fix(python): reject unsupported index accelerators

Cargo.lock

@@ -5442,7 +5442,6 @@ dependencies = [
  "pprof 0.14.1",
  "rand 0.9.5",
  "random_word",
- "rayon",
  "regex",
  "reqwest 0.12.28",
  "rstest",

Cargo.toml

@@ -60,7 +60,6 @@ moka = { version = "0.12", features = ["future"] }
 object_store = "0.13.2"
 pin-project = "1.0.7"
 rand = "0.9"
-rayon = "1"
 snafu = "0.8"
 url = "2"
 num-traits = "0.2"

python/python/lancedb/_lancedb.pyi

@@ -261,6 +261,7 @@ class Table:
     def name(self) -> str: ...
     def __repr__(self) -> str: ...
     def is_open(self) -> bool: ...
+    def _is_native(self) -> bool: ...
     def close(self) -> None: ...
     async def schema(self) -> pa.Schema: ...
     async def add(

python/python/lancedb/index.py

@@ -314,8 +314,7 @@ class HnswPq:
     m: int = 20
     ef_construction: int = 300
     target_partition_size: Optional[int] = None
-    # Name of the accelerator (e.g. "cuda") to use for IVF training. When set,
-    # create_index() dispatches to pylance to build the index on the accelerator.
+    # Reserved for future accelerator support. create_index() currently raises if set.
     accelerator: Optional[str] = None
 
 
@@ -422,8 +421,7 @@ class HnswSq:
     m: int = 20
     ef_construction: int = 300
     target_partition_size: Optional[int] = None
-    # Name of the accelerator (e.g. "cuda") to use for IVF training. When set,
-    # create_index() dispatches to pylance to build the index on the accelerator.
+    # Reserved for future accelerator support. create_index() currently raises if set.
     accelerator: Optional[str] = None
 
 
@@ -618,8 +616,7 @@ class IvfFlat:
     max_iterations: int = 50
     sample_rate: int = 256
     target_partition_size: Optional[int] = None
-    # Name of the accelerator (e.g. "cuda") to use for IVF training. When set,
-    # create_index() dispatches to pylance to build the index on the accelerator.
+    # Reserved for future accelerator support. create_index() currently raises if set.
     accelerator: Optional[str] = None
 
 
@@ -651,8 +648,7 @@ class IvfSq:
     max_iterations: int = 50
     sample_rate: int = 256
     target_partition_size: Optional[int] = None
-    # Name of the accelerator (e.g. "cuda") to use for IVF training. When set,
-    # create_index() dispatches to pylance to build the index on the accelerator.
+    # Reserved for future accelerator support. create_index() currently raises if set.
     accelerator: Optional[str] = None
 
 
@@ -769,13 +765,6 @@ class IvfPq:
 
         The default value is 256.
 
-    seed: int, optional
-        Seed used for deterministic sampling and training. Given identical data in
-        the same row order and identical index parameters, the same seed produces
-        the same IVF centroids and PQ codebook. This option is supported for local
-        CPU index builds; remote and accelerator builds reject it explicitly. If
-        omitted, training remains random.
-
     target_partition_size: int, default is 8192
 
         The target size of each partition.
@@ -790,18 +779,11 @@ class IvfPq:
     num_bits: int = 8
     max_iterations: int = 50
     sample_rate: int = 256
-    seed: Optional[int] = None
     target_partition_size: Optional[int] = None
-    # Name of the accelerator (e.g. "cuda") to use for IVF training. When set,
+    # Name of the accelerator ("cuda" or "mps") to use for IVF training. When set,
     # create_index() dispatches to pylance to build the index on the accelerator.
     accelerator: Optional[str] = None
 
-    def __post_init__(self):
-        if self.seed is not None and self.accelerator is not None:
-            raise ValueError(
-                "IvfPq seed is not supported with accelerator-based index training"
-            )
-
 
 @dataclass
 class IvfRq:
@@ -854,8 +836,7 @@ class IvfRq:
     max_iterations: int = 50
     sample_rate: int = 256
     target_partition_size: Optional[int] = None
-    # Name of the accelerator (e.g. "cuda") to use for IVF training. When set,
-    # create_index() dispatches to pylance to build the index on the accelerator.
+    # Reserved for future accelerator support. create_index() currently raises if set.
     accelerator: Optional[str] = None
 
 

python/python/lancedb/remote/table.py

@@ -68,6 +68,14 @@ from ..table import AsyncTable, BlobMode, Branches, IndexStatistics, Query, Tabl
 from ..types import BaseTokenizerType
 
 
+def _reject_index_accelerator(
+    config: Optional[IndexConfigType] = None,
+    accelerator: Optional[str] = None,
+) -> None:
+    if accelerator is not None or getattr(config, "accelerator", None) is not None:
+        raise ValueError("Index accelerators are not supported on LanceDB Cloud.")
+
+
 class RemoteTable(Table):
     def __init__(
         self,
@@ -457,6 +465,8 @@ class RemoteTable(Table):
         ...     "l2", vector_column_name="vector"
         ... )
         """
+        _reject_index_accelerator(config, accelerator)
+
         # Detect whether this is a legacy API call
         is_legacy = self._is_legacy_create_index_call(
             metric,
@@ -484,12 +494,6 @@ class RemoteTable(Table):
 
             column = vector_column_name
 
-            if accelerator is not None:
-                logging.warning(
-                    "GPU accelerator is not yet supported on LanceDB cloud."
-                    "If you have 100M+ vectors to index,"
-                    "please contact us at contact@lancedb.com"
-                )
             if replace is not None:
                 logging.warning(
                     "replace is not supported on LanceDB cloud."
@@ -557,6 +561,8 @@ class RemoteTable(Table):
         The job may already be complete when returned; callers must not assume
         the index exists until :meth:`Job.wait` returns.
         """
+        _reject_index_accelerator(config)
+
         return Job(
             LOOP.run(
                 self._table.create_index_async(

python/python/lancedb/table.py

@@ -214,6 +214,45 @@ IndexConfigType = Union[
 # Known distance metrics for legacy API detection
 KNOWN_METRICS = {"l2", "cosine", "dot", "hamming"}
 
+_PYLANCE_ACCELERATED_INDEX_TYPE = "IVF_PQ"
+
+
+def _pylance_accelerated_index_options(
+    config: IndexConfigType,
+    *,
+    accelerator: Optional[str] = None,
+    index_type: Optional[str] = None,
+) -> Optional[Dict[str, Any]]:
+    """Translate an accelerated vector config into PyLance index options."""
+    if accelerator is None:
+        accelerator = getattr(config, "accelerator", None)
+    if accelerator is None:
+        return None
+
+    if index_type is None:
+        index_type = (
+            _PYLANCE_ACCELERATED_INDEX_TYPE
+            if isinstance(config, IvfPq)
+            else type(config).__name__
+        )
+    if index_type.upper() != _PYLANCE_ACCELERATED_INDEX_TYPE:
+        raise ValueError(
+            f"Index type {index_type} does not support an accelerator; "
+            f"only {_PYLANCE_ACCELERATED_INDEX_TYPE} supports acceleration"
+        )
+
+    return {
+        "index_type": index_type,
+        "metric": getattr(config, "distance_type", "l2"),
+        "num_partitions": getattr(config, "num_partitions", None),
+        "num_sub_vectors": getattr(config, "num_sub_vectors", None),
+        "accelerator": accelerator,
+        "num_bits": getattr(config, "num_bits", 8),
+        "m": getattr(config, "m", 20),
+        "ef_construction": getattr(config, "ef_construction", 300),
+        "target_partition_size": getattr(config, "target_partition_size", None),
+    }
+
 
 def _into_pyarrow_reader(
     data, schema: Optional[pa.Schema] = None
@@ -2737,20 +2776,17 @@ class LanceTable(Table):
             )
 
             # Handle accelerator through pylance
-            if accelerator is not None:
+            accelerated_options = _pylance_accelerated_index_options(
+                config, accelerator=accelerator, index_type=index_type
+            )
+            if accelerated_options is not None:
                 self.to_lance().create_index(
                     column=column,
-                    index_type=index_type,
-                    metric=metric,
-                    num_partitions=num_partitions,
-                    num_sub_vectors=num_sub_vectors,
                     replace=replace,
-                    accelerator=accelerator,
                     index_cache_size=index_cache_size,
-                    num_bits=num_bits,
-                    m=m,
-                    ef_construction=ef_construction,
-                    target_partition_size=target_partition_size,
+                    name=name,
+                    train=train,
+                    **accelerated_options,
                 )
                 self.checkout_latest()
                 return
@@ -2758,44 +2794,21 @@ class LanceTable(Table):
             # New API: metric is the column name
             column = metric
 
-            # Check if config has accelerator set and dispatch to pylance
-            if config is not None and hasattr(config, "accelerator"):
-                acc = getattr(config, "accelerator", None)
-                if acc is not None:
-                    if isinstance(config, IvfPq) and config.seed is not None:
-                        raise ValueError(
-                            "IvfPq seed is not supported with accelerator-based "
-                            "index training"
-                        )
-                    # Dispatch to pylance for GPU acceleration
-                    index_type_map = {
-                        "IvfFlat": "IVF_FLAT",
-                        "IvfSq": "IVF_SQ",
-                        "IvfPq": "IVF_PQ",
-                        "IvfRq": "IVF_RQ",
-                        "HnswPq": "IVF_HNSW_PQ",
-                        "HnswSq": "IVF_HNSW_SQ",
-                    }
-                    cfg_type = type(config).__name__
-                    lance_index_type = index_type_map.get(cfg_type, "IVF_PQ")
-
-                    self.to_lance().create_index(
-                        column=column,
-                        index_type=lance_index_type,
-                        metric=getattr(config, "distance_type", "l2"),
-                        num_partitions=getattr(config, "num_partitions", None),
-                        num_sub_vectors=getattr(config, "num_sub_vectors", None),
-                        replace=replace,
-                        accelerator=acc,
-                        num_bits=getattr(config, "num_bits", 8),
-                        m=getattr(config, "m", 20),
-                        ef_construction=getattr(config, "ef_construction", 300),
-                        target_partition_size=getattr(
-                            config, "target_partition_size", None
-                        ),
-                    )
-                    self.checkout_latest()
-                    return
+            accelerated_options = (
+                _pylance_accelerated_index_options(config)
+                if config is not None
+                else None
+            )
+            if accelerated_options is not None:
+                self.to_lance().create_index(
+                    column=column,
+                    replace=replace,
+                    name=name,
+                    train=train,
+                    **accelerated_options,
+                )
+                self.checkout_latest()
+                return
 
         return LOOP.run(
             self._table.create_index(
@@ -2823,6 +2836,11 @@ class LanceTable(Table):
         The job may already be complete when returned; callers must not assume
         the index exists until :meth:`Job.wait` returns.
         """
+        if _pylance_accelerated_index_options(config) is not None:
+            raise ValueError(
+                "Accelerated index creation does not support create_index_async; "
+                "use create_index instead."
+            )
         return Job(
             LOOP.run(
                 self._table.create_index_async(
@@ -4835,6 +4853,7 @@ class AsyncTable:
         config: Optional[
             Union[
                 IvfFlat,
+                IvfSq,
                 IvfPq,
                 IvfRq,
                 HnswPq,
@@ -4905,6 +4924,23 @@ class AsyncTable:
                     " BTree, Bitmap, LabelList, Fm, or FTS, but got "
                     + str(type(config))
                 )
+        accelerated_options = (
+            _pylance_accelerated_index_options(config) if config is not None else None
+        )
+        if accelerated_options is not None:
+            if not self._inner._is_native():
+                raise ValueError("GPU accelerator is not supported on LanceDB Cloud.")
+            dataset = await self.to_lance()
+            await asyncio.to_thread(
+                dataset.create_index,
+                column=column,
+                replace=True if replace is None else replace,
+                name=name,
+                train=train,
+                **accelerated_options,
+            )
+            await self.checkout_latest()
+            return
         try:
             await self._inner.create_index(
                 column,
@@ -4931,6 +4967,7 @@ class AsyncTable:
         config: Optional[
             Union[
                 IvfFlat,
+                IvfSq,
                 IvfPq,
                 IvfRq,
                 HnswPq,
@@ -4953,6 +4990,11 @@ class AsyncTable:
         be complete when returned; callers must not assume the index exists
         until :meth:`AsyncJob.wait` resolves.
         """
+        if config is not None and _pylance_accelerated_index_options(config):
+            raise ValueError(
+                "Accelerated index creation does not support create_index_async; "
+                "use create_index instead."
+            )
         job = await self._inner.create_index_async(
             column,
             index=config,

python/python/tests/test_index.py

@@ -26,7 +26,7 @@ from lancedb.index import (
     HnswFlat,
     FTS,
 )
-from lancedb.table import IndexStatistics, LanceTable
+from lancedb.table import IndexStatistics
 
 
 @pytest_asyncio.fixture
@@ -375,7 +375,7 @@ async def test_create_vector_index(some_table: AsyncTable):
 @pytest.mark.asyncio
 async def test_create_4bit_ivfpq_index(some_table: AsyncTable):
     # Can create
-    await some_table.create_index("vector", config=IvfPq(num_bits=4, seed=42))
+    await some_table.create_index("vector", config=IvfPq(num_bits=4))
     # Can recreate if replace=True
     await some_table.create_index("vector", config=IvfPq(num_bits=4), replace=True)
     # Can't recreate if replace=False
@@ -395,16 +395,6 @@ async def test_create_4bit_ivfpq_index(some_table: AsyncTable):
     assert stats.num_indices == 1
 
 
-def test_seeded_ivfpq_rejects_accelerator():
-    with pytest.raises(ValueError, match="seed is not supported with accelerator"):
-        IvfPq(seed=42, accelerator="cuda")
-
-    config = IvfPq(seed=42)
-    config.accelerator = "cuda"
-    with pytest.raises(ValueError, match="seed is not supported with accelerator"):
-        object.__new__(LanceTable).create_index("vector", config=config)
-
-
 @pytest.mark.asyncio
 async def test_create_ivfrq_index(some_table: AsyncTable):
     await some_table.create_index("vector", config=IvfRq(num_bits=1))

python/python/tests/test_remote_db.py

@@ -875,6 +875,25 @@ def test_remote_create_index_async_returns_job():
         job.cancel()
 
 
+def test_remote_create_index_rejects_accelerator():
+    from lancedb.index import IvfPq
+    from lancedb.remote.table import RemoteTable
+
+    inner = MagicMock()
+    inner.name = "test"
+    table = RemoteTable(inner, "dev")
+
+    with pytest.raises(ValueError, match="not supported on LanceDB Cloud"):
+        table.create_index(accelerator="mps")
+    with pytest.raises(ValueError, match="not supported on LanceDB Cloud"):
+        table.create_index("vector", config=IvfPq(accelerator="mps"))
+    with pytest.raises(ValueError, match="not supported on LanceDB Cloud"):
+        table.create_index_async("vector", config=IvfPq(accelerator="mps"))
+
+    inner.create_index.assert_not_called()
+    inner.create_index_async.assert_not_called()
+
+
 def test_remote_job_wait_raises_on_failure():
     from lancedb.exceptions import JobFailedError
     from lancedb.index import BTree

python/python/tests/test_table.py

@@ -10,11 +10,21 @@ from concurrent.futures import ThreadPoolExecutor
 from datetime import date, datetime, timedelta
 from time import sleep
 from typing import List
-from unittest.mock import patch
+from unittest.mock import AsyncMock, MagicMock, patch
 
 import lancedb
 from lancedb.dependencies import _PANDAS_AVAILABLE
-from lancedb.index import BTree, FTS, HnswFlat, HnswPq, HnswSq, IvfPq
+from lancedb.index import (
+    BTree,
+    FTS,
+    HnswFlat,
+    HnswPq,
+    HnswSq,
+    IvfFlat,
+    IvfPq,
+    IvfRq,
+    IvfSq,
+)
 import numpy as np
 import polars as pl
 import pyarrow as pa
@@ -25,7 +35,7 @@ from lancedb.db import AsyncConnection, DBConnection
 from lancedb.embeddings import EmbeddingFunctionConfig, EmbeddingFunctionRegistry
 from lancedb.expr import col, lit
 from lancedb.pydantic import LanceModel, Vector
-from lancedb.table import LanceTable
+from lancedb.table import AsyncTable, LanceTable
 from pydantic import BaseModel
 
 
@@ -1412,6 +1422,174 @@ def test_create_index_async_returns_done_job(mem_db: DBConnection):
     job.cancel()
 
 
+def test_create_index_dispatches_mps_to_pylance(mem_db: DBConnection):
+    table = mem_db.create_table(
+        "mps_sync",
+        data=[
+            {"vector": [3.1, 4.1]},
+            {"vector": [5.9, 26.5]},
+        ],
+    )
+    dataset = MagicMock()
+
+    with (
+        patch.object(table, "to_lance", return_value=dataset),
+        patch.object(table, "checkout_latest") as checkout_latest,
+    ):
+        with pytest.warns(DeprecationWarning, match="create_index"):
+            table.create_index(
+                metric="cosine",
+                num_partitions=4,
+                num_sub_vectors=2,
+                accelerator="mps",
+                replace=False,
+                name="vector_mps",
+            )
+
+    dataset.create_index.assert_called_once_with(
+        column="vector",
+        replace=False,
+        index_cache_size=None,
+        name="vector_mps",
+        train=True,
+        index_type="IVF_PQ",
+        metric="cosine",
+        num_partitions=4,
+        num_sub_vectors=2,
+        accelerator="mps",
+        num_bits=8,
+        m=20,
+        ef_construction=300,
+        target_partition_size=None,
+    )
+    checkout_latest.assert_called_once_with()
+
+
+@pytest.mark.parametrize(
+    "config",
+    [
+        IvfFlat(accelerator="mps"),
+        IvfSq(accelerator="mps"),
+        IvfRq(accelerator="mps"),
+        HnswPq(accelerator="mps"),
+        HnswSq(accelerator="mps"),
+    ],
+)
+def test_create_index_rejects_unsupported_accelerated_format(
+    mem_db: DBConnection, config
+):
+    table = mem_db.create_table(
+        "unsupported_accelerator",
+        data=[{"vector": [3.1, 4.1]}, {"vector": [5.9, 26.5]}],
+    )
+
+    with (
+        patch.object(table, "to_lance") as to_lance,
+        pytest.raises(ValueError, match="only IVF_PQ supports acceleration"),
+    ):
+        table.create_index("vector", config=config)
+
+    to_lance.assert_not_called()
+
+
+def test_legacy_create_index_rejects_unsupported_accelerated_format(
+    mem_db: DBConnection,
+):
+    table = mem_db.create_table(
+        "unsupported_legacy_accelerator",
+        data=[{"vector": [3.1, 4.1]}, {"vector": [5.9, 26.5]}],
+    )
+
+    with (
+        pytest.warns(DeprecationWarning, match="create_index"),
+        patch.object(table, "to_lance") as to_lance,
+        pytest.raises(ValueError, match="only IVF_PQ supports acceleration"),
+    ):
+        table.create_index(index_type="IVF_FLAT", accelerator="mps")
+
+    to_lance.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_async_create_index_dispatches_mps_to_pylance():
+    inner = MagicMock()
+    inner._is_native.return_value = True
+    inner.checkout_latest = AsyncMock()
+    table = AsyncTable(inner)
+    dataset = MagicMock()
+
+    with patch.object(table, "to_lance", AsyncMock(return_value=dataset)):
+        await table.create_index(
+            "vector",
+            config=IvfPq(
+                distance_type="cosine",
+                num_partitions=4,
+                num_sub_vectors=2,
+                accelerator="mps",
+            ),
+            name="vector_mps",
+        )
+
+    dataset.create_index.assert_called_once_with(
+        column="vector",
+        replace=True,
+        name="vector_mps",
+        train=True,
+        index_type="IVF_PQ",
+        metric="cosine",
+        num_partitions=4,
+        num_sub_vectors=2,
+        accelerator="mps",
+        num_bits=8,
+        m=20,
+        ef_construction=300,
+        target_partition_size=None,
+    )
+    inner.create_index.assert_not_called()
+    inner.checkout_latest.assert_awaited_once_with()
+
+
+@pytest.mark.asyncio
+async def test_async_create_index_rejects_unsupported_accelerated_format():
+    inner = MagicMock()
+    inner._is_native.return_value = True
+    table = AsyncTable(inner)
+
+    with (
+        patch.object(table, "to_lance", AsyncMock()) as to_lance,
+        pytest.raises(ValueError, match="only IVF_PQ supports acceleration"),
+    ):
+        await table.create_index("vector", config=IvfFlat(accelerator="mps"))
+
+    to_lance.assert_not_awaited()
+    inner.create_index.assert_not_called()
+
+
+@pytest.mark.asyncio
+async def test_async_background_index_rejects_accelerator():
+    inner = MagicMock()
+    inner.create_index_async = AsyncMock()
+    table = AsyncTable(inner)
+
+    with pytest.raises(ValueError, match="Accelerated index creation does not support"):
+        await table.create_index_async("vector", config=IvfPq(accelerator="mps"))
+
+    inner.create_index_async.assert_not_awaited()
+
+
+def test_background_index_rejects_accelerator(mem_db: DBConnection):
+    table = mem_db.create_table(
+        "mps_background",
+        data=[
+            {"vector": [3.1, 4.1]},
+            {"vector": [5.9, 26.5]},
+        ],
+    )
+
+    with pytest.raises(ValueError, match="Accelerated index creation does not support"):
+        table.create_index_async("vector", config=IvfPq(accelerator="mps"))
+
+
 @patch("lancedb.table.AsyncTable.create_index")
 def test_create_index_method(mock_create_index, mem_db: DBConnection):
     table = mem_db.create_table(

python/src/index.rs

@@ -90,9 +90,6 @@ pub fn extract_index_params(source: &Option<Bound<'_, PyAny>>) -> PyResult<Lance
                     .max_iterations(params.max_iterations)
                     .sample_rate(params.sample_rate)
                     .num_bits(params.num_bits);
-                if let Some(seed) = params.seed {
-                    ivf_pq_builder = ivf_pq_builder.seed(seed);
-                }
                 if let Some(num_partitions) = params.num_partitions {
                     ivf_pq_builder = ivf_pq_builder.num_partitions(num_partitions);
                 }
@@ -235,7 +232,6 @@ struct IvfPqParams {
     num_bits: u32,
     max_iterations: u32,
     sample_rate: u32,
-    seed: Option<u64>,
     target_partition_size: Option<u32>,
 }
 

python/src/table.rs

@@ -622,6 +622,10 @@ impl Table {
         self.inner.is_some()
     }
 
+    pub fn _is_native(&self) -> PyResult<bool> {
+        Ok(self.inner_ref()?.as_native().is_some())
+    }
+
     /// Closes the table, releasing any resources associated with it.
     pub fn close(&mut self) {
         self.inner.take();

rust/lancedb/Cargo.toml

@@ -61,7 +61,6 @@ futures.workspace = true
 num-traits.workspace = true
 url.workspace = true
 rand.workspace = true
-rayon.workspace = true
 regex.workspace = true
 serde = { version = "^1" }
 serde_json = { version = "1" }

rust/lancedb/src/index/vector.rs

@@ -274,8 +274,6 @@ pub struct IvfPqIndexBuilder {
     pub(crate) sample_rate: u32,
     pub(crate) max_iterations: u32,
     #[serde(skip_serializing_if = "Option::is_none")]
-    pub(crate) seed: Option<u64>,
-    #[serde(skip_serializing_if = "Option::is_none")]
     pub(crate) target_partition_size: Option<u32>,
 
     // PQ
@@ -294,7 +292,6 @@ impl Default for IvfPqIndexBuilder {
             num_bits: None,
             sample_rate: 256,
             max_iterations: 50,
-            seed: None,
             target_partition_size: None,
         }
     }
@@ -304,30 +301,6 @@ impl IvfPqIndexBuilder {
     impl_distance_type_setter!();
     impl_ivf_params_setter!();
     impl_pq_params_setter!();
-
-    /// Use a deterministic seed when sampling and training the IVF and PQ models.
-    ///
-    /// Given identical data in the same row order and identical index parameters,
-    /// using the same seed produces the same IVF centroids and PQ codebook. This is
-    /// useful when independently-built tables need reproducible approximate-search
-    /// results.
-    ///
-    /// Seeded training is supported by native tables. Remote backends reject this
-    /// option unless they can provide the same deterministic training contract.
-    ///
-    /// If no seed is provided, index training uses random sampling and initialization.
-    ///
-    /// # Examples
-    ///
-    /// ```
-    /// use lancedb::index::vector::IvfPqIndexBuilder;
-    ///
-    /// let index = IvfPqIndexBuilder::default().seed(42);
-    /// ```
-    pub fn seed(mut self, seed: u64) -> Self {
-        self.seed = Some(seed);
-        self
-    }
 }
 
 pub(crate) fn suggested_num_sub_vectors(dim: u32) -> u32 {

rust/lancedb/src/remote/table.rs

@@ -339,12 +339,6 @@ impl<S: HttpSend> RemoteTable<S> {
         // Auto is special-cased since it needs schema inspection.
         let (index_type_str, params) = match &index.index {
             Index::IvfFlat(p) => ("IVF_FLAT", Some(to_json(p)?)),
-            Index::IvfPq(p) if p.seed.is_some() => {
-                return Err(Error::NotSupported {
-                    message: "Deterministic IVF PQ training is not supported on remote tables"
-                        .to_string(),
-                });
-            }
             Index::IvfPq(p) => ("IVF_PQ", Some(to_json(p)?)),
             Index::IvfSq(p) => ("IVF_SQ", Some(to_json(p)?)),
             Index::IvfHnswSq(p) => ("IVF_HNSW_SQ", Some(to_json(p)?)),
@@ -5163,41 +5157,6 @@ mod tests {
         }
     }
 
-    #[tokio::test]
-    async fn test_seeded_ivf_pq_is_rejected_for_remote_tables() {
-        let table = Table::new_with_handler("my_table", |request| match request.url().path() {
-            "/v1/table/my_table/describe/" => {
-                let schema = Schema::new(vec![Field::new(
-                    "vector",
-                    DataType::FixedSizeList(
-                        Arc::new(Field::new("item", DataType::Float32, true)),
-                        8,
-                    ),
-                    false,
-                )]);
-                http::Response::builder()
-                    .status(200)
-                    .body(describe_response(&schema))
-                    .unwrap()
-            }
-            path => panic!("Unexpected request for unsupported seeded index: {path}"),
-        });
-
-        let error = table
-            .create_index(
-                &["vector"],
-                Index::IvfPq(IvfPqIndexBuilder::default().seed(42)),
-            )
-            .execute()
-            .await
-            .unwrap_err();
-        assert!(matches!(
-            error,
-            Error::NotSupported { message }
-                if message.contains("not supported on remote tables")
-        ));
-    }
-
     #[tokio::test]
     async fn test_create_index_returns_job() {
         let describe_calls = std::sync::Arc::new(std::sync::atomic::AtomicUsize::new(0));