Bump version: 0.16.1-beta.0 → 0.17.0-beta.0

Users who call the remote SDK from code that uses futures (either
`ThreadPoolExecutor` or `asyncio`) can get odd errors like:

```
Traceback (most recent call last):
  File "/usr/lib/python3.12/asyncio/events.py", line 88, in _run
    self._context.run(self._callback, *self._args)
RuntimeError: cannot enter context: <_contextvars.Context object at 0x7cfe94cdc900> is already entered
```

This PR fixes that by executing all LanceDB futures in a dedicated
thread pool running on a background thread. That way, it doesn't
interact with their threadpool.

docs/src/guides/scalar_index.md

@@ -1,23 +1,35 @@
-# Building Scalar Index
+# Building a Scalar Index
 
-Similar to many SQL databases, LanceDB supports several types of Scalar indices to accelerate search
+Scalar indices organize data by scalar attributes (e.g. numbers, categorical values), enabling fast filtering of vector data. In vector databases, scalar indices accelerate the retrieval of scalar data associated with vectors, thus enhancing the query performance when searching for vectors that meet certain scalar criteria. 
+
+Similar to many SQL databases, LanceDB supports several types of scalar indices to accelerate search
 over scalar columns.
 
-- `BTREE`: The most common type is BTREE. This index is inspired by the btree data structure
+- `BTREE`: The most common type is BTREE. The index stores a copy of the
-  although only the first few layers of the btree are cached in memory.
+  column in sorted order. This sorted copy allows a binary search to be used to
-  It will perform well on columns with a large number of unique values and few rows per value.
+  satisfy queries.
-- `BITMAP`: this index stores a bitmap for each unique value in the column.
+- `BITMAP`: this index stores a bitmap for each unique value in the column. It 
-  This index is useful for columns with a finite number of unique values and many rows per value.
+  uses a series of bits to indicate whether a value is present in a row of a table
-  For example, columns that represent "categories", "labels", or "tags"
+- `LABEL_LIST`: a special index that can be used on `List<T>` columns to
-- `LABEL_LIST`: a special index that is used to index list columns whose values have a finite set of possibilities.
+  support queries with `array_contains_all` and `array_contains_any`
+  using an underlying bitmap index.
   For example, a column that contains lists of tags (e.g. `["tag1", "tag2", "tag3"]`) can be indexed with a `LABEL_LIST` index.
 
+!!! tips "How to choose the right scalar index type"
+
+    `BTREE`: This index is good for scalar columns with mostly distinct values and does best when the query is highly selective.
+    
+    `BITMAP`: This index works best for low-cardinality numeric or string columns, where the number of unique values is small (i.e., less than a few thousands).
+    
+    `LABEL_LIST`: This index should be used for columns containing list-type data.
+
 | Data Type                                                       | Filter                                    | Index Type   |
 | --------------------------------------------------------------- | ----------------------------------------- | ------------ |
 | Numeric, String, Temporal                                       | `<`, `=`, `>`, `in`, `between`, `is null` | `BTREE`      |
 | Boolean, numbers or strings with fewer than 1,000 unique values | `<`, `=`, `>`, `in`, `between`, `is null` | `BITMAP`     |
 | List of low cardinality of numbers or strings                   | `array_has_any`, `array_has_all`          | `LABEL_LIST` |
 
+### Create a scalar index
 === "Python"
 
     ```python
@@ -46,7 +58,7 @@ over scalar columns.
         await tlb.create_index("publisher", { config: lancedb.Index.bitmap() })
         ```
 
-For example, the following scan will be faster if the column `my_col` has a scalar index:
+The following scan will be faster if the column `book_id` has a scalar index:
 
 === "Python"
 
@@ -106,3 +118,30 @@ Scalar indices can also speed up scans containing a vector search or full text s
           .limit(10)
           .toArray();
         ```
+### Update a scalar index
+Updating the table data (adding, deleting, or modifying records) requires that you also update the scalar index. This can be done by calling `optimize`, which will trigger an update to the existing scalar index.
+=== "Python"
+
+    ```python
+    table.add([{"vector": [7, 8], "book_id": 4}])
+    table.optimize()
+    ```
+
+=== "TypeScript"
+
+    ```typescript
+    await tbl.add([{ vector: [7, 8], book_id: 4 }]);
+    await tbl.optimize();
+    ```
+
+=== "Rust"
+
+    ```rust
+    let more_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
+    tbl.add(more_data).execute().await?;
+    tbl.optimize(OptimizeAction::All).execute().await?;
+    ```
+
+!!! note
+
+    New data added after creating the scalar index will still appear in search results if optimize is not used, but with increased latency due to a flat search on the unindexed portion. LanceDB Cloud automates the optimize process, minimizing the impact on search speed.

docs/src/sql.md

@@ -7,6 +7,10 @@ performed on the top-k results returned by the vector search. However, pre-filte
 option that performs the filter prior to vector search. This can be useful to narrow down on
 the search space on a very large dataset to reduce query latency.
 
+Note that both pre-filtering and post-filtering can yield false positives. For pre-filtering, if the filter is too selective, it might eliminate relevant items that the vector search would have otherwise identified as a good match. In this case, increasing `nprobes` parameter will help reduce such false positives. It is recommended to set `use_index=false` if you know that the filter is highly selective.
+
+Similarly, a highly selective post-filter can lead to false positives. Increasing both `nprobes` and `refine_factor` can mitigate this issue. When deciding between pre-filtering and post-filtering, pre-filtering is generally the safer choice if you're uncertain.
+
 <!-- Setup Code
 ```python
 import lancedb
@@ -57,6 +61,9 @@ const tbl = await db.createTable('myVectors', data)
         ```ts
         --8<-- "docs/src/sql_legacy.ts:search"
         ```
+!!! note
+
+    Creating a [scalar index](guides/scalar_index.md) accelerates filtering
 
 ## SQL filters
 

node/package.json

@@ -84,6 +84,8 @@
       "aarch64-apple-darwin": "@lancedb/vectordb-darwin-arm64",
       "x86_64-unknown-linux-gnu": "@lancedb/vectordb-linux-x64-gnu",
       "aarch64-unknown-linux-gnu": "@lancedb/vectordb-linux-arm64-gnu",
+      "x86_64-unknown-linux-musl": "@lancedb/vectordb-linux-x64-musl",
+      "aarch64-unknown-linux-musl": "@lancedb/vectordb-linux-arm64-musl",
       "x86_64-pc-windows-msvc": "@lancedb/vectordb-win32-x64-msvc",
       "aarch64-pc-windows-msvc": "@lancedb/vectordb-win32-arm64-msvc"
     }

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.16.1-beta.0"
+current_version = "0.17.0-beta.0"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

python/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.16.1-beta.0"
+version = "0.17.0-beta.0"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true
@@ -17,11 +17,17 @@ crate-type = ["cdylib"]
 arrow = { version = "52.1", features = ["pyarrow"] }
 lancedb = { path = "../rust/lancedb", default-features = false }
 env_logger.workspace = true
-pyo3 = { version = "0.21", features = ["extension-module", "abi3-py38", "gil-refs"] }
+pyo3 = { version = "0.21", features = [
+    "extension-module",
+    "abi3-py39",
+    "gil-refs"
+] }
 # Using this fork for now: https://github.com/awestlake87/pyo3-asyncio/issues/119
 # pyo3-asyncio = { version = "0.20", features = ["attributes", "tokio-runtime"] }
-pyo3-asyncio-0-21 = { version = "0.21.0", features = ["attributes", "tokio-runtime"] }
+pyo3-asyncio-0-21 = { version = "0.21.0", features = [
-
+    "attributes",
+    "tokio-runtime"
+] }
 pin-project = "1.1.5"
 futures.workspace = true
 tokio = { version = "1.36.0", features = ["sync"] }
@@ -29,14 +35,13 @@ tokio = { version = "1.36.0", features = ["sync"] }
 [build-dependencies]
 pyo3-build-config = { version = "0.20.3", features = [
     "extension-module",
-    "abi3-py38",
+    "abi3-py39",
 ] }
 
 [features]
 default = ["default-tls", "remote"]
 fp16kernels = ["lancedb/fp16kernels"]
 remote = ["lancedb/remote"]
-
 # TLS
 default-tls = ["lancedb/default-tls"]
 native-tls = ["lancedb/native-tls"]

python/pyproject.toml

@@ -3,7 +3,6 @@ name = "lancedb"
 # version in Cargo.toml
 dependencies = [
     "deprecation",
-    "nest-asyncio~=1.0",
     "pylance==0.20.0b2",
     "tqdm>=4.27.0",
     "pydantic>=1.10",
@@ -31,7 +30,6 @@ classifiers = [
     "Programming Language :: Python",
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3 :: Only",
-    "Programming Language :: Python :: 3.8",
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",

python/python/lancedb/embeddings/openai.py

@@ -83,25 +83,33 @@ class OpenAIEmbeddings(TextEmbeddingFunction):
         """
         openai = attempt_import_or_raise("openai")
 
+        valid_texts = []
+        valid_indices = []
+        for idx, text in enumerate(texts):
+            if text:
+                valid_texts.append(text)
+                valid_indices.append(idx)
+
         # TODO retry, rate limit, token limit
         try:
-            if self.name == "text-embedding-ada-002":
+            kwargs = {
-                rs = self._openai_client.embeddings.create(input=texts, model=self.name)
+                "input": valid_texts,
-            else:
+                "model": self.name,
-                kwargs = {
+            }
-                    "input": texts,
+            if self.name != "text-embedding-ada-002":
-                    "model": self.name,
+                kwargs["dimensions"] = self.dim
-                }
+
-                if self.dim:
+            rs = self._openai_client.embeddings.create(**kwargs)
-                    kwargs["dimensions"] = self.dim
+            valid_embeddings = {
-                rs = self._openai_client.embeddings.create(**kwargs)
+                idx: v.embedding for v, idx in zip(rs.data, valid_indices)
+            }
         except openai.BadRequestError:
             logging.exception("Bad request: %s", texts)
             return [None] * len(texts)
         except Exception:
             logging.exception("OpenAI embeddings error")
             raise
-        return [v.embedding for v in rs.data]
+        return [valid_embeddings.get(idx, None) for idx in range(len(texts))]
 
     @cached_property
     def _openai_client(self):

python/python/lancedb/pydantic.py

@@ -1,15 +1,5 @@
-#  Copyright 2023 LanceDB Developers
+# SPDX-License-Identifier: Apache-2.0
-#
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
 
 """Pydantic (v1 / v2) adapter for LanceDB"""
 
@@ -30,6 +20,7 @@ from typing import (
     Type,
     Union,
     _GenericAlias,
+    GenericAlias,
 )
 
 import numpy as np
@@ -75,7 +66,7 @@ def vector(dim: int, value_type: pa.DataType = pa.float32()):
 
 
 def Vector(
-    dim: int, value_type: pa.DataType = pa.float32()
+    dim: int, value_type: pa.DataType = pa.float32(), nullable: bool = True
 ) -> Type[FixedSizeListMixin]:
     """Pydantic Vector Type.
 
@@ -88,6 +79,8 @@ def Vector(
         The dimension of the vector.
     value_type : pyarrow.DataType, optional
         The value type of the vector, by default pa.float32()
+    nullable : bool, optional
+        Whether the vector is nullable, by default it is True.
 
     Examples
     --------
@@ -103,7 +96,7 @@ def Vector(
     >>> assert schema == pa.schema([
     ...     pa.field("id", pa.int64(), False),
     ...     pa.field("url", pa.utf8(), False),
-    ...     pa.field("embeddings", pa.list_(pa.float32(), 768), False)
+    ...     pa.field("embeddings", pa.list_(pa.float32(), 768))
     ... ])
     """
 
@@ -112,6 +105,10 @@ def Vector(
         def __repr__(self):
             return f"FixedSizeList(dim={dim})"
 
+        @staticmethod
+        def nullable() -> bool:
+            return nullable
+
         @staticmethod
         def dim() -> int:
             return dim
@@ -205,9 +202,7 @@ else:
 def _pydantic_to_arrow_type(field: FieldInfo) -> pa.DataType:
     """Convert a Pydantic FieldInfo to Arrow DataType"""
 
-    if isinstance(field.annotation, _GenericAlias) or (
+    if isinstance(field.annotation, (_GenericAlias, GenericAlias)):
-        sys.version_info > (3, 9) and isinstance(field.annotation, types.GenericAlias)
-    ):
         origin = field.annotation.__origin__
         args = field.annotation.__args__
         if origin is list:
@@ -235,7 +230,7 @@ def _pydantic_to_arrow_type(field: FieldInfo) -> pa.DataType:
 
 def is_nullable(field: FieldInfo) -> bool:
     """Check if a Pydantic FieldInfo is nullable."""
-    if isinstance(field.annotation, _GenericAlias):
+    if isinstance(field.annotation, (_GenericAlias, GenericAlias)):
         origin = field.annotation.__origin__
         args = field.annotation.__args__
         if origin == Union:
@@ -246,6 +241,10 @@ def is_nullable(field: FieldInfo) -> bool:
         for typ in args:
             if typ is type(None):
                 return True
+    elif inspect.isclass(field.annotation) and issubclass(
+        field.annotation, FixedSizeListMixin
+    ):
+        return field.annotation.nullable()
     return False
 
 

python/python/lancedb/remote/background_loop.py

@@ -0,0 +1,25 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import asyncio
+import threading
+
+
+class BackgroundEventLoop:
+    """
+    A background event loop that can run futures.
+
+    Used to bridge sync and async code, without messing with users event loops.
+    """
+
+    def __init__(self):
+        self.loop = asyncio.new_event_loop()
+        self.thread = threading.Thread(
+            target=self.loop.run_forever,
+            name="LanceDBBackgroundEventLoop",
+            daemon=True,
+        )
+        self.thread.start()
+
+    def run(self, future):
+        return asyncio.run_coroutine_threadsafe(future, self.loop).result()

python/python/lancedb/remote/db.py

@@ -11,7 +11,6 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
-import asyncio
 from datetime import timedelta
 import logging
 from concurrent.futures import ThreadPoolExecutor
@@ -21,6 +20,7 @@ import warnings
 
 from lancedb import connect_async
 from lancedb.remote import ClientConfig
+from lancedb.remote.background_loop import BackgroundEventLoop
 import pyarrow as pa
 from overrides import override
 
@@ -31,6 +31,8 @@ from ..pydantic import LanceModel
 from ..table import Table
 from ..util import validate_table_name
 
+LOOP = BackgroundEventLoop()
+
 
 class RemoteDBConnection(DBConnection):
     """A connection to a remote LanceDB database."""
@@ -86,18 +88,9 @@ class RemoteDBConnection(DBConnection):
             raise ValueError(f"Invalid scheme: {parsed.scheme}, only accepts db://")
         self.db_name = parsed.netloc
 
-        import nest_asyncio
-
-        nest_asyncio.apply()
-        try:
-            self._loop = asyncio.get_running_loop()
-        except RuntimeError:
-            self._loop = asyncio.new_event_loop()
-            asyncio.set_event_loop(self._loop)
-
         self.client_config = client_config
 
-        self._conn = self._loop.run_until_complete(
+        self._conn = LOOP.run(
             connect_async(
                 db_url,
                 api_key=api_key,
@@ -127,9 +120,7 @@ class RemoteDBConnection(DBConnection):
         -------
         An iterator of table names.
         """
-        return self._loop.run_until_complete(
+        return LOOP.run(self._conn.table_names(start_after=page_token, limit=limit))
-            self._conn.table_names(start_after=page_token, limit=limit)
-        )
 
     @override
     def open_table(self, name: str, *, index_cache_size: Optional[int] = None) -> Table:
@@ -152,8 +143,8 @@ class RemoteDBConnection(DBConnection):
                 " (there is no local cache to configure)"
             )
 
-        table = self._loop.run_until_complete(self._conn.open_table(name))
+        table = LOOP.run(self._conn.open_table(name))
-        return RemoteTable(table, self.db_name, self._loop)
+        return RemoteTable(table, self.db_name)
 
     @override
     def create_table(
@@ -268,7 +259,7 @@ class RemoteDBConnection(DBConnection):
 
         from .table import RemoteTable
 
-        table = self._loop.run_until_complete(
+        table = LOOP.run(
             self._conn.create_table(
                 name,
                 data,
@@ -278,7 +269,7 @@ class RemoteDBConnection(DBConnection):
                 fill_value=fill_value,
             )
         )
-        return RemoteTable(table, self.db_name, self._loop)
+        return RemoteTable(table, self.db_name)
 
     @override
     def drop_table(self, name: str):
@@ -289,7 +280,7 @@ class RemoteDBConnection(DBConnection):
         name: str
             The name of the table.
         """
-        self._loop.run_until_complete(self._conn.drop_table(name))
+        LOOP.run(self._conn.drop_table(name))
 
     @override
     def rename_table(self, cur_name: str, new_name: str):
@@ -302,7 +293,7 @@ class RemoteDBConnection(DBConnection):
         new_name: str
             The new name of the table.
         """
-        self._loop.run_until_complete(self._conn.rename_table(cur_name, new_name))
+        LOOP.run(self._conn.rename_table(cur_name, new_name))
 
     async def close(self):
         """Close the connection to the database."""

python/python/lancedb/remote/table.py

@@ -12,12 +12,12 @@
 #  limitations under the License.
 
 from datetime import timedelta
-import asyncio
 import logging
 from functools import cached_property
 from typing import Dict, Iterable, List, Optional, Union, Literal
 
 from lancedb.index import FTS, BTree, Bitmap, HnswPq, HnswSq, IvfPq, LabelList
+from lancedb.remote.db import LOOP
 import pyarrow as pa
 
 from lancedb.common import DATA, VEC, VECTOR_COLUMN_NAME
@@ -33,9 +33,7 @@ class RemoteTable(Table):
         self,
         table: AsyncTable,
         db_name: str,
-        loop: Optional[asyncio.AbstractEventLoop] = None,
     ):
-        self._loop = loop
         self._table = table
         self.db_name = db_name
 
@@ -56,12 +54,12 @@ class RemoteTable(Table):
         of this Table
 
         """
-        return self._loop.run_until_complete(self._table.schema())
+        return LOOP.run(self._table.schema())
 
     @property
     def version(self) -> int:
         """Get the current version of the table"""
-        return self._loop.run_until_complete(self._table.version())
+        return LOOP.run(self._table.version())
 
     @cached_property
     def embedding_functions(self) -> dict:
@@ -98,11 +96,11 @@ class RemoteTable(Table):
 
     def list_indices(self):
         """List all the indices on the table"""
-        return self._loop.run_until_complete(self._table.list_indices())
+        return LOOP.run(self._table.list_indices())
 
     def index_stats(self, index_uuid: str):
         """List all the stats of a specified index"""
-        return self._loop.run_until_complete(self._table.index_stats(index_uuid))
+        return LOOP.run(self._table.index_stats(index_uuid))
 
     def create_scalar_index(
         self,
@@ -132,9 +130,7 @@ class RemoteTable(Table):
         else:
             raise ValueError(f"Unknown index type: {index_type}")
 
-        self._loop.run_until_complete(
+        LOOP.run(self._table.create_index(column, config=config, replace=replace))
-            self._table.create_index(column, config=config, replace=replace)
-        )
 
     def create_fts_index(
         self,
@@ -144,9 +140,7 @@ class RemoteTable(Table):
         with_position: bool = True,
     ):
         config = FTS(with_position=with_position)
-        self._loop.run_until_complete(
+        LOOP.run(self._table.create_index(column, config=config, replace=replace))
-            self._table.create_index(column, config=config, replace=replace)
-        )
 
     def create_index(
         self,
@@ -227,9 +221,7 @@ class RemoteTable(Table):
                 " 'IVF_PQ', 'IVF_HNSW_PQ', 'IVF_HNSW_SQ'"
             )
 
-        self._loop.run_until_complete(
+        LOOP.run(self._table.create_index(vector_column_name, config=config))
-            self._table.create_index(vector_column_name, config=config)
-        )
 
     def add(
         self,
@@ -261,7 +253,7 @@ class RemoteTable(Table):
             The value to use when filling vectors. Only used if on_bad_vectors="fill".
 
         """
-        self._loop.run_until_complete(
+        LOOP.run(
             self._table.add(
                 data, mode=mode, on_bad_vectors=on_bad_vectors, fill_value=fill_value
             )
@@ -349,9 +341,7 @@ class RemoteTable(Table):
     def _execute_query(
         self, query: Query, batch_size: Optional[int] = None
     ) -> pa.RecordBatchReader:
-        return self._loop.run_until_complete(
+        return LOOP.run(self._table._execute_query(query, batch_size=batch_size))
-            self._table._execute_query(query, batch_size=batch_size)
-        )
 
     def merge_insert(self, on: Union[str, Iterable[str]]) -> LanceMergeInsertBuilder:
         """Returns a [`LanceMergeInsertBuilder`][lancedb.merge.LanceMergeInsertBuilder]
@@ -368,9 +358,7 @@ class RemoteTable(Table):
         on_bad_vectors: str,
         fill_value: float,
     ):
-        self._loop.run_until_complete(
+        LOOP.run(self._table._do_merge(merge, new_data, on_bad_vectors, fill_value))
-            self._table._do_merge(merge, new_data, on_bad_vectors, fill_value)
-        )
 
     def delete(self, predicate: str):
         """Delete rows from the table.
@@ -419,7 +407,7 @@ class RemoteTable(Table):
            x      vector  _distance # doctest: +SKIP
         0  2  [3.0, 4.0]       85.0 # doctest: +SKIP
         """
-        self._loop.run_until_complete(self._table.delete(predicate))
+        LOOP.run(self._table.delete(predicate))
 
     def update(
         self,
@@ -469,7 +457,7 @@ class RemoteTable(Table):
         2  2  [10.0, 10.0] # doctest: +SKIP
 
         """
-        self._loop.run_until_complete(
+        LOOP.run(
             self._table.update(where=where, updates=values, updates_sql=values_sql)
         )
 
@@ -499,7 +487,7 @@ class RemoteTable(Table):
         )
 
     def count_rows(self, filter: Optional[str] = None) -> int:
-        return self._loop.run_until_complete(self._table.count_rows(filter))
+        return LOOP.run(self._table.count_rows(filter))
 
     def add_columns(self, transforms: Dict[str, str]):
         raise NotImplementedError(

python/python/tests/test_embeddings.py

@@ -90,10 +90,13 @@ def test_embedding_with_bad_results(tmp_path):
             self, texts: Union[List[str], np.ndarray]
         ) -> list[Union[np.array, None]]:
             # Return None, which is bad if field is non-nullable
-            return [
+            a = [
-                None if i % 2 == 0 else np.random.randn(self.ndims())
+                np.full(self.ndims(), np.nan)
+                if i % 2 == 0
+                else np.random.randn(self.ndims())
                 for i in range(len(texts))
             ]
+            return a
 
     db = lancedb.connect(tmp_path)
     registry = EmbeddingFunctionRegistry.get_instance()

python/python/tests/test_embeddings_slow.py

@@ -1,15 +1,6 @@
-#  Copyright (c) 2023. LanceDB Developers
+# SPDX-License-Identifier: Apache-2.0
-#
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
-#  Licensed under the Apache License, Version 2.0 (the "License");
+
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
 import importlib
 import io
 import os
@@ -17,6 +8,7 @@ import os
 import lancedb
 import numpy as np
 import pandas as pd
+import pyarrow as pa
 import pytest
 from lancedb.embeddings import get_registry
 from lancedb.pydantic import LanceModel, Vector
@@ -444,6 +436,30 @@ def test_watsonx_embedding(tmp_path):
         assert tbl.search("hello").limit(1).to_pandas()["text"][0] == "hello world"
 
 
+@pytest.mark.slow
+@pytest.mark.skipif(
+    os.environ.get("OPENAI_API_KEY") is None, reason="OPENAI_API_KEY not set"
+)
+def test_openai_with_empty_strs(tmp_path):
+    model = get_registry().get("openai").create(max_retries=0)
+
+    class TextModel(LanceModel):
+        text: str = model.SourceField()
+        vector: Vector(model.ndims()) = model.VectorField()
+
+    df = pd.DataFrame({"text": ["hello world", ""]})
+    db = lancedb.connect(tmp_path)
+    tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+    tbl.add(df, on_bad_vectors="skip")
+    tb = tbl.to_arrow()
+    assert tb.schema.field_by_name("vector").type == pa.list_(
+        pa.float32(), model.ndims()
+    )
+    assert len(tb) == 2
+    assert tb["vector"].is_null().to_pylist() == [False, True]
+
+
 @pytest.mark.slow
 @pytest.mark.skipif(
     importlib.util.find_spec("ollama") is None, reason="Ollama not installed"

python/python/tests/test_pydantic.py

@@ -1,16 +1,5 @@
-#  Copyright 2023 LanceDB Developers
+# SPDX-License-Identifier: Apache-2.0
-#
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
-
 
 import json
 import sys
@@ -172,6 +161,26 @@ def test_pydantic_to_arrow_py38():
     assert schema == expect_schema
 
 
+def test_nullable_vector():
+    class NullableModel(pydantic.BaseModel):
+        vec: Vector(16, nullable=False)
+
+    schema = pydantic_to_schema(NullableModel)
+    assert schema == pa.schema([pa.field("vec", pa.list_(pa.float32(), 16), False)])
+
+    class DefaultModel(pydantic.BaseModel):
+        vec: Vector(16)
+
+    schema = pydantic_to_schema(DefaultModel)
+    assert schema == pa.schema([pa.field("vec", pa.list_(pa.float32(), 16), True)])
+
+    class NotNullableModel(pydantic.BaseModel):
+        vec: Vector(16)
+
+    schema = pydantic_to_schema(NotNullableModel)
+    assert schema == pa.schema([pa.field("vec", pa.list_(pa.float32(), 16), True)])
+
+
 def test_fixed_size_list_field():
     class TestModel(pydantic.BaseModel):
         vec: Vector(16)
@@ -192,7 +201,7 @@ def test_fixed_size_list_field():
     schema = pydantic_to_schema(TestModel)
     assert schema == pa.schema(
         [
-            pa.field("vec", pa.list_(pa.float32(), 16), False),
+            pa.field("vec", pa.list_(pa.float32(), 16)),
             pa.field("li", pa.list_(pa.int64()), False),
         ]
     )

python/python/tests/test_remote_db.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
+from concurrent.futures import ThreadPoolExecutor
 import contextlib
 from datetime import timedelta
 import http.server
@@ -187,6 +188,47 @@ async def test_retry_error():
         assert cause.status_code == 429
 
 
+def test_table_add_in_threadpool():
+    def handler(request):
+        if request.path == "/v1/table/test/insert/":
+            request.send_response(200)
+            request.end_headers()
+        elif request.path == "/v1/table/test/create/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(b"{}")
+        elif request.path == "/v1/table/test/describe/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            payload = json.dumps(
+                dict(
+                    version=1,
+                    schema=dict(
+                        fields=[
+                            dict(name="id", type={"type": "int64"}, nullable=False),
+                        ]
+                    ),
+                )
+            )
+            request.wfile.write(payload.encode())
+        else:
+            request.send_response(404)
+            request.end_headers()
+
+    with mock_lancedb_connection(handler) as db:
+        table = db.create_table("test", [{"id": 1}])
+        with ThreadPoolExecutor(3) as executor:
+            futures = []
+            for _ in range(10):
+                future = executor.submit(table.add, [{"id": 1}])
+                futures.append(future)
+
+            for future in futures:
+                future.result()
+
+
 @contextlib.contextmanager
 def query_test_table(query_handler):
     def handler(request):