Files
noema/internal/cognition/handlers.go
T
Andy Dixon 99a89ba847 feat: add the relationship graph with extraction, corrections and explorer
Phase 11. Entities are mind-scoped nodes with canonical matching,
aliases and deterministic kind guessing (hostnames and hyphenated ids
are systems, capitalised names people, file suffixes documents).
Relationships carry a relation from the shared vocabulary, confidence,
source, reinforcement count and the event that produced them; links tie
entities to memories, beliefs, events, goals and predictions.

The perceive and attend handlers upsert the entities a message or
observation mentions and record co-mention edges cited to the event, so
the graph grows only from what was actually seen. Operators can merge
("these are the same": edges and links move, the old name becomes an
alias, the old id still resolves), record that two entities are
different, correct kinds and add typed edges; every correction is an
operator action and a corrections row.

The explorer renders a bounded subgraph on a canvas with a small
force-directed layout in plain JavaScript, with kind filters, focus on
a neighbourhood and a side panel per entity. API, entity page, CLI and
docs/graph.md.
2026-06-29 19:28:55 +01:00

195 lines
8.8 KiB
Go

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