved, Source: "brain.memory", Summary: t.Summary, Salience: t.Score, Confidence: t.Confidence,
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CausationID: t.EventID, ParentIDs: []ids.ID{t.EventID}, Evidence: t.Evidence,
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Metadata: map[string]any{"memory_id": mid, "score": t.Content["score"]}, Payload: map[string]any{"memory_id": mid, "score": t.Content["score"], "components": t.Content["components"], "query": t.Content["query"]}})
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c.Runtime.Hold(workspace.Item{Key: "memory:" + ref, Content: t.Summary, Kind: "retrieval", Ref: ref, SourceEvent: ids.ID(src)})
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return nil
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}
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var errSkipEncode = errors.New("skip")
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