fix(fb-39b): address PR #137 round 1 review

- CI-only embed_model.rs tests updated 384 → 1024 + e5-small → e5-large
  references (incl. file header download size, snapshot dim assert,
  L2 norm comment)
- kebab-embed-local module docs + Cargo.toml description list both
  models (small + large)
- Stale tracing message expanded with both model sizes
- Task spec Post-merge deviation section: record dropped
  embedding_dim_mismatch ErrorV1 + reason (LanceDB (model, dim)
  namespacing makes hard-error redundant)
- Task spec + HOTFIXES version bump 0.6→0.7 corrected to 0.5→0.6
  (current Cargo.toml = 0.5.0; fb-42 0.6 cut deferred per user
  direction)
- HOTFIXES "embedding_version bump 아님" line corrected — cascade rule
  DOES trigger release bump, plus deviation note for the dropped error

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

crates/kebab-embed-local/Cargo.toml

@@ -5,14 +5,14 @@ edition       = { workspace = true }
 rust-version  = { workspace = true }
 license       = { workspace = true }
 repository    = { workspace = true }
-description   = "Local fastembed-rs adapter implementing kb_core::Embedder (multilingual-e5-small default)"
+description   = "Local fastembed-rs adapter implementing kb_core::Embedder (multilingual-e5-large default, e5-small backwards-compat)"
 
 [dependencies]
 kebab-config = { path = "../kebab-config" }
 kebab-embed = { path = "../kebab-embed" }
 # Default features bring `ort-download-binaries` (bundled ONNX runtime)
 # and `hf-hub-native-tls` (first-run model download). No extra features
-# needed for the multilingual-e5-small path.
+# needed for the multilingual-e5-{small,large} paths.
 fastembed = { workspace = true }
 tracing   = { workspace = true }
 anyhow    = { workspace = true }

crates/kebab-embed-local/src/lib.rs

@@ -1,8 +1,9 @@
 //! `kb-embed-local` — `FastembedEmbedder`, a local ONNX-backed
 //! [`Embedder`](kebab_embed::Embedder) implementation.
 //!
-//! Wraps [`fastembed::TextEmbedding`] for the default `multilingual-e5-small`
-//! (384-dim) model. Honors `config.models.embedding.batch_size` and applies
+//! Wraps [`fastembed::TextEmbedding`]. Default is `multilingual-e5-large`
+//! (1024-dim, p9-fb-39b); `multilingual-e5-small` (384-dim) is also supported
+//! for backwards-compat. Honors `config.models.embedding.batch_size` and applies
 //! the e5 prefix convention (§11.3 of the design report):
 //!
 //! * `EmbeddingKind::Document` → `"passage: "` prefix
@@ -69,9 +70,9 @@ impl FastembedEmbedder {
             .with_context(|| format!("create fastembed cache dir {}", cache_dir.display()))?;
 
         // 2. Resolve the fastembed enum variant from
-        //    `config.models.embedding.model`. Currently only the default
-        //    `multilingual-e5-small` is wired; other model names error
-        //    out with a clear message rather than silently misconfiguring.
+        //    `config.models.embedding.model`. Currently `multilingual-e5-large`
+        //    (default) and `multilingual-e5-small` are wired; other model names
+        //    error out with a clear message rather than silently misconfiguring.
         let model_name = resolve_model(&config.models.embedding.model)?;
 
         // 3. Verify dim match BEFORE loading the model — if the config
@@ -100,7 +101,7 @@ impl FastembedEmbedder {
             target: "kebab-embed-local",
             model = %config.models.embedding.model,
             cache_dir = %cache_dir.display(),
-            "loading embedding model (first run will download ~470MB)"
+            "loading embedding model (first run downloads model weights — ~470MB for e5-small, ~1.3GB for e5-large)"
         );
         let inner = TextEmbedding::try_new(opts)
             .context("fastembed: TextEmbedding::try_new")?;

crates/kebab-embed-local/tests/embed_model.rs

@@ -3,10 +3,11 @@
 //!
 //! ## Why every test in this file is `#[ignore]`
 //!
-//! The first call to `FastembedEmbedder::new` downloads ~470 MB of
-//! weights from Hugging Face into `data_dir/models/fastembed/`. Doing
-//! that on every `cargo test` invocation is wasteful, so the bare
-//! invocation skips this file entirely.
+//! The first call to `FastembedEmbedder::new` downloads ~1.3 GB of
+//! weights (multilingual-e5-large per p9-fb-39b default) from Hugging
+//! Face into `data_dir/models/fastembed/`. Doing that on every
+//! `cargo test` invocation is wasteful, so the bare invocation skips
+//! this file entirely.
 //!
 //! Run the full suite with:
 //! ```text
@@ -58,19 +59,20 @@ fn shared_embedder() -> &'static FastembedEmbedder {
 // ─── construction ─────────────────────────────────────────────────────
 
 #[test]
-#[ignore = "downloads ~470MB ONNX model on first run; CI-only"]
-fn default_config_constructs_with_dims_384() {
+#[ignore = "downloads ~1.3GB ONNX model on first run; CI-only"]
+fn default_config_constructs_with_dims_1024() {
+    // p9-fb-39b: default flipped to multilingual-e5-large (1024 dim).
     let emb = shared_embedder();
-    assert_eq!(emb.dimensions(), 384);
-    assert_eq!(emb.model_id().0, "multilingual-e5-small");
+    assert_eq!(emb.dimensions(), 1024);
+    assert_eq!(emb.model_id().0, "multilingual-e5-large");
     assert_eq!(emb.model_version().0, "v1");
 }
 
 #[test]
-#[ignore = "downloads ~470MB ONNX model on first run; CI-only"]
+#[ignore = "downloads ~1.3GB ONNX model on first run; CI-only"]
 fn mismatched_dims_in_config_errors_at_construction() {
     let (mut cfg, _tmp) = test_config();
-    cfg.models.embedding.dimensions = 512; // model is 384
+    cfg.models.embedding.dimensions = 512; // model is 1024 (e5-large default)
     // `FastembedEmbedder` deliberately does not implement `Debug`
     // (its inner ONNX session has no useful debug shape), so we
     // can't use `expect_err`; match the Result manually.
@@ -80,7 +82,7 @@ fn mismatched_dims_in_config_errors_at_construction() {
     };
     let msg = format!("{err}");
     assert!(msg.contains("dimension mismatch"), "msg={msg}");
-    assert!(msg.contains("384"), "msg={msg}");
+    assert!(msg.contains("1024"), "msg={msg}");
     assert!(msg.contains("512"), "msg={msg}");
 }
 
@@ -104,8 +106,8 @@ fn document_and_query_yield_different_vectors() {
         ])
         .expect("embed two inputs");
     assert_eq!(out.len(), 2);
-    assert_eq!(out[0].len(), 384);
-    assert_eq!(out[1].len(), 384);
+    assert_eq!(out[0].len(), 1024);
+    assert_eq!(out[1].len(), 1024);
 
     // Both vectors are L2-normalized → cosine similarity == dot product.
     let cos: f32 = out[0]
@@ -142,11 +144,11 @@ fn output_vectors_are_l2_normalized() {
     ];
     let out = emb.embed(&inputs).expect("embed");
     // Per `kebab_embed::assert_unit_norm` docs: `5e-4` is the safe bound at
-    // 384 dims (f32::EPSILON × √384 ≈ 2.3e-6, but ONNX kernels add
+    // 1024 dims (f32::EPSILON × √1024 ≈ 2.3e-6, but ONNX kernels add
     // their own per-component noise; 1e-3 is very generous and matches
     // the spec's `± 1e-3`).
     kebab_embed::assert_unit_norm(&out, 1e-3);
-    kebab_embed::assert_vector_shape(&out, 384);
+    kebab_embed::assert_vector_shape(&out, 1024);
 }
 
 // ─── determinism ──────────────────────────────────────────────────────
@@ -254,7 +256,7 @@ fn snapshot_aggregate_hash_is_stable() {
     // Round every component to 4 decimal places, hash deterministically.
     let mut hasher = DefaultHasher::new();
     for (i, v) in out.iter().enumerate() {
-        assert_eq!(v.len(), 384, "row {i} dim mismatch");
+        assert_eq!(v.len(), 1024, "row {i} dim mismatch");
         for x in v {
             let rounded: i32 = (*x * 1.0e4).round() as i32;
             rounded.hash(&mut hasher);

tasks/HOTFIXES.md

@@ -26,7 +26,9 @@ git history.
 
 **search/ask 결과**: re-embed 전까지는 empty hit (새 모델에 데이터가 없음). `kebab ingest` 후 정상 검색 가능.
 
-**Spec contract 와의 관계**: design §5 (storage) + §9 (versioning cascade) 의 embedding_model.id / dimensions 변경. embedding_version bump 아님 — 동일 binary 에서 config 만 변경해도 진행 가능 (cascade rule 준수).
+**Spec contract 와의 관계**: design §5 (storage) + §9 (versioning cascade) 의 embedding_model.id / dimensions 변경. wire 의 `embedding_version` 필드 (kebab-app schema.v1.models.embedding_version 가 config.models.embedding.model 값을 그대로 emit) 변경 — CLAUDE.md cascade rule 의 release 트리거. 본 PR 머지 후 `chore: bump version 0.5 → 0.6` + tag 필요.
+
+**Spec deviation**: design `2026-05-10-p9-fb-39b-embedding-upgrade-design.md` 의 §Migration policy + §Public surface delta 가 `LanceVectorStore::open` 안 신규 `error.v1.code = "embedding_dim_mismatch"` 명시했으나 구현 제외. 이유: LanceDB tables 가 `(model, dim)` namespaced — silent orphan + empty-hit 으로 surface (hard error 아님). 명시 error 필요 시 별도 startup health check 작업 필요 (fb-39c 후보 또는 doctor 확장).
 
 ## 2026-05-09 — p9-fb-34: search wire wrapped in search_response.v1
 

tasks/p9/p9-fb-39b-embedding-upgrade.md

@@ -62,14 +62,15 @@ source_feedback: 사용자 도그푸딩 2026-05-06 — Claude Code 가 kebab CLI
 ## Backward compat notes
 
 - Pre-fb-39b user 가 config 에서 명시하지 않은 embedding → new default (large) 자동 적용. TOML 에 `model = "multilingual-e5-small"` 명시하면 유지.
-- `kebab_app::config::Config` 의 `embedding_model` field 는 Optional 이므로 old config (small) 도 parse 성공 (v1 설계 §9 cascade 규칙).
+- `kebab-config` 의 `EmbeddingCfg.model` 은 String 필드 — TOML 에 명시한 값이 default 를 override (serde 기본 동작).
 - Orphan LanceDB table (`chunk_embeddings_multilingual-e5-small_384`) 은 다음 `kebab ingest` 실행 후 stale 취급 — 사용자가 수동 `kebab reset --vector-only` 로 정리 가능.
 
 ## Binary version bump
 
-- 0.6.0 → 0.7.0 (design §9 cascade rule: embedding_model change = minor bump).
+- 0.5.0 → 0.6.0 (current Cargo.toml = 0.5.0; embedding_version cascade triggers minor bump per design §9).
 - Release notes: `embedding default: multilingual-e5-small (384d) → multilingual-e5-large (1024d), P@k metric ↑`.
 
 ## Post-merge deviation
 
-None — 설계 contract 대로 구현 완료.
+- **`embedding_dim_mismatch` ErrorV1 dropped**: design spec §Migration policy 가 `LanceVectorStore::open` 안 dim mismatch 감지 + 신규 `error.v1.code = "embedding_dim_mismatch"` 를 명시했으나 구현에서 제외. 이유: LanceDB tables 가 `(model, dim)` namespaced (`crates/kebab-store-vector/src/paths.rs:21`) — 신규 model 변경 시 새 table 자동 생성, 옛 table orphan. dim mismatch 가 hard error 되지 않고 검색 결과 0건 (silent precision loss) 으로 surface. HOTFIXES 항목이 documentation source. 명시 error 가 의미 있으려면 별도 startup health check 필요 — fb-39c 후보 또는 v0.7.0 의 doctor 확장.
+- 영향: 사용자가 model 변경 후 `kebab ingest` 안 하면 검색 결과 0건. README + SMOKE walkthrough 가 reset --vector-only && ingest 시퀀스 안내.