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Sometimes it is acceptable to users to only search indexed data and skip and new un-indexed data. For example, if un-indexed data will be shortly indexed and they don't mind the delay. In these cases, we can save a lot of CPU time in search, and provide better latency. Users can activate this on queries using `fast_search()`.
LanceDB
A Python library for LanceDB.
Installation
pip install 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
LanceDb is based on the rust crate lancedb and is built with maturin. In order to build with maturin
you will either need a conda environment or a virtual environment (venv).
python -m venv venv
. ./venv/bin/activate
Install the necessary packages:
python -m pip install .[tests,dev]
To build the python package you can use maturin:
# This will build the rust bindings and place them in the appropriate place
# in your venv or conda environment
maturin develop
To run the unit tests:
pytest
To run the doc tests:
pytest --doctest-modules python/lancedb
To run linter and automatically fix all errors:
ruff format python
ruff --fix python
If any packages are missing, install them with:
pip install <PACKAGE_NAME>
For Windows users, there may be errors when installing packages, so these commands may be helpful:
Activate the virtual environment:
. .\venv\Scripts\activate
You may need to run the installs separately:
pip install -e .[tests]
pip install -e .[dev]
tantivy requires rust to be installed, so install it with conda, as it doesn't support windows installation:
pip install wheel
pip install cargo
conda install rust
pip install tantivy