feat(embed): candle 임베딩 provider (NUMA-안전, opt-in) + v0.22.0

duo-socket NUMA 서버에서 fastembed(onnxruntime)가 intra-op 스레드를 48개로
하드코딩해 NUMA 힙 손상 → double-free 로 ingest 가 죽는 문제를 회피하기 위해,
같은 multilingual-e5-large 모델을 순수 Rust(candle)로 돌리는 opt-in 임베딩
provider 를 추가한다.

- 신규 crate kebab-embed-candle: CandleEmbedder (kebab_core::Embedder).
  hf-hub safetensors → XLMRobertaModel forward → mask mean-pool → L2 → e5
  prefix. candle 의존성 트리를 이 crate 에 격리 (core/config 외 kebab-* 의존 0).
- 스레드 캡: [models.embedding].num_threads + env KEBAB_EMBED_THREADS →
  글로벌 rayon 풀 1회 캡 (NUMA-안전 레버).
- kebab-app::embedder() 가 provider 분기 (fastembed/onnx/"" → 기존 경로 불변,
  candle → CandleEmbedder, 미지값 → 에러).
- Phase 0 스파이크 crate 제거 (production 흡수).
- 버전 0.21.1 → 0.22.0 (신규 config surface, pre-1.0 minor bump).

패리티: cosine_min=1.000000, max abs diff=2.01e-7 (< 1e-5) → embedding_version
유지, 재색인 0. fastembed default 동작/벡터 불변. wire schema 변경 없음.

검증(파일+exit code): clippy -D warnings EXIT=0(warning 0), test EXIT=0
(candle unit 5 + thread_cap rayon=4 + config 68), parity #[ignore] EXIT=0.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

crates/kebab-embed-candle/tests/parity.rs

@@ -0,0 +1,88 @@
+//! Parity test (spec §7, `#[ignore]` — needs the ~2GB model + network).
+//!
+//! Confirms the candle backend reproduces the onnxruntime `FastembedEmbedder`
+//! vectors closely enough that no re-index is required (spec D-reindex):
+//! per-sentence cosine ≥ 0.9999, and reports the dimension-wise max absolute
+//! difference (the number the re-index decision hangs on).
+//!
+//! Run manually:
+//!   CARGO_TARGET_DIR=/build/out/cargo-target/target \
+//!   cargo test -p kebab-embed-candle --release -- --ignored --nocapture
+//!
+//! Uses the canonical dogfood config so both backends resolve the same model
+//! identifiers and cache roots.
+
+use kebab_config::Config;
+use kebab_core::{Embedder, EmbeddingInput, EmbeddingKind};
+use kebab_embed_candle::CandleEmbedder;
+use kebab_embed_local::FastembedEmbedder;
+
+const DOGFOOD_CONFIG: &str = "/build/dogfood/config.toml";
+
+/// Mixed Korean / English parity set (≥ 8 sentences, mirrors the Phase 0 spike).
+const SENTENCES: &[&str] = &[
+    "The quick brown fox jumps over the lazy dog.",
+    "오늘 날씨가 정말 좋아서 산책을 나가고 싶다.",
+    "Rust is a systems programming language focused on safety and performance.",
+    "벡터 검색은 임베딩 사이의 코사인 유사도를 이용한다.",
+    "Machine learning models require large amounts of training data.",
+    "한국어와 영어가 섞인 문장도 멀티링구얼 모델은 잘 처리한다.",
+    "The capital of France is Paris, a city known for its art and culture.",
+    "이 프로젝트는 로컬 우선 지식 베이스와 검색 증강 생성을 목표로 한다.",
+    "Database indexing dramatically speeds up query performance.",
+    "임베딩 모델을 candle 로 옮기면 NUMA 서버에서 안전하게 돌릴 수 있다.",
+];
+
+fn cosine(a: &[f32], b: &[f32]) -> f32 {
+    let dot: f32 = a.iter().zip(b).map(|(x, y)| x * y).sum();
+    let na: f32 = a.iter().map(|x| x * x).sum::<f32>().sqrt();
+    let nb: f32 = b.iter().map(|x| x * x).sum::<f32>().sqrt();
+    dot / (na * nb)
+}
+
+#[test]
+#[ignore = "needs ~2GB model + network; run manually for the re-index decision"]
+fn candle_matches_fastembed() {
+    let config = Config::load(Some(std::path::Path::new(DOGFOOD_CONFIG)))
+        .expect("load dogfood config for parity baseline");
+
+    let candle = CandleEmbedder::new(&config).expect("build CandleEmbedder");
+    let fastembed = FastembedEmbedder::new(&config).expect("build FastembedEmbedder");
+
+    let inputs: Vec<EmbeddingInput> = SENTENCES
+        .iter()
+        .map(|s| EmbeddingInput {
+            text: s,
+            kind: EmbeddingKind::Document,
+        })
+        .collect();
+
+    let cv = candle.embed(&inputs).expect("candle embed");
+    let fv = fastembed.embed(&inputs).expect("fastembed embed");
+
+    assert_eq!(cv.len(), fv.len(), "embedding counts must match");
+    assert_eq!(candle.dimensions(), 1024);
+
+    let mut min_cos = f32::INFINITY;
+    let mut max_abs_diff = 0f32;
+    for (i, s) in SENTENCES.iter().enumerate() {
+        assert_eq!(cv[i].len(), 1024, "candle dim");
+        assert_eq!(fv[i].len(), 1024, "fastembed dim");
+        let c = cosine(&cv[i], &fv[i]);
+        min_cos = min_cos.min(c);
+        let diff = cv[i]
+            .iter()
+            .zip(&fv[i])
+            .map(|(a, b)| (a - b).abs())
+            .fold(0f32, f32::max);
+        max_abs_diff = max_abs_diff.max(diff);
+        let preview: String = s.chars().take(40).collect();
+        println!("[{i:>2}] cos={c:.6} max_abs_diff={diff:.6e}  {preview}");
+    }
+
+    println!("PARITY_SUMMARY cosine_min={min_cos:.6} max_abs_diff={max_abs_diff:.6e}");
+    assert!(
+        min_cos >= 0.9999,
+        "candle vs fastembed cosine_min={min_cos:.6} < 0.9999 — investigate before merge"
+    );
+}