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ecdee4d2b1
Reviving #1966. Closes #1938 The `search()` method can apply embeddings for the user. This simplifies hybrid search, so instead of writing: ```python vector_query = embeddings.compute_query_embeddings("flower moon")[0] await ( async_tbl.query() .nearest_to(vector_query) .nearest_to_text("flower moon") .to_pandas() ) ``` You can write: ```python await (await async_tbl.search("flower moon", query_type="hybrid")).to_pandas() ``` Unfortunately, we had to do a double-await here because `search()` needs to be async. This is because it often needs to do IO to retrieve and run an embedding function.
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.