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feat: add a basic async python client starting point (#1014)
This changes `lancedb` from a "pure python" setuptools project to a maturin project and adds a rust lancedb dependency. The async python client is extremely minimal (only `connect` and `Connection.table_names` are supported). The purpose of this PR is to get the infrastructure in place for building out the rest of the async client. Although this is not technically a breaking change (no APIs are changing) it is still a considerable change in the way the wheels are built because they now include the native shared library.
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114
python/python/tests/test_embeddings.py
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114
python/python/tests/test_embeddings.py
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# Copyright 2023 LanceDB Developers
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import sys
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import lance
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import lancedb
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import numpy as np
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import pyarrow as pa
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import pytest
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from lancedb.conftest import MockTextEmbeddingFunction
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from lancedb.embeddings import (
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EmbeddingFunctionConfig,
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EmbeddingFunctionRegistry,
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with_embeddings,
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)
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from lancedb.pydantic import LanceModel, Vector
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def mock_embed_func(input_data):
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return [np.random.randn(128).tolist() for _ in range(len(input_data))]
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def test_with_embeddings():
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for wrap_api in [True, False]:
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if wrap_api and sys.version_info.minor >= 11:
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# ratelimiter package doesn't work on 3.11
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continue
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data = pa.Table.from_arrays(
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[
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pa.array(["foo", "bar"]),
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pa.array([10.0, 20.0]),
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],
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names=["text", "price"],
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)
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data = with_embeddings(mock_embed_func, data, wrap_api=wrap_api)
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assert data.num_columns == 3
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assert data.num_rows == 2
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assert data.column_names == ["text", "price", "vector"]
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assert data.column("text").to_pylist() == ["foo", "bar"]
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assert data.column("price").to_pylist() == [10.0, 20.0]
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def test_embedding_function(tmp_path):
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registry = EmbeddingFunctionRegistry.get_instance()
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# let's create a table
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table = pa.table(
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{
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"text": pa.array(["hello world", "goodbye world"]),
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"vector": [np.random.randn(10), np.random.randn(10)],
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}
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)
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conf = EmbeddingFunctionConfig(
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source_column="text",
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vector_column="vector",
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function=MockTextEmbeddingFunction(),
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)
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metadata = registry.get_table_metadata([conf])
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table = table.replace_schema_metadata(metadata)
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# Write it to disk
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lance.write_dataset(table, tmp_path / "test.lance")
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# Load this back
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ds = lance.dataset(tmp_path / "test.lance")
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# can we get the serialized version back out?
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configs = registry.parse_functions(ds.schema.metadata)
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conf = configs["vector"]
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func = conf.function
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actual = func.compute_query_embeddings("hello world")
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# And we make sure we can call it
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expected = func.compute_query_embeddings("hello world")
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assert np.allclose(actual, expected)
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@pytest.mark.slow
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def test_embedding_function_rate_limit(tmp_path):
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def _get_schema_from_model(model):
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class Schema(LanceModel):
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text: str = model.SourceField()
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vector: Vector(model.ndims()) = model.VectorField()
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return Schema
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db = lancedb.connect(tmp_path)
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registry = EmbeddingFunctionRegistry.get_instance()
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model = registry.get("test-rate-limited").create(max_retries=0)
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schema = _get_schema_from_model(model)
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table = db.create_table("test", schema=schema, mode="overwrite")
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table.add([{"text": "hello world"}])
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with pytest.raises(Exception):
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table.add([{"text": "hello world"}])
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assert len(table) == 1
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model = registry.get("test-rate-limited").create()
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schema = _get_schema_from_model(model)
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table = db.create_table("test", schema=schema, mode="overwrite")
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table.add([{"text": "hello world"}])
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table.add([{"text": "hello world"}])
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assert len(table) == 2
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