feat: add long-term memory systems with fused retrieval and memory explorer

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.

internal/embeddings/embeddings.go

@@ -0,0 +1,49 @@
+// Package embeddings defines the embedding provider interface and helpers for
+// storing vectors in pgvector. Providers arrive with the LLM layer; None keeps
+// the system fully functional without them (spec §46).
+package embeddings
+
+import (
+	"context"
+	"errors"
+	"strconv"
+	"strings"
+)
+
+// Embedder turns texts into vectors.
+type Embedder interface {
+	// Model returns a stable identifier such as "openai/text-embedding-3-small"
+	// so vectors from different models are never compared.
+	Model() string
+	Dims() int
+	Embed(ctx context.Context, texts []string) ([][]float32, error)
+}
+
+// ErrUnavailable is returned by None.
+var ErrUnavailable = errors.New("embeddings unavailable")
+
+// None is the embedder used when no provider is configured.
+type None struct{}
+
+// Model implements Embedder.
+func (None) Model() string { return "" }
+
+// Dims implements Embedder.
+func (None) Dims() int { return 0 }
+
+// Embed implements Embedder.
+func (None) Embed(context.Context, []string) ([][]float32, error) { return nil, ErrUnavailable }
+
+// Format renders a vector in pgvector's text syntax.
+func Format(v []float32) string {
+	var b strings.Builder
+	b.WriteByte('[')
+	for i, f := range v {
+		if i > 0 {
+			b.WriteByte(',')
+		}
+		b.WriteString(strconv.FormatFloat(float64(f), 'f', -1, 32))
+	}
+	b.WriteByte(']')
+	return b.String()
+}