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Fixes #2612 This PR exposes the private _fast_search attribute via a public fast_search() method in the synchronous LanceVectorQueryBuilder. Previously, enabling fast search in the sync API required accessing a private member (query._fast_search = True). This change aligns the synchronous API with the Async and Remote APIs, allowing for cleaner, more Pythonic method chaining. Changes: Added fast_search() method to LanceVectorQueryBuilder in python/python/lancedb/query.py. Added a unit test verifying the flag works with high-dimensional data (2560 dims) and chaining. Example Usage: Before: ``` query = table.search(vector) query._fast_search = True # Private attribute usage results = query.limit(10).to_pandas() ``` After: ``` results = ( table.search(vector) .fast_search() .limit(10) .to_pandas() ) ``` Verification: I have added a test case (test_fast_search_high_dimension) that replicates the scenario described in the issue (2560 dimensions, cosine distance) to ensure the pipeline constructs the query correctly without errors. Checklist: - [ ] I have added tests to cover my changes. - [ ] All new and existing tests passed. - [ ] Documentation has been updated (inline docstrings). Signed-off-by: Rashidul Islam <rasidulislam71@gmail.com>
LanceDB
A Python library for LanceDB.
Installation
pip install lancedb
Preview Releases
Stable releases are created about every 2 weeks. For the latest features and bug fixes, you can install the preview release. These releases receive the same level of testing as stable releases, but are not guaranteed to be available for more than 6 months after they are released. Once your application is stable, we recommend switching to stable releases.
pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb
Usage
Basic Example
import lancedb
db = lancedb.connect('<PATH_TO_LANCEDB_DATASET>')
table = db.open_table('my_table')
results = table.search([0.1, 0.3]).limit(20).to_list()
print(results)
Development
See CONTRIBUTING.md for information on how to contribute to LanceDB.