Swap the embedder to multilingual-e5-small (Vikunja #371, #372)

The old model was a symmetric paraphrase model, so it scored "do these
look alike" instead of "does this note answer this question". Also fixes
the file mismatch: the Makefile, the deploy config and both evals now all
name the same quantized file, and the quantized one is what gets measured.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CGeSZxh1DCtRxmFVSYVGvJ

AGENTS.md

@@ -43,14 +43,17 @@ notes. Without it, the floor `HashEmbedder` is used — deterministic but weak
 (Russian recall rarely clears the confidence gate, many commands fall to
 "clarify").
 
-**Download the embedder** (ONNX, ~90 MB):
+**Download the embedder** (ONNX, ~120 MB):
 
 ```sh
 make download-embedder
 ```
 
-This fetches `paraphrase-multilingual-MiniLM-L12-v2` (384-dim, 12-layer,
-supports 50+ languages including Russian) to `models/embedder/`.
+This fetches `multilingual-e5-small` (384-dim, 12-layer, Russian and English)
+to `models/embedder/multilingual-e5-small/`. It is an asymmetric retrieval
+model: the code puts `query: ` in front of a question and `passage: ` in front
+of a stored note, which is how e5 was trained. The quantized file is the one
+that is downloaded, deployed and measured.
 
 **Also need ONNX Runtime** (`libonnxruntime.so`):
 
@@ -64,8 +67,8 @@ sudo cp onnxruntime-linux-x64-1.15.1/lib/libonnxruntime.so* /usr/local/lib/
 ```json
 "voice": {
   "embedder": {
-    "model_path": "models/embedder/model_quantized.onnx",
-    "tokenizer_path": "models/embedder/tokenizer.json",
+    "model_path": "models/embedder/multilingual-e5-small/model_quantized.onnx",
+    "tokenizer_path": "models/embedder/multilingual-e5-small/tokenizer.json",
     "lib_path": "/usr/local/lib/libonnxruntime.so"
   }
 }

Makefile

@@ -115,9 +115,13 @@ deps-piper:
 		-o /tmp/piper.tar.gz
 	tar -xzf /tmp/piper.tar.gz -C deps/
 
-EMBEDDER_DIR := $(shell pwd)/models/embedder
-EMBEDDER_MODEL_URL := https://huggingface.co/Xenova/paraphrase-multilingual-MiniLM-L12-v2/resolve/main/onnx/model_quantized.onnx
-EMBEDDER_TOKENIZER_URL := https://huggingface.co/Xenova/paraphrase-multilingual-MiniLM-L12-v2/resolve/main/tokenizer.json
+# multilingual-e5-small: an asymmetric retrieval model. It is trained to match
+# a short question against a longer passage, which is what note recall is.
+# The quantized file is the one we download, deploy and measure — see
+# RECALL-EVAL-31-07-2026.md.
+EMBEDDER_DIR := $(shell pwd)/models/embedder/multilingual-e5-small
+EMBEDDER_MODEL_URL := https://huggingface.co/Xenova/multilingual-e5-small/resolve/main/onnx/model_quantized.onnx
+EMBEDDER_TOKENIZER_URL := https://huggingface.co/Xenova/multilingual-e5-small/resolve/main/tokenizer.json
 
 download-embedder:
 	mkdir -p $(EMBEDDER_DIR)

deploy/mavend.json

@@ -36,8 +36,8 @@
     "stt": { "socket": "/run/maven/stt.sock", "lang": "ru" },
     "tts": { "socket": "/run/maven/tts.sock", "lang": "ru" },
     "embedder": {
-      "model_path": "/opt/maven/models/embedder/model.onnx",
-      "tokenizer_path": "/opt/maven/models/embedder/tokenizer.json",
+      "model_path": "/opt/maven/models/embedder/multilingual-e5-small/model_quantized.onnx",
+      "tokenizer_path": "/opt/maven/models/embedder/multilingual-e5-small/tokenizer.json",
       "lib_path": "/opt/maven/lib/libonnxruntime.so"
     },
     "tool_timeout": "30s",

internal/memory/recalleval/recalleval.go

@@ -117,18 +117,40 @@ type cachingEmbedder struct {
 	seen  map[string][]float32
 }
 
+var _ router.AsymmetricEmbedder = (*cachingEmbedder)(nil)
+
 func (c *cachingEmbedder) Dim() int     { return c.inner.Dim() }
 func (c *cachingEmbedder) Close() error { return nil } // the caller owns inner
 
 func (c *cachingEmbedder) Embed(ctx context.Context, text string) ([]float32, error) {
-	if v, ok := c.seen[text]; ok {
+	return c.cached(ctx, "embed:"+text, func() ([]float32, error) {
+		return c.inner.Embed(ctx, text)
+	})
+}
+
+// The two sides of an asymmetric embedder give different vectors for the same
+// string, so the cache key has to say which side asked.
+func (c *cachingEmbedder) EmbedQuery(ctx context.Context, text string) ([]float32, error) {
+	return c.cached(ctx, "query:"+text, func() ([]float32, error) {
+		return router.EmbedQuery(ctx, c.inner, text)
+	})
+}
+
+func (c *cachingEmbedder) EmbedPassage(ctx context.Context, text string) ([]float32, error) {
+	return c.cached(ctx, "passage:"+text, func() ([]float32, error) {
+		return router.EmbedPassage(ctx, c.inner, text)
+	})
+}
+
+func (c *cachingEmbedder) cached(_ context.Context, key string, embed func() ([]float32, error)) ([]float32, error) {
+	if v, ok := c.seen[key]; ok {
 		return v, nil
 	}
-	v, err := c.inner.Embed(ctx, text)
+	v, err := embed()
 	if err != nil {
 		return nil, err
 	}
-	c.seen[text] = v
+	c.seen[key] = v
 	return v, nil
 }
 
@@ -305,7 +327,7 @@ func scoreCase(ctx context.Context, emb router.Embedder, newStore NewStore, minS
 
 	all := append(append([]StoredNote(nil), c.Notes...), filler...)
 	for _, n := range all {
-		vec, err := emb.Embed(ctx, n.Text)
+		vec, err := router.EmbedPassage(ctx, emb, n.Text)
 		if err != nil {
 			return Outcome{}, fmt.Errorf("%s: embed note %s: %w", c.ID, n.ID, err)
 		}
@@ -317,7 +339,7 @@ func scoreCase(ctx context.Context, emb router.Embedder, newStore NewStore, minS
 
 	o := Outcome{Case: c}
 	start := time.Now()
-	qvec, err := emb.Embed(ctx, c.Query)
+	qvec, err := router.EmbedQuery(ctx, emb, c.Query)
 	if err != nil {
 		o.Latency = time.Since(start)
 		o.Err = err

internal/memory/recalleval/recalleval_test.go

@@ -246,8 +246,8 @@ func TestONNXRecall(t *testing.T) {
 	if lib == "" {
 		t.Skip("MAVEN_ONNX_LIB unset — see AGENTS.md § Embedder model for intent routing")
 	}
-	model := filepath.Join("../../..", "models/embedder/model.onnx")
-	tok := filepath.Join("../../..", "models/embedder/tokenizer.json")
+	model := filepath.Join("../../..", "models/embedder/multilingual-e5-small/model_quantized.onnx")
+	tok := filepath.Join("../../..", "models/embedder/multilingual-e5-small/tokenizer.json")
 	for _, p := range []string{lib, model, tok} {
 		if _, err := os.Stat(p); err != nil {
 			t.Skipf("missing %s: %v", p, err)

internal/router/eval/eval_test.go

@@ -185,8 +185,8 @@ func TestONNXBaseline(t *testing.T) {
 	if lib == "" {
 		t.Skip("MAVEN_ONNX_LIB unset — see AGENTS.md § Embedder model for intent routing")
 	}
-	model := filepath.Join("../../..", "models/embedder/model.onnx")
-	tok := filepath.Join("../../..", "models/embedder/tokenizer.json")
+	model := filepath.Join("../../..", "models/embedder/multilingual-e5-small/model_quantized.onnx")
+	tok := filepath.Join("../../..", "models/embedder/multilingual-e5-small/tokenizer.json")
 	for _, p := range []string{lib, model, tok} {
 		if _, err := os.Stat(p); err != nil {
 			t.Skipf("missing %s: %v", p, err)