mirror of
https://github.com/lancedb/lancedb.git
synced 2026-01-06 20:02:58 +00:00
fix(python)!: handle bad openai embeddings gracefully (#1873)
BREAKING-CHANGE: change Pydantic Vector field to be nullable by default. Closes #1577
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@@ -83,25 +83,33 @@ class OpenAIEmbeddings(TextEmbeddingFunction):
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"""
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openai = attempt_import_or_raise("openai")
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valid_texts = []
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valid_indices = []
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for idx, text in enumerate(texts):
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if text:
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valid_texts.append(text)
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valid_indices.append(idx)
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# TODO retry, rate limit, token limit
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try:
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if self.name == "text-embedding-ada-002":
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rs = self._openai_client.embeddings.create(input=texts, model=self.name)
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else:
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kwargs = {
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"input": texts,
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"model": self.name,
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}
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if self.dim:
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kwargs["dimensions"] = self.dim
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rs = self._openai_client.embeddings.create(**kwargs)
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kwargs = {
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"input": valid_texts,
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"model": self.name,
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}
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if self.name != "text-embedding-ada-002":
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kwargs["dimensions"] = self.dim
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rs = self._openai_client.embeddings.create(**kwargs)
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valid_embeddings = {
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idx: v.embedding for v, idx in zip(rs.data, valid_indices)
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}
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except openai.BadRequestError:
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logging.exception("Bad request: %s", texts)
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return [None] * len(texts)
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except Exception:
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logging.exception("OpenAI embeddings error")
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raise
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return [v.embedding for v in rs.data]
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return [valid_embeddings.get(idx, None) for idx in range(len(texts))]
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@cached_property
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def _openai_client(self):
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@@ -1,15 +1,5 @@
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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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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright The LanceDB Authors
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"""Pydantic (v1 / v2) adapter for LanceDB"""
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@@ -30,6 +20,7 @@ from typing import (
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Type,
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Union,
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_GenericAlias,
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GenericAlias,
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)
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import numpy as np
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@@ -75,7 +66,7 @@ def vector(dim: int, value_type: pa.DataType = pa.float32()):
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def Vector(
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dim: int, value_type: pa.DataType = pa.float32()
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dim: int, value_type: pa.DataType = pa.float32(), nullable: bool = True
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) -> Type[FixedSizeListMixin]:
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"""Pydantic Vector Type.
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@@ -88,6 +79,8 @@ def Vector(
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The dimension of the vector.
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value_type : pyarrow.DataType, optional
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The value type of the vector, by default pa.float32()
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nullable : bool, optional
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Whether the vector is nullable, by default it is True.
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Examples
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--------
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@@ -103,7 +96,7 @@ def Vector(
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>>> assert schema == pa.schema([
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... pa.field("id", pa.int64(), False),
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... pa.field("url", pa.utf8(), False),
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... pa.field("embeddings", pa.list_(pa.float32(), 768), False)
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... pa.field("embeddings", pa.list_(pa.float32(), 768))
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... ])
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"""
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@@ -112,6 +105,10 @@ def Vector(
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def __repr__(self):
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return f"FixedSizeList(dim={dim})"
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@staticmethod
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def nullable() -> bool:
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return nullable
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@staticmethod
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def dim() -> int:
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return dim
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@@ -205,9 +202,7 @@ else:
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def _pydantic_to_arrow_type(field: FieldInfo) -> pa.DataType:
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"""Convert a Pydantic FieldInfo to Arrow DataType"""
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if isinstance(field.annotation, _GenericAlias) or (
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sys.version_info > (3, 9) and isinstance(field.annotation, types.GenericAlias)
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):
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if isinstance(field.annotation, (_GenericAlias, GenericAlias)):
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origin = field.annotation.__origin__
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args = field.annotation.__args__
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if origin is list:
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@@ -235,7 +230,7 @@ def _pydantic_to_arrow_type(field: FieldInfo) -> pa.DataType:
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def is_nullable(field: FieldInfo) -> bool:
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"""Check if a Pydantic FieldInfo is nullable."""
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if isinstance(field.annotation, _GenericAlias):
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if isinstance(field.annotation, (_GenericAlias, GenericAlias)):
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origin = field.annotation.__origin__
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args = field.annotation.__args__
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if origin == Union:
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@@ -246,6 +241,10 @@ def is_nullable(field: FieldInfo) -> bool:
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for typ in args:
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if typ is type(None):
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return True
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elif inspect.isclass(field.annotation) and issubclass(
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field.annotation, FixedSizeListMixin
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):
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return field.annotation.nullable()
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return False
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@@ -90,10 +90,13 @@ def test_embedding_with_bad_results(tmp_path):
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self, texts: Union[List[str], np.ndarray]
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) -> list[Union[np.array, None]]:
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# Return None, which is bad if field is non-nullable
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return [
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None if i % 2 == 0 else np.random.randn(self.ndims())
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a = [
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np.full(self.ndims(), np.nan)
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if i % 2 == 0
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else np.random.randn(self.ndims())
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for i in range(len(texts))
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]
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return a
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db = lancedb.connect(tmp_path)
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registry = EmbeddingFunctionRegistry.get_instance()
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@@ -1,15 +1,6 @@
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# Copyright (c) 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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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright The LanceDB Authors
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import importlib
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import io
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import os
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@@ -17,6 +8,7 @@ import os
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import lancedb
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import numpy as np
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import pandas as pd
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import pyarrow as pa
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import pytest
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from lancedb.embeddings import get_registry
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from lancedb.pydantic import LanceModel, Vector
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@@ -444,6 +436,30 @@ def test_watsonx_embedding(tmp_path):
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assert tbl.search("hello").limit(1).to_pandas()["text"][0] == "hello world"
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@pytest.mark.slow
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@pytest.mark.skipif(
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os.environ.get("OPENAI_API_KEY") is None, reason="OPENAI_API_KEY not set"
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)
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def test_openai_with_empty_strs(tmp_path):
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model = get_registry().get("openai").create(max_retries=0)
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class TextModel(LanceModel):
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text: str = model.SourceField()
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vector: Vector(model.ndims()) = model.VectorField()
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df = pd.DataFrame({"text": ["hello world", ""]})
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db = lancedb.connect(tmp_path)
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tbl = db.create_table("test", schema=TextModel, mode="overwrite")
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tbl.add(df, on_bad_vectors="skip")
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tb = tbl.to_arrow()
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assert tb.schema.field_by_name("vector").type == pa.list_(
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pa.float32(), model.ndims()
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)
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assert len(tb) == 2
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assert tb["vector"].is_null().to_pylist() == [False, True]
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@pytest.mark.slow
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@pytest.mark.skipif(
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importlib.util.find_spec("ollama") is None, reason="Ollama not installed"
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@@ -1,16 +1,5 @@
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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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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright The LanceDB Authors
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import json
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import sys
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@@ -172,6 +161,26 @@ def test_pydantic_to_arrow_py38():
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assert schema == expect_schema
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def test_nullable_vector():
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class NullableModel(pydantic.BaseModel):
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vec: Vector(16, nullable=False)
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schema = pydantic_to_schema(NullableModel)
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assert schema == pa.schema([pa.field("vec", pa.list_(pa.float32(), 16), False)])
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class DefaultModel(pydantic.BaseModel):
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vec: Vector(16)
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schema = pydantic_to_schema(DefaultModel)
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assert schema == pa.schema([pa.field("vec", pa.list_(pa.float32(), 16), True)])
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class NotNullableModel(pydantic.BaseModel):
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vec: Vector(16)
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schema = pydantic_to_schema(NotNullableModel)
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assert schema == pa.schema([pa.field("vec", pa.list_(pa.float32(), 16), True)])
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def test_fixed_size_list_field():
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class TestModel(pydantic.BaseModel):
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vec: Vector(16)
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@@ -192,7 +201,7 @@ def test_fixed_size_list_field():
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schema = pydantic_to_schema(TestModel)
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assert schema == pa.schema(
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[
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pa.field("vec", pa.list_(pa.float32(), 16), False),
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pa.field("vec", pa.list_(pa.float32(), 16)),
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pa.field("li", pa.list_(pa.int64()), False),
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]
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)
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