Embed a question and a stored note differently (Vikunja #371)

Note recall is asymmetric: a short question goes in, a longer note comes
out. Adds EmbedQuery/EmbedPassage helpers and the e5 prefixes, and points
the note/fact write path at the passage side and the query path at the
query side. Reviewers: the three call sites in voice.go.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CGeSZxh1DCtRxmFVSYVGvJ
This commit is contained in:
kami
2026-07-31 11:38:07 +04:00
parent 8a174c1c70
commit 751c2a705f
4 changed files with 119 additions and 4 deletions
+3 -3
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@@ -567,7 +567,7 @@ func (h *reactiveHandler) applyAction(ctx context.Context, dec router.Decision)
// fail the fact write). Facts aren't in the notes table, so this is the
// only recall path for them — "когда я пил воду?" reads back from here.
if h.memStore != nil {
if vec, err := h.embedder.Embed(ctx, dec.Utterance); err != nil {
if vec, err := router.EmbedPassage(ctx, h.embedder, dec.Utterance); err != nil {
log.Printf("voice: embed fact for memory: %v", err)
} else if err := h.memStore.Insert(ctx, "fact:"+dec.Slots.Key+":"+strconv.FormatInt(now.Unix(), 10), vec, map[string]string{
"source": "voice",
@@ -681,7 +681,7 @@ func (h *reactiveHandler) applyAction(ctx context.Context, dec router.Decision)
// embed the note text with the same model the classifier uses, persist
// via CoreAPI (source=tap:voice). Semantic recall lives in `notes`, not
// facts — no predicate reads it (spec's two-memory split).
vec, err := h.embedder.Embed(ctx, dec.Utterance)
vec, err := router.EmbedPassage(ctx, h.embedder, dec.Utterance)
if err != nil {
log.Printf("voice: embed note: %v", err)
return "не получилось сохранить заметку."
@@ -758,7 +758,7 @@ func (h *reactiveHandler) applyAction(ctx context.Context, dec router.Decision)
return fmt.Sprintf("в %s сейчас %.0f градусов, %s.", w.Location, w.Temperature, w.Condition)
}
vec, err := h.embedder.Embed(ctx, dec.Utterance)
vec, err := router.EmbedQuery(ctx, h.embedder, dec.Utterance)
if err != nil {
log.Printf("voice: embed query: %v", err)
return "не получилось найти ответ."
+31
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@@ -19,6 +19,37 @@ type Embedder interface {
Close() error
}
// AsymmetricEmbedder — an embedder that wants to know whether a text is a
// search query or a stored passage. Recall is asymmetric: a short question
// goes in, a longer note comes out. The e5 family is trained for exactly that
// and needs the side written into the text ("query: " / "passage: ").
//
// Optional on purpose: HashEmbedder has no such notion, so callers go through
// EmbedQuery and EmbedPassage below, which fall back to plain Embed.
type AsymmetricEmbedder interface {
Embedder
EmbedQuery(ctx context.Context, text string) ([]float32, error)
EmbedPassage(ctx context.Context, text string) ([]float32, error)
}
// EmbedQuery embeds text that is being searched WITH — a question.
func EmbedQuery(ctx context.Context, e Embedder, text string) ([]float32, error) {
if a, ok := e.(AsymmetricEmbedder); ok {
return a.EmbedQuery(ctx, text)
}
return e.Embed(ctx, text)
}
// EmbedPassage embeds text that is being searched FOR — a note or a fact on
// its way into the store. Store and lookup must use these two calls, not one
// of them twice, or the asymmetry buys nothing.
func EmbedPassage(ctx context.Context, e Embedder, text string) ([]float32, error) {
if a, ok := e.(AsymmetricEmbedder); ok {
return a.EmbedPassage(ctx, text)
}
return e.Embed(ctx, text)
}
// HashEmbedder — a deterministic bag-of-words embedder used for tests and as a
// non-zero default floor. NOT semantically meaningful across languages; the
// real classifier swaps in the multilingual ONNX model wholesale.
+57
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@@ -37,3 +37,60 @@ func TestHashEmbedderCyrillic(t *testing.T) {
t.Fatalf("cosine(shared)=%.3f not > cosine(disjoint)=%.3f", cosine(a, b), cosine(a, c))
}
}
// recordingEmbedder — an asymmetric embedder that only remembers which side
// was asked for. Enough to pin the dispatch; real vectors need the model.
type recordingEmbedder struct{ calls []string }
func (r *recordingEmbedder) Dim() int { return 2 }
func (r *recordingEmbedder) Close() error { return nil }
func (r *recordingEmbedder) Embed(_ context.Context, _ string) ([]float32, error) {
r.calls = append(r.calls, "embed")
return []float32{1, 0}, nil
}
func (r *recordingEmbedder) EmbedQuery(_ context.Context, _ string) ([]float32, error) {
r.calls = append(r.calls, "query")
return []float32{1, 0}, nil
}
func (r *recordingEmbedder) EmbedPassage(_ context.Context, _ string) ([]float32, error) {
r.calls = append(r.calls, "passage")
return []float32{0, 1}, nil
}
// TestEmbedQueryAndPassageSplit — a question and a stored note must not take
// the same path. If both ended up on the same call the asymmetric model buys
// nothing, which is the whole reason for the swap.
func TestEmbedQueryAndPassageSplit(t *testing.T) {
rec := &recordingEmbedder{}
if _, err := EmbedQuery(context.Background(), rec, "где логи?"); err != nil {
t.Fatalf("EmbedQuery: %v", err)
}
if _, err := EmbedPassage(context.Background(), rec, "логи в /var/log"); err != nil {
t.Fatalf("EmbedPassage: %v", err)
}
if len(rec.calls) != 2 || rec.calls[0] != "query" || rec.calls[1] != "passage" {
t.Errorf("calls %v, want [query passage]", rec.calls)
}
}
// TestEmbedFallsBackToPlainEmbed — HashEmbedder has no sides, so both helpers
// must still work and give the same vector.
func TestEmbedFallsBackToPlainEmbed(t *testing.T) {
h := NewHashEmbedder(64)
q, err := EmbedQuery(context.Background(), h, "text")
if err != nil {
t.Fatalf("EmbedQuery: %v", err)
}
p, err := EmbedPassage(context.Background(), h, "text")
if err != nil {
t.Fatalf("EmbedPassage: %v", err)
}
for i := range q {
if q[i] != p[i] {
t.Fatalf("hash embedder gave two different vectors for the same text")
}
}
}
+28 -1
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@@ -12,6 +12,15 @@ import (
"golang.org/x/text/unicode/norm"
)
// The deployed model is multilingual-e5-small. e5 was trained with these two
// words glued to the front of every text, and it scores badly without them —
// they are part of the model, not a style choice. Swapping back to a symmetric
// paraphrase model means dropping them again.
const (
queryPrefix = "query: "
passagePrefix = "passage: "
)
const (
padTokenID = 1
unkTokenID = 3
@@ -54,7 +63,25 @@ func NewONNXEmbedder(modelPath, tokenizerPath, libPath string) (*onnxEmbedder, e
func (e *onnxEmbedder) Dim() int { return embedDim }
// Embed treats the text as a query. The classifier compares one short
// utterance to another short seed phrase, so both sides get the same prefix
// and the comparison stays fair. The recall path must call EmbedQuery and
// EmbedPassage instead.
func (e *onnxEmbedder) Embed(ctx context.Context, text string) ([]float32, error) {
return e.embed(ctx, queryPrefix+text)
}
// EmbedQuery — the question the user just asked.
func (e *onnxEmbedder) EmbedQuery(ctx context.Context, text string) ([]float32, error) {
return e.embed(ctx, queryPrefix+text)
}
// EmbedPassage — a note or fact being stored, or re-scored at lookup time.
func (e *onnxEmbedder) EmbedPassage(ctx context.Context, text string) ([]float32, error) {
return e.embed(ctx, passagePrefix+text)
}
func (e *onnxEmbedder) embed(ctx context.Context, text string) ([]float32, error) {
inputIDs, attentionMask, _ := e.tokenizer.Encode(text)
inputShape := ort.NewShape(1, int64(maxLength))
@@ -313,4 +340,4 @@ func preTokenize(text string) []string {
return out
}
var _ Embedder = (*onnxEmbedder)(nil)
var _ AsymmetricEmbedder = (*onnxEmbedder)(nil)