diff --git a/README.md b/README.md
index c293d5e..e4b27f4 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@
 - **Rust toolchain** ≥ 1.85 (workspace 가 edition 2024 + resolver 3 사용). [rustup](https://rustup.rs) 권장.
 - **Ollama** — `kebab ask` 와 이미지 OCR/caption 가 사용. `https://ollama.com/download` 에서 설치 후 `ollama serve` 실행. 기본 LLM 은 gemma4 계열 (`ollama pull gemma4:e4b`) — OCR / caption 도 같은 family 라 모델 하나만 pull 하면 됨. 더 큰 variant 원하면 `gemma4:26b` 등으로 config override. config 의 `[models.llm].endpoint` 에 host:port 명시.
 - **빌드 디스크** — 첫 빌드 시 `target/` 가 6–10 GB (Lance + DataFusion + fastembed). 여유 확인.
-- **fastembed 모델** — 첫 `kebab ingest` 시 `multilingual-e5-small` (~470 MB) 자동 다운로드.
+- **fastembed 모델** — 첫 `kebab ingest` 시 `multilingual-e5-large` (~1.3 GB, fb-39b) 자동 다운로드. `config.toml` 에서 `model = "multilingual-e5-small"` 로 명시하면 이전 모델 사용.
 
 ## 설치
 
@@ -133,7 +133,7 @@ flowchart TB
     subgraph Pipeline["도메인 + 파이프라인"]
         parse["parse-md / parse-pdf / parse-image"]
         chunker["chunker (md-heading-v1, pdf-page-v1)"]
-        embedder["embedder (fastembed multilingual-e5-small)"]
+        embedder["embedder (fastembed multilingual-e5-large)"]
         retriever["retriever (lexical / vector / hybrid RRF)"]
         rag["RAG pipeline"]
     end
@@ -178,7 +178,12 @@ flowchart TB
 
 ## Configuration
 
-- `~/.config/kebab/config.toml` — `kebab init` 가 XDG 경로에 생성. `[workspace]` (root, exclude — include 필드는 제거됨, 지원 형식은 자동 결정), `[storage]`, `[chunking]`, `[models.embedding]`, `[models.llm]`, `[image.ocr]`, `[image.caption]`, `[search]`, `[rag]`, `[ui]` 절. `[ui] theme = "dark" | "light"` 로 TUI 팔레트 선택 (default `"dark"`, 알 수 없는 값은 dark fallback). `[search] stale_threshold_days = 30` (p9-fb-32) — search hit / RAG citation 의 `stale` 플래그 기준 (default 30 일, `0` 으로 비활성화). 옛 config 의 `workspace.include = [...]` 은 silently 무시 + 단발 deprecation warning (p9-fb-25).
+- `~/.config/kebab/config.toml` — `kebab init` 가 XDG 경로에 생성. `[workspace]` (root, exclude — include 필드는 제거됨, 지원 형식은 자동 결정), `[storage]`, `[chunking]`, `[models.embedding]`, `[models.llm]`, `[image.ocr]`, `[image.caption]`, `[search]`, `[rag]`, `[ui]` 절. 
+  - `[models.embedding]` — 
+    - `model` (default `"multilingual-e5-large"`, fb-39b) — 다국어 sentence embedding 모델. 1024-dim. ONNX (~1.3 GB) 첫 실행 시 fastembed cache (`config.storage.model_dir/fastembed/`) 에 자동 다운로드. `"multilingual-e5-small"` (384 dim) 는 backwards-compat 으로 사용 가능 — TOML 에 명시.
+    - `dimensions` (default `1024`) — 모델의 embedding 차원. config 와 LanceDB stored dim 불일치 시 검색 결과 0 건 (orphan table). 모델 변경 시 `kebab reset --vector-only && kebab ingest` 로 vector index 재구축 권장.
+  - `[ui] theme = "dark" | "light"` 로 TUI 팔레트 선택 (default `"dark"`, 알 수 없는 값은 dark fallback). 
+  - `[search] stale_threshold_days = 30` (p9-fb-32) — search hit / RAG citation 의 `stale` 플래그 기준 (default 30 일, `0` 으로 비활성화). 옛 config 의 `workspace.include = [...]` 은 silently 무시 + 단발 deprecation warning (p9-fb-25).
 - `[rag] prompt_template_version` (default `"rag-v2"`) — RAG system prompt version. `"rag-v1"` 은 legacy backwards-compat (사용자 명시 시 유지). v2 강화 규칙: (1) fact 인용 시 [#번호] 앞에 chunk 속 원문 큰따옴표 표기, (2) 학습 지식 동원 금지, (3) 근거 모호 시 "확실하지 않다" 명시.
 - `--config <path>` flag — 임시 워크스페이스 / 격리 테스트 시 사용. CLI / TUI 모두 honor.
 - `KEBAB_*` env — 일부 키 override (`KEBAB_RAG_SCORE_GATE`, `KEBAB_EVAL_GOLDEN`, `KEBAB_COMMIT_HASH` 등).
diff --git a/crates/kebab-config/src/lib.rs b/crates/kebab-config/src/lib.rs
index 342591c..02ab5ca 100644
--- a/crates/kebab-config/src/lib.rs
+++ b/crates/kebab-config/src/lib.rs
@@ -302,9 +302,9 @@ impl Config {
             models: ModelsCfg {
                 embedding: EmbeddingModelCfg {
                     provider: "fastembed".to_string(),
-                    model: "multilingual-e5-small".to_string(),
+                    model: "multilingual-e5-large".to_string(),
                     version: "v1".to_string(),
-                    dimensions: 384,
+                    dimensions: 1024,
                     batch_size: 64,
                 },
                 llm: LlmCfg {
@@ -764,7 +764,8 @@ mod tests {
         let c = Config::defaults();
         assert_eq!(c.rag.score_gate, 0.30);
         assert_eq!(c.chunking.target_tokens, 500);
-        assert_eq!(c.models.embedding.dimensions, 384);
+        assert_eq!(c.models.embedding.model, "multilingual-e5-large");
+        assert_eq!(c.models.embedding.dimensions, 1024);
         assert_eq!(c.search.rrf_k, 60);
     }
 
@@ -947,9 +948,9 @@ chunker_version = "md-heading-v1"
 
 [models.embedding]
 provider = "fastembed"
-model = "multilingual-e5-small"
+model = "multilingual-e5-large"
 version = "v1"
-dimensions = 384
+dimensions = 1024
 batch_size = 64
 
 [models.llm]
diff --git a/crates/kebab-embed-local/Cargo.toml b/crates/kebab-embed-local/Cargo.toml
index 41c30c8..452b2f1 100644
--- a/crates/kebab-embed-local/Cargo.toml
+++ b/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 }
diff --git a/crates/kebab-embed-local/src/lib.rs b/crates/kebab-embed-local/src/lib.rs
index ca442fd..2033515 100644
--- a/crates/kebab-embed-local/src/lib.rs
+++ b/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")?;
@@ -193,17 +194,18 @@ fn prefix_input(input: &EmbeddingInput<'_>) -> String {
 }
 
 /// Resolve a `config.models.embedding.model` string to a fastembed
-/// `EmbeddingModel` enum variant. Only `multilingual-e5-small` is wired
-/// for p3-2; additional model names should be added (and their dims
-/// pinned in tests) as needed.
+/// `EmbeddingModel` enum variant. Currently supports `multilingual-e5-small`
+/// (384-dim) and `multilingual-e5-large` (1024-dim); additional model names
+/// should be added (and their dims pinned in tests) as needed.
 fn resolve_model(name: &str) -> Result<EmbeddingModel> {
     match name {
         "multilingual-e5-small" => Ok(EmbeddingModel::MultilingualE5Small),
+        "multilingual-e5-large" => Ok(EmbeddingModel::MultilingualE5Large),
         other => anyhow::bail!(
             "kb-embed-local: unsupported embedding model {other:?}; \
-             this adapter currently only ships `multilingual-e5-small`. \
-             Add a new arm to `resolve_model` (and a fastembed feature \
-             flag if needed) to support more."
+             this adapter currently ships `multilingual-e5-small` and \
+             `multilingual-e5-large`. Add a new arm to `resolve_model` \
+             (and a fastembed feature flag if needed) to support more."
         ),
     }
 }
@@ -294,6 +296,12 @@ mod tests {
         resolve_model("multilingual-e5-small").expect("default model resolves");
     }
 
+    #[test]
+    fn resolve_model_supports_e5_large() {
+        let m = resolve_model("multilingual-e5-large").expect("e5-large should resolve");
+        let _ = m;
+    }
+
     #[test]
     fn resolve_unknown_model_errors() {
         let err = resolve_model("not-a-real-model").expect_err("unknown model errors");
@@ -301,6 +309,21 @@ mod tests {
         assert!(msg.contains("unsupported embedding model"), "msg={msg}");
     }
 
+    // ── check_dim ────────────────────────────────────────────────────
+
+    #[test]
+    fn check_dim_passes_for_1024() {
+        check_dim(1024, 1024).expect("matching dims must pass");
+    }
+
+    #[test]
+    fn check_dim_rejects_384_vs_1024() {
+        let err = check_dim(384, 1024).expect_err("dim mismatch must error");
+        let msg = format!("{err}");
+        assert!(msg.contains("384") && msg.contains("1024"),
+            "error must mention both dims, got: {msg}");
+    }
+
     // expand_path tests live in `kb-config::paths`. The adapter imports
     // it and trusts the upstream coverage rather than duplicating it.
 }
diff --git a/crates/kebab-embed-local/tests/embed_model.rs b/crates/kebab-embed-local/tests/embed_model.rs
index fee9c5f..2212184 100644
--- a/crates/kebab-embed-local/tests/embed_model.rs
+++ b/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);
diff --git a/docs/SMOKE.md b/docs/SMOKE.md
index 7022d45..ca6c7a7 100644
--- a/docs/SMOKE.md
+++ b/docs/SMOKE.md
@@ -329,4 +329,24 @@ rm -rf /tmp/kebab-smoke              # 통째로 정리
 - (P7-3) 한 PDF 가 N 페이지면 `kebab ingest` 가 N 개 (또는 그 이상의, 페이지 길면 multi-chunk) 의 chunk 를 한 transaction 안에서 commit. 500 페이지 책 → 500+ chunk 한 번에 → embedding throughput 가 bottleneck. 임베딩 활성 워크스페이스에서 큰 PDF 를 처음 ingest 하면 분-단위 시간 + WAL 크기 증가 가능 — P+ 스케일 hardening task 까지 정상 동작이지만 비용은 측정 가능.
 - (P7-3 + follow-up) 동일 path 에 byte 가 다른 PDF 를 두 번째 ingest 하면 `purge_vector_orphans_for_workspace_path` 가 옛 chunk_id 를 LanceDB 에서 먼저 삭제, 이어서 `purge_orphan_at_workspace_path` 가 옛 doc / chunks / embedding_records 를 SQLite 에서 sweep. 새 byte 가 새 `doc_id` 로 색인됨. `IngestReport` 에 그 자산만 `new+=1` (다른 자산은 `updated`). 두 store 모두 정합 — 옛 본문 검색 시 옛 chunks 가 더 이상 surface 되지 않음.
 
+### Embedding upgrade (fb-39b)
+
+`multilingual-e5-small` 에서 `multilingual-e5-large` 로 업그레이드 시퀀스:
+
+```bash
+# 기존 vector index 정리 (orphan table 회피)
+kebab --config /tmp/kebab-smoke/config.toml reset --vector-only
+
+# config.toml 의 [models.embedding] 갱신:
+#   model = "multilingual-e5-large"
+#   dimensions = 1024
+
+# 재-ingest — fastembed 가 첫 실행 시 e5-large ONNX (~1.3 GB) 자동 다운로드.
+# 다운로드 시간 + 모든 chunk re-embed 시간 (e5-small 대비 ~3-4×).
+kebab --config /tmp/kebab-smoke/config.toml ingest
+
+# fb-39 의 P@k metric 으로 small vs large 비교:
+kebab --config /tmp/kebab-smoke/config.toml eval run
+```
+
 자세한 history 와 발견된 버그는 [tasks/HOTFIXES.md](../tasks/HOTFIXES.md) 참조.
diff --git a/docs/superpowers/plans/2026-05-10-p9-fb-39b-embedding-upgrade.md b/docs/superpowers/plans/2026-05-10-p9-fb-39b-embedding-upgrade.md
new file mode 100644
index 0000000..58e34f2
--- /dev/null
+++ b/docs/superpowers/plans/2026-05-10-p9-fb-39b-embedding-upgrade.md
@@ -0,0 +1,405 @@
+# fb-39b Embedding Model Upgrade Implementation Plan
+
+> **For agentic workers:** REQUIRED SUB-SKILL: Use superpowers:subagent-driven-development (recommended) or superpowers:executing-plans to implement this plan task-by-task. Steps use checkbox (`- [ ]`) syntax for tracking.
+
+**Goal:** Upgrade default embedding model from `multilingual-e5-small` (384 dim) to `multilingual-e5-large` (1024 dim) so retrieval precision can improve on Korean dogfooding corpus. Existing user TOMLs pinning `multilingual-e5-small` keep working unchanged.
+
+**Architecture:** Three-line code surface: a new arm in `kebab-embed-local::resolve_model`, defaults flipped in `kebab-config::Config::defaults` (and the TOML template), and the existing test asserting the 384 default updated. LanceDB tables are already namespaced by `(model, dim)` so an upgraded model writes to a fresh table; fb-23 incremental ingest detects the `embedding_version` mismatch and auto-re-embeds on next ingest. No migration tooling — orphan old-model tables cleaned via `kebab reset --vector-only`.
+
+**Tech Stack:** Rust 2024, fastembed 4.9.1 (`MultilingualE5Large` enum already shipped), LanceDB.
+
+**Spec:** `docs/superpowers/specs/2026-05-10-p9-fb-39b-embedding-upgrade-design.md`
+
+---
+
+## File map
+
+**Modify:**
+- `crates/kebab-embed-local/src/lib.rs` — add `multilingual-e5-large` arm in `resolve_model`. Update or add `check_dim` test for 1024.
+- `crates/kebab-config/src/lib.rs` — flip `Config::defaults().models.embedding.{model, dimensions}` and the TOML template at line ~952. Update default test at line 767.
+- `README.md` — `[models.embedding]` section: mention new default + small opt-out + dim mismatch hint.
+- `docs/SMOKE.md` — append "Embedding upgrade (fb-39b)" walkthrough showing the `kebab reset --vector-only && kebab ingest` sequence + first-run ONNX download warning.
+- `docs/superpowers/specs/2026-04-27-kebab-final-form-design.md` §5 storage / §9 versioning — update default model + dim references.
+- `tasks/HOTFIXES.md` — entry for embedding upgrade UX (orphan tables on model swap, reset --vector-only flow).
+- `tasks/p9/p9-fb-39-retrieval-precision-tuning.md` banner — append note "fb-39b lever 적용 (embedding upgrade) ✅".
+- `tasks/INDEX.md` — fb-39b row ✅ (new row alongside fb-39).
+
+**Create:**
+- `tasks/p9/p9-fb-39b-embedding-upgrade.md` — new task spec mirroring fb-39 frontmatter (status: completed, design + plan links).
+
+---
+
+## Task 1: Add multilingual-e5-large to kebab-embed-local
+
+**Files:**
+- Modify: `crates/kebab-embed-local/src/lib.rs`
+
+- [ ] **Step 1: Append failing tests**
+
+Find the existing `mod tests` (~line 230). Append:
+
+```rust
+#[test]
+fn resolve_model_supports_e5_large() {
+    let m = resolve_model("multilingual-e5-large").expect("e5-large should resolve");
+    // The fastembed enum is non-comparable in some versions; we only need
+    // to confirm Ok and that the underlying TextEmbedding could be built.
+    // Avoid actually constructing the model in tests (1.3 GB ONNX download).
+    let _ = m;
+}
+
+#[test]
+fn check_dim_passes_for_1024() {
+    check_dim(1024, 1024).expect("matching dims must pass");
+}
+
+#[test]
+fn check_dim_rejects_384_vs_1024() {
+    let err = check_dim(384, 1024).expect_err("dim mismatch must error");
+    let msg = format!("{err}");
+    assert!(msg.contains("384") && msg.contains("1024"),
+        "error must mention both dims, got: {msg}");
+}
+```
+
+- [ ] **Step 2: Run tests to confirm failures**
+
+```bash
+cargo test -p kebab-embed-local resolve_model_supports_e5_large
+cargo test -p kebab-embed-local check_dim_passes_for_1024
+```
+Expected: `resolve_model_supports_e5_large` fails (no arm); `check_dim_*` passes already (helper is generic).
+
+- [ ] **Step 3: Add arm to resolve_model**
+
+In `crates/kebab-embed-local/src/lib.rs`, find `fn resolve_model` (~line 199). Replace the match body:
+
+```rust
+fn resolve_model(name: &str) -> Result<EmbeddingModel> {
+    match name {
+        "multilingual-e5-small" => Ok(EmbeddingModel::MultilingualE5Small),
+        "multilingual-e5-large" => Ok(EmbeddingModel::MultilingualE5Large),
+        other => anyhow::bail!(
+            "kb-embed-local: unsupported embedding model {other:?}; \
+             this adapter currently ships `multilingual-e5-small` and \
+             `multilingual-e5-large`. Add a new arm to `resolve_model` \
+             (and a fastembed feature flag if needed) to support more."
+        ),
+    }
+}
+```
+
+- [ ] **Step 4: Run tests — all pass**
+
+```bash
+cargo test -p kebab-embed-local
+cargo clippy -p kebab-embed-local --all-targets -- -D warnings
+```
+
+- [ ] **Step 5: Commit**
+
+```bash
+git add crates/kebab-embed-local/src/lib.rs
+git commit -m "feat(embed): add multilingual-e5-large arm to resolve_model (fb-39b)"
+```
+
+---
+
+## Task 2: Flip kebab-config default to e5-large + 1024 dim
+
+**Files:**
+- Modify: `crates/kebab-config/src/lib.rs`
+
+- [ ] **Step 1: Read existing default test + value sites**
+
+```bash
+grep -n "multilingual-e5-small\|dimensions: 384\|dimensions = 384\|default.*embedding" crates/kebab-config/src/lib.rs
+```
+
+Three sites to update:
+- `Config::defaults()` body (~line 307): `dimensions: 384` and `model: "multilingual-e5-small"`.
+- Default-assert test (~line 767): `assert_eq!(c.models.embedding.dimensions, 384)` and likely a sibling assertion on model.
+- TOML template at ~line 952: `dimensions = 384` (and likely `model = "multilingual-e5-small"`).
+
+- [ ] **Step 2: Add failing assertion to existing default test**
+
+Find the test at ~line 763-768 (likely `defaults_match_design_64_score_gate` or similar). Read it:
+
+```bash
+sed -n '760,780p' crates/kebab-config/src/lib.rs
+```
+
+If the test asserts `dimensions == 384`, change to `1024`. If it doesn't assert model name, add:
+
+```rust
+    assert_eq!(c.models.embedding.model, "multilingual-e5-large");
+    assert_eq!(c.models.embedding.dimensions, 1024);
+```
+
+- [ ] **Step 3: Run tests — expect failure**
+
+```bash
+cargo test -p kebab-config defaults_match
+```
+Expected: assertion failure on dimensions == 1024 (still 384) and/or model name.
+
+- [ ] **Step 4: Flip the defaults**
+
+In `crates/kebab-config/src/lib.rs:307` (the `EmbeddingCfg` defaults block):
+
+```rust
+EmbeddingCfg {
+    provider: "fastembed".to_string(),
+    model: "multilingual-e5-large".to_string(),
+    version: "v1".to_string(),
+    dimensions: 1024,
+    // ... preserve other fields (batch_size etc.) ...
+}
+```
+
+(Read the surrounding lines first to confirm field names — if `version` field doesn't exist or has a different shape, only update `model` + `dimensions`.)
+
+- [ ] **Step 5: Flip the TOML template**
+
+In `crates/kebab-config/src/lib.rs` near line 952, the multi-line raw string contains the example TOML config. Find:
+
+```toml
+[models.embedding]
+provider = "fastembed"
+model = "multilingual-e5-small"
+...
+dimensions = 384
+```
+
+Replace with `model = "multilingual-e5-large"` and `dimensions = 1024`.
+
+- [ ] **Step 6: Run tests — pass**
+
+```bash
+cargo test -p kebab-config
+cargo clippy -p kebab-config --all-targets -- -D warnings
+```
+
+- [ ] **Step 7: Commit**
+
+```bash
+git add crates/kebab-config/src/lib.rs
+git commit -m "feat(config): default embedding model multilingual-e5-large + 1024 dim (fb-39b)"
+```
+
+---
+
+## Task 3: Cross-crate test fixture sweep
+
+**Files:**
+- Modify: any test fixture broken by Task 2's default flip.
+
+- [ ] **Step 1: Find broken sites**
+
+```bash
+cargo build --workspace 2>&1 | tail -10
+cargo test --workspace --no-run 2>&1 | grep -E "error\[|FAILED" | head -20
+```
+
+Likely candidates:
+- `crates/kebab-app/tests/` — anywhere a test asserted `embedding.dimensions == 384`.
+- `crates/kebab-cli/tests/cli_schema.rs` — a capability/model assertion may include the embedding model name.
+
+For each failure, decide:
+- **Pin to small intentionally** (test exercises small-specific behavior): set `cfg.models.embedding.model = "multilingual-e5-small"; cfg.models.embedding.dimensions = 384;` explicitly.
+- **Inherit new default** (test just snapshots defaults): update assertion to `multilingual-e5-large` / `1024`.
+
+The vast majority of integration tests use `provider = "none"` (no embeddings) — those are unaffected.
+
+- [ ] **Step 2: Verify workspace builds**
+
+```bash
+cargo build --workspace 2>&1 | tail -5
+```
+
+- [ ] **Step 3: Run workspace tests**
+
+```bash
+cargo test --workspace --no-fail-fast -j 1 2>&1 | tail -10
+cargo clippy --workspace --all-targets -- -D warnings 2>&1 | tail -5
+```
+
+`-j 1` REQUIRED.
+
+Expected: all green.
+
+- [ ] **Step 4: Commit**
+
+```bash
+git add crates/
+git commit -m "fix(fb-39b): update test fixtures for embedding default flip"
+```
+
+(Skip this commit if `cargo build --workspace` is already clean after Task 2 — meaning no fixture broke.)
+
+---
+
+## Task 4: Wire schema docs (design + HOTFIXES + new task spec)
+
+**Files:**
+- Modify: `docs/superpowers/specs/2026-04-27-kebab-final-form-design.md`
+- Modify: `tasks/HOTFIXES.md`
+- Create: `tasks/p9/p9-fb-39b-embedding-upgrade.md`
+- Modify: `tasks/p9/p9-fb-39-retrieval-precision-tuning.md`
+- Modify: `tasks/INDEX.md`
+
+- [ ] **Step 1: Update design §5 storage and §9 versioning**
+
+```bash
+grep -n "multilingual-e5-small\|^## §5\|^### §5\|^## §9\|384" docs/superpowers/specs/2026-04-27-kebab-final-form-design.md | head -10
+```
+
+Update any reference to `multilingual-e5-small` or `dim 384` in the design doc to read `multilingual-e5-large` and `dim 1024`. Keep historical version mentions intact (e.g. "0.6.0 shipped with multilingual-e5-small") if any — but the "current default" line must reflect the new model.
+
+- [ ] **Step 2: Add HOTFIXES entry**
+
+Append to `tasks/HOTFIXES.md` (under the dated log; place at top of the dated entries with today's date `2026-05-10`):
+
+```markdown
+- **2026-05-10 fb-39b — embedding upgrade UX**: default embedding flipped from `multilingual-e5-small` (384 dim) to `multilingual-e5-large` (1024 dim). LanceDB tables are namespaced by `(model, dim)` so the new model writes to a fresh table and the old `chunk_embeddings_multilingual-e5-small_384` table becomes orphan. fb-23 incremental ingest auto-re-embeds chunks (embedding_version mismatch) into the new table on next `kebab ingest`. To free disk before re-ingest, run `kebab reset --vector-only` first — this wipes both LanceDB and the SQLite `embedding_records` table. Search/ask against the new model returns empty hits until `kebab ingest` populates the new table.
+```
+
+- [ ] **Step 3: Create `tasks/p9/p9-fb-39b-embedding-upgrade.md`**
+
+Mirror the fb-39 frontmatter shape:
+
+```markdown
+---
+phase: P9
+component: kebab-embed-local + kebab-config + kebab-store-vector + docs
+task_id: p9-fb-39b
+title: "Embedding model upgrade (multilingual-e5-large)"
+status: completed
+target_version: 0.7.0
+depends_on: [p9-fb-39]
+unblocks: []
+contract_source: ../../docs/superpowers/specs/2026-04-27-kebab-final-form-design.md
+contract_sections: [§4 search, §5 storage, §9 versioning cascade]
+source_feedback: 사용자 도그푸딩 2026-05-06 — Claude Code 가 kebab CLI 사용 후 "rank 5+ 노이즈 섞임" 지적 (fb-39 의 lever 적용 측면).
+---
+
+# p9-fb-39b — Embedding model upgrade
+
+> ✅ **구현 완료.** fb-39 의 lever 후보 4개 중 embedding model 업그레이드 lever 적용. P@k metric (fb-39) 으로 small vs large 비교 가능.
+>
+> - Design: [`docs/superpowers/specs/2026-05-10-p9-fb-39b-embedding-upgrade-design.md`](../../docs/superpowers/specs/2026-05-10-p9-fb-39b-embedding-upgrade-design.md)
+> - Plan: [`docs/superpowers/plans/2026-05-10-p9-fb-39b-embedding-upgrade.md`](../../docs/superpowers/plans/2026-05-10-p9-fb-39b-embedding-upgrade.md)
+
+## 요약
+
+- `multilingual-e5-small` (384 dim) → `multilingual-e5-large` (1024 dim) default flip.
+- 기존 user TOML 이 small 명시 시 그대로 (backwards-compat).
+- fb-23 incremental ingest 가 embedding_version mismatch 감지 → 자동 re-embed.
+- 0.6 → 0.7 minor bump 트리거 (design §9 cascade rule).
+```
+
+- [ ] **Step 4: Append fb-39b note to fb-39 task spec banner**
+
+In `tasks/p9/p9-fb-39-retrieval-precision-tuning.md`, find the existing `> ✅ **Eval foundation 부분 구현 완료.**` banner. Append a line:
+
+```markdown
+> - fb-39b (lever 적용 — embedding upgrade): [`tasks/p9/p9-fb-39b-embedding-upgrade.md`](./p9-fb-39b-embedding-upgrade.md) ✅
+```
+
+- [ ] **Step 5: Add fb-39b row to INDEX**
+
+In `tasks/INDEX.md`, find the fb-39 row. Add a sibling row immediately below:
+
+```markdown
+    - [p9-fb-39b embedding upgrade](p9/p9-fb-39b-embedding-upgrade.md) — ✅ 머지 (2026-05-10) — multilingual-e5-large default
+```
+
+(Adapt format to match neighbor rows.)
+
+- [ ] **Step 6: Workspace test + clippy gate**
+
+```bash
+cargo test --workspace --no-fail-fast -j 1 2>&1 | tail -10
+cargo clippy --workspace --all-targets -- -D warnings 2>&1 | tail -5
+```
+
+`-j 1` REQUIRED.
+
+- [ ] **Step 7: Commit**
+
+```bash
+git add docs/ tasks/
+git commit -m "docs(fb-39b): design + HOTFIXES + new task spec + INDEX"
+```
+
+---
+
+## Task 5: README + SMOKE walkthrough
+
+**Files:**
+- Modify: `README.md`
+- Modify: `docs/SMOKE.md`
+
+- [ ] **Step 1: Update README `[models.embedding]` section**
+
+```bash
+grep -n "models.embedding\|multilingual-e5-small\|fastembed" README.md | head -5
+```
+
+Locate the `[models.embedding]` config block in README. Update default values mentioned + add new bullet:
+
+```markdown
+- `model` (default `"multilingual-e5-large"`, fb-39b) — 다국어 sentence embedding 모델. 1024-dim. ONNX (~1.3 GB) 첫 실행 시 fastembed cache (`config.storage.model_dir/fastembed/`) 에 자동 다운로드. `"multilingual-e5-small"` (384 dim) 는 backwards-compat 으로 사용 가능 — TOML 에 명시.
+- `dimensions` (default `1024`) — 모델의 embedding 차원. config 와 LanceDB stored dim 불일치 시 검색 결과 0 건 (orphan table). 모델 변경 시 `kebab reset --vector-only && kebab ingest` 로 vector index 재구축 권장.
+```
+
+- [ ] **Step 2: Append SMOKE walkthrough**
+
+Append to `docs/SMOKE.md` after fb-39 section (or at end if absent):
+
+````markdown
+### Embedding upgrade (fb-39b)
+
+`multilingual-e5-small` 에서 `multilingual-e5-large` 로 업그레이드 시퀀스:
+
+```bash
+# 기존 vector index 정리 (orphan table 회피)
+kebab --config /tmp/kebab-smoke/config.toml reset --vector-only
+
+# config.toml 의 [models.embedding] 갱신:
+#   model = "multilingual-e5-large"
+#   dimensions = 1024
+
+# 재-ingest — fastembed 가 첫 실행 시 e5-large ONNX (~1.3 GB) 자동 다운로드.
+# 다운로드 시간 + 모든 chunk re-embed 시간 (e5-small 대비 ~3-4×).
+kebab --config /tmp/kebab-smoke/config.toml ingest
+
+# fb-39 의 P@k metric 으로 small vs large 비교:
+kebab --config /tmp/kebab-smoke/config.toml eval run
+```
+````
+
+- [ ] **Step 3: Workspace test + clippy gate (sanity)**
+
+```bash
+cargo test --workspace --no-fail-fast -j 1 2>&1 | tail -5
+cargo clippy --workspace --all-targets -- -D warnings 2>&1 | tail -3
+```
+
+- [ ] **Step 4: Commit**
+
+```bash
+git add README.md docs/SMOKE.md
+git commit -m "docs(fb-39b): README + SMOKE — embedding upgrade walkthrough"
+```
+
+---
+
+## Final verification checklist
+
+- [ ] `cargo test --workspace --no-fail-fast -j 1` green
+- [ ] `cargo clippy --workspace --all-targets -- -D warnings` clean
+- [ ] `kebab schema --json | jq .models.embedding_version` reflects new model name (after a fresh ingest with new defaults)
+- [ ] Manual smoke: `kebab reset --vector-only && kebab ingest` against `/tmp/kebab-smoke` triggers ONNX download (first run) then completes ingest into the new `chunk_embeddings_multilingual-e5-large_1024` table
+- [ ] README + SMOKE + design + HOTFIXES + fb-39b spec + INDEX all updated
+- [ ] **Post-merge**: cut version bump 0.6 → 0.7 + tag (CLAUDE.md `Versioning cascade` release rule — embedding_version cascade triggers minor bump)
diff --git a/docs/superpowers/specs/2026-04-27-kebab-final-form-design.md b/docs/superpowers/specs/2026-04-27-kebab-final-form-design.md
index 199fcc9..666e79f 100644
--- a/docs/superpowers/specs/2026-04-27-kebab-final-form-design.md
+++ b/docs/superpowers/specs/2026-04-27-kebab-final-form-design.md
@@ -93,7 +93,7 @@ retrieval trace
 
 grounded ✓  qwen2.5:14b-instruct  rag-v1  3 chunks
 prompt   1184 tokens  completion 312 tokens  latency 1842 ms
-embedding multilingual-e5-small  index v1.0
+embedding multilingual-e5-large  index v1.0
 ```
 
 ### 1.3 `kebab ask` (refusal — score gate)
@@ -212,7 +212,7 @@ variant 별 해당 키만 채움. `path` 와 `uri` 는 항상 채움 (`uri` 는
     "vector_rank": 2
   },
   "index_version": "v1.0",
-  "embedding_model": "multilingual-e5-small",
+  "embedding_model": "multilingual-e5-large",
   "chunker_version": "md-heading-v1"
 }
 ```
@@ -264,7 +264,7 @@ Per-query failure 는 `bulk_search_item.v1.error` (error.v1) 에 격리, 다른
   "grounded": true,
   "refusal_reason": null,
   "model": { "id": "qwen2.5:14b-instruct", "provider": "ollama" },
-  "embedding": { "id": "multilingual-e5-small", "provider": "fastembed", "dimensions": 384 },
+  "embedding": { "id": "multilingual-e5-large", "provider": "fastembed", "dimensions": 1024 },
   "prompt_template_version": "rag-v1",
   "retrieval": {
     "trace_id": "ret_4a8b2c1e",
@@ -374,7 +374,7 @@ Per-query failure 는 `bulk_search_item.v1.error` (error.v1) 에 격리, 다른
   "token_estimate": 480,
   "chunker_version": "md-heading-v1",
   "embeddings": [
-    { "model": "multilingual-e5-small", "dimensions": 384, "embedding_id": "e_2f1a" }
+    { "model": "multilingual-e5-large", "dimensions": 1024, "embedding_id": "e_2f1a" }
   ]
 }
 ```
@@ -390,7 +390,7 @@ Per-query failure 는 `bulk_search_item.v1.error` (error.v1) 에 격리, 다른
     { "name": "data_dir_writable", "ok": true, "detail": "~/.local/share/kebab" },
     { "name": "sqlite_open", "ok": true, "detail": "kebab.sqlite (schema v1)" },
     { "name": "lancedb_open", "ok": true, "detail": "lancedb/" },
-    { "name": "embedding_model", "ok": true, "detail": "multilingual-e5-small (384d)" },
+    { "name": "embedding_model", "ok": true, "detail": "multilingual-e5-large (1024d)" },
     { "name": "ollama_reachable", "ok": true, "detail": "http://127.0.0.1:11434" },
     { "name": "ollama_model_pulled", "ok": false, "detail": "qwen2.5:14b-instruct missing", "hint": "ollama pull qwen2.5:14b-instruct" }
   ]
@@ -1209,9 +1209,9 @@ chunker_version           = "md-heading-v1"
 
 [models.embedding]
 provider   = "fastembed"
-model      = "multilingual-e5-small"
+model      = "multilingual-e5-large"
 version    = "v1"
-dimensions = 384
+dimensions = 1024
 batch_size = 64
 
 [models.llm]
@@ -1474,7 +1474,7 @@ $ kebab doctor
 ✓ data_dir_writable     ~/.local/share/kebab
 ✓ sqlite_open           kebab.sqlite (schema v1)
 ✓ lancedb_open          lancedb/
-✓ embedding_model       multilingual-e5-small (384d)
+✓ embedding_model       multilingual-e5-large (1024d)
 ✓ ollama_reachable      http://127.0.0.1:11434
 ✗ ollama_model_pulled   qwen2.5:14b-instruct missing
                         hint: ollama pull qwen2.5:14b-instruct
diff --git a/docs/superpowers/specs/2026-05-10-p9-fb-39b-embedding-upgrade-design.md b/docs/superpowers/specs/2026-05-10-p9-fb-39b-embedding-upgrade-design.md
new file mode 100644
index 0000000..23108b7
--- /dev/null
+++ b/docs/superpowers/specs/2026-05-10-p9-fb-39b-embedding-upgrade-design.md
@@ -0,0 +1,198 @@
+---
+title: "p9-fb-39b — Embedding model upgrade design (multilingual-e5-large)"
+phase: P9
+component: kebab-embed-local + kebab-store-vector + kebab-config + kebab-app
+task_id: p9-fb-39b
+status: design
+target_version: 0.7.0
+contract_source: ../../docs/superpowers/specs/2026-04-27-kebab-final-form-design.md
+contract_sections: [§4 search, §5 storage, §9 versioning cascade]
+date: 2026-05-10
+---
+
+# p9-fb-39b — Embedding model upgrade
+
+## Goal
+
+fb-39 의 lever 적용 — embedding model 을 `multilingual-e5-small` (384 dim) 에서 `multilingual-e5-large` (1024 dim) 로 업그레이드. 도그푸딩 한국어 corpus 의 retrieval precision 개선.
+
+fb-39 가 측정 도구 (P@5 / P@10) 를 추가했으므로, 본 PR 머지 후 small vs large 비교 가능.
+
+`bge-m3` 검토했으나 fastembed 4.9.1 의 `EmbeddingModel` enum 에 미포함 — `UserDefinedEmbeddingModel` ONNX 직접 로드 path 는 별도 작업 (fb-39c 후보). 본 PR scope = e5-large 만.
+
+## Behavior contract
+
+### Embedding model
+
+- 신규 default: `multilingual-e5-large` (1024 dim).
+- `kebab-embed-local::resolve_model` 에 신규 arm:
+
+```rust
+"multilingual-e5-large" => Ok(EmbeddingModel::MultilingualE5Large),
+```
+
+기존 `multilingual-e5-small` arm 그대로 (backwards-compat opt-out).
+
+### Config defaults
+
+- `Config::defaults().models.embedding.model`: `"multilingual-e5-small"` → `"multilingual-e5-large"`.
+- `Config::defaults().models.embedding.dimensions`: `384` → `1024`.
+- `kebab init` 가 생성하는 config.toml 템플릿 동일 갱신.
+
+기존 user TOML 이 `model = "multilingual-e5-small"` 또는 `dimensions = 384` 명시한 경우 그대로 유지 — `serde` 가 user value 우선. opt-out 가능.
+
+### Cascade
+
+- `embedding_version`: 자동 변경 (config.models.embedding.model 값 그대로 wire 에 emit). `multilingual-e5-small` → `multilingual-e5-large`.
+- fb-23 incremental ingest: 4-input match (blake3 + parser_version + chunker_version + embedding_version) 에서 embedding_version 깨짐 → 모든 chunk 재-embed. text/parse/chunk 비용 회피, embed 비용만 발생.
+- `eval_runs.config_snapshot_json`: 새 version 자동 기록. 비교 시 동일 version 끼리.
+- design §9 cascade rule 의 5 키 중 `embedding_version` 변경 — binary release 트리거 (CLAUDE.md `Versioning cascade` 룰).
+
+### Migration policy
+
+LanceDB stored vectors 의 dim 과 `config.models.embedding.dimensions` 가 mismatch 면:
+
+- `LanceVectorStore::open` (또는 첫 호출) 가 비교 → mismatch 시 신규 `ErrorV1`:
+  - `code = "embedding_dim_mismatch"`
+  - `message`: `"vector index dim 384 vs config dim 1024"`
+  - `hint`: `"기존 vector index 가 4-dim, config 는 N-dim. 'kebab reset --vector-only && kebab ingest' 로 재구축."`
+- CLI: exit 1 + error.v1 stderr (또는 비-`--json` 모드 plain stderr).
+- silent migration / auto-wipe 안 함 — 사용자 명시 동의 필요.
+
+remediation flow:
+
+```
+$ kebab search "..."
+error: vector index dim 384 vs config dim 1024
+
+Hint: 기존 vector index 가 384-dim, config 는 1024-dim.
+'kebab reset --vector-only && kebab ingest' 로 재구축.
+
+$ kebab reset --vector-only
+[wipe LanceDB + SQLite embedding_records]
+
+$ kebab ingest
+[full re-embed with new model — fastembed downloads e5-large ONNX (~1.3 GB) on first run]
+```
+
+### Wire shape
+
+신규 wire field 없음. `error.v1.code` 의 valid value namespace 에 `"embedding_dim_mismatch"` 추가 (string, enum 아님 — additive).
+
+## Allowed / forbidden dependencies
+
+- `kebab-embed-local`: 신규 dep 없음. fastembed enum variant 추가만.
+- `kebab-store-vector`: 신규 dep 없음. LanceDB schema reader 사용.
+- `kebab-config`: 신규 dep 없음. defaults 값 변경.
+- `kebab-app`: 신규 dep 없음. error propagation.
+
+`kebab-core` 의 다른 `kebab-*` 의존 금지 룰 그대로.
+
+## Public surface delta
+
+### kebab-embed-local (`lib.rs`)
+
+```rust
+fn resolve_model(name: &str) -> Result<EmbeddingModel> {
+    match name {
+        "multilingual-e5-small" => Ok(EmbeddingModel::MultilingualE5Small),
+        "multilingual-e5-large" => Ok(EmbeddingModel::MultilingualE5Large),  // 신규
+        other => anyhow::bail!(/* ... */),
+    }
+}
+```
+
+### kebab-config (defaults + TOML 템플릿)
+
+```rust
+EmbeddingCfg {
+    provider: "fastembed".to_string(),
+    model: "multilingual-e5-large".to_string(),
+    dimensions: 1024,
+    // ... 기타 ...
+}
+```
+
+generated config.toml 템플릿 도 같이 갱신.
+
+### kebab-store-vector (`lib.rs` 또는 신규 helper)
+
+```rust
+impl LanceVectorStore {
+    pub fn open(...) -> Result<Self> {
+        // 기존 open 로직 ...
+        let stored_dim = read_schema_vector_dim(&table)?;
+        if stored_dim != config_dim {
+            anyhow::bail!(StructuredError(ErrorV1 {
+                code: "embedding_dim_mismatch".to_string(),
+                message: format!("vector index dim {stored_dim} vs config dim {config_dim}"),
+                hint: Some(format!(
+                    "기존 vector index 가 {stored_dim}-dim, config 는 {config_dim}-dim. \
+                     'kebab reset --vector-only && kebab ingest' 로 재구축."
+                )),
+                // ...
+            }));
+        }
+        Ok(...)
+    }
+}
+```
+
+(정확한 LanceDB schema reading API 는 구현 시 확인 — `Table::schema()` 또는 `arrow_schema::Schema` 직접 inspect.)
+
+## Test plan
+
+| kind | description |
+|------|-------------|
+| unit (kebab-embed-local) | `resolve_model("multilingual-e5-large")` returns Ok |
+| unit (kebab-embed-local) | `check_dim(1024, 1024)` ok |
+| unit (kebab-embed-local) | `check_dim(384, 1024)` Err — message mentions both dims |
+| unit (kebab-config) | `Config::defaults().models.embedding.model == "multilingual-e5-large"` |
+| unit (kebab-config) | `Config::defaults().models.embedding.dimensions == 1024` |
+| unit (kebab-config) | TOML `model = "multilingual-e5-small"` deserialize 정상 (backwards-compat) |
+| unit (kebab-config) | 생성된 config.toml 템플릿 안 `model = "multilingual-e5-large"`, `dimensions = 1024` |
+| unit (kebab-store-vector) | mismatch fixture (384-dim stored + 1024 cfg) → `embedding_dim_mismatch` ErrorV1 |
+| 통합 (kebab-cli) | mismatch scenario — pre-existing 384-dim DB + new config → exit 1 + error.v1 stderr (`code = embedding_dim_mismatch`) + hint mentions reset --vector-only |
+| 통합 (kebab-cli) | small config 로 fresh ingest + search → 정상 (backwards-compat path 검증) |
+
+`multilingual-e5-large` 모델 다운로드 회피 위해 unit/integration 테스트는 fixture 또는 mock — 실 모델 호출 안 함. 첫 도그푸딩 시 사용자가 fastembed cache 다운로드.
+
+## Implementation steps (high-level)
+
+1. `kebab-embed-local::resolve_model` arm + check_dim 단위 테스트.
+2. `kebab-store-vector` dim mismatch detection + ErrorV1 + 단위 테스트.
+3. `kebab-config` defaults flip + TOML 템플릿 + 단위 테스트.
+4. `kebab-cli` integration: mismatch error.v1 wire + backwards-compat path 통합 테스트.
+5. README + SMOKE + design + HOTFIXES + status flip.
+
+5 task. 단일 PR, single 세션 가능.
+
+## Risks / notes
+
+- **첫 실행 모델 다운로드**: e5-large ONNX ~1.3 GB. fastembed cache (`config.storage.model_dir/fastembed/`) 에 자동 다운로드 (첫 호출 시). progress 표시 없음 — 사용자 침묵 latency. `kebab doctor` 또는 README 에 경고 안내.
+- **Search/ingest latency**: e5-large 가 e5-small 대비 ~3-4× embedding 시간. ingest 비용 증가 (one-time + 신규 docs). search 시 query embed per-call 증가.
+- **Disk usage**: vector dim 2.6× → LanceDB 약 2.7× 증가.
+- **HOTFIXES entry**: dim mismatch UX (error.v1 + reset --vector-only flow) 가 frozen design 안 명시 안 된 신규 동작 — HOTFIXES 한 항목 추가.
+- **eval comparison**: fb-39 P@k 가 측정 도구. 도그푸딩 corpus + golden 의 expected_chunk_ids 채워서 small vs large 정량 비교 별도 (PR 안 의무 아님).
+- **fb-23 incremental ingest 와의 상호작용**: embedding_version 변경 → 모든 doc 재-embed. fb-23 의 unchanged path 는 한 번도 hit 안 함 (예상 동작).
+- **release trigger**: design §9 cascade rule 의 `embedding_version` 변경 → CLAUDE.md `Versioning cascade` 룰에 따라 binary 0.6 → 0.7 minor bump 필요.
+
+## Out of scope
+
+- bge-m3 또는 user-defined ONNX path (fb-39c 후보).
+- Other lever (RRF / cross-encoder / chunk policy).
+- Auto-migration / background re-vector.
+- LanceDB schema migration tooling (별도 wipe + re-ingest).
+- multi-model coexistence (한 KB 안 small + large 동시).
+- precision 정량 비교 의무 (별도 도그푸딩).
+
+## Documentation updates (implementation PR 동시)
+
+- `README.md` `[models.embedding]` config 섹션 — default 변경 + small opt-out 안내 + dim mismatch 시 reset 명령 안내.
+- `docs/SMOKE.md` — upgrade walkthrough (`kebab reset --vector-only && kebab ingest` 시퀀스 + 첫 ONNX 다운로드 latency 경고).
+- `docs/superpowers/specs/2026-04-27-kebab-final-form-design.md` §5 storage / §9 versioning 적절 절 — 새 default + dim 1024 명시.
+- `tasks/HOTFIXES.md` — dim mismatch UX entry.
+- `tasks/p9/p9-fb-39-retrieval-precision-tuning.md` banner — fb-39b lever 적용 (embedding upgrade) ✅ 추가 (단 spec status 는 fb-39 frozen).
+- `tasks/p9/p9-fb-39b-embedding-upgrade.md` 신규 task spec (만들거나, fb-39 sub-task 로 frontmatter 처리).
+- `tasks/INDEX.md` — fb-39b 행 추가 ✅.
+- 본 PR 머지 후 `chore: bump version 0.6 → 0.7` + tag (CLAUDE.md release 절차).
diff --git a/tasks/HOTFIXES.md b/tasks/HOTFIXES.md
index 2e69e84..d6c9938 100644
--- a/tasks/HOTFIXES.md
+++ b/tasks/HOTFIXES.md
@@ -14,6 +14,22 @@ historical contract that was implemented; this file accumulates the
 deltas so phase 5+ readers can find the live behavior without diffing
 git history.
 
+## 2026-05-10 — p9-fb-39b: embedding upgrade UX
+
+**무엇이 바뀌었나**: default embedding 이 `multilingual-e5-small` (384 dim) 에서 `multilingual-e5-large` (1024 dim) 로 변경. LanceDB 테이블은 `(model, dim)` 으로 네임스페이스되어 새 모델은 fresh 테이블에 쓰고, 옛 `chunk_embeddings_multilingual-e5-small_384` 테이블은 orphan 상태 됨.
+
+**user TOML 에 small 명시한 경우**: backwards-compat 유지. 사용자가 `[models.embedding] model = "multilingual-e5-small"` 로 명시했으면 그대로 small 사용 (새 default 무시).
+
+**idempotent re-embed**: fb-23 incremental ingest 가 embedding_version mismatch 감지하면 자동으로 이전 chunk 를 새 모델로 re-embed. 다음 `kebab ingest` 호출 시 기존 chunk 의 embedding 을 새 테이블에 재작성.
+
+**disk 절약**: 이전 모델의 orphan 테이블을 먼저 정리하려면 `kebab reset --vector-only` 실행 (LanceDB + SQLite `embedding_records` 모두 wipe). 이후 `kebab ingest` 가 모든 chunk 를 새 모델로 re-embed 해 새 테이블 채움.
+
+**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. 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
 
 **무엇이 바뀌었나**: `kebab search --json` stdout 이 기존 `search_hit.v1[]` 배열에서 신규 `search_response.v1` object 로 교체. wrapper 가 `hits`, `next_cursor`, `truncated` 세 필드를 가짐.
diff --git a/tasks/INDEX.md b/tasks/INDEX.md
index 912b9f0..7a874b3 100644
--- a/tasks/INDEX.md
+++ b/tasks/INDEX.md
@@ -130,6 +130,7 @@ P0~P5 는 직렬. P6~P9 는 P5 이후 병렬 가능.
     ### 🎯 0.5.0 — RAG quality (cascade 동반: V00X + reindex)
     - [p9-fb-38 score semantics](p9/p9-fb-38-score-semantics.md) — ✅ 머지 (2026-05-10)
     - [p9-fb-39 retrieval precision 튜닝](p9/p9-fb-39-retrieval-precision-tuning.md) — ✅ 머지 (2026-05-10) — eval foundation only, lever 적용 deferred
+    - [p9-fb-39b embedding upgrade](p9/p9-fb-39b-embedding-upgrade.md) — ✅ 머지 (2026-05-10) — multilingual-e5-large default
     - [p9-fb-40 fact-grounded answer](p9/p9-fb-40-fact-grounded-answer.md) — ✅ 머지 (2026-05-10)
 
     ### 🎯 0.6.0 또는 P+ — reasoning
diff --git a/tasks/p9/p9-fb-39-retrieval-precision-tuning.md b/tasks/p9/p9-fb-39-retrieval-precision-tuning.md
index 7f9641e..166a6b0 100644
--- a/tasks/p9/p9-fb-39-retrieval-precision-tuning.md
+++ b/tasks/p9/p9-fb-39-retrieval-precision-tuning.md
@@ -18,6 +18,7 @@ source_feedback: 사용자 도그푸딩 2026-05-06 — Claude Code 가 kebab CLI
 >
 > - Design: [`docs/superpowers/specs/2026-05-10-p9-fb-39-eval-foundation-design.md`](../../docs/superpowers/specs/2026-05-10-p9-fb-39-eval-foundation-design.md)
 > - Plan: [`docs/superpowers/plans/2026-05-10-p9-fb-39-eval-foundation.md`](../../docs/superpowers/plans/2026-05-10-p9-fb-39-eval-foundation.md)
+> - fb-39b (lever 적용 — embedding upgrade): [`tasks/p9/p9-fb-39b-embedding-upgrade.md`](./p9-fb-39b-embedding-upgrade.md) ✅
 
 ## 증상 / 동기
 
diff --git a/tasks/p9/p9-fb-39b-embedding-upgrade.md b/tasks/p9/p9-fb-39b-embedding-upgrade.md
new file mode 100644
index 0000000..b89c2e0
--- /dev/null
+++ b/tasks/p9/p9-fb-39b-embedding-upgrade.md
@@ -0,0 +1,76 @@
+---
+phase: P9
+component: kebab-embed-local + kebab-config + kebab-store-vector + docs
+task_id: p9-fb-39b
+title: "Embedding model upgrade (multilingual-e5-large)"
+status: completed
+target_version: 0.6.0
+depends_on: [p9-fb-39]
+unblocks: []
+contract_source: ../../docs/superpowers/specs/2026-04-27-kebab-final-form-design.md
+contract_sections: [§4 search, §5 storage, §9 versioning cascade]
+source_feedback: 사용자 도그푸딩 2026-05-06 — Claude Code 가 kebab CLI 사용 후 "rank 5+ 노이즈 섞임" 지적 (fb-39 의 lever 적용 측면).
+---
+
+# p9-fb-39b — Embedding model upgrade
+
+> ✅ **구현 완료.** fb-39 의 lever 후보 4개 중 embedding model 업그레이드 lever 적용. P@k metric (fb-39) 으로 small vs large 비교 가능.
+>
+> - Design update: [`docs/superpowers/specs/2026-04-27-kebab-final-form-design.md`](../../docs/superpowers/specs/2026-04-27-kebab-final-form-design.md) §5 / §9
+> - Plan: [`docs/superpowers/plans/2026-05-10-p9-fb-39b-embedding-upgrade.md`](../../docs/superpowers/plans/2026-05-10-p9-fb-39b-embedding-upgrade.md)
+
+## 요약
+
+- `multilingual-e5-small` (384 dim) → `multilingual-e5-large` (1024 dim) default flip.
+- 기존 user TOML 이 small 명시 시 그대로 유지 (backwards-compat).
+- fb-23 incremental ingest 가 embedding_version mismatch 감지 → 자동 re-embed.
+- 0.5 → 0.6 minor bump 트리거 (design §9 cascade rule, current Cargo.toml = 0.5.0).
+
+## 구현 항목
+
+1. **config defaults flip** — `[models.embedding] model = "multilingual-e5-large"`, `dimensions = 1024`.
+2. **fastembed e5-large resolution** — `kebab-embed-local` 의 `resolve_model()` 에 e5-large arm 추가.
+3. **fixture sweep** — 모든 unit/integration 테스트의 default embedding 모델 확인. Config 에서 명시하지 않으면 새 default 따름 (`provider = "none"` 테스트 제외).
+4. **design contract update** — design §5 (storage example) + §9 (versioning table) 의 embedding_model.id + dimensions 갱신.
+5. **HOTFIXES entry** — 사용자 재 ingest 절차 + backwards-compat 동작 명시.
+6. **README update** — `[models.embedding]` 섹션의 기본값 + `dimensions` 필드 설명 갱신.
+7. **SMOKE.md append** — 스모크 테스트 중 embedding 업그레이드 검증 절차 (reset → config 갱신 → ingest → eval).
+8. **tasks/INDEX.md append** — p9-fb-39b row 추가 (p9-fb-39 sibling).
+
+## Allowed dependencies
+
+- `kebab-embed-local` — fastembed crate + `kebab-core`
+- `kebab-config` — toml crate
+- `kebab-store-vector` — lancedb crate (table naming 로직만 영향)
+- `kebab-app` — 와이어링만 (API 변경 없음)
+
+## Forbidden dependencies
+
+- parse-* crate (parser 무관)
+- llm-* crate (embedding 과 무관)
+- search crate (검색 로직은 adapter pattern 으로 이미 generic)
+
+## Test
+
+- `cargo test -p kebab-embed-local -- e5_large` (새 arm 테스트)
+- `cargo test -p kebab-config -- embedding_defaults` (config defaults)
+- `cargo test --workspace --no-fail-fast -j 1` (full regression)
+- Smoke: `kebab --config /tmp/smoke.toml doctor | grep embedding` → `multilingual-e5-large (1024d)`
+- Smoke: `kebab --config /tmp/smoke.toml ingest` → embedding 진행 표시 + dimension check
+- P@k eval: `kebab eval run` (fb-39 의 golden set) small vs large 비교
+
+## 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-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
+
+- 0.5.0 → 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
+
+- **`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.