Andy Dixon
03631e5685
Phase 5 (ADR-0003). pkg/concepts fixes the epistemic vocabulary (observation, fact, memory, claim, hypothesis, belief, prediction, assumption, inference, opinion), memory kinds and relation names. internal/memory stores episodic, semantic, procedural and autobiographical memories in one mind-scoped table with trust-derived credibility, importance, salience and decaying activation. Retrieval fuses full-text rank (any-term by default), optional pgvector similarity, recency, activation, importance and credibility, and returns the component scores so every recall can be explained. Decay lowers activation and never deletes; retraction and supersession keep rows for provenance. Relationships are constrained to one mind at insert time. Embeddings live in a conditional pgvector table keyed by model so vector spaces are never mixed; without an embedder the system degrades to text and graph retrieval. Attention gates encoding: the attend handler turns admitted observations into episodic memories (operator changes become autobiographical ones) with memory_created events; the retrieve process proposes recalled memories as candidates and admitted recalls are reinforced with memory_retrieved events carrying their scoring components. Memory explorer UI (search with component breakdown, timeline, filters, operator knowledge entry), memory detail with provenance, relationships, citing events and an inaccurate-memory correction; API; noemactl memory commands; decay and embed-missing background jobs.
61 lines
2.2 KiB
Go
61 lines
2.2 KiB
Go
package cognition
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import (
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"context"
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"encoding/json"
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"strings"
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"github.com/andydixon/noema/internal/attention"
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"github.com/andydixon/noema/internal/brain"
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"github.com/andydixon/noema/internal/memory"
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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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// Retrieve recalls memories relevant to each new external event and proposes
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// them as candidates. Retrieval scoring is deterministic and recorded on the
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// candidate so the UI can show why a memory came to mind.
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type Retrieve struct{}
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// Name implements brain.Process.
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func (Retrieve) Name() string { return "retrieve" }
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// minRetrievalScore filters weak matches before they compete for attention.
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const minRetrievalScore = 0.30
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// Observe implements brain.Process.
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func (Retrieve) Observe(ctx context.Context, s *brain.State) ([]brain.Candidate, error) {
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if s.Services.Memory == nil {
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return nil, nil
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}
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var out []brain.Candidate
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seen := map[ids.ID]bool{}
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for _, ev := range s.NewEvents {
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if strings.HasPrefix(ev.Source, "brain") || (ev.Type != events.Observation && ev.Type != events.Perception) {
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continue
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}
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text := ev.Summary
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var p events.ObservationPayload
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if json.Unmarshal(ev.Payload, &p) == nil && p.Text != "" {
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text = p.Text
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}
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hits, err := s.Services.Memory.Retrieve(ctx, s.Mind.ID, memory.Query{Text: text, Limit: 5, Now: s.Now, Semantic: true})
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if err != nil {
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return out, err
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}
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for _, h := range hits {
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m := h.Memory
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if h.Score < minRetrievalScore || m.OriginEvent == ev.ID || seen[m.ID] {
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continue
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}
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seen[m.ID] = true
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out = append(out, brain.Candidate{Kind: "retrieve", Subject: string(m.ID), Summary: "recalled: " + truncate(m.Content, 160),
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Content: map[string]any{"ref": string(m.ID), "memory_kind": m.Kind, "score": h.Score, "components": h.Components, "query": truncate(text, 200), "source_event": string(ev.ID)},
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Features: attention.Features{Salience: h.Score * 0.8, ContextRelevance: h.Score, Confidence: m.Confidence, Novelty: 0.2, Consequence: 0.3 + 0.4*m.Importance, Urgency: 0.1},
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Confidence: m.Confidence, SourceEvents: []ids.ID{ev.ID},
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Evidence: []events.EvidenceRef{{Kind: "memory", ID: string(m.ID), Note: "retrieved"}, {Kind: "event", ID: string(ev.ID), Note: "cue"}}})
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}
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}
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return out, nil
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}
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