81afd8a42f
## Summary
Fixes intermittent CI failures in `test_search_fts[False]` where boolean
FTS queries were returning fewer results than expected due to
non-deterministic test data generation.
## Problem
The test was using global `random` and `np.random` without seeding,
causing the boolean query `MatchQuery("puppy", "text") &
MatchQuery("runs", "text")` to sometimes return only 3 results instead
of the expected 5, leading to `AssertionError: assert 3 == 5`.
## Solution
- Replace global random calls with local `random.Random(42)` and
`np.random.RandomState(42)` objects in test fixtures
- Ensures deterministic test data while maintaining test isolation
- No impact on other tests since random state is scoped to fixtures only
## Test Results
- ✅ `test_search_fts[False]` now passes consistently
- ✅ All other FTS tests continue to pass
- ✅ No regression in other test suites (verified with `test_basic`)
- ✅ Maintains existing test behavior and coverage
The Multimodal AI Lakehouse
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The ultimate multimodal data platform for AI/ML applications.
LanceDB is designed for fast, scalable, and production-ready vector search. It is built on top of the Lance columnar format. You can store, index, and search over petabytes of multimodal data and vectors with ease. LanceDB is a central location where developers can build, train and analyze their AI workloads.
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Follow the Quickstart doc to set up LanceDB locally.
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| Interface | Documentation |
|---|---|
| Python SDK | https://lancedb.github.io/lancedb/python/python/ |
| Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
| Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
| REST API | https://docs.lancedb.com/api-reference/introduction |
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