Phase 10. Metacognition is a cognitive process with deterministic self-checks (insufficient evidence, single-source reliance, model output without evidence, repeated failed hypotheses, stale contradictions, calibration overconfidence); its warnings compete for attention and, when admitted, add counter-evidence or open an investigation goal. Reflection reviews the period since the last run (prediction errors, rejected beliefs, corrections, status changes, completed goals, contradictions, calibration), turns repeated failures into procedural lessons written once, and records a reflection event; a model may only word the prose within the mind's budget. Consolidation clusters unconsolidated episodes by shared stemmed terms, creates a belief whose confidence is computed from the episodes as evidence and a semantic memory linked to each episode by derived_from, and records jobs with inputs and outputs. Wording may come from the consolidate prompt within a per-run model budget; the job says which. Operators can run, pause and reject; originals are never touched. The narrative hook keeps a five-part first-person narrative built from state and stored with narrative_updated events. The notification hook applies explicit thresholded rules, aggregates repeats within a cooldown and records suppressions with reasons. A scheduled job runs consolidation and reflection per mind, switching the runtime mode while it works. API, Background tab, notifications page with unread badge, overview narrative and warnings, CLI commands, docs/cognition.md.
191 lines
8.6 KiB
Go
191 lines
8.6 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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r.Handle("metacognition", h.metacognition)
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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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