format

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.6.0"
+current_version = "0.5.2"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

Cargo.toml

@@ -20,18 +20,13 @@ keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]
 
 [workspace.dependencies]
-# lance = { "version" = "=0.13.0", "features" = ["dynamodb"] }
-# lance-index = { "version" = "=0.13.0" }
-# lance-linalg = { "version" = "=0.13.0" }
-# lance-testing = { "version" = "=0.13.0" }
-# lance-datafusion = { "version" = "=0.13.0" }
-
-lance = { path = "../lance/rust/lance" }
-lance-index = { path = "../lance/rust/lance-index" }
-lance-linalg= { path = "../lance/rust/lance-linalg" }
-lance-testing = { path = "../lance/rust/lance-testing" }
-lance-datafusion = { path = "../lance/rust/lance-datafusion" }
-
+lance = { "version" = "=0.12.2", "features" = [
+    "dynamodb",
+], git = "https://github.com/lancedb/lance.git", tag = "v0.12.2-beta.2" }
+lance-index = { "version" = "=0.12.2", git = "https://github.com/lancedb/lance.git", tag = "v0.12.2-beta.2" }
+lance-linalg = { "version" = "=0.12.2", git = "https://github.com/lancedb/lance.git", tag = "v0.12.2-beta.2" }
+lance-testing = { "version" = "=0.12.2", git = "https://github.com/lancedb/lance.git", tag = "v0.12.2-beta.2" }
+lance-datafusion = { "version" = "=0.12.2", git = "https://github.com/lancedb/lance.git", tag = "v0.12.2-beta.2" }
 # Note that this one does not include pyarrow
 arrow = { version = "51.0", optional = false }
 arrow-array = "51.0"

docs/src/guides/tables.md

@@ -116,21 +116,21 @@ This guide will show how to create tables, insert data into them, and update the
 
 ### From a Polars DataFrame
 
-LanceDB supports [Polars](https://pola.rs/), a modern, fast DataFrame library
-written in Rust. Just like in Pandas, the Polars integration is enabled by PyArrow
-under the hood. A deeper integration between LanceDB Tables and Polars DataFrames
-is on the way.
+    LanceDB supports [Polars](https://pola.rs/), a modern, fast DataFrame library
+    written in Rust. Just like in Pandas, the Polars integration is enabled by PyArrow
+    under the hood. A deeper integration between LanceDB Tables and Polars DataFrames
+    is on the way.
 
-```python
-import polars as pl
+    ```python
+    import polars as pl
 
-data = pl.DataFrame({
-    "vector": [[3.1, 4.1], [5.9, 26.5]],
-    "item": ["foo", "bar"],
-    "price": [10.0, 20.0]
-})
-table = db.create_table("pl_table", data=data)
-```
+    data = pl.DataFrame({
+        "vector": [[3.1, 4.1], [5.9, 26.5]],
+        "item": ["foo", "bar"],
+        "price": [10.0, 20.0]
+    })
+    table = db.create_table("pl_table", data=data)
+    ```
 
 ### From an Arrow Table
 === "Python"

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.6.0",
+  "version": "0.5.2",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.6.0",
+      "version": "0.5.2",
       "cpu": [
         "x64",
         "arm64"

node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vectordb",
-  "version": "0.6.0",
+  "version": "0.5.2",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",

nodejs/__test__/table.test.ts

@@ -39,9 +39,7 @@ describe.each([arrow, arrowOld])("Given a table", (arrow: any) => {
   let tmpDir: tmp.DirResult;
   let table: Table;
 
-  const schema:
-    | import("apache-arrow").Schema
-    | import("apache-arrow-old").Schema = new arrow.Schema([
+  const schema = new arrow.Schema([
     new arrow.Field("id", new arrow.Float64(), true),
   ]);
 
@@ -317,7 +315,7 @@ describe("When creating an index", () => {
       .query()
       .limit(2)
       .nearestTo(queryVec)
-      .distanceType("dot")
+      .distanceType("DoT")
       .toArrow();
     expect(rst.numRows).toBe(2);
 

nodejs/lancedb/arrow.ts

@@ -15,7 +15,6 @@
 import {
   Table as ArrowTable,
   Binary,
-  BufferType,
   DataType,
   Field,
   FixedSizeBinary,
@@ -38,68 +37,14 @@ import {
   type makeTable,
   vectorFromArray,
 } from "apache-arrow";
-import { Buffers } from "apache-arrow/data";
 import { type EmbeddingFunction } from "./embedding/embedding_function";
 import { EmbeddingFunctionConfig, getRegistry } from "./embedding/registry";
-import {
-  sanitizeField,
-  sanitizeSchema,
-  sanitizeTable,
-  sanitizeType,
-} from "./sanitize";
+import { sanitizeField, sanitizeSchema, sanitizeType } from "./sanitize";
 export * from "apache-arrow";
-export type SchemaLike =
-  | Schema
-  | {
-      fields: FieldLike[];
-      metadata: Map<string, string>;
-      get names(): unknown[];
-    };
-export type FieldLike =
-  | Field
-  | {
-      type: string;
-      name: string;
-      nullable?: boolean;
-      metadata?: Map<string, string>;
-    };
-
-export type DataLike =
-  // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-  | import("apache-arrow").Data<Struct<any>>
-  | {
-      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-      type: any;
-      length: number;
-      offset: number;
-      stride: number;
-      nullable: boolean;
-      children: DataLike[];
-      get nullCount(): number;
-      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-      values: Buffers<any>[BufferType.DATA];
-      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-      typeIds: Buffers<any>[BufferType.TYPE];
-      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-      nullBitmap: Buffers<any>[BufferType.VALIDITY];
-      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-      valueOffsets: Buffers<any>[BufferType.OFFSET];
-    };
-
-export type RecordBatchLike =
-  | RecordBatch
-  | {
-      schema: SchemaLike;
-      data: DataLike;
-    };
-
-export type TableLike =
-  | ArrowTable
-  | { schema: SchemaLike; batches: RecordBatchLike[] };
 
 export type IntoVector = Float32Array | Float64Array | number[];
 
-export function isArrowTable(value: object): value is TableLike {
+export function isArrowTable(value: object): value is ArrowTable {
   if (value instanceof ArrowTable) return true;
   return "schema" in value && "batches" in value;
 }
@@ -190,7 +135,7 @@ export function isFixedSizeList(value: unknown): value is FixedSizeList {
 }
 
 /** Data type accepted by NodeJS SDK */
-export type Data = Record<string, unknown>[] | TableLike;
+export type Data = Record<string, unknown>[] | ArrowTable;
 
 /*
  * Options to control how a column should be converted to a vector array
@@ -217,7 +162,7 @@ export class MakeArrowTableOptions {
    * The schema must be specified if there are no records (e.g. to make
    * an empty table)
    */
-  schema?: SchemaLike;
+  schema?: Schema;
 
   /*
    * Mapping from vector column name to expected type
@@ -365,7 +310,7 @@ export function makeArrowTable(
   if (opt.schema !== undefined && opt.schema !== null) {
     opt.schema = sanitizeSchema(opt.schema);
     opt.schema = validateSchemaEmbeddings(
-      opt.schema as Schema,
+      opt.schema,
       data,
       options?.embeddingFunction,
     );
@@ -449,7 +394,7 @@ export function makeArrowTable(
     // `new ArrowTable(schema, batches)` which does not do any schema inference
     const firstTable = new ArrowTable(columns);
     const batchesFixed = firstTable.batches.map(
-      (batch) => new RecordBatch(opt.schema as Schema, batch.data),
+      (batch) => new RecordBatch(opt.schema!, batch.data),
     );
     let schema: Schema;
     if (metadata !== undefined) {
@@ -462,9 +407,9 @@ export function makeArrowTable(
         }
       }
 
-      schema = new Schema(opt.schema.fields as Field[], schemaMetadata);
+      schema = new Schema(opt.schema.fields, schemaMetadata);
     } else {
-      schema = opt.schema as Schema;
+      schema = opt.schema;
     }
     return new ArrowTable(schema, batchesFixed);
   }
@@ -480,7 +425,7 @@ export function makeArrowTable(
  * Create an empty Arrow table with the provided schema
  */
 export function makeEmptyTable(
-  schema: SchemaLike,
+  schema: Schema,
   metadata?: Map<string, string>,
 ): ArrowTable {
   return makeArrowTable([], { schema }, metadata);
@@ -618,17 +563,18 @@ async function applyEmbeddingsFromMetadata(
 async function applyEmbeddings<T>(
   table: ArrowTable,
   embeddings?: EmbeddingFunctionConfig,
-  schema?: SchemaLike,
+  schema?: Schema,
 ): Promise<ArrowTable> {
-  if (schema !== undefined && schema !== null) {
-    schema = sanitizeSchema(schema);
-  }
   if (schema?.metadata.has("embedding_functions")) {
-    return applyEmbeddingsFromMetadata(table, schema! as Schema);
+    return applyEmbeddingsFromMetadata(table, schema!);
   } else if (embeddings == null || embeddings === undefined) {
     return table;
   }
 
+  if (schema !== undefined && schema !== null) {
+    schema = sanitizeSchema(schema);
+  }
+
   // Convert from ArrowTable to Record<String, Vector>
   const colEntries = [...Array(table.numCols).keys()].map((_, idx) => {
     const name = table.schema.fields[idx].name;
@@ -704,7 +650,7 @@ async function applyEmbeddings<T>(
         `When using embedding functions and specifying a schema the schema should include the embedding column but the column ${destColumn} was missing`,
       );
     }
-    return alignTable(newTable, schema as Schema);
+    return alignTable(newTable, schema);
   }
   return newTable;
 }
@@ -798,7 +744,7 @@ export async function fromRecordsToStreamBuffer(
 export async function fromTableToBuffer(
   table: ArrowTable,
   embeddings?: EmbeddingFunctionConfig,
-  schema?: SchemaLike,
+  schema?: Schema,
 ): Promise<Buffer> {
   if (schema !== undefined && schema !== null) {
     schema = sanitizeSchema(schema);
@@ -825,7 +771,7 @@ export async function fromDataToBuffer(
     schema = sanitizeSchema(schema);
   }
   if (isArrowTable(data)) {
-    return fromTableToBuffer(sanitizeTable(data), embeddings, schema);
+    return fromTableToBuffer(data, embeddings, schema);
   } else {
     const table = await convertToTable(data, embeddings, { schema });
     return fromTableToBuffer(table);
@@ -843,7 +789,7 @@ export async function fromDataToBuffer(
 export async function fromTableToStreamBuffer(
   table: ArrowTable,
   embeddings?: EmbeddingFunctionConfig,
-  schema?: SchemaLike,
+  schema?: Schema,
 ): Promise<Buffer> {
   const tableWithEmbeddings = await applyEmbeddings(table, embeddings, schema);
   const writer = RecordBatchStreamWriter.writeAll(tableWithEmbeddings);
@@ -908,6 +854,7 @@ function validateSchemaEmbeddings(
   for (let field of schema.fields) {
     if (isFixedSizeList(field.type)) {
       field = sanitizeField(field);
+
       if (data.length !== 0 && data?.[0]?.[field.name] === undefined) {
         if (schema.metadata.has("embedding_functions")) {
           const embeddings = JSON.parse(

nodejs/lancedb/connection.ts

@@ -12,7 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import { Data, Schema, SchemaLike, TableLike } from "./arrow";
+import { Table as ArrowTable, Data, Schema } from "./arrow";
 import { fromTableToBuffer, makeEmptyTable } from "./arrow";
 import { EmbeddingFunctionConfig, getRegistry } from "./embedding/registry";
 import { Connection as LanceDbConnection } from "./native";
@@ -50,7 +50,7 @@ export interface CreateTableOptions {
    * The default is true while the new format is in beta
    */
   useLegacyFormat?: boolean;
-  schema?: SchemaLike;
+  schema?: Schema;
   embeddingFunction?: EmbeddingFunctionConfig;
 }
 
@@ -167,12 +167,12 @@ export abstract class Connection {
   /**
    * Creates a new Table and initialize it with new data.
    * @param {string} name - The name of the table.
-   * @param {Record<string, unknown>[] | TableLike} data - Non-empty Array of Records
+   * @param {Record<string, unknown>[] | ArrowTable} data - Non-empty Array of Records
    * to be inserted into the table
    */
   abstract createTable(
     name: string,
-    data: Record<string, unknown>[] | TableLike,
+    data: Record<string, unknown>[] | ArrowTable,
     options?: Partial<CreateTableOptions>,
   ): Promise<Table>;
 
@@ -183,7 +183,7 @@ export abstract class Connection {
    */
   abstract createEmptyTable(
     name: string,
-    schema: import("./arrow").SchemaLike,
+    schema: Schema,
     options?: Partial<CreateTableOptions>,
   ): Promise<Table>;
 
@@ -235,7 +235,7 @@ export class LocalConnection extends Connection {
     nameOrOptions:
       | string
       | ({ name: string; data: Data } & Partial<CreateTableOptions>),
-    data?: Record<string, unknown>[] | TableLike,
+    data?: Record<string, unknown>[] | ArrowTable,
     options?: Partial<CreateTableOptions>,
   ): Promise<Table> {
     if (typeof nameOrOptions !== "string" && "name" in nameOrOptions) {
@@ -259,7 +259,7 @@ export class LocalConnection extends Connection {
 
   async createEmptyTable(
     name: string,
-    schema: import("./arrow").SchemaLike,
+    schema: Schema,
     options?: Partial<CreateTableOptions>,
   ): Promise<Table> {
     let mode: string = options?.mode ?? "create";

nodejs/lancedb/query.ts

@@ -300,9 +300,7 @@ export class VectorQuery extends QueryBase<NativeVectorQuery, VectorQuery> {
    *
    * By default "l2" is used.
    */
-  distanceType(
-    distanceType: Required<IvfPqOptions>["distanceType"],
-  ): VectorQuery {
+  distanceType(distanceType: string): VectorQuery {
     this.inner.distanceType(distanceType);
     return this;
   }

nodejs/lancedb/remote/connection.ts

@@ -1,10 +1,5 @@
 import { Schema } from "apache-arrow";
-import {
-  Data,
-  SchemaLike,
-  fromTableToStreamBuffer,
-  makeEmptyTable,
-} from "../arrow";
+import { Data, fromTableToStreamBuffer, makeEmptyTable } from "../arrow";
 import {
   Connection,
   CreateTableOptions,
@@ -161,7 +156,7 @@ export class RemoteConnection extends Connection {
 
   async createEmptyTable(
     name: string,
-    schema: SchemaLike,
+    schema: Schema,
     options?: Partial<CreateTableOptions> | undefined,
   ): Promise<Table> {
     if (options?.mode) {

nodejs/lancedb/sanitize.ts

@@ -20,12 +20,10 @@
 // comes from the exact same library instance.  This is not always the case
 // and so we must sanitize the input to ensure that it is compatible.
 
-import { BufferType, Data } from "apache-arrow";
 import type { IntBitWidth, TKeys, TimeBitWidth } from "apache-arrow/type";
 import {
   Binary,
   Bool,
-  DataLike,
   DataType,
   DateDay,
   DateMillisecond,
@@ -58,14 +56,9 @@ import {
   Map_,
   Null,
   type Precision,
-  RecordBatch,
-  RecordBatchLike,
   Schema,
-  SchemaLike,
   SparseUnion,
   Struct,
-  Table,
-  TableLike,
   Time,
   TimeMicrosecond,
   TimeMillisecond,
@@ -495,7 +488,7 @@ export function sanitizeField(fieldLike: unknown): Field {
  * instance because they might be using a different instance of apache-arrow
  * than lancedb is using.
  */
-export function sanitizeSchema(schemaLike: SchemaLike): Schema {
+export function sanitizeSchema(schemaLike: unknown): Schema {
   if (schemaLike instanceof Schema) {
     return schemaLike;
   }
@@ -521,68 +514,3 @@ export function sanitizeSchema(schemaLike: SchemaLike): Schema {
   );
   return new Schema(sanitizedFields, metadata);
 }
-
-export function sanitizeTable(tableLike: TableLike): Table {
-  if (tableLike instanceof Table) {
-    return tableLike;
-  }
-  if (typeof tableLike !== "object" || tableLike === null) {
-    throw Error("Expected a Table but object was null/undefined");
-  }
-  if (!("schema" in tableLike)) {
-    throw Error(
-      "The table passed in does not appear to be a table (no 'schema' property)",
-    );
-  }
-  if (!("batches" in tableLike)) {
-    throw Error(
-      "The table passed in does not appear to be a table (no 'columns' property)",
-    );
-  }
-  const schema = sanitizeSchema(tableLike.schema);
-
-  const batches = tableLike.batches.map(sanitizeRecordBatch);
-  return new Table(schema, batches);
-}
-
-function sanitizeRecordBatch(batchLike: RecordBatchLike): RecordBatch {
-  if (batchLike instanceof RecordBatch) {
-    return batchLike;
-  }
-  if (typeof batchLike !== "object" || batchLike === null) {
-    throw Error("Expected a RecordBatch but object was null/undefined");
-  }
-  if (!("schema" in batchLike)) {
-    throw Error(
-      "The record batch passed in does not appear to be a record batch (no 'schema' property)",
-    );
-  }
-  if (!("data" in batchLike)) {
-    throw Error(
-      "The record batch passed in does not appear to be a record batch (no 'data' property)",
-    );
-  }
-  const schema = sanitizeSchema(batchLike.schema);
-  const data = sanitizeData(batchLike.data);
-  return new RecordBatch(schema, data);
-}
-function sanitizeData(
-  dataLike: DataLike,
-  // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-): import("apache-arrow").Data<Struct<any>> {
-  if (dataLike instanceof Data) {
-    return dataLike;
-  }
-  return new Data(
-    dataLike.type,
-    dataLike.offset,
-    dataLike.length,
-    dataLike.nullCount,
-    {
-      [BufferType.OFFSET]: dataLike.valueOffsets,
-      [BufferType.DATA]: dataLike.values,
-      [BufferType.VALIDITY]: dataLike.nullBitmap,
-      [BufferType.TYPE]: dataLike.typeIds,
-    },
-  );
-}

nodejs/lancedb/table.ts

@@ -17,7 +17,6 @@ import {
   Data,
   IntoVector,
   Schema,
-  TableLike,
   fromDataToBuffer,
   fromTableToBuffer,
   fromTableToStreamBuffer,
@@ -39,8 +38,6 @@ import {
   Table as _NativeTable,
 } from "./native";
 import { Query, VectorQuery } from "./query";
-import { sanitizeTable } from "./sanitize";
-export { IndexConfig } from "./native";
 
 /**
  * Options for adding data to a table.
@@ -384,7 +381,8 @@ export abstract class Table {
   abstract indexStats(name: string): Promise<IndexStatistics | undefined>;
 
   static async parseTableData(
-    data: Record<string, unknown>[] | TableLike,
+    // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+    data: Record<string, unknown>[] | ArrowTable<any>,
     options?: Partial<CreateTableOptions>,
     streaming = false,
   ) {
@@ -397,9 +395,9 @@ export abstract class Table {
 
     let table: ArrowTable;
     if (isArrowTable(data)) {
-      table = sanitizeTable(data);
+      table = data;
     } else {
-      table = makeArrowTable(data as Record<string, unknown>[], options);
+      table = makeArrowTable(data, options);
     }
     if (streaming) {
       const buf = await fromTableToStreamBuffer(

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.6.0",
+	"version": "0.5.2",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",

nodejs/npm/darwin-x64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-x64",
-	"version": "0.6.0",
+	"version": "0.5.2",
 	"os": ["darwin"],
 	"cpu": ["x64"],
 	"main": "lancedb.darwin-x64.node",

nodejs/npm/linux-arm64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.6.0",
+	"version": "0.5.2",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",

nodejs/npm/linux-x64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.6.0",
+	"version": "0.5.2",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",

nodejs/npm/win32-x64-msvc/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.6.0",
+	"version": "0.5.2",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package.json

@@ -10,7 +10,7 @@
     "vector database",
     "ann"
   ],
-  "version": "0.6.0",
+  "version": "0.5.2",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.9.0"
+current_version = "0.8.2"
 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 = "0.9.0"
+version = "0.8.2"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true

python/pyproject.toml

@@ -3,7 +3,7 @@ name = "lancedb"
 # version in Cargo.toml
 dependencies = [
     "deprecation",
-    "pylance==0.13.0",
+    "pylance==0.12.2-beta.2",
     "ratelimiter~=1.0",
     "requests>=2.31.0",
     "retry>=0.9.2",

python/python/lancedb/rerankers/base.py

@@ -119,7 +119,9 @@ class Reranker(ABC):
         fts_results : pa.Table
             The results from the FTS search
         """
-        combined = pa.concat_tables([vector_results, fts_results], promote=True)
+        combined = pa.concat_tables(
+            [vector_results, fts_results], promote_options="default"
+        )
         row_id = combined.column("_rowid")
 
         # deduplicate

rust/ffi/node/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-node"
-version = "0.6.0"
+version = "0.5.2"
 description = "Serverless, low-latency vector database for AI applications"
 license.workspace = true
 edition.workspace = true

rust/lancedb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.6.0"
+version = "0.5.2"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true

rust/lancedb/README.md

@@ -6,12 +6,3 @@
 LanceDB Rust SDK, a serverless vector database.
 
 Read more at: https://lancedb.com/
-
-> [!TIP]
-> A transitive dependency of `lancedb` is `lzma-sys`, which uses dynamic linking
-> by default. If you want to statically link `lzma-sys`, you should activate it's
-> `static` feature by adding the following to your dependencies:
->
-> ```toml
-> lzma-sys = { version = "*", features = ["static"] }
-> ```

rust/lancedb/src/table.rs

@@ -1889,7 +1889,6 @@ impl TableInternal for NativeTable {
                 }
                 columns.push(field.name.clone());
             }
-
             let index_type = if is_vector {
                 crate::index::IndexType::IvfPq
             } else {