kebab/tasks/p3/p3-2-fastembed-adapter.md

phase: P3 component: kebab-embed-local (fastembed adapter) task_id: p3-2 title: "fastembed-rs Embedder for multilingual-e5-small" status: completed depends_on: [p3-1] unblocks: [p3-3, p3-4] contract_source: ../../docs/superpowers/specs/2026-04-27-kebab-final-form-design.md contract_sections: [design §7.2 Embedder, report §11.3 local embedding, design §6.4 [models.embedding], design §9 versioning]

p3-2 — fastembed adapter

Goal

Provide FastembedEmbedder implementing Embedder for multilingual-e5-small (default) using fastembed-rs (ONNX runtime). Apply Document/Query prefix per §11.3. Honor batch_size from config.

Why now / why this size

First real Embedder. Drives EmbeddingId recipe (model_id + model_version + dims) downstream. Isolated from store/search so model swaps remain config-only.

Allowed dependencies

• kebab-core
• kebab-config
• kebab-embed
• fastembed = "4" (or current stable)
• tokenizers
• ort (transitive via fastembed)
• tracing
• thiserror

Forbidden dependencies

• kebab-source-fs, kebab-parse-md, kebab-normalize, kebab-chunk, kebab-store-*, kebab-search, kebab-llm*, kebab-rag, kebab-tui, kebab-desktop, network HTTP libs (model download is fastembed's responsibility)

Inputs

input	type	source
kebab-config::Config.models.embedding	settings	runtime
EmbeddingInput[..]	kebab_core::EmbeddingInput<'_>[]	callers
model cache	data_dir/models/fastembed/	filesystem

Outputs

output	type	downstream
Vec<Vec<f32>>	row-aligned, dimensions = 384	kebab-store-vector, query vectors for hybrid search
model identity	(EmbeddingModelId, EmbeddingVersion, usize)	record fields, embedding_id recipe

Public surface (signatures only — no new types)

pub struct FastembedEmbedder { /* internal: TextEmbedding instance + model meta */ }

impl FastembedEmbedder {
    pub fn new(config: &kebab_config::Config) -> anyhow::Result<Self>;
}

impl kebab_core::Embedder for FastembedEmbedder {
    fn model_id(&self) -> kebab_core::EmbeddingModelId;
    fn model_version(&self) -> kebab_core::EmbeddingVersion;
    fn dimensions(&self) -> usize;
    fn embed(&self, inputs: &[kebab_core::EmbeddingInput<'_>]) -> anyhow::Result<Vec<Vec<f32>>>;
}

Behavior contract

• Default model multilingual-e5-small (384 dims). model_id() returns EmbeddingModelId("multilingual-e5-small").
• model_version() returns EmbeddingVersion("v1") initially. Bump per §9 if fastembed upgrades the bundled weights.
• Apply e5 prefix per §11.3: input prefixed with "passage: " for EmbeddingKind::Document, "query: " for EmbeddingKind::Query BEFORE tokenization.
• Batch processing respects config.models.embedding.batch_size. Inputs longer than the batch are split into multiple inference calls and concatenated.
• L2-normalize each vector before returning (e5 convention).
• Dimensions must equal config.models.embedding.dimensions AND the model's actual dim. Mismatch returns anyhow::Error at construction (not at first embed).
• Model files cached under config.storage.model_dir/fastembed/ (downloaded on first use).
• Determinism: identical input + identical model version → identical vectors (tolerance < 1e-6 on aggregate hash for snapshot tests).
• No async runtime: the trait is synchronous. fastembed is sync internally.

Storage / wire effects

• Reads/writes data_dir/models/fastembed/ (model cache).
• Otherwise no DB or wire effects.

Test plan

kind	description	fixture / data
unit	construction with default config returns dims=384	tmp config
unit	construction with mismatched dims returns error	tmp config
unit	EmbeddingKind::Query vs Document for same text yield different vectors (cosine < 1.0)	inline
unit	output vectors are L2-normalized (norm ~= 1.0 ± 1e-3)	inline
determinism	identical input twice → identical output (hash-of-floats compare)	inline
performance	batch of 64 short inputs completes in < 5s on CI host	tmp config (skip on slow CI via #[ignore])
snapshot	aggregate hash of vectors for 5 known sentences stable across runs	fixtures/embed/known-sentences.json

All tests under cargo test -p kebab-embed-local. Mark slow tests #[ignore] and run via cargo test -- --ignored in dedicated CI lane.

Definition of Done

• cargo check -p kebab-embed-local passes
• cargo test -p kebab-embed-local passes (excluding #[ignore])
• First-run model download works under data_dir/models/fastembed/
• No imports outside Allowed dependencies
• PR links design §11.3, §6.4, §9

Out of scope

• Reranker (P+).
• Other model providers (Ollama embedding endpoint, candle) — separate adapter crates.
• Visual / image embeddings (P6).

Risks / notes

• ONNX runtime first-load latency on M-series Macs (Metal) can be 1-2 s; tests share a OnceCell<FastembedEmbedder>.
• Forgetting the e5 prefix silently degrades retrieval quality. Tests must assert query/document yield distinct vectors.
• Bumping EmbeddingVersion invalidates every embedding_id. Treat as a versioning event per §9 — provides justification in PR body.