kebab/tasks/p3/p3-3-lancedb-store.md

phase, component, task_id, title, status, depends_on, unblocks, contract_source, contract_sections

phase	component	task_id	title	status	depends_on	unblocks	contract_source	contract_sections
P3	kb-store-vector (LanceDB)	p3-3	LanceDB VectorStore + embedding_records writer	planned	p3-2 p1-6	p3-4	../../docs/superpowers/specs/2026-04-27-kb-final-form-design.md	§5.6 embedding_records §6.3 lancedb table naming §7.2 VectorStore §9 versioning

p3-3 — LanceDB VectorStore

Goal

Implement VectorStore over LanceDB (embedded). Stores per-model tables (chunk_embeddings_<model>_<dim>.lance), upserts vectors transactionally with a row in embedding_records (SQLite), and serves search for the vector retrieval mode.

Why now / why this size

Closes the loop chunk → vector. Splits cleanly from kb-search so hybrid (p3-4) can compose lexical + vector retrievers without leaking storage details.

Allowed dependencies

• kb-core
• kb-config
• kb-store-sqlite (only for writing/reading rows in embedding_records)
• lancedb
• arrow (and arrow-array, arrow-schema)
• serde, serde_json
• tracing
• thiserror

Forbidden dependencies

• kb-source-fs, kb-parse-md, kb-normalize, kb-chunk, kb-embed* (consumes Vec<f32> via input only — no embedding logic here), kb-search, kb-llm*, kb-rag, kb-tui, kb-desktop

Inputs

input	type	source
VectorRecord[..]	kb_core::VectorRecord	kb-app::embed_index (P3 facade)
query vector	&[f32]	kb-embed-local (Embedder::embed for query)
filters	kb_core::SearchFilters	SearchQuery
kb-config::Config.storage.vector_dir	path	runtime

Outputs

output	type	downstream
Lance tables under vector_dir/chunk_embeddings_<model>_<dim>.lance/	filesystem	future searches
embedding_records rows	SQLite	reverse lookup, reindex bookkeeping
Vec<VectorHit>	kb_core::VectorHit	hybrid retriever (p3-4)

Public surface (signatures only — no new types)

pub struct LanceVectorStore { /* internal: connection + sqlite handle */ }

impl LanceVectorStore {
    pub fn new(config: &kb_config::Config, sqlite: std::sync::Arc<kb_store_sqlite::SqliteStore>) -> anyhow::Result<Self>;
}

impl kb_core::VectorStore for LanceVectorStore {
    fn ensure_table(&self, model: &kb_core::EmbeddingModelId, dim: usize) -> anyhow::Result<kb_core::IndexId>;
    fn upsert(&self, recs: &[kb_core::VectorRecord]) -> anyhow::Result<()>;
    fn search(&self, query_vec: &[f32], k: usize, filters: &kb_core::SearchFilters) -> anyhow::Result<Vec<kb_core::VectorHit>>;
}

Behavior contract

• Table naming: chunk_embeddings_<model_id>_<dim>.lance. Model IDs must be sanitized (replace non [A-Za-z0-9-] with _) to avoid filesystem issues.
• ensure_table is idempotent: opens existing or creates with explicit Arrow schema:

  chunk_id : Utf8 (primary)
  doc_id   : Utf8
  embedding: FixedSizeList<Float32, dim>
  model_id : Utf8
  embedding_version : Utf8
  text     : Utf8
  heading_path : Utf8
  created_at : Timestamp(Microsecond, UTC)
  

• For corpora < 100k rows, no IVF index — flat cosine. Above that threshold, the next migration task (P+) introduces IVF; this task does not.
• upsert is best-effort 2-step (Lance commit, then SQLite INSERT OR REPLACE INTO embedding_records). On SQLite failure after Lance commit, log a warning; the next upsert reconciles via the UNIQUE(chunk_id, model_id, model_version, dimensions) constraint.
• Dimension mismatch (record dim ≠ table dim) returns anyhow::Error from upsert and writes nothing.
• search performs cosine similarity, applies SearchFilters post-fetch (filter-then-limit may over-fetch internally — fetch 2 * k then trim).
• VectorHit { chunk_id, score, doc_id, text, heading_path }; score in [0, 1] (cosine similarity, clamped).
• search returns empty Vec (not error) when table absent.
• index_id for ensure_table per design §4.2 with collection = "chunk_embeddings", index_kind = "flat", params_hash = blake3(serde_json(table_schema)).

Storage / wire effects

• Writes Lance tables under data_dir/lancedb/.
• Writes/reads embedding_records rows.
• Reads chunks/documents not from this crate (the caller pre-fetches text + heading via VectorRecord).

Test plan

kind	description	fixture / data
unit	ensure_table creates dir; second call returns same IndexId	tmp data_dir
unit	upsert of 10 records makes them retrievable via search (k=5)	tmp data_dir
unit	dimension mismatch → error, no Lance row written	tmp data_dir
unit	filter tags_any removes non-matching docs	tmp data_dir + seeded sqlite tags
unit	model isolation: two models live in two directories with same chunk_id	tmp data_dir
unit	search before any upsert returns empty Vec	tmp data_dir
determinism	same query vector + same data → same top-k order	tmp data_dir
snapshot	Vec<VectorHit> JSON for fixed corpus stable	fixtures/vector/run-1.json

All tests under cargo test -p kb-store-vector.

Definition of Done

• cargo check -p kb-store-vector passes
• cargo test -p kb-store-vector passes
• No imports outside Allowed dependencies
• embedding_records rows align 1:1 with Lance rows after a successful upsert batch
• PR links design §5.6, §6.3, §7.2

Out of scope

• IVF / PQ index tuning (P+).
• Image / multimodal vector tables (P6).
• kb-app orchestration of indexing jobs (embed_index facade method body).

Risks / notes

• LanceDB's Rust API requires Arrow batches; constructing them per upsert is allocation-heavy — batch by configurable chunk size to avoid memory spikes.
• Filter-then-limit can starve k results; over-fetch by 2 * k initially and double on retry up to a cap.
• WAL stability: ensure Lance commits before SQLite INSERT INTO embedding_records to avoid orphan SQLite rows.