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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.
66 lines
1.9 KiB
Python
66 lines
1.9 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright The LanceDB Authors
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import shutil
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import pytest
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# --8<-- [start:imports]
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import lancedb
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import numpy as np
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# --8<-- [end:imports]
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shutil.rmtree("data/distance_range_demo", ignore_errors=True)
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def test_binary_vector():
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# --8<-- [start:sync_distance_range]
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db = lancedb.connect("data/distance_range_demo")
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data = [
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{
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"id": i,
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"vector": np.random.random(256),
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}
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for i in range(1024)
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]
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tbl = db.create_table("my_table", data=data)
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query = np.random.random(256)
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# Search for the vectors within the range of [0.1, 0.5)
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tbl.search(query).distance_range(0.1, 0.5).to_arrow()
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# Search for the vectors with the distance less than 0.5
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tbl.search(query).distance_range(upper_bound=0.5).to_arrow()
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# Search for the vectors with the distance greater or equal to 0.1
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tbl.search(query).distance_range(lower_bound=0.1).to_arrow()
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# --8<-- [end:sync_distance_range]
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db.drop_table("my_table")
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@pytest.mark.asyncio
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async def test_binary_vector_async():
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# --8<-- [start:async_distance_range]
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db = await lancedb.connect_async("data/distance_range_demo")
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data = [
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{
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"id": i,
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"vector": np.random.random(256),
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}
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for i in range(1024)
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]
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tbl = await db.create_table("my_table", data=data)
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query = np.random.random(256)
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# Search for the vectors within the range of [0.1, 0.5)
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await (await tbl.search(query)).distance_range(0.1, 0.5).to_arrow()
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# Search for the vectors with the distance less than 0.5
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await (await tbl.search(query)).distance_range(upper_bound=0.5).to_arrow()
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# Search for the vectors with the distance greater or equal to 0.1
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await (await tbl.search(query)).distance_range(lower_bound=0.1).to_arrow()
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# --8<-- [end:async_distance_range]
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await db.drop_table("my_table")
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