lancedb git:20260703.774344bA

Immutable. This exact content is served forever at /api/v1/blob/79650cc211b0afdd.

---
name: lancedb
description: Use when working on LanceDB vector storage, table creation/querying, LeRobot/BDD100K imports, UDF backfills, materialized views, CLIP embeddings, or AV/perception data flows.
---

# LanceDB

## When To Use

Use this skill for vector-search workbench changes, perception dataset imports,
BDD100K failure-mode slices, materialized views, CLIP embedding backfills, and
LanceDB CLI/API/SDK parity reviews.

## Procedure

1. Pick the data shape first. LanceDB is best for frame-aligned records such as
   image paths, annotations, metadata, and vectors. It is not the right store for
   raw multi-rate sensor streams.
2. Create or inspect tables before ingestion:

   ```bash
   npa workbench lancedb create-table --help
   npa workbench lancedb query --help
   ```

3. Import supported datasets through current commands:

   ```bash
   npa workbench lancedb import-lerobot --help
   npa workbench lancedb import-bdd100k --help
   ```

4. Add derived fields through `backfill`, then materialize reusable SQL slices
   with `create-mv`, `refresh-mv`, and `query-table`.

## Three-Tier Contract

- CLI: `deploy`, `status`, `list`, `create-table`, `query`,
  `import-lerobot`, `import-bdd100k`, `backfill`, `create-mv`, `refresh-mv`,
  and `query-table`.
- SDK/API: keep table import, backfill, and query behavior in shared
  implementation paths so CLI, SDK, and service endpoints produce equivalent
  manifests and row counts.
- YAML: workflow tasks should pass S3-backed LanceDB URIs and table names through
  environment variables, not hardcoded project paths.

## BDD100K Contract

BDD100K UDFs:

- `has_person`
- `has_rider`
- `person_bbox_area_pct`
- `dhash`
- `is_duplicate`
- `clip_embedding`

`PERSON_CATEGORIES = {"person", "pedestrian"}`. Real BDD100K uses
`pedestrian`; synthetic data may use `person`. Both must be accepted.

Materialized views are SQL-defined failure-mode slices such as `rider_train`,
`nighttime_person_train`, and `distant_person_train`. CLIP embeddings are
512-dimensional `float32`, use a GPU UDF, and route to H100.

## Gotchas

- Do not document stale `launch` or `load-dataset` commands for LanceDB.
- Inject detection-training label maps through workflow env vars such as
  `BDD100K_LABEL_MAP`; do not hardcode them in tool source.
- Use `https://storage.eu-north1.nebius.cloud` for primary-region object
  storage.

## Verify

```bash
npa/.venv/bin/python -m pytest npa/tests/guardrails/test_skills_index.py -q
```

The smoke test invokes current LanceDB command help and fails if stale commands
return to the manifest.