The tokenizer read every long word backwards (V-664)

encodeWord backtracks the Viterbi path from the end of the word and
prepends each piece, which puts them back in reading order. A second
reverse after that loop undid it. So "query: вода" tokenized to
[0 12 1294 41 12489 2] where the reference tokenizer gives
[0 41 1294 12 12489 2], and every multi-piece Russian word reached the
model with its pieces in the wrong order.

Measured on the recall fixture, same 27 cases either way:

  recall@1  70.4% -> 77.8%
  recall@3  85.2% -> 96.3%
  answered after gate  63.0% -> 66.7%
  false recall  0/5 -> 1/5

The classifier barely moves, 76.0% to 75.0% on the routing fixture,
because seeds and queries were mangled the same way and cosine survived
it. Recall is where it cost, because a stored passage and a live query
are different lengths and break differently.

The embedder id now names a tokenizer revision. Stored vectors were
written under rev 1 and no longer sit in the same space as a query
embedded now, and the model file's name never moved, so nothing would
have triggered ReembedAll.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_013ptwopxyo3Z2kwFckHkLvN

internal/router/embedderid_test.go

@@ -1,10 +1,13 @@
 package router
 
-import "testing"
+import (
+	"strings"
+	"testing"
+)
 
 func TestEmbedderIDFromModelPath(t *testing.T) {
 	got := modelIDFromPath("/opt/maven/models/embedder/multilingual-e5-small.onnx")
-	if got != "multilingual-e5-small@384" {
+	if got != "multilingual-e5-small@384/tok2" {
 		t.Fatalf("modelIDFromPath = %q", got)
 	}
 	// A different model file must produce a different id, even at 384 dim.
@@ -12,6 +15,13 @@ func TestEmbedderIDFromModelPath(t *testing.T) {
 	if old == got {
 		t.Fatal("two different models share one id")
 	}
+	// The tokenizer is half of what makes a vector, and it changes under a
+	// model file whose name never moves (V-664). An id that ignored it would
+	// leave stored passages in one space and every new query in another, with
+	// nothing to trigger the re-embed.
+	if !strings.Contains(got, "/tok") {
+		t.Fatalf("id %q does not name the tokenizer revision", got)
+	}
 }
 
 func TestEmbedderIDIncludesDim(t *testing.T) {

internal/router/onnxembedder.go

@@ -66,12 +66,19 @@ func NewONNXEmbedder(modelPath, tokenizerPath, libPath string) (*onnxEmbedder, e
 func (e *onnxEmbedder) Dim() int { return embedDim }
 
 // ID names the loaded model for the DB marker (Vikunja #378): the model file's
-// own name plus the dimension, so pointing the config at another model changes
+// own name, the dimension, and the tokenizer revision, so pointing the config
-// the string on its own.
+// at another model changes the string on its own.
 func (e *onnxEmbedder) ID() string { return e.id }
 
+// tokenizerRev — bumped whenever the tokenizer changes what it emits for the
+// same text, because that changes every vector while the model file's name
+// stays put. Rev 2 is the fix for the reversed word pieces (V-664): stored
+// passages embedded under rev 1 no longer sit in the same space as a query
+// embedded now, and ReembedAll rewrites them because this string moved.
+const tokenizerRev = 2
+
 // modelIDFromPath turns /opt/.../multilingual-e5-small.onnx into
-// "multilingual-e5-small@384".
+// "multilingual-e5-small@384/tok2".
 func modelIDFromPath(modelPath string) string {
 	name := modelPath
 	if i := strings.LastIndexAny(name, "/\\"); i >= 0 {
@@ -81,7 +88,7 @@ func modelIDFromPath(modelPath string) string {
 	if name == "" {
 		name = "onnx"
 	}
-	return fmt.Sprintf("%s@%d", name, embedDim)
+	return fmt.Sprintf("%s@%d/tok%d", name, embedDim, tokenizerRev)
 }
 
 // Embed treats the text as a query. The classifier compares one short
@@ -339,14 +346,17 @@ func (t *unigramTokenizer) encodeWord(word string) []int64 {
 		}
 	}
 
+	// Backtracking walks the word from its end, and prepending each piece puts
+	// it back in reading order. There used to be a second reverse after this
+	// loop, which undid it: every multi-piece word came out backwards, and
+	// "query: вода" tokenized to [0 12 1294 41 12489 2] where the reference
+	// tokenizer gives [0 41 1294 12 12489 2] (V-664). A transformer reads
+	// position, so the pieces of a long Russian word were being read in the
+	// wrong order on every turn.
 	var result []int64
 	for i := n; i > 0; i = prev[i] {
 		result = append([]int64{bestID[i]}, result...)
 	}
-	// Reverse
-	for l, r := 0, len(result)-1; l < r; l, r = l+1, r-1 {
-		result[l], result[r] = result[r], result[l]
-	}
 	return result
 }