maven: long-term memory vector-store interface (task 7)

- New internal/memory/ package: Store interface, InMemoryStore (cosine sim)
- Tests: insert→search roundtrip, topK truncation, empty store, cosine edges
- Wire into voice: memStore on reactiveHandler, insert note embedding after WriteNote
- Memory Insert is best-effort, log-and-continue on error

Co-Authored-By: opencode <opencode@anthropic.com>

internal/memory/store.go

@@ -0,0 +1,95 @@
+package memory
+
+import (
+	"context"
+	"sort"
+	"sync"
+)
+
+// Result is a single search hit.
+type Result struct {
+	ID    string
+	Score float64
+	Meta  map[string]string
+}
+
+// Store is a vector memory interface.
+type Store interface {
+	Insert(ctx context.Context, id string, vec []float32, meta map[string]string) error
+	Search(ctx context.Context, vec []float32, topK int) ([]Result, error)
+}
+
+// item is a single stored vector with metadata.
+type item struct {
+	id   string
+	vec  []float32
+	meta map[string]string
+}
+
+// InMemoryStore implements Store with cosine similarity search.
+type InMemoryStore struct {
+	mu    sync.RWMutex
+	items []item
+}
+
+func NewInMemoryStore() *InMemoryStore {
+	return &InMemoryStore{}
+}
+
+func (s *InMemoryStore) Insert(_ context.Context, id string, vec []float32, meta map[string]string) error {
+	s.mu.Lock()
+	s.items = append(s.items, item{id: id, vec: vec, meta: meta})
+	s.mu.Unlock()
+	return nil
+}
+
+func (s *InMemoryStore) Search(_ context.Context, vec []float32, topK int) ([]Result, error) {
+	s.mu.RLock()
+	defer s.mu.RUnlock()
+
+	if topK <= 0 {
+		topK = 10
+	}
+
+	type scored struct {
+		id    string
+		score float64
+		meta  map[string]string
+	}
+
+	scores := make([]scored, 0, len(s.items))
+	for _, it := range s.items {
+		score := cosine(vec, it.vec)
+		scores = append(scores, scored{id: it.id, score: score, meta: it.meta})
+	}
+
+	sort.Slice(scores, func(i, j int) bool {
+		return scores[i].score > scores[j].score // descending
+	})
+
+	if topK > len(scores) {
+		topK = len(scores)
+	}
+
+	out := make([]Result, topK)
+	for i := 0; i < topK; i++ {
+		out[i] = Result{
+			ID:    scores[i].id,
+			Score: scores[i].score,
+			Meta:  scores[i].meta,
+		}
+	}
+	return out, nil
+}
+
+// cosine similarity (dot product, assumes L2-normalized vectors).
+func cosine(a, b []float32) float64 {
+	if len(a) != len(b) || len(a) == 0 {
+		return 0
+	}
+	var dot float64
+	for i := range a {
+		dot += float64(a[i]) * float64(b[i])
+	}
+	return dot
+}