# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright The LanceDB Authors import re from datetime import timedelta import os import lancedb import numpy as np import pandas as pd import pyarrow as pa import pytest from lancedb.pydantic import LanceModel, Vector @pytest.mark.parametrize("use_tantivy", [True, False]) def test_basic(tmp_path, use_tantivy): db = lancedb.connect(tmp_path) assert db.uri == str(tmp_path) assert db.table_names() == [] class SimpleModel(LanceModel): item: str price: float vector: Vector(2) table = db.create_table( "test", data=[ {"vector": [3.1, 4.1], "item": "foo", "price": 10.0}, {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}, ], schema=SimpleModel, ) with pytest.raises( ValueError, match="Cannot add a single LanceModel to a table. Use a list." ): table.add(SimpleModel(item="baz", price=30.0, vector=[1.0, 2.0])) rs = table.search([100, 100]).limit(1).to_pandas() assert len(rs) == 1 assert rs["item"].iloc[0] == "bar" rs = table.search([100, 100]).where("price < 15").limit(2).to_pandas() assert len(rs) == 1 assert rs["item"].iloc[0] == "foo" table.create_fts_index("item", use_tantivy=use_tantivy) rs = table.search("bar", query_type="fts").to_pandas() assert len(rs) == 1 assert rs["item"].iloc[0] == "bar" assert db.table_names() == ["test"] assert "test" in db assert len(db) == 1 assert db.open_table("test").name == db["test"].name def test_ingest_pd(tmp_path): db = lancedb.connect(tmp_path) assert db.uri == str(tmp_path) assert db.table_names() == [] data = pd.DataFrame( { "vector": [[3.1, 4.1], [5.9, 26.5]], "item": ["foo", "bar"], "price": [10.0, 20.0], } ) table = db.create_table("test", data=data) rs = table.search([100, 100]).limit(1).to_pandas() assert len(rs) == 1 assert rs["item"].iloc[0] == "bar" rs = table.search([100, 100]).where("price < 15").limit(2).to_pandas() assert len(rs) == 1 assert rs["item"].iloc[0] == "foo" assert db.table_names() == ["test"] assert "test" in db assert len(db) == 1 assert db.open_table("test").name == db["test"].name def test_ingest_iterator(mem_db: lancedb.DBConnection): class PydanticSchema(LanceModel): vector: Vector(2) item: str price: float arrow_schema = pa.schema( [ pa.field("vector", pa.list_(pa.float32(), 2)), pa.field("item", pa.utf8()), pa.field("price", pa.float32()), ] ) def make_batches(): for _ in range(5): yield from [ # pandas pd.DataFrame( { "vector": [[3.1, 4.1], [1, 1]], "item": ["foo", "bar"], "price": [10.0, 20.0], } ), # pylist [ {"vector": [3.1, 4.1], "item": "foo", "price": 10.0}, {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}, ], # recordbatch pa.RecordBatch.from_arrays( [ pa.array([[3.1, 4.1], [5.9, 26.5]], pa.list_(pa.float32(), 2)), pa.array(["foo", "bar"]), pa.array([10.0, 20.0]), ], ["vector", "item", "price"], ), # pa Table pa.Table.from_arrays( [ pa.array([[3.1, 4.1], [5.9, 26.5]], pa.list_(pa.float32(), 2)), pa.array(["foo", "bar"]), pa.array([10.0, 20.0]), ], ["vector", "item", "price"], ), # pydantic list [ PydanticSchema(vector=[3.1, 4.1], item="foo", price=10.0), PydanticSchema(vector=[5.9, 26.5], item="bar", price=20.0), ], # TODO: test pydict separately. it is unique column number and # name constraints ] def run_tests(schema): tbl = mem_db.create_table("table2", make_batches(), schema=schema) tbl.to_pandas() assert tbl.search([3.1, 4.1]).limit(1).to_pandas()["_distance"][0] == 0.0 assert tbl.search([5.9, 26.5]).limit(1).to_pandas()["_distance"][0] == 0.0 tbl_len = len(tbl) tbl.add(make_batches()) assert tbl_len == 50 assert len(tbl) == tbl_len * 2 assert len(tbl.list_versions()) == 2 mem_db.drop_database() run_tests(arrow_schema) run_tests(PydanticSchema) def test_table_names(tmp_db: lancedb.DBConnection): data = pd.DataFrame( { "vector": [[3.1, 4.1], [5.9, 26.5]], "item": ["foo", "bar"], "price": [10.0, 20.0], } ) tmp_db.create_table("test2", data=data) tmp_db.create_table("test1", data=data) tmp_db.create_table("test3", data=data) assert tmp_db.table_names() == ["test1", "test2", "test3"] # Test that positional arguments for page_token and limit result = list(tmp_db.table_names("test1", 1)) # page_token="test1", limit=1 assert result == ["test2"], f"Expected ['test2'], got {result}" # Test mixed positional and keyword arguments result = list(tmp_db.table_names("test2", limit=2)) assert result == ["test3"], f"Expected ['test3'], got {result}" # Test that namespace parameter can be passed as keyword result = list(tmp_db.table_names(namespace=[])) assert len(result) == 3 @pytest.mark.asyncio async def test_table_names_async(tmp_path): db = lancedb.connect(tmp_path) data = pd.DataFrame( { "vector": [[3.1, 4.1], [5.9, 26.5]], "item": ["foo", "bar"], "price": [10.0, 20.0], } ) db.create_table("test2", data=data) db.create_table("test1", data=data) db.create_table("test3", data=data) db = await lancedb.connect_async(tmp_path) assert await db.table_names() == ["test1", "test2", "test3"] assert await db.table_names(limit=1) == ["test1"] assert await db.table_names(start_after="test1", limit=1) == ["test2"] assert await db.table_names(start_after="test1") == ["test2", "test3"] def test_create_mode(tmp_db: lancedb.DBConnection): data = pd.DataFrame( { "vector": [[3.1, 4.1], [5.9, 26.5]], "item": ["foo", "bar"], "price": [10.0, 20.0], } ) tmp_db.create_table("test", data=data) with pytest.raises(Exception): tmp_db.create_table("test", data=data) new_data = pd.DataFrame( { "vector": [[3.1, 4.1], [5.9, 26.5]], "item": ["fizz", "buzz"], "price": [10.0, 20.0], } ) tbl = tmp_db.create_table("test", data=new_data, mode="overwrite") assert tbl.to_pandas().item.tolist() == ["fizz", "buzz"] def test_create_table_from_iterator(mem_db: lancedb.DBConnection): def gen_data(): for _ in range(10): yield pa.RecordBatch.from_arrays( [ pa.array([[3.1, 4.1]], pa.list_(pa.float32(), 2)), pa.array(["foo"]), pa.array([10.0]), ], ["vector", "item", "price"], ) table = mem_db.create_table("test", data=gen_data()) assert table.count_rows() == 10 @pytest.mark.asyncio async def test_create_table_from_iterator_async(mem_db_async: lancedb.AsyncConnection): def gen_data(): for _ in range(10): yield pa.RecordBatch.from_arrays( [ pa.array([[3.1, 4.1]], pa.list_(pa.float32(), 2)), pa.array(["foo"]), pa.array([10.0]), ], ["vector", "item", "price"], ) table = await mem_db_async.create_table("test", data=gen_data()) assert await table.count_rows() == 10 def test_create_exist_ok(tmp_db: lancedb.DBConnection): data = pd.DataFrame( { "vector": [[3.1, 4.1], [5.9, 26.5]], "item": ["foo", "bar"], "price": [10.0, 20.0], } ) tbl = tmp_db.create_table("test", data=data) with pytest.raises(ValueError): tmp_db.create_table("test", data=data) # open the table but don't add more rows tbl2 = tmp_db.create_table("test", data=data, exist_ok=True) assert tbl.name == tbl2.name assert tbl.schema == tbl2.schema assert len(tbl) == len(tbl2) schema = pa.schema( [ pa.field("vector", pa.list_(pa.float32(), list_size=2)), pa.field("item", pa.utf8()), pa.field("price", pa.float64()), ] ) tbl3 = tmp_db.create_table("test", schema=schema, exist_ok=True) assert tbl3.schema == schema bad_schema = pa.schema( [ pa.field("vector", pa.list_(pa.float32(), list_size=2)), pa.field("item", pa.utf8()), pa.field("price", pa.float64()), pa.field("extra", pa.float32()), ] ) with pytest.raises(ValueError): tmp_db.create_table("test", schema=bad_schema, exist_ok=True) @pytest.mark.asyncio async def test_connect(tmp_path): db = await lancedb.connect_async(tmp_path) assert str(db) == f"ListingDatabase(uri={tmp_path}, read_consistency_interval=None)" db = await lancedb.connect_async( tmp_path, read_consistency_interval=timedelta(seconds=5) ) assert str(db) == f"ListingDatabase(uri={tmp_path}, read_consistency_interval=5s)" @pytest.mark.asyncio async def test_close(mem_db_async: lancedb.AsyncConnection): assert mem_db_async.is_open() mem_db_async.close() assert not mem_db_async.is_open() with pytest.raises(RuntimeError, match="is closed"): await mem_db_async.table_names() @pytest.mark.asyncio async def test_context_manager(): with await lancedb.connect_async("memory://") as db: assert db.is_open() assert not db.is_open() @pytest.mark.asyncio async def test_create_mode_async(tmp_db_async: lancedb.AsyncConnection): data = pd.DataFrame( { "vector": [[3.1, 4.1], [5.9, 26.5]], "item": ["foo", "bar"], "price": [10.0, 20.0], } ) await tmp_db_async.create_table("test", data=data) with pytest.raises(ValueError, match="already exists"): await tmp_db_async.create_table("test", data=data) new_data = pd.DataFrame( { "vector": [[3.1, 4.1], [5.9, 26.5]], "item": ["fizz", "buzz"], "price": [10.0, 20.0], } ) _tbl = await tmp_db_async.create_table("test", data=new_data, mode="overwrite") # MIGRATION: to_pandas() is not available in async # assert tbl.to_pandas().item.tolist() == ["fizz", "buzz"] @pytest.mark.asyncio async def test_create_exist_ok_async(tmp_db_async: lancedb.AsyncConnection): data = pd.DataFrame( { "vector": [[3.1, 4.1], [5.9, 26.5]], "item": ["foo", "bar"], "price": [10.0, 20.0], } ) tbl = await tmp_db_async.create_table("test", data=data) with pytest.raises(ValueError, match="already exists"): await tmp_db_async.create_table("test", data=data) # open the table but don't add more rows tbl2 = await tmp_db_async.create_table("test", data=data, exist_ok=True) assert tbl.name == tbl2.name assert await tbl.schema() == await tbl2.schema() schema = pa.schema( [ pa.field("vector", pa.list_(pa.float32(), list_size=2)), pa.field("item", pa.utf8()), pa.field("price", pa.float64()), ] ) tbl3 = await tmp_db_async.create_table("test", schema=schema, exist_ok=True) assert await tbl3.schema() == schema # Migration: When creating a table, but the table already exists, but # the schema is different, it should raise an error. # bad_schema = pa.schema( # [ # pa.field("vector", pa.list_(pa.float32(), list_size=2)), # pa.field("item", pa.utf8()), # pa.field("price", pa.float64()), # pa.field("extra", pa.float32()), # ] # ) # with pytest.raises(ValueError): # await db.create_table("test", schema=bad_schema, exist_ok=True) @pytest.mark.asyncio async def test_create_table_v2_manifest_paths_async(tmp_path): db_with_v2_paths = await lancedb.connect_async( tmp_path, storage_options={"new_table_enable_v2_manifest_paths": "true"} ) db_no_v2_paths = await lancedb.connect_async( tmp_path, storage_options={"new_table_enable_v2_manifest_paths": "false"} ) # Create table in v2 mode with v2 manifest paths enabled tbl = await db_with_v2_paths.create_table( "test_v2_manifest_paths", data=[{"id": 0}], ) assert await tbl.uses_v2_manifest_paths() manifests_dir = tmp_path / "test_v2_manifest_paths.lance" / "_versions" for manifest in os.listdir(manifests_dir): assert re.match(r"\d{20}\.manifest", manifest) # Start a table in V1 mode then migrate tbl = await db_no_v2_paths.create_table( "test_v2_migration", data=[{"id": 0}], ) assert not await tbl.uses_v2_manifest_paths() manifests_dir = tmp_path / "test_v2_migration.lance" / "_versions" for manifest in os.listdir(manifests_dir): assert re.match(r"\d\.manifest", manifest) await tbl.migrate_manifest_paths_v2() assert await tbl.uses_v2_manifest_paths() for manifest in os.listdir(manifests_dir): assert re.match(r"\d{20}\.manifest", manifest) def test_open_table_sync(tmp_db: lancedb.DBConnection): tmp_db.create_table("test", data=[{"id": 0}]) assert tmp_db.open_table("test").count_rows() == 1 assert tmp_db.open_table("test", index_cache_size=0).count_rows() == 1 with pytest.raises(ValueError, match="Table 'does_not_exist' was not found"): tmp_db.open_table("does_not_exist") @pytest.mark.asyncio async def test_open_table(tmp_path): db = await lancedb.connect_async(tmp_path) data = pd.DataFrame( { "vector": [[3.1, 4.1], [5.9, 26.5]], "item": ["foo", "bar"], "price": [10.0, 20.0], } ) await db.create_table("test", data=data) tbl = await db.open_table("test") assert tbl.name == "test" assert ( re.search( r"NativeTable\(test, uri=.*test\.lance, read_consistency_interval=None\)", str(tbl), ) is not None ) assert await tbl.schema() == pa.schema( { "vector": pa.list_(pa.float32(), list_size=2), "item": pa.utf8(), "price": pa.float64(), } ) # No way to verify this yet, but at least make sure we # can pass the parameter await db.open_table("test", index_cache_size=0) with pytest.raises(ValueError, match="was not found"): await db.open_table("does_not_exist") def test_delete_table(tmp_db: lancedb.DBConnection): data = pd.DataFrame( { "vector": [[3.1, 4.1], [5.9, 26.5]], "item": ["foo", "bar"], "price": [10.0, 20.0], } ) tmp_db.create_table("test", data=data) with pytest.raises(Exception): tmp_db.create_table("test", data=data) assert tmp_db.table_names() == ["test"] tmp_db.drop_table("test") assert tmp_db.table_names() == [] tmp_db.create_table("test", data=data) assert tmp_db.table_names() == ["test"] # dropping a table that does not exist should pass # if ignore_missing=True tmp_db.drop_table("does_not_exist", ignore_missing=True) tmp_db.drop_all_tables() assert tmp_db.table_names() == [] @pytest.mark.asyncio async def test_delete_table_async(tmp_db: lancedb.DBConnection): data = pd.DataFrame( { "vector": [[3.1, 4.1], [5.9, 26.5]], "item": ["foo", "bar"], "price": [10.0, 20.0], } ) tmp_db.create_table("test", data=data) with pytest.raises(Exception): tmp_db.create_table("test", data=data) assert tmp_db.table_names() == ["test"] tmp_db.drop_table("test") assert tmp_db.table_names() == [] tmp_db.create_table("test", data=data) assert tmp_db.table_names() == ["test"] tmp_db.drop_table("does_not_exist", ignore_missing=True) def test_drop_database(tmp_db: lancedb.DBConnection): data = pd.DataFrame( { "vector": [[3.1, 4.1], [5.9, 26.5]], "item": ["foo", "bar"], "price": [10.0, 20.0], } ) new_data = pd.DataFrame( { "vector": [[5.1, 4.1], [5.9, 10.5]], "item": ["kiwi", "avocado"], "price": [12.0, 17.0], } ) tmp_db.create_table("test", data=data) with pytest.raises(Exception): tmp_db.create_table("test", data=data) assert tmp_db.table_names() == ["test"] tmp_db.create_table("new_test", data=new_data) tmp_db.drop_database() assert tmp_db.table_names() == [] # it should pass when no tables are present tmp_db.create_table("test", data=new_data) tmp_db.drop_table("test") assert tmp_db.table_names() == [] tmp_db.drop_database() assert tmp_db.table_names() == [] # creating an empty database with schema schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))]) tmp_db.create_table("empty_table", schema=schema) # dropping a empty database should pass tmp_db.drop_database() assert tmp_db.table_names() == [] def test_empty_or_nonexistent_table(mem_db: lancedb.DBConnection): with pytest.raises(Exception): mem_db.create_table("test_with_no_data") with pytest.raises(Exception): mem_db.open_table("does_not_exist") schema = pa.schema([pa.field("a", pa.int64(), nullable=False)]) test = mem_db.create_table("test", schema=schema) class TestModel(LanceModel): a: int test2 = mem_db.create_table("test2", schema=TestModel) assert test.schema == test2.schema @pytest.mark.asyncio async def test_create_in_v2_mode(): def make_data(): for i in range(10): yield pa.record_batch([pa.array([x for x in range(1024)])], names=["x"]) def make_table(): return pa.table([pa.array([x for x in range(10 * 1024)])], names=["x"]) schema = pa.schema([pa.field("x", pa.int64())]) # Create table in v1 mode v1_db = await lancedb.connect_async( "memory://", storage_options={"new_table_data_storage_version": "legacy"} ) tbl = await v1_db.create_table("test", data=make_data(), schema=schema) async def is_in_v2_mode(tbl): batches = ( await tbl.query().limit(10 * 1024).to_batches(max_batch_length=1024 * 10) ) num_batches = 0 async for batch in batches: num_batches += 1 return num_batches < 10 assert not await is_in_v2_mode(tbl) # Create table in v2 mode v2_db = await lancedb.connect_async( "memory://", storage_options={"new_table_data_storage_version": "stable"} ) tbl = await v2_db.create_table("test_v2", data=make_data(), schema=schema) assert await is_in_v2_mode(tbl) # Add data (should remain in v2 mode) await tbl.add(make_table()) assert await is_in_v2_mode(tbl) # Create empty table in v2 mode and add data tbl = await v2_db.create_table("test_empty_v2", data=None, schema=schema) await tbl.add(make_table()) assert await is_in_v2_mode(tbl) # Db uses v2 mode by default db = await lancedb.connect_async("memory://") tbl = await db.create_table("test_empty_v2_default", data=None, schema=schema) await tbl.add(make_table()) assert await is_in_v2_mode(tbl) def test_replace_index(mem_db: lancedb.DBConnection): table = mem_db.create_table( "test", [ {"vector": np.random.rand(32), "item": "foo", "price": float(i)} for i in range(512) ], ) table.create_index( num_partitions=2, num_sub_vectors=2, ) with pytest.raises(Exception): table.create_index( num_partitions=2, num_sub_vectors=4, replace=False, ) table.create_index( num_partitions=1, num_sub_vectors=2, replace=True, index_cache_size=10, ) def test_prefilter_with_index(mem_db: lancedb.DBConnection): data = [ {"vector": np.random.rand(32), "item": "foo", "price": float(i)} for i in range(512) ] sample_key = data[100]["vector"] table = mem_db.create_table( "test", data, ) table.create_index( num_partitions=2, num_sub_vectors=2, ) table = ( table.search(sample_key) .where("price == 500", prefilter=True) .limit(5) .to_arrow() ) assert table.num_rows == 1 def test_create_table_with_invalid_names(tmp_db: lancedb.DBConnection): data = [{"vector": np.random.rand(128), "item": "foo"} for i in range(10)] with pytest.raises(ValueError): tmp_db.create_table("foo/bar", data) with pytest.raises(ValueError): tmp_db.create_table("foo bar", data) with pytest.raises(ValueError): tmp_db.create_table("foo$$bar", data) tmp_db.create_table("foo.bar", data) def test_bypass_vector_index_sync(tmp_db: lancedb.DBConnection): data = [{"vector": np.random.rand(32)} for _ in range(512)] sample_key = data[100]["vector"] table = tmp_db.create_table( "test", data, ) table.create_index( num_partitions=2, num_sub_vectors=2, ) plan_with_index = table.search(sample_key).explain_plan(verbose=True) assert "ANN" in plan_with_index plan_without_index = ( table.search(sample_key).bypass_vector_index().explain_plan(verbose=True) ) assert "KNN" in plan_without_index def test_local_namespace_operations(tmp_path): """Test that local mode namespace operations behave as expected.""" # Create a local database connection db = lancedb.connect(tmp_path) # Test list_namespaces returns empty list for root namespace namespaces = list(db.list_namespaces()) assert namespaces == [] # Test list_namespaces with non-empty namespace raises NotImplementedError with pytest.raises( NotImplementedError, match="Namespace operations are not supported for listing database", ): list(db.list_namespaces(namespace=["test"])) def test_local_create_namespace_not_supported(tmp_path): """Test that create_namespace is not supported in local mode.""" db = lancedb.connect(tmp_path) with pytest.raises( NotImplementedError, match="Namespace operations are not supported for listing database", ): db.create_namespace(["test_namespace"]) def test_local_drop_namespace_not_supported(tmp_path): """Test that drop_namespace is not supported in local mode.""" db = lancedb.connect(tmp_path) with pytest.raises( NotImplementedError, match="Namespace operations are not supported for listing database", ): db.drop_namespace(["test_namespace"]) def test_local_table_operations_with_namespace_raise_error(tmp_path): """ Test that table operations with namespace parameter raise ValueError in local mode. """ db = lancedb.connect(tmp_path) # Create some test data data = [{"vector": [1.0, 2.0], "item": "test"}] schema = pa.schema( [pa.field("vector", pa.list_(pa.float32(), 2)), pa.field("item", pa.string())] ) # Test create_table with namespace - should raise ValueError with pytest.raises( NotImplementedError, match="Namespace parameter is not supported for listing database", ): db.create_table( "test_table_with_ns", data=data, schema=schema, namespace=["test_ns"] ) # Create table normally for other tests db.create_table("test_table", data=data, schema=schema) assert "test_table" in db.table_names() # Test open_table with namespace - should raise ValueError with pytest.raises( NotImplementedError, match="Namespace parameter is not supported for listing database", ): db.open_table("test_table", namespace=["test_ns"]) # Test table_names with namespace - should raise ValueError with pytest.raises( NotImplementedError, match="Namespace parameter is not supported for listing database", ): list(db.table_names(namespace=["test_ns"])) # Test drop_table with namespace - should raise ValueError with pytest.raises( NotImplementedError, match="Namespace parameter is not supported for listing database", ): db.drop_table("test_table", namespace=["test_ns"]) # Test table_names without namespace - should work normally tables_root = list(db.table_names()) assert "test_table" in tables_root def test_clone_table_latest_version(tmp_path): """Test cloning a table with the latest version (default behavior)""" import os db = lancedb.connect(tmp_path) # Create source table with some data data = [ {"id": 1, "text": "hello", "vector": [1.0, 2.0]}, {"id": 2, "text": "world", "vector": [3.0, 4.0]}, ] source_table = db.create_table("source", data=data) # Add more data to create a new version more_data = [{"id": 3, "text": "test", "vector": [5.0, 6.0]}] source_table.add(more_data) # Clone the table (should get latest version with 3 rows) source_uri = os.path.join(tmp_path, "source.lance") cloned_table = db.clone_table("cloned", source_uri) # Verify cloned table has all 3 rows assert cloned_table.count_rows() == 3 assert "cloned" in db.table_names() # Verify data matches cloned_data = cloned_table.to_pandas() assert len(cloned_data) == 3 assert set(cloned_data["id"].tolist()) == {1, 2, 3} def test_clone_table_specific_version(tmp_path): """Test cloning a table from a specific version""" import os db = lancedb.connect(tmp_path) # Create source table with initial data data = [ {"id": 1, "text": "hello", "vector": [1.0, 2.0]}, {"id": 2, "text": "world", "vector": [3.0, 4.0]}, ] source_table = db.create_table("source", data=data) # Get the initial version initial_version = source_table.version # Add more data to create a new version more_data = [{"id": 3, "text": "test", "vector": [5.0, 6.0]}] source_table.add(more_data) # Verify source now has 3 rows assert source_table.count_rows() == 3 # Clone from the initial version (should have only 2 rows) source_uri = os.path.join(tmp_path, "source.lance") cloned_table = db.clone_table("cloned", source_uri, source_version=initial_version) # Verify cloned table has only the initial 2 rows assert cloned_table.count_rows() == 2 cloned_data = cloned_table.to_pandas() assert set(cloned_data["id"].tolist()) == {1, 2} def test_clone_table_with_tag(tmp_path): """Test cloning a table from a tagged version""" import os db = lancedb.connect(tmp_path) # Create source table with initial data data = [ {"id": 1, "text": "hello", "vector": [1.0, 2.0]}, {"id": 2, "text": "world", "vector": [3.0, 4.0]}, ] source_table = db.create_table("source", data=data) # Create a tag for the current version source_table.tags.create("v1.0", source_table.version) # Add more data after the tag more_data = [{"id": 3, "text": "test", "vector": [5.0, 6.0]}] source_table.add(more_data) # Verify source now has 3 rows assert source_table.count_rows() == 3 # Clone from the tagged version (should have only 2 rows) source_uri = os.path.join(tmp_path, "source.lance") cloned_table = db.clone_table("cloned", source_uri, source_tag="v1.0") # Verify cloned table has only the tagged version's 2 rows assert cloned_table.count_rows() == 2 cloned_data = cloned_table.to_pandas() assert set(cloned_data["id"].tolist()) == {1, 2} def test_clone_table_deep_clone_fails(tmp_path): """Test that deep clone raises an unsupported error""" import os db = lancedb.connect(tmp_path) # Create source table with some data data = [ {"id": 1, "text": "hello", "vector": [1.0, 2.0]}, {"id": 2, "text": "world", "vector": [3.0, 4.0]}, ] db.create_table("source", data=data) # Try to create a deep clone (should fail) source_uri = os.path.join(tmp_path, "source.lance") with pytest.raises(Exception, match="Deep clone is not yet implemented"): db.clone_table("cloned", source_uri, is_shallow=False)