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2 Commits
type-reorg
...
python-v0.
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600bfd7237 | ||
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d087e7891d |
@@ -1,5 +1,5 @@
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[bumpversion]
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current_version = 0.3.4
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current_version = 0.3.5
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commit = True
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message = [python] Bump version: {current_version} → {new_version}
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tag = True
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@@ -17,7 +17,7 @@ import inspect
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import os
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from abc import ABC, abstractmethod
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from functools import cached_property
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from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Union
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from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Union
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import lance
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import numpy as np
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@@ -30,7 +30,7 @@ from .common import DATA, VEC, VECTOR_COLUMN_NAME
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from .embeddings import EmbeddingFunctionConfig, EmbeddingFunctionRegistry
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from .pydantic import LanceModel
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from .query import LanceQueryBuilder, Query
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from .util import fs_from_uri, safe_import_pandas
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from .util import fs_from_uri, safe_import_pandas, value_to_sql
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from .utils.events import register_event
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if TYPE_CHECKING:
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@@ -913,30 +913,35 @@ class LanceTable(Table):
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def delete(self, where: str):
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self._dataset.delete(where)
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def update(self, where: str, values: dict):
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def update(
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self,
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where: Optional[str] = None,
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values: Optional[dict] = None,
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*,
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values_sql: Optional[Dict[str, str]] = None,
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):
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"""
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EXPERIMENTAL: Update rows in the table (not threadsafe).
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This can be used to update zero to all rows depending on how many
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rows match the where clause.
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Parameters
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----------
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where: str
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where: str, optional
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The SQL where clause to use when updating rows. For example, 'x = 2'
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or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error.
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values: dict
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values: dict, optional
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The values to update. The keys are the column names and the values
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are the values to set.
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values_sql: dict, optional
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The values to update, expressed as SQL expression strings. These can
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reference existing columns. For example, {"x": "x + 1"} will increment
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the x column by 1.
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Examples
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--------
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>>> import lancedb
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>>> data = [
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... {"x": 1, "vector": [1, 2]},
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... {"x": 2, "vector": [3, 4]},
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... {"x": 3, "vector": [5, 6]}
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... ]
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>>> import pandas as pd
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>>> data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1, 2], [3, 4], [5, 6]]})
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>>> db = lancedb.connect("./.lancedb")
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>>> table = db.create_table("my_table", data)
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>>> table.to_pandas()
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@@ -952,18 +957,15 @@ class LanceTable(Table):
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2 2 [10.0, 10.0]
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"""
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orig_data = self._dataset.to_table(filter=where).combine_chunks()
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if len(orig_data) == 0:
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return
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for col, val in values.items():
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i = orig_data.column_names.index(col)
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if i < 0:
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raise ValueError(f"Column {col} does not exist")
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orig_data = orig_data.set_column(
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i, col, pa.array([val] * len(orig_data), type=orig_data[col].type)
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)
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self.delete(where)
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self.add(orig_data, mode="append")
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if values is not None and values_sql is not None:
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raise ValueError("Only one of values or values_sql can be provided")
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if values is None and values_sql is None:
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raise ValueError("Either values or values_sql must be provided")
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if values is not None:
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values_sql = {k: value_to_sql(v) for k, v in values.items()}
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self.to_lance().update(values_sql, where)
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self._reset_dataset()
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register_event("update")
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@@ -12,9 +12,12 @@
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# limitations under the License.
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import os
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from datetime import date, datetime
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from functools import singledispatch
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from typing import Tuple
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from urllib.parse import urlparse
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import numpy as np
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import pyarrow.fs as pa_fs
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@@ -88,3 +91,53 @@ def safe_import_pandas():
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return pd
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except ImportError:
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return None
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@singledispatch
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def value_to_sql(value):
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raise NotImplementedError("SQL conversion is not implemented for this type")
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@value_to_sql.register(str)
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def _(value: str):
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return f"'{value}'"
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@value_to_sql.register(int)
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def _(value: int):
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return str(value)
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@value_to_sql.register(float)
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def _(value: float):
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return str(value)
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@value_to_sql.register(bool)
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def _(value: bool):
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return str(value).upper()
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@value_to_sql.register(type(None))
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def _(value: type(None)):
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return "NULL"
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@value_to_sql.register(datetime)
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def _(value: datetime):
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return f"'{value.isoformat()}'"
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@value_to_sql.register(date)
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def _(value: date):
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return f"'{value.isoformat()}'"
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@value_to_sql.register(list)
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def _(value: list):
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return "[" + ", ".join(map(value_to_sql, value)) + "]"
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@value_to_sql.register(np.ndarray)
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def _(value: np.ndarray):
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return value_to_sql(value.tolist())
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@@ -1,12 +1,12 @@
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[project]
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name = "lancedb"
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version = "0.3.4"
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version = "0.3.5"
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dependencies = [
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"deprecation",
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"pylance==0.8.17",
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"pylance==0.8.21",
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"ratelimiter~=1.0",
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"retry>=0.9.2",
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"tqdm>=4.1.0",
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"tqdm>=4.27.0",
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"aiohttp",
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"pydantic>=1.10",
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"attrs>=21.3.0",
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@@ -12,7 +12,7 @@
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# limitations under the License.
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import functools
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from datetime import timedelta
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from datetime import date, datetime, timedelta
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from pathlib import Path
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from typing import List
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from unittest.mock import PropertyMock, patch
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@@ -348,14 +348,79 @@ def test_update(db):
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assert len(table) == 2
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assert len(table.list_versions()) == 2
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table.update(where="id=0", values={"vector": [1.1, 1.1]})
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assert len(table.list_versions()) == 4
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assert table.version == 4
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assert len(table.list_versions()) == 3
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assert table.version == 3
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assert len(table) == 2
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v = table.to_arrow()["vector"].combine_chunks()
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v = v.values.to_numpy().reshape(2, 2)
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assert np.allclose(v, np.array([[1.2, 1.9], [1.1, 1.1]]))
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def test_update_types(db):
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table = LanceTable.create(
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db,
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"my_table",
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data=[
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{
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"id": 0,
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"str": "foo",
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"float": 1.1,
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"timestamp": datetime(2021, 1, 1),
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"date": date(2021, 1, 1),
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"vector1": [1.0, 0.0],
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"vector2": [1.0, 1.0],
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}
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],
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)
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# Update with SQL
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table.update(
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values_sql=dict(
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id="1",
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str="'bar'",
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float="2.2",
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timestamp="TIMESTAMP '2021-01-02 00:00:00'",
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date="DATE '2021-01-02'",
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vector1="[2.0, 2.0]",
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vector2="[3.0, 3.0]",
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)
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)
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actual = table.to_arrow().to_pylist()[0]
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expected = dict(
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id=1,
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str="bar",
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float=2.2,
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timestamp=datetime(2021, 1, 2),
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date=date(2021, 1, 2),
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vector1=[2.0, 2.0],
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vector2=[3.0, 3.0],
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)
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assert actual == expected
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# Update with values
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table.update(
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values=dict(
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id=2,
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str="baz",
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float=3.3,
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timestamp=datetime(2021, 1, 3),
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date=date(2021, 1, 3),
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vector1=[3.0, 3.0],
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vector2=np.array([4.0, 4.0]),
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)
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)
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actual = table.to_arrow().to_pylist()[0]
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expected = dict(
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id=2,
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str="baz",
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float=3.3,
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timestamp=datetime(2021, 1, 3),
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date=date(2021, 1, 3),
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vector1=[3.0, 3.0],
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vector2=[4.0, 4.0],
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)
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assert actual == expected
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def test_create_with_embedding_function(db):
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class MyTable(LanceModel):
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text: str
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