stuff

Fixes #1078

Cargo.toml

@@ -28,13 +28,14 @@ arrow-schema = "50.0"
 arrow-arith = "50.0"
 arrow-cast = "50.0"
 async-trait = "0"
-chrono = "0.4.23"
+chrono = "0.4.35"
 half = { "version" = "=2.3.1", default-features = false, features = [
     "num-traits",
 ] }
 futures = "0"
 log = "0.4"
 object_store = "0.9.0"
+pin-project = "1.0.7"
 snafu = "0.7.4"
 url = "2"
 num-traits = "0.2"

node/src/index.ts

@@ -176,6 +176,10 @@ export async function connect (
     opts = { uri: arg }
   } else {
     // opts = { uri: arg.uri, awsCredentials = arg.awsCredentials }
+    const keys = Object.keys(arg)
+    if (keys.length === 1 && keys[0] === 'uri' && typeof arg.uri === 'string') {
+      opts = { uri: arg.uri }
+    } else {
       opts = Object.assign(
         {
           uri: '',
@@ -187,6 +191,7 @@ export async function connect (
         arg
       )
     }
+  }
 
   if (opts.uri.startsWith('db://')) {
     // Remote connection

node/src/test/test.ts

@@ -128,6 +128,11 @@ describe('LanceDB client', function () {
       assertResults(results)
       results = await table.where('id % 2 = 0').execute()
       assertResults(results)
+
+      // Should reject a bad filter
+      await expect(table.filter('id % 2 = 0 AND').execute()).to.be.rejectedWith(
+        /.*sql parser error: Expected an expression:, found: EOF.*/
+      )
     })
 
     it('uses a filter / where clause', async function () {
@@ -283,7 +288,8 @@ describe('LanceDB client', function () {
 
     it('create a table from an Arrow Table', async function () {
       const dir = await track().mkdir('lancejs')
-      const con = await lancedb.connect(dir)
+      // Also test the connect function with an object
+      const con = await lancedb.connect({ uri: dir })
 
       const i32s = new Int32Array(new Array<number>(10))
       const i32 = makeVector(i32s)
@@ -745,11 +751,11 @@ describe('LanceDB client', function () {
         num_sub_vectors: 2
       })
       await expect(createIndex).to.be.rejectedWith(
-        /VectorIndex requires the column data type to be fixed size list of float32s/
+        "index cannot be created on the column `name` which has data type Utf8"
       )
     })
 
-    it('it should fail when the column is not a vector', async function () {
+    it('it should fail when num_partitions is invalid', async function () {
       const uri = await createTestDB(32, 300)
       const con = await lancedb.connect(uri)
       const table = await con.openTable('vectors')

nodejs/Cargo.toml

@@ -14,12 +14,10 @@ crate-type = ["cdylib"]
 [dependencies]
 arrow-ipc.workspace = true
 futures.workspace = true
-lance-linalg.workspace = true
-lance.workspace = true
 lancedb = { path = "../rust/lancedb" }
 napi = { version = "2.15", default-features = false, features = [
     "napi7",
-    "async"
+    "async",
 ] }
 napi-derive = "2"
 

nodejs/__test__/table.test.ts

@@ -27,6 +27,7 @@ import {
   Float64,
 } from "apache-arrow";
 import { makeArrowTable } from "../dist/arrow";
+import { Index } from "../dist/indices";
 
 describe("Given a table", () => {
   let tmpDir: tmp.DirResult;
@@ -65,21 +66,36 @@ describe("Given a table", () => {
     expect(table.isOpen()).toBe(false);
     expect(table.countRows()).rejects.toThrow("Table some_table is closed");
   });
+
+  it("should let me update values", async () => {
+    await table.add([{ id: 1 }]);
+    expect(await table.countRows("id == 1")).toBe(1);
+    expect(await table.countRows("id == 7")).toBe(0);
+    await table.update({ id: "7" });
+    expect(await table.countRows("id == 1")).toBe(0);
+    expect(await table.countRows("id == 7")).toBe(1);
+    await table.add([{ id: 2 }]);
+    // Test Map as input
+    await table.update(new Map(Object.entries({ id: "10" })), {
+      where: "id % 2 == 0",
+    });
+    expect(await table.countRows("id == 2")).toBe(0);
+    expect(await table.countRows("id == 7")).toBe(1);
+    expect(await table.countRows("id == 10")).toBe(1);
+  });
 });
 
-describe("Test creating index", () => {
+describe("When creating an index", () => {
   let tmpDir: tmp.DirResult;
   const schema = new Schema([
     new Field("id", new Int32(), true),
     new Field("vec", new FixedSizeList(32, new Field("item", new Float32()))),
   ]);
+  let tbl: Table;
+  let queryVec: number[];
 
-  beforeEach(() => {
+  beforeEach(async () => {
     tmpDir = tmp.dirSync({ unsafeCleanup: true });
-  });
-  afterEach(() => tmpDir.removeCallback());
-
-  test("create vector index with no column", async () => {
     const db = await connect(tmpDir.name);
     const data = makeArrowTable(
       Array(300)
@@ -94,47 +110,66 @@ describe("Test creating index", () => {
         schema,
       },
     );
-    const tbl = await db.createTable("test", data);
-    await tbl.createIndex().build();
+    queryVec = data.toArray()[5].vec.toJSON();
+    tbl = await db.createTable("test", data);
+  });
+  afterEach(() => tmpDir.removeCallback());
+
+  it("should create a vector index on vector columns", async () => {
+    await tbl.createIndex("vec");
 
     // check index directory
     const indexDir = path.join(tmpDir.name, "test.lance", "_indices");
     expect(fs.readdirSync(indexDir)).toHaveLength(1);
-    // TODO: check index type.
+    const indices = await tbl.listIndices();
+    expect(indices.length).toBe(1);
+    expect(indices[0]).toEqual({
+      indexType: "IvfPq",
+      columns: ["vec"],
+    });
 
     // Search without specifying the column
-    const queryVector = data.toArray()[5].vec.toJSON();
-    const rst = await tbl.query().nearestTo(queryVector).limit(2).toArrow();
+    const rst = await tbl.query().nearestTo(queryVec).limit(2).toArrow();
     expect(rst.numRows).toBe(2);
 
     // Search with specifying the column
-    const rst2 = await tbl.search(queryVector, "vec").limit(2).toArrow();
+    const rst2 = await tbl.search(queryVec, "vec").limit(2).toArrow();
     expect(rst2.numRows).toBe(2);
     expect(rst.toString()).toEqual(rst2.toString());
   });
 
-  test("no vector column available", async () => {
-    const db = await connect(tmpDir.name);
-    const tbl = await db.createTable(
-      "no_vec",
-      makeArrowTable([
-        { id: 1, val: 2 },
-        { id: 2, val: 3 },
-      ]),
-    );
-    await expect(tbl.createIndex().build()).rejects.toThrow(
-      "No vector column found",
-    );
+  it("should allow parameters to be specified", async () => {
+    await tbl.createIndex("vec", {
+      config: Index.ivfPq({
+        numPartitions: 10,
+      }),
+    });
 
-    await tbl.createIndex("val").build();
-    const indexDir = path.join(tmpDir.name, "no_vec.lance", "_indices");
+    // TODO: Verify parameters when we can load index config as part of list indices
+  });
+
+  it("should allow me to replace (or not) an existing index", async () => {
+    await tbl.createIndex("id");
+    // Default is replace=true
+    await tbl.createIndex("id");
+    await expect(tbl.createIndex("id", { replace: false })).rejects.toThrow(
+      "already exists",
+    );
+    await tbl.createIndex("id", { replace: true });
+  });
+
+  test("should create a scalar index on scalar columns", async () => {
+    await tbl.createIndex("id");
+    const indexDir = path.join(tmpDir.name, "test.lance", "_indices");
     expect(fs.readdirSync(indexDir)).toHaveLength(1);
 
     for await (const r of tbl.query().filter("id > 1").select(["id"])) {
-      expect(r.numRows).toBe(1);
+      expect(r.numRows).toBe(298);
     }
   });
 
+  // TODO: Move this test to the query API test (making sure we can reject queries
+  // when the dimension is incorrect)
   test("two columns with different dimensions", async () => {
     const db = await connect(tmpDir.name);
     const schema = new Schema([
@@ -164,14 +199,9 @@ describe("Test creating index", () => {
     );
 
     // Only build index over v1
-    await expect(tbl.createIndex().build()).rejects.toThrow(
-      /.*More than one vector columns found.*/,
-    );
-    tbl
-      .createIndex("vec")
-      // eslint-disable-next-line @typescript-eslint/naming-convention
-      .ivf_pq({ num_partitions: 2, num_sub_vectors: 2 })
-      .build();
+    await tbl.createIndex("vec", {
+      config: Index.ivfPq({ numPartitions: 2, numSubVectors: 2 }),
+    });
 
     const rst = await tbl
       .query()
@@ -205,30 +235,6 @@ describe("Test creating index", () => {
     expect(rst64Query.toString()).toEqual(rst64Search.toString());
     expect(rst64Query.numRows).toBe(2);
   });
-
-  test("create scalar index", async () => {
-    const db = await connect(tmpDir.name);
-    const data = makeArrowTable(
-      Array(300)
-        .fill(1)
-        .map((_, i) => ({
-          id: i,
-          vec: Array(32)
-            .fill(1)
-            .map(() => Math.random()),
-        })),
-      {
-        schema,
-      },
-    );
-    const tbl = await db.createTable("test", data);
-    await tbl.createIndex("id").build();
-
-    // check index directory
-    const indexDir = path.join(tmpDir.name, "test.lance", "_indices");
-    expect(fs.readdirSync(indexDir)).toHaveLength(1);
-    // TODO: check index type.
-  });
 });
 
 describe("Read consistency interval", () => {
@@ -348,3 +354,48 @@ describe("schema evolution", function () {
     expect(await table.schema()).toEqual(expectedSchema);
   });
 });
+
+describe("when dealing with versioning", () => {
+  let tmpDir: tmp.DirResult;
+  beforeEach(() => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  });
+  afterEach(() => {
+    tmpDir.removeCallback();
+  });
+
+  it("can travel in time", async () => {
+    // Setup
+    const con = await connect(tmpDir.name);
+    const table = await con.createTable("vectors", [
+      { id: 1n, vector: [0.1, 0.2] },
+    ]);
+    const version = await table.version();
+    await table.add([{ id: 2n, vector: [0.1, 0.2] }]);
+    expect(await table.countRows()).toBe(2);
+    // Make sure we can rewind
+    await table.checkout(version);
+    expect(await table.countRows()).toBe(1);
+    // Can't add data in time travel mode
+    await expect(table.add([{ id: 3n, vector: [0.1, 0.2] }])).rejects.toThrow(
+      "table cannot be modified when a specific version is checked out",
+    );
+    // Can go back to normal mode
+    await table.checkoutLatest();
+    expect(await table.countRows()).toBe(2);
+    // Should be able to add data again
+    await table.add([{ id: 2n, vector: [0.1, 0.2] }]);
+    expect(await table.countRows()).toBe(3);
+    // Now checkout and restore
+    await table.checkout(version);
+    await table.restore();
+    expect(await table.countRows()).toBe(1);
+    // Should be able to add data
+    await table.add([{ id: 2n, vector: [0.1, 0.2] }]);
+    expect(await table.countRows()).toBe(2);
+    // Can't use restore if not checked out
+    await expect(table.restore()).rejects.toThrow(
+      "checkout before running restore",
+    );
+  });
+});

nodejs/lancedb/index.ts

@@ -18,15 +18,9 @@ import {
   ConnectionOptions,
 } from "./native.js";
 
-export {
-  ConnectionOptions,
-  WriteOptions,
-  Query,
-  MetricType,
-} from "./native.js";
-export { Connection } from "./connection";
-export { Table } from "./table";
-export { IvfPQOptions, IndexBuilder } from "./indexer";
+export { ConnectionOptions, WriteOptions, Query } from "./native.js";
+export { Connection, CreateTableOptions } from "./connection";
+export { Table, AddDataOptions } from "./table";
 
 /**
  * Connect to a LanceDB instance at the given URI.

nodejs/lancedb/indexer.ts

@@ -1,105 +0,0 @@
-// Copyright 2024 Lance Developers.
-//
-// 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.
-
-// TODO: Re-enable this as part of https://github.com/lancedb/lancedb/pull/1052
-/* eslint-disable @typescript-eslint/naming-convention */
-
-import {
-  MetricType,
-  IndexBuilder as NativeBuilder,
-  Table as NativeTable,
-} from "./native";
-
-/** Options to create `IVF_PQ` index */
-export interface IvfPQOptions {
-  /** Number of IVF partitions. */
-  num_partitions?: number;
-
-  /** Number of sub-vectors in PQ coding. */
-  num_sub_vectors?: number;
-
-  /** Number of bits used for each PQ code.
-   */
-  num_bits?: number;
-
-  /** Metric type to calculate the distance between vectors.
-   *
-   * Supported metrics: `L2`, `Cosine` and `Dot`.
-   */
-  metric_type?: MetricType;
-
-  /** Number of iterations to train K-means.
-   *
-   * Default is 50. The more iterations it usually yield better results,
-   * but it takes longer to train.
-   */
-  max_iterations?: number;
-
-  sample_rate?: number;
-}
-
-/**
- * Building an index on LanceDB {@link Table}
- *
- * @see {@link Table.createIndex} for detailed usage.
- */
-export class IndexBuilder {
-  private inner: NativeBuilder;
-
-  constructor(tbl: NativeTable) {
-    this.inner = tbl.createIndex();
-  }
-
-  /** Instruct the builder to build an `IVF_PQ` index */
-  ivf_pq(options?: IvfPQOptions): IndexBuilder {
-    this.inner.ivfPq(
-      options?.metric_type,
-      options?.num_partitions,
-      options?.num_sub_vectors,
-      options?.num_bits,
-      options?.max_iterations,
-      options?.sample_rate,
-    );
-    return this;
-  }
-
-  /** Instruct the builder to build a Scalar index. */
-  scalar(): IndexBuilder {
-    this.scalar();
-    return this;
-  }
-
-  /** Set the column(s) to create index on top of. */
-  column(col: string): IndexBuilder {
-    this.inner.column(col);
-    return this;
-  }
-
-  /** Set to true to replace existing index. */
-  replace(val: boolean): IndexBuilder {
-    this.inner.replace(val);
-    return this;
-  }
-
-  /** Specify the name of the index. Optional */
-  name(n: string): IndexBuilder {
-    this.inner.name(n);
-    return this;
-  }
-
-  /** Building the index. */
-  async build() {
-    await this.inner.build();
-  }
-}

nodejs/lancedb/indices.ts

@@ -0,0 +1,195 @@
+// Copyright 2024 Lance Developers.
+//
+// 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 { Index as LanceDbIndex } from "./native";
+
+/**
+ * Options to create an `IVF_PQ` index
+ */
+export interface IvfPqOptions {
+  /** The number of IVF partitions to create.
+   *
+   * This value should generally scale with the number of rows in the dataset.
+   * By default the number of partitions is the square root of the number of
+   * rows.
+   *
+   * If this value is too large then the first part of the search (picking the
+   * right partition) will be slow.  If this value is too small then the second
+   * part of the search (searching within a partition) will be slow.
+   */
+  numPartitions?: number;
+
+  /** Number of sub-vectors of PQ.
+   *
+   * This value controls how much the vector is compressed during the quantization step.
+   * The more sub vectors there are the less the vector is compressed.  The default is
+   * the dimension of the vector divided by 16.  If the dimension is not evenly divisible
+   * by 16 we use the dimension divded by 8.
+   *
+   * The above two cases are highly preferred.  Having 8 or 16 values per subvector allows
+   * us to use efficient SIMD instructions.
+   *
+   * If the dimension is not visible by 8 then we use 1 subvector.  This is not ideal and
+   * will likely result in poor performance.
+   */
+  numSubVectors?: number;
+
+  /** [DistanceType] to use to build the index.
+   *
+   * Default value is [DistanceType::L2].
+   *
+   * This is used when training the index to calculate the IVF partitions
+   * (vectors are grouped in partitions with similar vectors according to this
+   * distance type) and to calculate a subvector's code during quantization.
+   *
+   * The distance type used to train an index MUST match the distance type used
+   * to search the index.  Failure to do so will yield inaccurate results.
+   *
+   * The following distance types are available:
+   *
+   * "l2" - Euclidean distance. This is a very common distance metric that
+   * accounts for both magnitude and direction when determining the distance
+   * between vectors. L2 distance has a range of [0, ∞).
+   *
+   * "cosine" - Cosine distance.  Cosine distance is a distance metric
+   * calculated from the cosine similarity between two vectors. Cosine
+   * similarity is a measure of similarity between two non-zero vectors of an
+   * inner product space. It is defined to equal the cosine of the angle
+   * between them.  Unlike L2, the cosine distance is not affected by the
+   * magnitude of the vectors.  Cosine distance has a range of [0, 2].
+   *
+   * Note: the cosine distance is undefined when one (or both) of the vectors
+   * are all zeros (there is no direction).  These vectors are invalid and may
+   * never be returned from a vector search.
+   *
+   * "dot" - Dot product. Dot distance is the dot product of two vectors. Dot
+   * distance has a range of (-∞, ∞). If the vectors are normalized (i.e. their
+   * L2 norm is 1), then dot distance is equivalent to the cosine distance.
+   */
+  distanceType?: "l2" | "cosine" | "dot";
+
+  /** Max iteration to train IVF kmeans.
+   *
+   * When training an IVF PQ index we use kmeans to calculate the partitions.  This parameter
+   * controls how many iterations of kmeans to run.
+   *
+   * Increasing this might improve the quality of the index but in most cases these extra
+   * iterations have diminishing returns.
+   *
+   * The default value is 50.
+   */
+  maxIterations?: number;
+
+  /** The number of vectors, per partition, to sample when training IVF kmeans.
+   *
+   * When an IVF PQ index is trained, we need to calculate partitions.  These are groups
+   * of vectors that are similar to each other.  To do this we use an algorithm called kmeans.
+   *
+   * Running kmeans on a large dataset can be slow.  To speed this up we run kmeans on a
+   * random sample of the data.  This parameter controls the size of the sample.  The total
+   * number of vectors used to train the index is `sample_rate * num_partitions`.
+   *
+   * Increasing this value might improve the quality of the index but in most cases the
+   * default should be sufficient.
+   *
+   * The default value is 256.
+   */
+  sampleRate?: number;
+}
+
+export class Index {
+  private readonly inner: LanceDbIndex;
+  private constructor(inner: LanceDbIndex) {
+    this.inner = inner;
+  }
+
+  /**
+   * Create an IvfPq index
+   *
+   * This index stores a compressed (quantized) copy of every vector.  These vectors
+   * are grouped into partitions of similar vectors.  Each partition keeps track of
+   * a centroid which is the average value of all vectors in the group.
+   *
+   * During a query the centroids are compared with the query vector to find the closest
+   * partitions.  The compressed vectors in these partitions are then searched to find
+   * the closest vectors.
+   *
+   * The compression scheme is called product quantization.  Each vector is divided into
+   * subvectors and then each subvector is quantized into a small number of bits.  the
+   * parameters `num_bits` and `num_subvectors` control this process, providing a tradeoff
+   * between index size (and thus search speed) and index accuracy.
+   *
+   * The partitioning process is called IVF and the `num_partitions` parameter controls how
+   * many groups to create.
+   *
+   * Note that training an IVF PQ index on a large dataset is a slow operation and
+   * currently is also a memory intensive operation.
+   */
+  static ivfPq(options?: Partial<IvfPqOptions>) {
+    return new Index(
+      LanceDbIndex.ivfPq(
+        options?.distanceType,
+        options?.numPartitions,
+        options?.numSubVectors,
+        options?.maxIterations,
+        options?.sampleRate,
+      ),
+    );
+  }
+
+  /** Create a btree index
+   *
+   * A btree index is an index on a scalar columns.  The index stores a copy of the column
+   * in sorted order.  A header entry is created for each block of rows (currently the
+   * block size is fixed at 4096).  These header entries are stored in a separate
+   * cacheable structure (a btree).  To search for data the header is used to determine
+   * which blocks need to be read from disk.
+   *
+   * For example, a btree index in a table with 1Bi rows requires sizeof(Scalar) * 256Ki
+   * bytes of memory and will generally need to read sizeof(Scalar) * 4096 bytes to find
+   * the correct row ids.
+   *
+   * This index is good for scalar columns with mostly distinct values and does best when
+   * the query is highly selective.
+   *
+   * The btree index does not currently have any parameters though parameters such as the
+   * block size may be added in the future.
+   */
+  static btree() {
+    return new Index(LanceDbIndex.btree());
+  }
+}
+
+export interface IndexOptions {
+  /** Advanced index configuration
+   *
+   * This option allows you to specify a specfic index to create and also
+   * allows you to pass in configuration for training the index.
+   *
+   * See the static methods on Index for details on the various index types.
+   *
+   * If this is not supplied then column data type(s) and column statistics
+   * will be used to determine the most useful kind of index to create.
+   */
+  config?: Index;
+  /** Whether to replace the existing index
+   *
+   * If this is false, and another index already exists on the same columns
+   * and the same name, then an error will be returned.  This is true even if
+   * that index is out of date.
+   *
+   * The default is true
+   */
+  replace?: boolean;
+}

nodejs/lancedb/native.d.ts

@@ -3,14 +3,17 @@
 
 /* auto-generated by NAPI-RS */
 
-export const enum IndexType {
-  Scalar = 0,
-  IvfPq = 1
-}
-export const enum MetricType {
-  L2 = 0,
-  Cosine = 1,
-  Dot = 2
+/** A description of an index currently configured on a column */
+export interface IndexConfig {
+  /** The type of the index */
+  indexType: string
+  /**
+   * The columns in the index
+   *
+   * Currently this is always an array of size 1.  In the future there may
+   * be more columns to represent composite indices.
+   */
+  columns: Array<string>
 }
 /**
  *  A definition of a column alteration. The alteration changes the column at
@@ -93,13 +96,9 @@ export class Connection {
   /** Drop table with the name. Or raise an error if the table does not exist. */
   dropTable(name: string): Promise<void>
 }
-export class IndexBuilder {
-  replace(v: boolean): void
-  column(c: string): void
-  name(name: string): void
-  ivfPq(metricType?: MetricType | undefined | null, numPartitions?: number | undefined | null, numSubVectors?: number | undefined | null, numBits?: number | undefined | null, maxIterations?: number | undefined | null, sampleRate?: number | undefined | null): void
-  scalar(): void
-  build(): Promise<void>
+export class Index {
+  static ivfPq(distanceType?: string | undefined | null, numPartitions?: number | undefined | null, numSubVectors?: number | undefined | null, maxIterations?: number | undefined | null, sampleRate?: number | undefined | null): Index
+  static btree(): Index
 }
 /** Typescript-style Async Iterator over RecordBatches  */
 export class RecordBatchIterator {
@@ -125,9 +124,15 @@ export class Table {
   add(buf: Buffer, mode: string): Promise<void>
   countRows(filter?: string | undefined | null): Promise<number>
   delete(predicate: string): Promise<void>
-  createIndex(): IndexBuilder
+  createIndex(index: Index | undefined | null, column: string, replace?: boolean | undefined | null): Promise<void>
+  update(onlyIf: string | undefined | null, columns: Array<[string, string]>): Promise<void>
   query(): Query
   addColumns(transforms: Array<AddColumnsSql>): Promise<void>
   alterColumns(alterations: Array<ColumnAlteration>): Promise<void>
   dropColumns(columns: Array<string>): Promise<void>
+  version(): Promise<number>
+  checkout(version: number): Promise<void>
+  checkoutLatest(): Promise<void>
+  restore(): Promise<void>
+  listIndices(): Promise<Array<IndexConfig>>
 }

nodejs/lancedb/native.js

@@ -295,12 +295,10 @@ if (!nativeBinding) {
   throw new Error(`Failed to load native binding`)
 }
 
-const { Connection, IndexType, MetricType, IndexBuilder, RecordBatchIterator, Query, Table, WriteMode, connect } = nativeBinding
+const { Connection, Index, RecordBatchIterator, Query, Table, WriteMode, connect } = nativeBinding
 
 module.exports.Connection = Connection
-module.exports.IndexType = IndexType
-module.exports.MetricType = MetricType
-module.exports.IndexBuilder = IndexBuilder
+module.exports.Index = Index
 module.exports.RecordBatchIterator = RecordBatchIterator
 module.exports.Query = Query
 module.exports.Table = Table

nodejs/lancedb/table.ts

@@ -16,12 +16,14 @@ import { Schema, tableFromIPC } from "apache-arrow";
 import {
   AddColumnsSql,
   ColumnAlteration,
+  IndexConfig,
   Table as _NativeTable,
 } from "./native";
 import { Query } from "./query";
-import { IndexBuilder } from "./indexer";
+import { IndexOptions } from "./indices";
 import { Data, fromDataToBuffer } from "./arrow";
 
+export { IndexConfig } from "./native";
 /**
  * Options for adding data to a table.
  */
@@ -33,6 +35,20 @@ export interface AddDataOptions {
   mode: "append" | "overwrite";
 }
 
+export interface UpdateOptions {
+  /**
+   * A filter that limits the scope of the update.
+   *
+   * This should be an SQL filter expression.
+   *
+   * Only rows that satisfy the expression will be updated.
+   *
+   * For example, this could be 'my_col == 0' to replace all instances
+   * of 0 in a column with some other default value.
+   */
+  where: string;
+}
+
 /**
  * A Table is a collection of Records in a LanceDB Database.
  *
@@ -93,6 +109,45 @@ export class Table {
     await this.inner.add(buffer, mode);
   }
 
+  /**
+   * Update existing records in the Table
+   *
+   * An update operation can be used to adjust existing values.  Use the
+   * returned builder to specify which columns to update.  The new value
+   * can be a literal value (e.g. replacing nulls with some default value)
+   * or an expression applied to the old value (e.g. incrementing a value)
+   *
+   * An optional condition can be specified (e.g. "only update if the old
+   * value is 0")
+   *
+   * Note: if your condition is something like "some_id_column == 7" and
+   * you are updating many rows (with different ids) then you will get
+   * better performance with a single [`merge_insert`] call instead of
+   * repeatedly calilng this method.
+   *
+   * @param updates the columns to update
+   *
+   * Keys in the map should specify the name of the column to update.
+   * Values in the map provide the new value of the column.  These can
+   * be SQL literal strings (e.g. "7" or "'foo'") or they can be expressions
+   * based on the row being updated (e.g. "my_col + 1")
+   *
+   * @param options additional options to control the update behavior
+   */
+  async update(
+    updates: Map<string, string> | Record<string, string>,
+    options?: Partial<UpdateOptions>,
+  ) {
+    const onlyIf = options?.where;
+    let columns: [string, string][];
+    if (updates instanceof Map) {
+      columns = Array.from(updates.entries());
+    } else {
+      columns = Object.entries(updates);
+    }
+    await this.inner.update(onlyIf, columns);
+  }
+
   /** Count the total number of rows in the dataset. */
   async countRows(filter?: string): Promise<number> {
     return await this.inner.countRows(filter);
@@ -103,24 +158,28 @@ export class Table {
     await this.inner.delete(predicate);
   }
 
-  /** Create an index over the columns.
+  /** Create an index to speed up queries.
    *
-   * @param {string} column The column to create the index on. If not specified,
-   *                        it will create an index on vector field.
+   * Indices can be created on vector columns or scalar columns.
+   * Indices on vector columns will speed up vector searches.
+   * Indices on scalar columns will speed up filtering (in both
+   * vector and non-vector searches)
    *
    * @example
    *
-   * By default, it creates vector idnex on one vector column.
+   * If the column has a vector (fixed size list) data type then
+   * an IvfPq vector index will be created.
    *
    * ```typescript
    * const table = await conn.openTable("my_table");
-   * await table.createIndex().build();
+   * await table.createIndex(["vector"]);
    * ```
    *
-   * You can specify `IVF_PQ` parameters via `ivf_pq({})` call.
+   * For advanced control over vector index creation you can specify
+   * the index type and options.
    * ```typescript
    * const table = await conn.openTable("my_table");
-   * await table.createIndex("my_vec_col")
+   * await table.createIndex(["vector"], I)
    *   .ivf_pq({ num_partitions: 128, num_sub_vectors: 16 })
    *   .build();
    * ```
@@ -131,12 +190,11 @@ export class Table {
    * await table.createIndex("my_float_col").build();
    * ```
    */
-  createIndex(column?: string): IndexBuilder {
-    let builder = new IndexBuilder(this.inner);
-    if (column !== undefined) {
-      builder = builder.column(column);
-    }
-    return builder;
+  async createIndex(column: string, options?: Partial<IndexOptions>) {
+    // Bit of a hack to get around the fact that TS has no package-scope.
+    // eslint-disable-next-line @typescript-eslint/no-explicit-any
+    const nativeIndex = (options?.config as any)?.inner;
+    await this.inner.createIndex(nativeIndex, column, options?.replace);
   }
 
   /**
@@ -232,4 +290,65 @@ export class Table {
   async dropColumns(columnNames: string[]): Promise<void> {
     await this.inner.dropColumns(columnNames);
   }
+
+  /** Retrieve the version of the table
+   *
+   * LanceDb supports versioning.  Every operation that modifies the table increases
+   * version.  As long as a version hasn't been deleted you can `[Self::checkout]` that
+   * version to view the data at that point.  In addition, you can `[Self::restore]` the
+   * version to replace the current table with a previous version.
+   */
+  async version(): Promise<number> {
+    return await this.inner.version();
+  }
+
+  /** Checks out a specific version of the Table
+   *
+   * Any read operation on the table will now access the data at the checked out version.
+   * As a consequence, calling this method will disable any read consistency interval
+   * that was previously set.
+   *
+   * This is a read-only operation that turns the table into a sort of "view"
+   * or "detached head".  Other table instances will not be affected.  To make the change
+   * permanent you can use the `[Self::restore]` method.
+   *
+   * Any operation that modifies the table will fail while the table is in a checked
+   * out state.
+   *
+   * To return the table to a normal state use `[Self::checkout_latest]`
+   */
+  async checkout(version: number): Promise<void> {
+    await this.inner.checkout(version);
+  }
+
+  /** Ensures the table is pointing at the latest version
+   *
+   * This can be used to manually update a table when the read_consistency_interval is None
+   * It can also be used to undo a `[Self::checkout]` operation
+   */
+  async checkoutLatest(): Promise<void> {
+    await this.inner.checkoutLatest();
+  }
+
+  /** Restore the table to the currently checked out version
+   *
+   * This operation will fail if checkout has not been called previously
+   *
+   * This operation will overwrite the latest version of the table with a
+   * previous version.  Any changes made since the checked out version will
+   * no longer be visible.
+   *
+   * Once the operation concludes the table will no longer be in a checked
+   * out state and the read_consistency_interval, if any, will apply.
+   */
+  async restore(): Promise<void> {
+    await this.inner.restore();
+  }
+
+  /**
+   * List all indices that have been created with Self::create_index
+   */
+  async listIndices(): Promise<IndexConfig[]> {
+    return await this.inner.listIndices();
+  }
 }

nodejs/src/error.rs

@@ -0,0 +1,12 @@
+pub type Result<T> = napi::Result<T>;
+
+pub trait NapiErrorExt<T> {
+    /// Convert to a napi error using from_reason(err.to_string())
+    fn default_error(self) -> Result<T>;
+}
+
+impl<T> NapiErrorExt<T> for std::result::Result<T, lancedb::Error> {
+    fn default_error(self) -> Result<T> {
+        self.map_err(|err| napi::Error::from_reason(err.to_string()))
+    }
+}

nodejs/src/index.rs

@@ -14,126 +14,73 @@
 
 use std::sync::Mutex;
 
-use lance_linalg::distance::MetricType as LanceMetricType;
-use lancedb::index::IndexBuilder as LanceDbIndexBuilder;
-use lancedb::Table as LanceDbTable;
+use lancedb::index::scalar::BTreeIndexBuilder;
+use lancedb::index::vector::IvfPqIndexBuilder;
+use lancedb::index::Index as LanceDbIndex;
+use lancedb::DistanceType;
 use napi_derive::napi;
 
 #[napi]
-pub enum IndexType {
-    Scalar,
-    IvfPq,
+pub struct Index {
+    inner: Mutex<Option<LanceDbIndex>>,
 }
 
-#[napi]
-pub enum MetricType {
-    L2,
-    Cosine,
-    Dot,
-}
-
-impl From<MetricType> for LanceMetricType {
-    fn from(metric: MetricType) -> Self {
-        match metric {
-            MetricType::L2 => Self::L2,
-            MetricType::Cosine => Self::Cosine,
-            MetricType::Dot => Self::Dot,
-        }
+impl Index {
+    pub fn consume(&self) -> napi::Result<LanceDbIndex> {
+        self.inner
+            .lock()
+            .unwrap()
+            .take()
+            .ok_or(napi::Error::from_reason(
+                "attempt to use an index more than once",
+            ))
     }
 }
 
 #[napi]
-pub struct IndexBuilder {
-    inner: Mutex<Option<LanceDbIndexBuilder>>,
-}
-
-impl IndexBuilder {
-    fn modify(
-        &self,
-        mod_fn: impl Fn(LanceDbIndexBuilder) -> LanceDbIndexBuilder,
-    ) -> napi::Result<()> {
-        let mut inner = self.inner.lock().unwrap();
-        let inner_builder = inner.take().ok_or_else(|| {
-            napi::Error::from_reason("IndexBuilder has already been consumed".to_string())
-        })?;
-        let inner_builder = mod_fn(inner_builder);
-        inner.replace(inner_builder);
-        Ok(())
-    }
-}
-
-#[napi]
-impl IndexBuilder {
-    pub fn new(tbl: &LanceDbTable) -> Self {
-        let inner = tbl.create_index(&[]);
-        Self {
-            inner: Mutex::new(Some(inner)),
-        }
-    }
-
-    #[napi]
-    pub fn replace(&self, v: bool) -> napi::Result<()> {
-        self.modify(|b| b.replace(v))
-    }
-
-    #[napi]
-    pub fn column(&self, c: String) -> napi::Result<()> {
-        self.modify(|b| b.columns(&[c.as_str()]))
-    }
-
-    #[napi]
-    pub fn name(&self, name: String) -> napi::Result<()> {
-        self.modify(|b| b.name(name.as_str()))
-    }
-
-    #[napi]
+impl Index {
+    #[napi(factory)]
     pub fn ivf_pq(
-        &self,
-        metric_type: Option<MetricType>,
+        distance_type: Option<String>,
         num_partitions: Option<u32>,
         num_sub_vectors: Option<u32>,
-        num_bits: Option<u32>,
         max_iterations: Option<u32>,
         sample_rate: Option<u32>,
-    ) -> napi::Result<()> {
-        self.modify(|b| {
-            let mut b = b.ivf_pq();
-            if let Some(metric_type) = metric_type {
-                b = b.metric_type(metric_type.into());
+    ) -> napi::Result<Self> {
+        let mut ivf_pq_builder = IvfPqIndexBuilder::default();
+        if let Some(distance_type) = distance_type {
+            let distance_type = match distance_type.as_str() {
+                "l2" => Ok(DistanceType::L2),
+                "cosine" => Ok(DistanceType::Cosine),
+                "dot" => Ok(DistanceType::Dot),
+                _ => Err(napi::Error::from_reason(format!(
+                    "Invalid distance type '{}'.  Must be one of l2, cosine, or dot",
+                    distance_type
+                ))),
+            }?;
+            ivf_pq_builder = ivf_pq_builder.distance_type(distance_type);
         }
         if let Some(num_partitions) = num_partitions {
-                b = b.num_partitions(num_partitions);
+            ivf_pq_builder = ivf_pq_builder.num_partitions(num_partitions);
         }
         if let Some(num_sub_vectors) = num_sub_vectors {
-                b = b.num_sub_vectors(num_sub_vectors);
-            }
-            if let Some(num_bits) = num_bits {
-                b = b.num_bits(num_bits);
+            ivf_pq_builder = ivf_pq_builder.num_sub_vectors(num_sub_vectors);
         }
         if let Some(max_iterations) = max_iterations {
-                b = b.max_iterations(max_iterations);
+            ivf_pq_builder = ivf_pq_builder.max_iterations(max_iterations);
         }
         if let Some(sample_rate) = sample_rate {
-                b = b.sample_rate(sample_rate);
+            ivf_pq_builder = ivf_pq_builder.sample_rate(sample_rate);
         }
-            b
+        Ok(Self {
+            inner: Mutex::new(Some(LanceDbIndex::IvfPq(ivf_pq_builder))),
         })
     }
 
-    #[napi]
-    pub fn scalar(&self) -> napi::Result<()> {
-        self.modify(|b| b.scalar())
+    #[napi(factory)]
+    pub fn btree() -> Self {
+        Self {
+            inner: Mutex::new(Some(LanceDbIndex::BTree(BTreeIndexBuilder::default()))),
         }
-
-    #[napi]
-    pub async fn build(&self) -> napi::Result<()> {
-        let inner = self.inner.lock().unwrap().take().ok_or_else(|| {
-            napi::Error::from_reason("IndexBuilder has already been consumed".to_string())
-        })?;
-        inner
-            .build()
-            .await
-            .map_err(|e| napi::Error::from_reason(format!("Failed to build index: {}", e)))?;
-        Ok(())
     }
 }

nodejs/src/iterator.rs

@@ -13,7 +13,7 @@
 // limitations under the License.
 
 use futures::StreamExt;
-use lance::io::RecordBatchStream;
+use lancedb::arrow::SendableRecordBatchStream;
 use lancedb::ipc::batches_to_ipc_file;
 use napi::bindgen_prelude::*;
 use napi_derive::napi;
@@ -21,12 +21,12 @@ use napi_derive::napi;
 /** Typescript-style Async Iterator over RecordBatches */
 #[napi]
 pub struct RecordBatchIterator {
-    inner: Box<dyn RecordBatchStream + Unpin>,
+    inner: SendableRecordBatchStream,
 }
 
 #[napi]
 impl RecordBatchIterator {
-    pub(crate) fn new(inner: Box<dyn RecordBatchStream + Unpin>) -> Self {
+    pub(crate) fn new(inner: SendableRecordBatchStream) -> Self {
         Self { inner }
     }
 

nodejs/src/lib.rs

@@ -16,6 +16,7 @@ use connection::Connection;
 use napi_derive::*;
 
 mod connection;
+mod error;
 mod index;
 mod iterator;
 mod query;

nodejs/src/query.rs

@@ -74,6 +74,6 @@ impl Query {
         let inner_stream = self.inner.execute_stream().await.map_err(|e| {
             napi::Error::from_reason(format!("Failed to execute query stream: {}", e))
         })?;
-        Ok(RecordBatchIterator::new(Box::new(inner_stream)))
+        Ok(RecordBatchIterator::new(inner_stream))
     }
 }

nodejs/src/table.rs

@@ -13,13 +13,16 @@
 // limitations under the License.
 
 use arrow_ipc::writer::FileWriter;
-use lance::dataset::ColumnAlteration as LanceColumnAlteration;
 use lancedb::ipc::ipc_file_to_batches;
-use lancedb::table::{AddDataMode, Table as LanceDbTable};
+use lancedb::table::{
+    AddDataMode, ColumnAlteration as LanceColumnAlteration, NewColumnTransform,
+    Table as LanceDbTable,
+};
 use napi::bindgen_prelude::*;
 use napi_derive::napi;
 
-use crate::index::IndexBuilder;
+use crate::error::NapiErrorExt;
+use crate::index::Index;
 use crate::query::Query;
 
 #[napi]
@@ -129,8 +132,38 @@ impl Table {
     }
 
     #[napi]
-    pub fn create_index(&self) -> napi::Result<IndexBuilder> {
-        Ok(IndexBuilder::new(self.inner_ref()?))
+    pub async fn create_index(
+        &self,
+        index: Option<&Index>,
+        column: String,
+        replace: Option<bool>,
+    ) -> napi::Result<()> {
+        let lancedb_index = if let Some(index) = index {
+            index.consume()?
+        } else {
+            lancedb::index::Index::Auto
+        };
+        let mut builder = self.inner_ref()?.create_index(&[column], lancedb_index);
+        if let Some(replace) = replace {
+            builder = builder.replace(replace);
+        }
+        builder.execute().await.default_error()
+    }
+
+    #[napi]
+    pub async fn update(
+        &self,
+        only_if: Option<String>,
+        columns: Vec<(String, String)>,
+    ) -> napi::Result<()> {
+        let mut op = self.inner_ref()?.update();
+        if let Some(only_if) = only_if {
+            op = op.only_if(only_if);
+        }
+        for (column_name, value) in columns {
+            op = op.column(column_name, value);
+        }
+        op.execute().await.default_error()
     }
 
     #[napi]
@@ -144,7 +177,7 @@ impl Table {
             .into_iter()
             .map(|sql| (sql.name, sql.value_sql))
             .collect::<Vec<_>>();
-        let transforms = lance::dataset::NewColumnTransform::SqlExpressions(transforms);
+        let transforms = NewColumnTransform::SqlExpressions(transforms);
         self.inner_ref()?
             .add_columns(transforms, None)
             .await
@@ -197,6 +230,67 @@ impl Table {
             })?;
         Ok(())
     }
+
+    #[napi]
+    pub async fn version(&self) -> napi::Result<i64> {
+        self.inner_ref()?
+            .version()
+            .await
+            .map(|val| val as i64)
+            .default_error()
+    }
+
+    #[napi]
+    pub async fn checkout(&self, version: i64) -> napi::Result<()> {
+        self.inner_ref()?
+            .checkout(version as u64)
+            .await
+            .default_error()
+    }
+
+    #[napi]
+    pub async fn checkout_latest(&self) -> napi::Result<()> {
+        self.inner_ref()?.checkout_latest().await.default_error()
+    }
+
+    #[napi]
+    pub async fn restore(&self) -> napi::Result<()> {
+        self.inner_ref()?.restore().await.default_error()
+    }
+
+    #[napi]
+    pub async fn list_indices(&self) -> napi::Result<Vec<IndexConfig>> {
+        Ok(self
+            .inner_ref()?
+            .list_indices()
+            .await
+            .default_error()?
+            .into_iter()
+            .map(IndexConfig::from)
+            .collect::<Vec<_>>())
+    }
+}
+
+#[napi(object)]
+/// A description of an index currently configured on a column
+pub struct IndexConfig {
+    /// The type of the index
+    pub index_type: String,
+    /// The columns in the index
+    ///
+    /// Currently this is always an array of size 1.  In the future there may
+    /// be more columns to represent composite indices.
+    pub columns: Vec<String>,
+}
+
+impl From<lancedb::index::IndexConfig> for IndexConfig {
+    fn from(value: lancedb::index::IndexConfig) -> Self {
+        let index_type = format!("{:?}", value.index_type);
+        Self {
+            index_type,
+            columns: value.columns,
+        }
+    }
 }
 
 ///  A definition of a column alteration. The alteration changes the column at

python/pyproject.toml

@@ -57,6 +57,7 @@ tests = [
     "duckdb",
     "pytz",
     "polars>=0.19",
+    "pillow",
 ]
 dev = ["ruff", "pre-commit"]
 docs = [

python/python/lancedb/__init__.py

@@ -23,8 +23,9 @@ from ._lancedb import connect as lancedb_connect
 from .common import URI, sanitize_uri
 from .db import AsyncConnection, DBConnection, LanceDBConnection
 from .remote.db import RemoteDBConnection
-from .schema import vector  # noqa: F401
-from .utils import sentry_log  # noqa: F401
+from .schema import vector
+from .table import AsyncTable
+from .utils import sentry_log
 
 
 def connect(
@@ -188,3 +189,19 @@ async def connect_async(
             read_consistency_interval_secs,
         )
     )
+
+
+__all__ = [
+    "connect",
+    "connect_async",
+    "AsyncConnection",
+    "AsyncTable",
+    "URI",
+    "sanitize_uri",
+    "sentry_log",
+    "vector",
+    "DBConnection",
+    "LanceDBConnection",
+    "RemoteDBConnection",
+    "__version__",
+]

python/python/lancedb/_lancedb.pyi

@@ -1,7 +1,19 @@
-from typing import Optional
+from typing import Dict, List, Optional
 
 import pyarrow as pa
 
+class Index:
+    @staticmethod
+    def ivf_pq(
+        distance_type: Optional[str],
+        num_partitions: Optional[int],
+        num_sub_vectors: Optional[int],
+        max_iterations: Optional[int],
+        sample_rate: Optional[int],
+    ) -> Index: ...
+    @staticmethod
+    def btree() -> Index: ...
+
 class Connection(object):
     async def table_names(
         self, start_after: Optional[str], limit: Optional[int]
@@ -13,10 +25,25 @@ class Connection(object):
         self, name: str, mode: str, schema: pa.Schema
     ) -> Table: ...
 
-class Table(object):
+class Table:
     def name(self) -> str: ...
     def __repr__(self) -> str: ...
     async def schema(self) -> pa.Schema: ...
+    async def add(self, data: pa.RecordBatchReader, mode: str) -> None: ...
+    async def update(self, updates: Dict[str, str], where: Optional[str]) -> None: ...
+    async def count_rows(self, filter: Optional[str]) -> int: ...
+    async def create_index(
+        self, column: str, config: Optional[Index], replace: Optional[bool]
+    ): ...
+    async def version(self) -> int: ...
+    async def checkout(self, version): ...
+    async def checkout_latest(self): ...
+    async def restore(self): ...
+    async def list_indices(self) -> List[IndexConfig]: ...
+
+class IndexConfig:
+    index_type: str
+    columns: List[str]
 
 async def connect(
     uri: str,

python/python/lancedb/db.py

@@ -529,7 +529,7 @@ class AsyncConnection(object):
         on_bad_vectors: Optional[str] = None,
         fill_value: Optional[float] = None,
         embedding_functions: Optional[List[EmbeddingFunctionConfig]] = None,
-    ) -> Table:
+    ) -> AsyncTable:
         """Create a [Table][lancedb.table.Table] in the database.
 
         Parameters

python/python/lancedb/embeddings/open_clip.py

@@ -126,6 +126,10 @@ class OpenClipEmbeddings(EmbeddingFunction):
         """
         Issue concurrent requests to retrieve the image data
         """
+        return [
+            self.generate_image_embedding(image) for image in tqdm(images)
+        ]
+
         with concurrent.futures.ThreadPoolExecutor() as executor:
             futures = [
                 executor.submit(self.generate_image_embedding, image)

python/python/lancedb/index.py

@@ -0,0 +1,163 @@
+from typing import Optional
+
+from ._lancedb import (
+    Index as LanceDbIndex,
+)
+from ._lancedb import (
+    IndexConfig,
+)
+
+
+class BTree(object):
+    """Describes a btree index configuration
+
+    A btree index is an index on scalar columns.  The index stores a copy of the
+    column in sorted order.  A header entry is created for each block of rows
+    (currently the block size is fixed at 4096).  These header entries are stored
+    in a separate cacheable structure (a btree).  To search for data the header is
+    used to determine which blocks need to be read from disk.
+
+    For example, a btree index in a table with 1Bi rows requires
+    sizeof(Scalar) * 256Ki bytes of memory and will generally need to read
+    sizeof(Scalar) * 4096 bytes to find the correct row ids.
+
+    This index is good for scalar columns with mostly distinct values and does best
+    when the query is highly selective.
+
+    The btree index does not currently have any parameters though parameters such as
+    the block size may be added in the future.
+    """
+
+    def __init__(self):
+        self._inner = LanceDbIndex.btree()
+
+
+class IvfPq(object):
+    """Describes an IVF PQ Index
+
+    This index stores a compressed (quantized) copy of every vector.  These vectors
+    are grouped into partitions of similar vectors.  Each partition keeps track of
+    a centroid which is the average value of all vectors in the group.
+
+    During a query the centroids are compared with the query vector to find the
+    closest partitions.  The compressed vectors in these partitions are then
+    searched to find the closest vectors.
+
+    The compression scheme is called product quantization.  Each vector is divide
+    into subvectors and then each subvector is quantized into a small number of
+    bits.  the parameters `num_bits` and `num_subvectors` control this process,
+    providing a tradeoff between index size (and thus search speed) and index
+    accuracy.
+
+    The partitioning process is called IVF and the `num_partitions` parameter
+    controls how many groups to create.
+
+    Note that training an IVF PQ index on a large dataset is a slow operation and
+    currently is also a memory intensive operation.
+    """
+
+    def __init__(
+        self,
+        *,
+        distance_type: Optional[str] = None,
+        num_partitions: Optional[int] = None,
+        num_sub_vectors: Optional[int] = None,
+        max_iterations: Optional[int] = None,
+        sample_rate: Optional[int] = None,
+    ):
+        """
+        Create an IVF PQ index config
+
+        Parameters
+        ----------
+        distance_type: str, default "L2"
+            The distance metric used to train the index
+
+            This is used when training the index to calculate the IVF partitions
+            (vectors are grouped in partitions with similar vectors according to this
+            distance type) and to calculate a subvector's code during quantization.
+
+            The distance type used to train an index MUST match the distance type used
+            to search the index.  Failure to do so will yield inaccurate results.
+
+            The following distance types are available:
+
+            "l2" - Euclidean distance. This is a very common distance metric that
+            accounts for both magnitude and direction when determining the distance
+            between vectors. L2 distance has a range of [0, ∞).
+
+            "cosine" - Cosine distance.  Cosine distance is a distance metric
+            calculated from the cosine similarity between two vectors. Cosine
+            similarity is a measure of similarity between two non-zero vectors of an
+            inner product space. It is defined to equal the cosine of the angle
+            between them.  Unlike L2, the cosine distance is not affected by the
+            magnitude of the vectors.  Cosine distance has a range of [0, 2].
+
+            Note: the cosine distance is undefined when one (or both) of the vectors
+            are all zeros (there is no direction).  These vectors are invalid and may
+            never be returned from a vector search.
+
+            "dot" - Dot product. Dot distance is the dot product of two vectors. Dot
+            distance has a range of (-∞, ∞). If the vectors are normalized (i.e. their
+            L2 norm is 1), then dot distance is equivalent to the cosine distance.
+        num_partitions: int, default sqrt(num_rows)
+            The number of IVF partitions to create.
+
+            This value should generally scale with the number of rows in the dataset.
+            By default the number of partitions is the square root of the number of
+            rows.
+
+            If this value is too large then the first part of the search (picking the
+            right partition) will be slow.  If this value is too small then the second
+            part of the search (searching within a partition) will be slow.
+        num_sub_vectors: int, default is vector dimension / 16
+            Number of sub-vectors of PQ.
+
+            This value controls how much the vector is compressed during the
+            quantization step.  The more sub vectors there are the less the vector is
+            compressed.  The default is the dimension of the vector divided by 16.  If
+            the dimension is not evenly divisible by 16 we use the dimension divded by
+            8.
+
+            The above two cases are highly preferred.  Having 8 or 16 values per
+            subvector allows us to use efficient SIMD instructions.
+
+            If the dimension is not visible by 8 then we use 1 subvector.  This is not
+            ideal and will likely result in poor performance.
+        max_iterations: int, default 50
+            Max iteration to train kmeans.
+
+            When training an IVF PQ index we use kmeans to calculate the partitions.
+            This parameter controls how many iterations of kmeans to run.
+
+            Increasing this might improve the quality of the index but in most cases
+            these extra iterations have diminishing returns.
+
+            The default value is 50.
+        sample_rate: int, default 256
+            The rate used to calculate the number of training vectors for kmeans.
+
+            When an IVF PQ index is trained, we need to calculate partitions.  These
+            are groups of vectors that are similar to each other.  To do this we use an
+            algorithm called kmeans.
+
+            Running kmeans on a large dataset can be slow.  To speed this up we run
+            kmeans on a random sample of the data.  This parameter controls the size of
+            the sample.  The total number of vectors used to train the index is
+            `sample_rate * num_partitions`.
+
+            Increasing this value might improve the quality of the index but in most
+            cases the default should be sufficient.
+
+            The default value is 256.
+        """
+        self._inner = LanceDbIndex.ivf_pq(
+            distance_type=distance_type,
+            num_partitions=num_partitions,
+            num_sub_vectors=num_sub_vectors,
+            max_iterations=max_iterations,
+            sample_rate=sample_rate,
+        )
+
+
+__all__ = ["BTree", "IvfPq", "IndexConfig"]

python/python/lancedb/pydantic.py

@@ -16,6 +16,7 @@
 from __future__ import annotations
 
 import inspect
+import io
 import sys
 import types
 from abc import ABC, abstractmethod
@@ -26,7 +27,9 @@ from typing import (
     Callable,
     Dict,
     Generator,
+    Iterable,
     List,
+    Tuple,
     Type,
     Union,
     _GenericAlias,
@@ -36,19 +39,30 @@ import numpy as np
 import pyarrow as pa
 import pydantic
 import semver
+from lance.arrow import (
+    EncodedImageType,
+)
+from lance.util import _check_huggingface
+from pydantic.fields import FieldInfo
+from pydantic_core import core_schema
+
+from .util import attempt_import_or_raise
 
 PYDANTIC_VERSION = semver.Version.parse(pydantic.__version__)
-try:
-    from pydantic_core import CoreSchema, core_schema
-except ImportError:
-    if PYDANTIC_VERSION >= (2,):
-        raise
 
 if TYPE_CHECKING:
     from pydantic.fields import FieldInfo
 
     from .embeddings import EmbeddingFunctionConfig
 
+    try:
+        from pydantic import GetJsonSchemaHandler
+        from pydantic.json_schema import JsonSchemaValue
+        from pydantic_core import CoreSchema
+    except ImportError:
+        if PYDANTIC_VERSION >= (2,):
+            raise
+
 
 class FixedSizeListMixin(ABC):
     @staticmethod
@@ -123,7 +137,7 @@ def Vector(
         @classmethod
         def __get_pydantic_core_schema__(
             cls, _source_type: Any, _handler: pydantic.GetCoreSchemaHandler
-        ) -> CoreSchema:
+        ) -> "CoreSchema":
             return core_schema.no_info_after_validator_function(
                 cls,
                 core_schema.list_schema(
@@ -181,24 +195,117 @@ def _py_type_to_arrow_type(py_type: Type[Any], field: FieldInfo) -> pa.DataType:
     elif getattr(py_type, "__origin__", None) in (list, tuple):
         child = py_type.__args__[0]
         return pa.list_(_py_type_to_arrow_type(child, field))
+    elif _safe_is_huggingface_image():
+        import datasets
+        
     raise TypeError(
         f"Converting Pydantic type to Arrow Type: unsupported type {py_type}."
     )
 
 
-if PYDANTIC_VERSION.major < 2:
+class ImageMixin(ABC):
+    @staticmethod
+    @abstractmethod
+    def value_arrow_type() -> pa.DataType:
+        raise NotImplementedError
 
-    def _pydantic_model_to_fields(model: pydantic.BaseModel) -> List[pa.Field]:
-        return [
-            _pydantic_to_field(name, field) for name, field in model.__fields__.items()
+
+def EncodedImage():
+    attempt_import_or_raise("PIL", "pillow or pip install lancedb[embeddings]")
+    import PIL.Image
+
+    class EncodedImage(bytes, ImageMixin):
+        """Pydantic type for inlined images.
+
+        !!! warning
+            Experimental feature.
+
+        Examples
+        --------
+
+        >>> import pydantic
+        >>> from lancedb.pydantic import EncodedImage
+        ...
+        >>> class MyModel(pydantic.BaseModel):
+        ...     image: EncodedImage()
+        >>> schema = pydantic_to_schema(MyModel)
+        >>> assert schema == pa.schema([
+        ...     pa.field("image", pa.binary(), False)
+        ... ])
+        """
+
+        def __repr__(self):
+            return "EncodedImage()"
+
+        @staticmethod
+        def value_arrow_type() -> pa.DataType:
+            return EncodedImageType()
+
+        @classmethod
+        def __get_pydantic_core_schema__(
+            cls, _source_type: Any, _handler: pydantic.GetCoreSchemaHandler
+        ) -> "CoreSchema":
+            from_bytes_schema = core_schema.bytes_schema()
+
+            return core_schema.json_or_python_schema(
+                json_schema=from_bytes_schema,
+                python_schema=core_schema.union_schema(
+                    [
+                        core_schema.is_instance_schema(PIL.Image.Image),
+                        from_bytes_schema,
                     ]
+                ),
+                serialization=core_schema.plain_serializer_function_ser_schema(
+                    lambda instance: cls.validate(instance)
+                ),
+            )
 
+        @classmethod
+        def __get_pydantic_json_schema__(
+            cls, _core_schema: "CoreSchema", handler: "GetJsonSchemaHandler"
+        ) -> "JsonSchemaValue":
+            return handler(core_schema.bytes_schema())
+
+        @classmethod
+        def __get_validators__(cls) -> Generator[Callable, None, None]:
+            yield cls.validate
+
+        # For pydantic v2
+        @classmethod
+        def validate(cls, v):
+            if isinstance(v, bytes):
+                return v
+            if isinstance(v, PIL.Image.Image):
+                with io.BytesIO() as output:
+                    v.save(output, format=v.format)
+                    return output.getvalue()
+            raise TypeError(
+                "EncodedImage can take bytes or PIL.Image.Image "
+                f"as input but got {type(v)}"
+            )
+
+        if PYDANTIC_VERSION < (2, 0):
+
+            @classmethod
+            def __modify_schema__(cls, field_schema: Dict[str, Any]):
+                field_schema["type"] = "string"
+                field_schema["format"] = "binary"
+
+    return EncodedImage
+
+
+if PYDANTIC_VERSION.major < 2:
+    def _safe_get_fields(model: pydantic.BaseModel):
+        return model.__fields__
 else:
+    def _safe_get_fields(model: pydantic.BaseModel):    
+        return model.model_fields
 
-    def _pydantic_model_to_fields(model: pydantic.BaseModel) -> List[pa.Field]:
+
+def _pydantic_model_to_fields(model: pydantic.BaseModel) -> List[pa.Field]:
     return [
         _pydantic_to_field(name, field)
-            for name, field in model.model_fields.items()
+        for name, field in _safe_get_fields(model).items()
     ]
 
 
@@ -230,6 +337,9 @@ def _pydantic_to_arrow_type(field: FieldInfo) -> pa.DataType:
             return pa.struct(fields)
         elif issubclass(field.annotation, FixedSizeListMixin):
             return pa.list_(field.annotation.value_arrow_type(), field.annotation.dim())
+        elif issubclass(field.annotation, ImageMixin):
+            return field.annotation.value_arrow_type()
+
     return _py_type_to_arrow_type(field.annotation, field)
 
 
@@ -335,13 +445,7 @@ class LanceModel(pydantic.BaseModel):
         """
         Get the field names of this model.
         """
-        return list(cls.safe_get_fields().keys())
-
-    @classmethod
-    def safe_get_fields(cls):
-        if PYDANTIC_VERSION.major < 2:
-            return cls.__fields__
-        return cls.model_fields
+        return list(_safe_get_fields(cls).keys())
 
     @classmethod
     def parse_embedding_functions(cls) -> List["EmbeddingFunctionConfig"]:
@@ -351,14 +455,16 @@ class LanceModel(pydantic.BaseModel):
         from .embeddings import EmbeddingFunctionConfig
 
         vec_and_function = []
-        for name, field_info in cls.safe_get_fields().items():
-            func = get_extras(field_info, "vector_column_for")
+        def get_vector_column(name, field_info):
+            fun = get_extras(field_info, "vector_column_for")
             if func is not None:
                 vec_and_function.append([name, func])
+        visit_fields(_safe_get_fields(cls).items(), get_vector_column)
 
         configs = []
+        # find the source columns for each one
         for vec, func in vec_and_function:
-            for source, field_info in cls.safe_get_fields().items():
+            def get_source_column(source, field_info):
                 src_func = get_extras(field_info, "source_column_for")
                 if src_func is func:
                     # note we can't use == here since the function is a pydantic
@@ -371,20 +477,48 @@ class LanceModel(pydantic.BaseModel):
                             source_column=source, vector_column=vec, function=func
                         )
                     )
+            visit_fields(_safe_get_fields(cls).items(), get_source_column)
         return configs
     
 
-def get_extras(field_info: FieldInfo, key: str) -> Any:
+def visit_fields(fields: Iterable[Tuple[str, FieldInfo]], 
+                 visitor: Callable[[str, FieldInfo], Any]):
     """
-    Get the extra metadata from a Pydantic FieldInfo.
+    Visit all the leaf fields in a Pydantic model.
+
+    Parameters
+    ----------
+    fields : Iterable[Tuple(str, FieldInfo)]
+        The fields to visit.
+    visitor : Callable[[str, FieldInfo], Any]
+        The visitor function.
     """
-    if PYDANTIC_VERSION.major >= 2:
-        return (field_info.json_schema_extra or {}).get(key)
-    return (field_info.field_info.extra or {}).get("json_schema_extra", {}).get(key)
+    for name, field_info in fields:
+        # if the field is a pydantic model then
+        # visit all subfields
+        if (isinstance(getattr(field_info, "annotation"), type) 
+            and issubclass(field_info.annotation, pydantic.BaseModel)):
+            visit_fields(_safe_get_fields(field_info.annotation).items(), 
+                           _add_prefix(visitor, name))
+        else:
+            visitor(name, field_info)
+
+
+def _add_prefix(visitor: Callable[[str, FieldInfo], Any], prefix: str) -> Callable[[str, FieldInfo], Any]:
+    def prefixed_visitor(name: str, field: FieldInfo):
+        return visitor(f"{prefix}.{name}", field)
+    return prefixed_visitor
 
 
 if PYDANTIC_VERSION.major < 2:
 
+    def get_extras(field_info: FieldInfo, key: str) -> Any:
+        """
+        Get the extra metadata from a Pydantic FieldInfo.
+        """
+        return (field_info.field_info.extra or {}).get("json_schema_extra", {}).get(key)
+
+
     def model_to_dict(model: pydantic.BaseModel) -> Dict[str, Any]:
         """
         Convert a Pydantic model to a dictionary.
@@ -393,6 +527,13 @@ if PYDANTIC_VERSION.major < 2:
 
 else:
 
+    def get_extras(field_info: FieldInfo, key: str) -> Any:
+        """
+        Get the extra metadata from a Pydantic FieldInfo.
+        """
+        return (field_info.json_schema_extra or {}).get(key)
+    
+
     def model_to_dict(model: pydantic.BaseModel) -> Dict[str, Any]:
         """
         Convert a Pydantic model to a dictionary.

python/python/lancedb/table.py

@@ -37,6 +37,7 @@ import pyarrow as pa
 import pyarrow.compute as pc
 import pyarrow.fs as pa_fs
 from lance import LanceDataset
+from lance.dependencies import _check_for_hugging_face
 from lance.vector import vec_to_table
 
 from .common import DATA, VEC, VECTOR_COLUMN_NAME
@@ -60,6 +61,7 @@ if TYPE_CHECKING:
 
     from ._lancedb import Table as LanceDBTable
     from .db import LanceDBConnection
+    from .index import BTree, IndexConfig, IvfPq
 
 
 pd = safe_import_pandas()
@@ -73,7 +75,16 @@ def _sanitize_data(
     on_bad_vectors: str,
     fill_value: Any,
 ):
-    if isinstance(data, list):
+    import pdb; pdb.set_trace()
+    if _check_for_hugging_face(data):
+        # Huggingface datasets
+        import datasets
+
+        if isinstance(data, datasets.Dataset):
+            if schema is None:
+                schema = data.features.arrow_schema
+            data = data.data.to_batches()
+    elif isinstance(data, list):
         # convert to list of dict if data is a bunch of LanceModels
         if isinstance(data[0], LanceModel):
             schema = data[0].__class__.to_arrow_schema()
@@ -135,12 +146,11 @@ def _to_record_batch_generator(
     data: Iterable, schema, metadata, on_bad_vectors, fill_value
 ):
     for batch in data:
-        if not isinstance(batch, pa.RecordBatch):
+        if isinstance(batch, pa.RecordBatch):
+            batch = pa.Table.from_batches([batch])
         table = _sanitize_data(batch, schema, metadata, on_bad_vectors, fill_value)
         for batch in table.to_batches():
             yield batch
-        else:
-            yield batch
 
 
 class Table(ABC):
@@ -1917,112 +1927,48 @@ class AsyncTable:
         raise NotImplementedError
 
     async def create_index(
-        self,
-        metric="L2",
-        num_partitions=256,
-        num_sub_vectors=96,
-        vector_column_name: str = VECTOR_COLUMN_NAME,
-        replace: bool = True,
-        accelerator: Optional[str] = None,
-        index_cache_size: Optional[int] = None,
-    ):
-        """Create an index on the table.
-
-        Parameters
-        ----------
-        metric: str, default "L2"
-            The distance metric to use when creating the index.
-            Valid values are "L2", "cosine", or "dot".
-            L2 is euclidean distance.
-        num_partitions: int, default 256
-            The number of IVF partitions to use when creating the index.
-            Default is 256.
-        num_sub_vectors: int, default 96
-            The number of PQ sub-vectors to use when creating the index.
-            Default is 96.
-        vector_column_name: str, default "vector"
-            The vector column name to create the index.
-        replace: bool, default True
-            - If True, replace the existing index if it exists.
-
-            - If False, raise an error if duplicate index exists.
-        accelerator: str, default None
-            If set, use the given accelerator to create the index.
-            Only support "cuda" for now.
-        index_cache_size : int, optional
-            The size of the index cache in number of entries. Default value is 256.
-        """
-        raise NotImplementedError
-
-    async def create_scalar_index(
         self,
         column: str,
         *,
-        replace: bool = True,
+        replace: Optional[bool] = None,
+        config: Optional[Union[IvfPq, BTree]] = None,
     ):
-        """Create a scalar index on a column.
+        """Create an index to speed up queries
 
-        Scalar indices, like vector indices, can be used to speed up scans.  A scalar
-        index can speed up scans that contain filter expressions on the indexed column.
-        For example, the following scan will be faster if the column ``my_col`` has
-        a scalar index:
-
-        .. code-block:: python
-
-            import lancedb
-
-            db = lancedb.connect("/data/lance")
-            img_table = db.open_table("images")
-            my_df = img_table.search().where("my_col = 7", prefilter=True).to_pandas()
-
-        Scalar indices can also speed up scans containing a vector search and a
-        prefilter:
-
-        .. code-block::python
-
-            import lancedb
-
-            db = lancedb.connect("/data/lance")
-            img_table = db.open_table("images")
-            img_table.search([1, 2, 3, 4], vector_column_name="vector")
-                .where("my_col != 7", prefilter=True)
-                .to_pandas()
-
-        Scalar indices can only speed up scans for basic filters using
-        equality, comparison, range (e.g. ``my_col BETWEEN 0 AND 100``), and set
-        membership (e.g. `my_col IN (0, 1, 2)`)
-
-        Scalar indices can be used if the filter contains multiple indexed columns and
-        the filter criteria are AND'd or OR'd together
-        (e.g. ``my_col < 0 AND other_col> 100``)
-
-        Scalar indices may be used if the filter contains non-indexed columns but,
-        depending on the structure of the filter, they may not be usable.  For example,
-        if the column ``not_indexed`` does not have a scalar index then the filter
-        ``my_col = 0 OR not_indexed = 1`` will not be able to use any scalar index on
-        ``my_col``.
-
-        **Experimental API**
+        Indices can be created on vector columns or scalar columns.
+        Indices on vector columns will speed up vector searches.
+        Indices on scalar columns will speed up filtering (in both
+        vector and non-vector searches)
 
         Parameters
         ----------
-        column : str
-            The column to be indexed.  Must be a boolean, integer, float,
-            or string column.
-        replace : bool, default True
-            Replace the existing index if it exists.
+        index: Index
+            The index to create.
 
-        Examples
-        --------
+            LanceDb supports multiple types of indices.  See the static methods on
+            the Index class for more details.
+        column: str, default None
+            The column to index.
 
-        .. code-block:: python
+            When building a scalar index this must be set.
 
-            import lance
+            When building a vector index, this is optional.  The default will look
+            for any columns of type fixed-size-list with floating point values.  If
+            there is only one column of this type then it will be used.  Otherwise
+            an error will be returned.
+        replace: bool, default True
+            Whether to replace the existing index
 
-            dataset = lance.dataset("./images.lance")
-            dataset.create_scalar_index("category")
+            If this is false, and another index already exists on the same columns
+            and the same name, then an error will be returned.  This is true even if
+            that index is out of date.
+
+            The default is True
         """
-        raise NotImplementedError
+        index = None
+        if config is not None:
+            index = config._inner
+        await self._inner.create_index(column, index=index, replace=replace)
 
     async def add(
         self,
@@ -2066,6 +2012,8 @@ class AsyncTable:
             on_bad_vectors=on_bad_vectors,
             fill_value=fill_value,
         )
+        if isinstance(data, pa.Table):
+            data = pa.RecordBatchReader.from_batches(data.schema, data.to_batches())
         await self._inner.add(data, mode)
         register_event("add")
 
@@ -2275,58 +2223,57 @@ class AsyncTable:
 
     async def update(
         self,
-        where: Optional[str] = None,
-        values: Optional[dict] = None,
+        updates: Optional[Dict[str, Any]] = None,
         *,
-        values_sql: Optional[Dict[str, str]] = None,
+        where: Optional[str] = None,
+        updates_sql: Optional[Dict[str, str]] = None,
     ):
         """
-        This can be used to update zero to all rows depending on how many
-        rows match the where clause. If no where clause is provided, then
-        all rows will be updated.
+        This can be used to update zero to all rows in the table.
 
-        Either `values` or `values_sql` must be provided. You cannot provide
-        both.
+        If a filter is provided with `where` then only rows matching the
+        filter will be updated.  Otherwise all rows will be updated.
 
         Parameters
         ----------
+        updates: dict, optional
+            The updates to apply.  The keys should be the name of the column to
+            update.  The values should be the new values to assign.  This is
+            required unless updates_sql is supplied.
         where: str, optional
-            The SQL where clause to use when updating rows. For example, 'x = 2'
-            or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error.
-        values: dict, optional
-            The values to update. The keys are the column names and the values
-            are the values to set.
-        values_sql: dict, optional
-            The values to update, expressed as SQL expression strings. These can
-            reference existing columns. For example, {"x": "x + 1"} will increment
-            the x column by 1.
+            An SQL filter that controls which rows are updated. For example, 'x = 2'
+            or 'x IN (1, 2, 3)'.  Only rows that satisfy this filter will be udpated.
+        updates_sql: dict, optional
+            The updates to apply, expressed as SQL expression strings.  The keys should
+            be column names. The values should be SQL expressions.  These can be SQL
+            literals (e.g. "7" or "'foo'") or they can be expressions based on the
+            previous value of the row (e.g. "x + 1" to increment the x column by 1)
 
         Examples
         --------
+        >>> import asyncio
         >>> import lancedb
         >>> import pandas as pd
-        >>> data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1, 2], [3, 4], [5, 6]]})
-        >>> db = lancedb.connect("./.lancedb")
-        >>> table = db.create_table("my_table", data)
-        >>> table.to_pandas()
-           x      vector
-        0  1  [1.0, 2.0]
-        1  2  [3.0, 4.0]
-        2  3  [5.0, 6.0]
-        >>> table.update(where="x = 2", values={"vector": [10, 10]})
-        >>> table.to_pandas()
-           x        vector
-        0  1    [1.0, 2.0]
-        1  3    [5.0, 6.0]
-        2  2  [10.0, 10.0]
-        >>> table.update(values_sql={"x": "x + 1"})
-        >>> table.to_pandas()
-           x        vector
-        0  2    [1.0, 2.0]
-        1  4    [5.0, 6.0]
-        2  3  [10.0, 10.0]
+        >>> async def demo_update():
+        ...     data = pd.DataFrame({"x": [1, 2], "vector": [[1, 2], [3, 4]]})
+        ...     db = await lancedb.connect_async("./.lancedb")
+        ...     table = await db.create_table("my_table", data)
+        ...     # x is [1, 2], vector is [[1, 2], [3, 4]]
+        ...     await table.update({"vector": [10, 10]}, where="x = 2")
+        ...     # x is [1, 2], vector is [[1, 2], [10, 10]]
+        ...     await table.update(updates_sql={"x": "x + 1"})
+        ...     # x is [2, 3], vector is [[1, 2], [10, 10]]
+        >>> asyncio.run(demo_update())
         """
-        raise NotImplementedError
+        if updates is not None and updates_sql is not None:
+            raise ValueError("Only one of updates or updates_sql can be provided")
+        if updates is None and updates_sql is None:
+            raise ValueError("Either updates or updates_sql must be provided")
+
+        if updates is not None:
+            updates_sql = {k: value_to_sql(v) for k, v in updates.items()}
+
+        return await self._inner.update(updates_sql, where)
 
     async def cleanup_old_versions(
         self,
@@ -2423,3 +2370,65 @@ class AsyncTable:
             The names of the columns to drop.
         """
         raise NotImplementedError
+
+    async def version(self) -> int:
+        """
+        Retrieve the version of the table
+
+        LanceDb supports versioning.  Every operation that modifies the table increases
+        version.  As long as a version hasn't been deleted you can `[Self::checkout]`
+        that version to view the data at that point.  In addition, you can
+        `[Self::restore]` the version to replace the current table with a previous
+        version.
+        """
+        return await self._inner.version()
+
+    async def checkout(self, version):
+        """
+        Checks out a specific version of the Table
+
+        Any read operation on the table will now access the data at the checked out
+        version. As a consequence, calling this method will disable any read consistency
+        interval that was previously set.
+
+        This is a read-only operation that turns the table into a sort of "view"
+        or "detached head".  Other table instances will not be affected.  To make the
+        change permanent you can use the `[Self::restore]` method.
+
+        Any operation that modifies the table will fail while the table is in a checked
+        out state.
+
+        To return the table to a normal state use `[Self::checkout_latest]`
+        """
+        await self._inner.checkout(version)
+
+    async def checkout_latest(self):
+        """
+        Ensures the table is pointing at the latest version
+
+        This can be used to manually update a table when the read_consistency_interval
+        is None
+        It can also be used to undo a `[Self::checkout]` operation
+        """
+        await self._inner.checkout_latest()
+
+    async def restore(self):
+        """
+        Restore the table to the currently checked out version
+
+        This operation will fail if checkout has not been called previously
+
+        This operation will overwrite the latest version of the table with a
+        previous version.  Any changes made since the checked out version will
+        no longer be visible.
+
+        Once the operation concludes the table will no longer be in a checked
+        out state and the read_consistency_interval, if any, will apply.
+        """
+        await self._inner.restore()
+
+    async def list_indices(self) -> IndexConfig:
+        """
+        List all indices that have been created with Self::create_index
+        """
+        return await self._inner.list_indices()

python/python/tests/test_index.py

@@ -0,0 +1,69 @@
+from datetime import timedelta
+
+import pyarrow as pa
+import pytest
+import pytest_asyncio
+from lancedb import AsyncConnection, AsyncTable, connect_async
+from lancedb.index import BTree, IvfPq
+
+
+@pytest_asyncio.fixture
+async def db_async(tmp_path) -> AsyncConnection:
+    return await connect_async(tmp_path, read_consistency_interval=timedelta(seconds=0))
+
+
+def sample_fixed_size_list_array(nrows, dim):
+    vector_data = pa.array([float(i) for i in range(dim * nrows)], pa.float32())
+    return pa.FixedSizeListArray.from_arrays(vector_data, dim)
+
+
+DIM = 8
+NROWS = 256
+
+
+@pytest_asyncio.fixture
+async def some_table(db_async):
+    data = pa.Table.from_pydict(
+        {
+            "id": list(range(256)),
+            "vector": sample_fixed_size_list_array(NROWS, DIM),
+        }
+    )
+    return await db_async.create_table(
+        "some_table",
+        data,
+    )
+
+
+@pytest.mark.asyncio
+async def test_create_scalar_index(some_table: AsyncTable):
+    # Can create
+    await some_table.create_index("id")
+    # Can recreate if replace=True
+    await some_table.create_index("id", replace=True)
+    indices = await some_table.list_indices()
+    assert len(indices) == 1
+    assert indices[0].index_type == "BTree"
+    assert indices[0].columns == ["id"]
+    # Can't recreate if replace=False
+    with pytest.raises(RuntimeError, match="already exists"):
+        await some_table.create_index("id", replace=False)
+    # can also specify index type
+    await some_table.create_index("id", config=BTree())
+
+
+@pytest.mark.asyncio
+async def test_create_vector_index(some_table: AsyncTable):
+    # Can create
+    await some_table.create_index("vector")
+    # Can recreate if replace=True
+    await some_table.create_index("vector", replace=True)
+    # Can't recreate if replace=False
+    with pytest.raises(RuntimeError, match="already exists"):
+        await some_table.create_index("vector", replace=False)
+    # Can also specify index type
+    await some_table.create_index("vector", config=IvfPq(num_partitions=100))
+    indices = await some_table.list_indices()
+    assert len(indices) == 1
+    assert indices[0].index_type == "IvfPq"
+    assert indices[0].columns == ["vector"]

python/python/tests/test_pydantic.py

@@ -12,17 +12,27 @@
 #  limitations under the License.
 
 
+import io
 import json
+import os
 import sys
 from datetime import date, datetime
+from pathlib import Path
 from typing import List, Optional, Tuple
 
 import pyarrow as pa
 import pydantic
 import pytest
-from lancedb.pydantic import PYDANTIC_VERSION, LanceModel, Vector, pydantic_to_schema
 from pydantic import Field
 
+from lancedb.pydantic import (
+    PYDANTIC_VERSION,
+    EncodedImage,
+    LanceModel,
+    Vector,
+    pydantic_to_schema,
+)
+
 
 @pytest.mark.skipif(
     sys.version_info < (3, 9),
@@ -243,3 +253,23 @@ def test_lance_model():
 
     t = TestModel()
     assert t == TestModel(vec=[0.0] * 16, li=[1, 2, 3])
+
+
+def test_schema_with_images():
+    pytest.importorskip("PIL")
+    import PIL.Image
+
+    class TestModel(LanceModel):
+        img: EncodedImage()
+
+    img_path = Path(os.path.dirname(__file__)) / "images/1.png"
+    with open(img_path, "rb") as f:
+        img_bytes = f.read()
+
+    m1 = TestModel(img=PIL.Image.open(img_path))
+    m2 = TestModel(img=img_bytes)
+
+    def tobytes(m):
+        return PIL.Image.open(io.BytesIO(m.model_dump()["img"])).tobytes()
+
+    assert tobytes(m1) == tobytes(m2)

python/python/tests/test_table.py

@@ -12,6 +12,8 @@
 #  limitations under the License.
 
 import functools
+import io
+import os
 from copy import copy
 from datetime import date, datetime, timedelta
 from pathlib import Path
@@ -20,19 +22,21 @@ from typing import List
 from unittest.mock import PropertyMock, patch
 
 import lance
-import lancedb
 import numpy as np
 import pandas as pd
 import polars as pl
 import pyarrow as pa
 import pytest
 import pytest_asyncio
+from lance.arrow import EncodedImageType
+from pydantic import BaseModel
+
+import lancedb
 from lancedb.conftest import MockTextEmbeddingFunction
 from lancedb.db import AsyncConnection, LanceDBConnection
 from lancedb.embeddings import EmbeddingFunctionConfig, EmbeddingFunctionRegistry
-from lancedb.pydantic import LanceModel, Vector
+from lancedb.pydantic import EncodedImage, LanceModel, Vector
 from lancedb.table import LanceTable
-from pydantic import BaseModel
 
 
 class MockDB:
@@ -85,12 +89,30 @@ async def test_close(db_async: AsyncConnection):
     assert str(table) == "ClosedTable(some_table)"
 
 
+@pytest.mark.asyncio
+async def test_update_async(db_async: AsyncConnection):
+    table = await db_async.create_table("some_table", data=[{"id": 0}])
+    assert await table.count_rows("id == 0") == 1
+    assert await table.count_rows("id == 7") == 0
+    await table.update({"id": 7})
+    assert await table.count_rows("id == 7") == 1
+    assert await table.count_rows("id == 0") == 0
+    await table.add([{"id": 2}])
+    await table.update(where="id % 2 == 0", updates_sql={"id": "5"})
+    assert await table.count_rows("id == 7") == 1
+    assert await table.count_rows("id == 2") == 0
+    assert await table.count_rows("id == 5") == 1
+    await table.update({"id": 10}, where="id == 5")
+    assert await table.count_rows("id == 10") == 1
+
+
 def test_create_table(db):
     schema = pa.schema(
         [
             pa.field("vector", pa.list_(pa.float32(), 2)),
             pa.field("item", pa.string()),
             pa.field("price", pa.float32()),
+            pa.field("encoded_image", EncodedImageType()),
         ]
     )
     expected = pa.Table.from_arrays(
@@ -98,13 +120,26 @@ def test_create_table(db):
             pa.FixedSizeListArray.from_arrays(pa.array([3.1, 4.1, 5.9, 26.5]), 2),
             pa.array(["foo", "bar"]),
             pa.array([10.0, 20.0]),
+            pa.ExtensionArray.from_storage(
+                EncodedImageType(), pa.array([b"foo", b"bar"], pa.binary())
+            ),
         ],
         schema=schema,
     )
     data = [
         [
-            {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
-            {"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
+            {
+                "vector": [3.1, 4.1],
+                "item": "foo",
+                "price": 10.0,
+                "encoded_image": b"foo",
+            },
+            {
+                "vector": [5.9, 26.5],
+                "item": "bar",
+                "price": 20.0,
+                "encoded_image": b"bar",
+            },
         ]
     ]
     df = pd.DataFrame(data[0])
@@ -974,3 +1009,56 @@ def test_drop_columns(tmp_path):
     table = LanceTable.create(db, "my_table", data=data)
     table.drop_columns(["category"])
     assert table.to_arrow().column_names == ["id"]
+
+
+@pytest.mark.asyncio
+async def test_time_travel(db_async: AsyncConnection):
+    # Setup
+    table = await db_async.create_table("some_table", data=[{"id": 0}])
+    version = await table.version()
+    await table.add([{"id": 1}])
+    assert await table.count_rows() == 2
+    # Make sure we can rewind
+    await table.checkout(version)
+    assert await table.count_rows() == 1
+    # Can't add data in time travel mode
+    with pytest.raises(
+        ValueError,
+        match="table cannot be modified when a specific version is checked out",
+    ):
+        await table.add([{"id": 2}])
+    # Can go back to normal mode
+    await table.checkout_latest()
+    assert await table.count_rows() == 2
+    # Should be able to add data again
+    await table.add([{"id": 3}])
+    assert await table.count_rows() == 3
+    # Now checkout and restore
+    await table.checkout(version)
+    await table.restore()
+    assert await table.count_rows() == 1
+    # Should be able to add data
+    await table.add([{"id": 4}])
+    assert await table.count_rows() == 2
+    # Can't use restore if not checked out
+    with pytest.raises(ValueError, match="checkout before running restore"):
+        await table.restore()
+
+        
+def test_add_image(tmp_path):
+    pytest.importorskip("PIL")
+    import PIL.Image
+
+    db = lancedb.connect(tmp_path)
+
+    class TestModel(LanceModel):
+        img: EncodedImage()
+
+    img_path = Path(os.path.dirname(__file__)) / "images/1.png"
+    m1 = TestModel(img=PIL.Image.open(img_path))
+
+    def tobytes(m):
+        return PIL.Image.open(io.BytesIO(m.model_dump()["img"])).tobytes()
+
+    table = LanceTable.create(db, "my_table", schema=TestModel)
+    table.add([m1])

python/src/index.rs

@@ -0,0 +1,109 @@
+// Copyright 2024 Lance Developers.
+//
+// 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.
+
+use std::sync::Mutex;
+
+use lancedb::{
+    index::{scalar::BTreeIndexBuilder, vector::IvfPqIndexBuilder, Index as LanceDbIndex},
+    DistanceType,
+};
+use pyo3::{
+    exceptions::{PyRuntimeError, PyValueError},
+    pyclass, pymethods, PyResult,
+};
+
+#[pyclass]
+pub struct Index {
+    inner: Mutex<Option<LanceDbIndex>>,
+}
+
+impl Index {
+    pub fn consume(&self) -> PyResult<LanceDbIndex> {
+        self.inner
+            .lock()
+            .unwrap()
+            .take()
+            .ok_or_else(|| PyRuntimeError::new_err("cannot use an Index more than once"))
+    }
+}
+
+#[pymethods]
+impl Index {
+    #[staticmethod]
+    pub fn ivf_pq(
+        distance_type: Option<String>,
+        num_partitions: Option<u32>,
+        num_sub_vectors: Option<u32>,
+        max_iterations: Option<u32>,
+        sample_rate: Option<u32>,
+    ) -> PyResult<Self> {
+        let mut ivf_pq_builder = IvfPqIndexBuilder::default();
+        if let Some(distance_type) = distance_type {
+            let distance_type = match distance_type.as_str() {
+                "l2" => Ok(DistanceType::L2),
+                "cosine" => Ok(DistanceType::Cosine),
+                "dot" => Ok(DistanceType::Dot),
+                _ => Err(PyValueError::new_err(format!(
+                    "Invalid distance type '{}'.  Must be one of l2, cosine, or dot",
+                    distance_type
+                ))),
+            }?;
+            ivf_pq_builder = ivf_pq_builder.distance_type(distance_type);
+        }
+        if let Some(num_partitions) = num_partitions {
+            ivf_pq_builder = ivf_pq_builder.num_partitions(num_partitions);
+        }
+        if let Some(num_sub_vectors) = num_sub_vectors {
+            ivf_pq_builder = ivf_pq_builder.num_sub_vectors(num_sub_vectors);
+        }
+        if let Some(max_iterations) = max_iterations {
+            ivf_pq_builder = ivf_pq_builder.max_iterations(max_iterations);
+        }
+        if let Some(sample_rate) = sample_rate {
+            ivf_pq_builder = ivf_pq_builder.sample_rate(sample_rate);
+        }
+        Ok(Self {
+            inner: Mutex::new(Some(LanceDbIndex::IvfPq(ivf_pq_builder))),
+        })
+    }
+
+    #[staticmethod]
+    pub fn btree() -> PyResult<Self> {
+        Ok(Self {
+            inner: Mutex::new(Some(LanceDbIndex::BTree(BTreeIndexBuilder::default()))),
+        })
+    }
+}
+
+#[pyclass(get_all)]
+/// A description of an index currently configured on a column
+pub struct IndexConfig {
+    /// The type of the index
+    pub index_type: String,
+    /// The columns in the index
+    ///
+    /// Currently this is always a list of size 1.  In the future there may
+    /// be more columns to represent composite indices.
+    pub columns: Vec<String>,
+}
+
+impl From<lancedb::index::IndexConfig> for IndexConfig {
+    fn from(value: lancedb::index::IndexConfig) -> Self {
+        let index_type = format!("{:?}", value.index_type);
+        Self {
+            index_type,
+            columns: value.columns,
+        }
+    }
+}

python/src/lib.rs

@@ -14,11 +14,15 @@
 
 use connection::{connect, Connection};
 use env_logger::Env;
+use index::{Index, IndexConfig};
 use pyo3::{pymodule, types::PyModule, wrap_pyfunction, PyResult, Python};
+use table::Table;
 
 pub mod connection;
 pub mod error;
+pub mod index;
 pub mod table;
+pub mod util;
 
 #[pymodule]
 pub fn _lancedb(_py: Python, m: &PyModule) -> PyResult<()> {
@@ -27,6 +31,9 @@ pub fn _lancedb(_py: Python, m: &PyModule) -> PyResult<()> {
         .write_style("LANCEDB_LOG_STYLE");
     env_logger::init_from_env(env);
     m.add_class::<Connection>()?;
+    m.add_class::<Table>()?;
+    m.add_class::<Index>()?;
+    m.add_class::<IndexConfig>()?;
     m.add_function(wrap_pyfunction!(connect, m)?)?;
     m.add("__version__", env!("CARGO_PKG_VERSION"))?;
     Ok(())

python/src/table.rs

@@ -5,11 +5,16 @@ use arrow::{
 use lancedb::table::{AddDataMode, Table as LanceDbTable};
 use pyo3::{
     exceptions::{PyRuntimeError, PyValueError},
-    pyclass, pymethods, PyAny, PyRef, PyResult, Python,
+    pyclass, pymethods,
+    types::{PyDict, PyString},
+    PyAny, PyRef, PyResult, Python,
 };
 use pyo3_asyncio::tokio::future_into_py;
 
-use crate::error::PythonErrorExt;
+use crate::{
+    error::PythonErrorExt,
+    index::{Index, IndexConfig},
+};
 
 #[pyclass]
 pub struct Table {
@@ -74,6 +79,28 @@ impl Table {
         })
     }
 
+    pub fn update<'a>(
+        self_: PyRef<'a, Self>,
+        updates: &PyDict,
+        r#where: Option<String>,
+    ) -> PyResult<&'a PyAny> {
+        let mut op = self_.inner_ref()?.update();
+        if let Some(only_if) = r#where {
+            op = op.only_if(only_if);
+        }
+        for (column_name, value) in updates.into_iter() {
+            let column_name: &PyString = column_name.downcast()?;
+            let column_name = column_name.to_str()?.to_string();
+            let value: &PyString = value.downcast()?;
+            let value = value.to_str()?.to_string();
+            op = op.column(column_name, value);
+        }
+        future_into_py(self_.py(), async move {
+            op.execute().await.infer_error()?;
+            Ok(())
+        })
+    }
+
     pub fn count_rows(self_: PyRef<'_, Self>, filter: Option<String>) -> PyResult<&PyAny> {
         let inner = self_.inner_ref()?.clone();
         future_into_py(self_.py(), async move {
@@ -81,10 +108,75 @@ impl Table {
         })
     }
 
+    pub fn create_index<'a>(
+        self_: PyRef<'a, Self>,
+        column: String,
+        index: Option<&Index>,
+        replace: Option<bool>,
+    ) -> PyResult<&'a PyAny> {
+        let index = if let Some(index) = index {
+            index.consume()?
+        } else {
+            lancedb::index::Index::Auto
+        };
+        let mut op = self_.inner_ref()?.create_index(&[column], index);
+        if let Some(replace) = replace {
+            op = op.replace(replace);
+        }
+
+        future_into_py(self_.py(), async move {
+            op.execute().await.infer_error()?;
+            Ok(())
+        })
+    }
+
+    pub fn list_indices(self_: PyRef<'_, Self>) -> PyResult<&PyAny> {
+        let inner = self_.inner_ref()?.clone();
+        future_into_py(self_.py(), async move {
+            Ok(inner
+                .list_indices()
+                .await
+                .infer_error()?
+                .into_iter()
+                .map(IndexConfig::from)
+                .collect::<Vec<_>>())
+        })
+    }
+
     pub fn __repr__(&self) -> String {
         match &self.inner {
             None => format!("ClosedTable({})", self.name),
             Some(inner) => inner.to_string(),
         }
     }
+
+    pub fn version(self_: PyRef<'_, Self>) -> PyResult<&PyAny> {
+        let inner = self_.inner_ref()?.clone();
+        future_into_py(
+            self_.py(),
+            async move { inner.version().await.infer_error() },
+        )
+    }
+
+    pub fn checkout(self_: PyRef<'_, Self>, version: u64) -> PyResult<&PyAny> {
+        let inner = self_.inner_ref()?.clone();
+        future_into_py(self_.py(), async move {
+            inner.checkout(version).await.infer_error()
+        })
+    }
+
+    pub fn checkout_latest(self_: PyRef<'_, Self>) -> PyResult<&PyAny> {
+        let inner = self_.inner_ref()?.clone();
+        future_into_py(self_.py(), async move {
+            inner.checkout_latest().await.infer_error()
+        })
+    }
+
+    pub fn restore(self_: PyRef<'_, Self>) -> PyResult<&PyAny> {
+        let inner = self_.inner_ref()?.clone();
+        future_into_py(
+            self_.py(),
+            async move { inner.restore().await.infer_error() },
+        )
+    }
 }

python/src/util.rs

@@ -0,0 +1,35 @@
+use std::sync::Mutex;
+
+use pyo3::{exceptions::PyRuntimeError, PyResult};
+
+/// A wrapper around a rust builder
+///
+/// Rust builders are often implemented so that the builder methods
+/// consume the builder and return a new one. This is not compatible
+/// with the pyo3, which, being garbage collected, cannot easily obtain
+/// ownership of an object.
+///
+/// This wrapper converts the compile-time safety of rust into runtime
+/// errors if any attempt to use the builder happens after it is consumed.
+pub struct BuilderWrapper<T> {
+    name: String,
+    inner: Mutex<Option<T>>,
+}
+
+impl<T> BuilderWrapper<T> {
+    pub fn new(name: impl AsRef<str>, inner: T) -> Self {
+        Self {
+            name: name.as_ref().to_string(),
+            inner: Mutex::new(Some(inner)),
+        }
+    }
+
+    pub fn consume<O>(&self, mod_fn: impl FnOnce(T) -> O) -> PyResult<O> {
+        let mut inner = self.inner.lock().unwrap();
+        let inner_builder = inner.take().ok_or_else(|| {
+            PyRuntimeError::new_err(format!("{} has already been consumed", self.name))
+        })?;
+        let result = mod_fn(inner_builder);
+        Ok(result)
+    }
+}

rust/ffi/node/src/index/scalar.rs

@@ -12,6 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+use lancedb::index::{scalar::BTreeIndexBuilder, Index};
 use neon::{
     context::{Context, FunctionContext},
     result::JsResult,
@@ -33,9 +34,9 @@ pub fn table_create_scalar_index(mut cx: FunctionContext) -> JsResult<JsPromise>
 
     rt.spawn(async move {
         let idx_result = table
-            .create_index(&[&column])
+            .create_index(&[column], Index::BTree(BTreeIndexBuilder::default()))
             .replace(replace)
-            .build()
+            .execute()
             .await;
 
         deferred.settle_with(&channel, move |mut cx| {

rust/ffi/node/src/index/vector.rs

@@ -13,12 +13,12 @@
 // limitations under the License.
 
 use lance_linalg::distance::MetricType;
-use lancedb::index::IndexBuilder;
+use lancedb::index::vector::IvfPqIndexBuilder;
+use lancedb::index::Index;
 use neon::context::FunctionContext;
 use neon::prelude::*;
 use std::convert::TryFrom;
 
-use crate::error::Error::InvalidIndexType;
 use crate::error::ResultExt;
 use crate::neon_ext::js_object_ext::JsObjectExt;
 use crate::runtime;
@@ -39,13 +39,20 @@ pub fn table_create_vector_index(mut cx: FunctionContext) -> JsResult<JsPromise>
         .map(|s| s.value(&mut cx))
         .unwrap_or("vector".to_string()); // Backward compatibility
 
+    let replace = index_params
+        .get_opt::<JsBoolean, _, _>(&mut cx, "replace")?
+        .map(|r| r.value(&mut cx));
+
     let tbl = table.clone();
-    let index_builder = tbl.create_index(&[&column_name]);
-    let index_builder =
-        get_index_params_builder(&mut cx, index_params, index_builder).or_throw(&mut cx)?;
+    let ivf_pq_builder = get_index_params_builder(&mut cx, index_params).or_throw(&mut cx)?;
+
+    let mut index_builder = tbl.create_index(&[column_name], Index::IvfPq(ivf_pq_builder));
+    if let Some(replace) = replace {
+        index_builder = index_builder.replace(replace);
+    }
 
     rt.spawn(async move {
-        let idx_result = index_builder.build().await;
+        let idx_result = index_builder.execute().await;
         deferred.settle_with(&channel, move |mut cx| {
             idx_result.or_throw(&mut cx)?;
             Ok(cx.boxed(JsTable::from(table)))
@@ -57,26 +64,17 @@ pub fn table_create_vector_index(mut cx: FunctionContext) -> JsResult<JsPromise>
 fn get_index_params_builder(
     cx: &mut FunctionContext,
     obj: Handle<JsObject>,
-    builder: IndexBuilder,
-) -> crate::error::Result<IndexBuilder> {
-    let mut builder = match obj.get::<JsString, _, _>(cx, "type")?.value(cx).as_str() {
-        "ivf_pq" => builder.ivf_pq(),
-        _ => {
-            return Err(InvalidIndexType {
-                index_type: "".into(),
-            })
+) -> crate::error::Result<IvfPqIndexBuilder> {
+    if obj.get_opt::<JsString, _, _>(cx, "index_name")?.is_some() {
+        return Err(crate::error::Error::LanceDB {
+            message: "Setting the index_name is no longer supported".to_string(),
+        });
     }
-    };
-
-    if let Some(index_name) = obj.get_opt::<JsString, _, _>(cx, "index_name")? {
-        builder = builder.name(index_name.value(cx).as_str());
-    }
-
+    let mut builder = IvfPqIndexBuilder::default();
     if let Some(metric_type) = obj.get_opt::<JsString, _, _>(cx, "metric_type")? {
         let metric_type = MetricType::try_from(metric_type.value(cx).as_str())?;
-        builder = builder.metric_type(metric_type);
+        builder = builder.distance_type(metric_type);
     }
-
     if let Some(np) = obj.get_opt_u32(cx, "num_partitions")? {
         builder = builder.num_partitions(np);
     }
@@ -86,11 +84,5 @@ fn get_index_params_builder(
     if let Some(max_iters) = obj.get_opt_u32(cx, "max_iters")? {
         builder = builder.max_iterations(max_iters);
     }
-    if let Some(num_bits) = obj.get_opt_u32(cx, "num_bits")? {
-        builder = builder.num_bits(num_bits);
-    }
-    if let Some(replace) = obj.get_opt::<JsBoolean, _, _>(cx, "replace")? {
-        builder = builder.replace(replace.value(cx));
-    }
     Ok(builder)
 }

rust/ffi/node/src/table.rs

@@ -297,11 +297,14 @@ impl JsTable {
 
             let predicate = predicate.as_deref();
 
-            let update_result = table
-                .as_native()
-                .unwrap()
-                .update(predicate, updates_arg)
-                .await;
+            let mut update_op = table.update();
+            if let Some(predicate) = predicate {
+                update_op = update_op.only_if(predicate);
+            }
+            for (column, value) in updates_arg {
+                update_op = update_op.column(column, value);
+            }
+            let update_result = update_op.execute().await;
             deferred.settle_with(&channel, move |mut cx| {
                 update_result.or_throw(&mut cx)?;
                 Ok(cx.boxed(Self::from(table)))

rust/lancedb/Cargo.toml

@@ -26,6 +26,7 @@ lance = { workspace = true }
 lance-index = { workspace = true }
 lance-linalg = { workspace = true }
 lance-testing = { workspace = true }
+pin-project = { workspace = true }
 tokio = { version = "1.23", features = ["rt-multi-thread"] }
 log.workspace = true
 async-trait = "0"

rust/lancedb/examples/simple.rs

@@ -20,6 +20,7 @@ use arrow_schema::{DataType, Field, Schema};
 use futures::TryStreamExt;
 
 use lancedb::connection::Connection;
+use lancedb::index::Index;
 use lancedb::{connect, Result, Table as LanceDbTable};
 
 #[tokio::main]
@@ -142,23 +143,18 @@ async fn create_empty_table(db: &Connection) -> Result<LanceDbTable> {
 
 async fn create_index(table: &LanceDbTable) -> Result<()> {
     // --8<-- [start:create_index]
-    table
-        .create_index(&["vector"])
-        .ivf_pq()
-        .num_partitions(8)
-        .build()
-        .await
+    table.create_index(&["vector"], Index::Auto).execute().await
     // --8<-- [end:create_index]
 }
 
 async fn search(table: &LanceDbTable) -> Result<Vec<RecordBatch>> {
     // --8<-- [start:search]
-    Ok(table
+    table
         .search(&[1.0; 128])
         .limit(2)
         .execute_stream()
         .await?
         .try_collect::<Vec<_>>()
-        .await?)
+        .await
     // --8<-- [end:search]
 }

rust/lancedb/src/arrow.rs

@@ -12,4 +12,92 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-pub use lance::arrow::*;
+use std::{pin::Pin, sync::Arc};
+
+pub use arrow_array;
+pub use arrow_schema;
+use futures::{Stream, StreamExt};
+
+use crate::error::Result;
+
+/// An iterator of batches that also has a schema
+pub trait RecordBatchReader: Iterator<Item = Result<arrow_array::RecordBatch>> {
+    /// Returns the schema of this `RecordBatchReader`.
+    ///
+    /// Implementation of this trait should guarantee that all `RecordBatch`'s returned by this
+    /// reader should have the same schema as returned from this method.
+    fn schema(&self) -> Arc<arrow_schema::Schema>;
+}
+
+/// A simple RecordBatchReader formed from the two parts (iterator + schema)
+pub struct SimpleRecordBatchReader<I: Iterator<Item = Result<arrow_array::RecordBatch>>> {
+    pub schema: Arc<arrow_schema::Schema>,
+    pub batches: I,
+}
+
+impl<I: Iterator<Item = Result<arrow_array::RecordBatch>>> Iterator for SimpleRecordBatchReader<I> {
+    type Item = Result<arrow_array::RecordBatch>;
+
+    fn next(&mut self) -> Option<Self::Item> {
+        self.batches.next()
+    }
+}
+
+impl<I: Iterator<Item = Result<arrow_array::RecordBatch>>> RecordBatchReader
+    for SimpleRecordBatchReader<I>
+{
+    fn schema(&self) -> Arc<arrow_schema::Schema> {
+        self.schema.clone()
+    }
+}
+
+/// A stream of batches that also has a schema
+pub trait RecordBatchStream: Stream<Item = Result<arrow_array::RecordBatch>> {
+    /// Returns the schema of this `RecordBatchStream`.
+    ///
+    /// Implementation of this trait should guarantee that all `RecordBatch`'s returned by this
+    /// stream should have the same schema as returned from this method.
+    fn schema(&self) -> Arc<arrow_schema::Schema>;
+}
+
+/// A boxed RecordBatchStream that is also Send
+pub type SendableRecordBatchStream = Pin<Box<dyn RecordBatchStream + Send>>;
+
+impl<I: lance::io::RecordBatchStream + 'static> From<I> for SendableRecordBatchStream {
+    fn from(stream: I) -> Self {
+        let schema = stream.schema();
+        let mapped_stream = Box::pin(stream.map(|r| r.map_err(Into::into)));
+        Box::pin(SimpleRecordBatchStream {
+            schema,
+            stream: mapped_stream,
+        })
+    }
+}
+
+/// A simple RecordBatchStream formed from the two parts (stream + schema)
+#[pin_project::pin_project]
+pub struct SimpleRecordBatchStream<S: Stream<Item = Result<arrow_array::RecordBatch>>> {
+    pub schema: Arc<arrow_schema::Schema>,
+    #[pin]
+    pub stream: S,
+}
+
+impl<S: Stream<Item = Result<arrow_array::RecordBatch>>> Stream for SimpleRecordBatchStream<S> {
+    type Item = Result<arrow_array::RecordBatch>;
+
+    fn poll_next(
+        self: Pin<&mut Self>,
+        cx: &mut std::task::Context<'_>,
+    ) -> std::task::Poll<Option<Self::Item>> {
+        let this = self.project();
+        this.stream.poll_next(cx)
+    }
+}
+
+impl<S: Stream<Item = Result<arrow_array::RecordBatch>>> RecordBatchStream
+    for SimpleRecordBatchStream<S>
+{
+    fn schema(&self) -> Arc<arrow_schema::Schema> {
+        self.schema.clone()
+    }
+}

rust/lancedb/src/connection.rs

@@ -356,6 +356,15 @@ pub struct ConnectBuilder {
     aws_creds: Option<AwsCredential>,
 
     /// The interval at which to check for updates from other processes.
+    ///
+    /// If None, then consistency is not checked. For performance
+    /// reasons, this is the default. For strong consistency, set this to
+    /// zero seconds. Then every read will check for updates from other
+    /// processes. As a compromise, you can set this to a non-zero timedelta
+    /// for eventual consistency. If more than that interval has passed since
+    /// the last check, then the table will be checked for updates. Note: this
+    /// consistency only applies to read operations. Write operations are
+    /// always consistent.
     read_consistency_interval: Option<std::time::Duration>,
 }
 

rust/lancedb/src/index.rs

@@ -12,181 +12,69 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-use std::{cmp::max, sync::Arc};
-
-use lance_index::IndexType;
-pub use lance_linalg::distance::MetricType;
-
-pub mod vector;
+use std::sync::Arc;
 
 use crate::{table::TableInternal, Result};
 
-/// Index Parameters.
-pub enum IndexParams {
-    Scalar {
-        replace: bool,
-    },
-    IvfPq {
-        replace: bool,
-        metric_type: MetricType,
-        num_partitions: u64,
-        num_sub_vectors: u32,
-        num_bits: u32,
-        sample_rate: u32,
-        max_iterations: u32,
-    },
+use self::{scalar::BTreeIndexBuilder, vector::IvfPqIndexBuilder};
+
+pub mod scalar;
+pub mod vector;
+
+pub enum Index {
+    Auto,
+    BTree(BTreeIndexBuilder),
+    IvfPq(IvfPqIndexBuilder),
 }
 
-/// Builder for Index Parameters.
-
+/// Builder for the create_index operation
+///
+/// The methods on this builder are used to specify options common to all indices.
 pub struct IndexBuilder {
     parent: Arc<dyn TableInternal>,
+    pub(crate) index: Index,
     pub(crate) columns: Vec<String>,
-    // General parameters
-    /// Index name.
-    pub(crate) name: Option<String>,
-    /// Replace the existing index.
     pub(crate) replace: bool,
-
-    pub(crate) index_type: IndexType,
-
-    // Scalar index parameters
-    // Nothing to set here.
-
-    // IVF_PQ parameters
-    pub(crate) metric_type: MetricType,
-    pub(crate) num_partitions: Option<u32>,
-    // PQ related
-    pub(crate) num_sub_vectors: Option<u32>,
-    pub(crate) num_bits: u32,
-
-    /// The rate to find samples to train kmeans.
-    pub(crate) sample_rate: u32,
-    /// Max iteration to train kmeans.
-    pub(crate) max_iterations: u32,
 }
 
 impl IndexBuilder {
-    pub(crate) fn new(parent: Arc<dyn TableInternal>, columns: &[&str]) -> Self {
+    pub(crate) fn new(parent: Arc<dyn TableInternal>, columns: Vec<String>, index: Index) -> Self {
         Self {
             parent,
-            columns: columns.iter().map(|c| c.to_string()).collect(),
-            name: None,
+            index,
+            columns,
             replace: true,
-            index_type: IndexType::Scalar,
-            metric_type: MetricType::L2,
-            num_partitions: None,
-            num_sub_vectors: None,
-            num_bits: 8,
-            sample_rate: 256,
-            max_iterations: 50,
         }
     }
 
-    /// Build a Scalar Index.
+    /// Whether to replace the existing index, the default is `true`.
     ///
-    /// Accepted parameters:
-    ///  - `replace`: Replace the existing index.
-    ///  - `name`: Index name. Default: `None`
-    pub fn scalar(mut self) -> Self {
-        self.index_type = IndexType::Scalar;
-        self
-    }
-
-    /// Build an IVF PQ index.
-    ///
-    /// Accepted parameters:
-    /// - `replace`: Replace the existing index.
-    /// - `name`: Index name. Default: `None`
-    /// - `metric_type`: [MetricType] to use to build Vector Index.
-    /// - `num_partitions`: Number of IVF partitions.
-    /// - `num_sub_vectors`: Number of sub-vectors of PQ.
-    /// - `num_bits`: Number of bits used for PQ centroids.
-    /// - `sample_rate`: The rate to find samples to train kmeans.
-    /// - `max_iterations`: Max iteration to train kmeans.
-    pub fn ivf_pq(mut self) -> Self {
-        self.index_type = IndexType::Vector;
-        self
-    }
-
-    /// The columns to build index on.
-    pub fn columns(mut self, cols: &[&str]) -> Self {
-        self.columns = cols.iter().map(|s| s.to_string()).collect();
-        self
-    }
-
-    /// Whether to replace the existing index, default is `true`.
+    /// If this is false, and another index already exists on the same columns
+    /// and the same name, then an error will be returned.  This is true even if
+    /// that index is out of date.
     pub fn replace(mut self, v: bool) -> Self {
         self.replace = v;
         self
     }
 
-    /// Set the index name.
-    pub fn name(mut self, name: &str) -> Self {
-        self.name = Some(name.to_string());
-        self
+    pub async fn execute(self) -> Result<()> {
+        self.parent.clone().create_index(self).await
     }
+}
 
-    /// [MetricType] to use to build Vector Index.
+#[derive(Debug, Clone, PartialEq)]
+pub enum IndexType {
+    IvfPq,
+    BTree,
+}
+
+/// A description of an index currently configured on a column
+pub struct IndexConfig {
+    /// The type of the index
+    pub index_type: IndexType,
+    /// The columns in the index
     ///
-    /// Default value is [MetricType::L2].
-    pub fn metric_type(mut self, metric_type: MetricType) -> Self {
-        self.metric_type = metric_type;
-        self
-    }
-
-    /// Number of IVF partitions.
-    pub fn num_partitions(mut self, num_partitions: u32) -> Self {
-        self.num_partitions = Some(num_partitions);
-        self
-    }
-
-    /// Number of sub-vectors of PQ.
-    pub fn num_sub_vectors(mut self, num_sub_vectors: u32) -> Self {
-        self.num_sub_vectors = Some(num_sub_vectors);
-        self
-    }
-
-    /// Number of bits used for PQ centroids.
-    pub fn num_bits(mut self, num_bits: u32) -> Self {
-        self.num_bits = num_bits;
-        self
-    }
-
-    /// The rate to find samples to train kmeans.
-    pub fn sample_rate(mut self, sample_rate: u32) -> Self {
-        self.sample_rate = sample_rate;
-        self
-    }
-
-    /// Max iteration to train kmeans.
-    pub fn max_iterations(mut self, max_iterations: u32) -> Self {
-        self.max_iterations = max_iterations;
-        self
-    }
-
-    /// Build the parameters.
-    pub async fn build(self) -> Result<()> {
-        self.parent.clone().do_create_index(self).await
-    }
-}
-
-pub(crate) fn suggested_num_partitions(rows: usize) -> u32 {
-    let num_partitions = (rows as f64).sqrt() as u32;
-    max(1, num_partitions)
-}
-
-pub(crate) fn suggested_num_sub_vectors(dim: u32) -> u32 {
-    if dim % 16 == 0 {
-        // Should be more aggressive than this default.
-        dim / 16
-    } else if dim % 8 == 0 {
-        dim / 8
-    } else {
-        log::warn!(
-            "The dimension of the vector is not divisible by 8 or 16, \
-                which may cause performance degradation in PQ"
-        );
-        1
-    }
+    /// Currently this is always a Vec of size 1.  In the future there may
+    /// be more columns to represent composite indices.
+    pub columns: Vec<String>,
 }

rust/lancedb/src/index/scalar.rs

@@ -0,0 +1,30 @@
+//! Scalar indices are exact indices that are used to quickly satisfy a variety of filters
+//! against a column of scalar values.
+//!
+//! Scalar indices are currently supported on numeric, string, boolean, and temporal columns.
+//!
+//! A scalar index will help with queries with filters like `x > 10`, `x < 10`, `x = 10`,
+//! etc.  Scalar indices can also speed up prefiltering for vector searches.  A single
+//! vector search with prefiltering can use both a scalar index and a vector index.
+
+/// Builder for a btree index
+///
+/// A btree index is an index on scalar columns.  The index stores a copy of the column
+/// in sorted order.  A header entry is created for each block of rows (currently the
+/// block size is fixed at 4096).  These header entries are stored in a separate
+/// cacheable structure (a btree).  To search for data the header is used to determine
+/// which blocks need to be read from disk.
+///
+/// For example, a btree index in a table with 1Bi rows requires sizeof(Scalar) * 256Ki
+/// bytes of memory and will generally need to read sizeof(Scalar) * 4096 bytes to find
+/// the correct row ids.
+///
+/// This index is good for scalar columns with mostly distinct values and does best when
+/// the query is highly selective.
+///
+/// The btree index does not currently have any parameters though parameters such as the
+/// block size may be added in the future.
+#[derive(Default, Debug, Clone)]
+pub struct BTreeIndexBuilder {}
+
+impl BTreeIndexBuilder {}

rust/lancedb/src/index/vector.rs

@@ -12,10 +12,19 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+//! Vector indices are approximate indices that are used to find rows similar to
+//! a query vector.  Vector indices speed up vector searches.
+//!
+//! Vector indices are only supported on fixed-size-list (tensor) columns of floating point
+//! values
+use std::cmp::max;
+
 use serde::Deserialize;
 
 use lance::table::format::{Index, Manifest};
 
+use crate::DistanceType;
+
 pub struct VectorIndex {
     pub columns: Vec<String>,
     pub index_name: String,
@@ -42,3 +51,145 @@ pub struct VectorIndexStatistics {
     pub num_indexed_rows: usize,
     pub num_unindexed_rows: usize,
 }
+
+/// Builder for an IVF PQ index.
+///
+/// This index stores a compressed (quantized) copy of every vector.  These vectors
+/// are grouped into partitions of similar vectors.  Each partition keeps track of
+/// a centroid which is the average value of all vectors in the group.
+///
+/// During a query the centroids are compared with the query vector to find the closest
+/// partitions.  The compressed vectors in these partitions are then searched to find
+/// the closest vectors.
+///
+/// The compression scheme is called product quantization.  Each vector is divided into
+/// subvectors and then each subvector is quantized into a small number of bits.  the
+/// parameters `num_bits` and `num_subvectors` control this process, providing a tradeoff
+/// between index size (and thus search speed) and index accuracy.
+///
+/// The partitioning process is called IVF and the `num_partitions` parameter controls how
+/// many groups to create.
+///
+/// Note that training an IVF PQ index on a large dataset is a slow operation and
+/// currently is also a memory intensive operation.
+#[derive(Debug, Clone)]
+pub struct IvfPqIndexBuilder {
+    pub(crate) distance_type: DistanceType,
+    pub(crate) num_partitions: Option<u32>,
+    pub(crate) num_sub_vectors: Option<u32>,
+    pub(crate) sample_rate: u32,
+    pub(crate) max_iterations: u32,
+}
+
+impl Default for IvfPqIndexBuilder {
+    fn default() -> Self {
+        Self {
+            distance_type: DistanceType::L2,
+            num_partitions: None,
+            num_sub_vectors: None,
+            sample_rate: 256,
+            max_iterations: 50,
+        }
+    }
+}
+
+impl IvfPqIndexBuilder {
+    /// [DistanceType] to use to build the index.
+    ///
+    /// Default value is [DistanceType::L2].
+    ///
+    /// This is used when training the index to calculate the IVF partitions (vectors are
+    /// grouped in partitions with similar vectors according to this distance type) and to
+    /// calculate a subvector's code during quantization.
+    ///
+    /// The metric type used to train an index MUST match the metric type used to search the
+    /// index.  Failure to do so will yield inaccurate results.
+    pub fn distance_type(mut self, distance_type: DistanceType) -> Self {
+        self.distance_type = distance_type;
+        self
+    }
+
+    /// The number of IVF partitions to create.
+    ///
+    /// This value should generally scale with the number of rows in the dataset.  By default
+    /// the number of partitions is the square root of the number of rows.
+    ///
+    /// If this value is too large then the first part of the search (picking the right partition)
+    /// will be slow.  If this value is too small then the second part of the search (searching
+    /// within a partition) will be slow.
+    pub fn num_partitions(mut self, num_partitions: u32) -> Self {
+        self.num_partitions = Some(num_partitions);
+        self
+    }
+
+    /// Number of sub-vectors of PQ.
+    ///
+    /// This value controls how much the vector is compressed during the quantization step.
+    /// The more sub vectors there are the less the vector is compressed.  The default is
+    /// the dimension of the vector divided by 16.  If the dimension is not evenly divisible
+    /// by 16 we use the dimension divded by 8.
+    ///
+    /// The above two cases are highly preferred.  Having 8 or 16 values per subvector allows
+    /// us to use efficient SIMD instructions.
+    ///
+    /// If the dimension is not visible by 8 then we use 1 subvector.  This is not ideal and
+    /// will likely result in poor performance.
+    pub fn num_sub_vectors(mut self, num_sub_vectors: u32) -> Self {
+        self.num_sub_vectors = Some(num_sub_vectors);
+        self
+    }
+
+    /// The rate used to calculate the number of training vectors for kmeans.
+    ///
+    /// When an IVF PQ index is trained, we need to calculate partitions.  These are groups
+    /// of vectors that are similar to each other.  To do this we use an algorithm called kmeans.
+    ///
+    /// Running kmeans on a large dataset can be slow.  To speed this up we run kmeans on a
+    /// random sample of the data.  This parameter controls the size of the sample.  The total
+    /// number of vectors used to train the index is `sample_rate * num_partitions`.
+    ///
+    /// Increasing this value might improve the quality of the index but in most cases the
+    /// default should be sufficient.
+    ///
+    /// The default value is 256.
+    pub fn sample_rate(mut self, sample_rate: u32) -> Self {
+        self.sample_rate = sample_rate;
+        self
+    }
+
+    /// Max iterations to train kmeans.
+    ///
+    /// When training an IVF PQ index we use kmeans to calculate the partitions.  This parameter
+    /// controls how many iterations of kmeans to run.
+    ///
+    /// Increasing this might improve the quality of the index but in most cases the parameter
+    /// is unused because kmeans will converge with fewer iterations.  The parameter is only
+    /// used in cases where kmeans does not appear to converge.  In those cases it is unlikely
+    /// that setting this larger will lead to the index converging anyways.
+    ///
+    /// The default value is 50.
+    pub fn max_iterations(mut self, max_iterations: u32) -> Self {
+        self.max_iterations = max_iterations;
+        self
+    }
+}
+
+pub(crate) fn suggested_num_partitions(rows: usize) -> u32 {
+    let num_partitions = (rows as f64).sqrt() as u32;
+    max(1, num_partitions)
+}
+
+pub(crate) fn suggested_num_sub_vectors(dim: u32) -> u32 {
+    if dim % 16 == 0 {
+        // Should be more aggressive than this default.
+        dim / 16
+    } else if dim % 8 == 0 {
+        dim / 8
+    } else {
+        log::warn!(
+            "The dimension of the vector is not divisible by 8 or 16, \
+                which may cause performance degradation in PQ"
+        );
+        1
+    }
+}

rust/lancedb/src/lib.rs

@@ -130,14 +130,13 @@
 //! # use arrow_array::{FixedSizeListArray, types::Float32Type, RecordBatch,
 //! #   RecordBatchIterator, Int32Array};
 //! # use arrow_schema::{Schema, Field, DataType};
+//! use lancedb::index::Index;
 //! # tokio::runtime::Runtime::new().unwrap().block_on(async {
 //! # let tmpdir = tempfile::tempdir().unwrap();
 //! # let db = lancedb::connect(tmpdir.path().to_str().unwrap()).execute().await.unwrap();
 //! # let tbl = db.open_table("idx_test").execute().await.unwrap();
-//! tbl.create_index(&["vector"])
-//!     .ivf_pq()
-//!     .num_partitions(256)
-//!     .build()
+//! tbl.create_index(&["vector"], Index::Auto)
+//!    .execute()
 //!    .await
 //!    .unwrap();
 //! # });
@@ -181,6 +180,7 @@
 //! # });
 //! ```
 
+pub mod arrow;
 pub mod connection;
 pub mod data;
 pub mod error;
@@ -194,6 +194,7 @@ pub mod table;
 pub mod utils;
 
 pub use error::{Error, Result};
+pub use lance_linalg::distance::DistanceType;
 pub use table::Table;
 
 /// Connect to a database

rust/lancedb/src/query.rs

@@ -15,9 +15,9 @@
 use std::sync::Arc;
 
 use arrow_array::Float32Array;
-use lance::dataset::scanner::DatasetRecordBatchStream;
 use lance_linalg::distance::MetricType;
 
+use crate::arrow::SendableRecordBatchStream;
 use crate::error::Result;
 use crate::table::TableInternal;
 
@@ -81,13 +81,15 @@ impl Query {
         }
     }
 
-    /// Convert the query plan to a [`DatasetRecordBatchStream`]
+    /// Convert the query plan to a [`SendableRecordBatchStream`]
     ///
     /// # Returns
     ///
-    /// * A [DatasetRecordBatchStream] with the query's results.
-    pub async fn execute_stream(&self) -> Result<DatasetRecordBatchStream> {
-        self.parent.clone().do_query(self).await
+    /// * A [SendableRecordBatchStream] with the query's results.
+    pub async fn execute_stream(&self) -> Result<SendableRecordBatchStream> {
+        Ok(SendableRecordBatchStream::from(
+            self.parent.clone().query(self).await?,
+        ))
     }
 
     /// Set the column to query
@@ -363,6 +365,10 @@ mod tests {
             let arr: &Int32Array = b["id"].as_primitive();
             assert!(arr.iter().all(|x| x.unwrap() % 2 == 0));
         }
+
+        // Reject bad filter
+        let result = table.query().filter("id = 0 AND").execute_stream().await;
+        assert!(result.is_err());
     }
 
     fn make_non_empty_batches() -> impl RecordBatchReader + Send + 'static {

rust/lancedb/src/remote/table.rs

@@ -5,11 +5,11 @@ use lance::dataset::{scanner::DatasetRecordBatchStream, ColumnAlteration, NewCol
 
 use crate::{
     error::Result,
-    index::IndexBuilder,
+    index::{IndexBuilder, IndexConfig},
     query::Query,
     table::{
         merge::MergeInsertBuilder, AddDataBuilder, NativeTable, OptimizeAction, OptimizeStats,
-        TableInternal,
+        TableInternal, UpdateBuilder,
     },
 };
 
@@ -45,25 +45,40 @@ impl TableInternal for RemoteTable {
     fn name(&self) -> &str {
         &self.name
     }
+    async fn version(&self) -> Result<u64> {
+        todo!()
+    }
+    async fn checkout(&self, _version: u64) -> Result<()> {
+        todo!()
+    }
+    async fn checkout_latest(&self) -> Result<()> {
+        todo!()
+    }
+    async fn restore(&self) -> Result<()> {
+        todo!()
+    }
     async fn schema(&self) -> Result<SchemaRef> {
         todo!()
     }
     async fn count_rows(&self, _filter: Option<String>) -> Result<usize> {
         todo!()
     }
-    async fn do_add(&self, _add: AddDataBuilder) -> Result<()> {
+    async fn add(&self, _add: AddDataBuilder) -> Result<()> {
         todo!()
     }
-    async fn do_query(&self, _query: &Query) -> Result<DatasetRecordBatchStream> {
+    async fn query(&self, _query: &Query) -> Result<DatasetRecordBatchStream> {
+        todo!()
+    }
+    async fn update(&self, _update: UpdateBuilder) -> Result<()> {
         todo!()
     }
     async fn delete(&self, _predicate: &str) -> Result<()> {
         todo!()
     }
-    async fn do_create_index(&self, _index: IndexBuilder) -> Result<()> {
+    async fn create_index(&self, _index: IndexBuilder) -> Result<()> {
         todo!()
     }
-    async fn do_merge_insert(
+    async fn merge_insert(
         &self,
         _params: MergeInsertBuilder,
         _new_data: Box<dyn RecordBatchReader + Send>,
@@ -86,4 +101,7 @@ impl TableInternal for RemoteTable {
     async fn drop_columns(&self, _columns: &[&str]) -> Result<()> {
         todo!()
     }
+    async fn list_indices(&self) -> Result<Vec<IndexConfig>> {
+        todo!()
+    }
 }

rust/lancedb/src/table/dataset.rs

@@ -83,6 +83,33 @@ impl DatasetRef {
         }
     }
 
+    async fn as_time_travel(&mut self, target_version: u64) -> Result<()> {
+        match self {
+            Self::Latest { dataset, .. } => {
+                *self = Self::TimeTravel {
+                    dataset: dataset.checkout_version(target_version).await?,
+                    version: target_version,
+                };
+            }
+            Self::TimeTravel { dataset, version } => {
+                if *version != target_version {
+                    *self = Self::TimeTravel {
+                        dataset: dataset.checkout_version(target_version).await?,
+                        version: target_version,
+                    };
+                }
+            }
+        }
+        Ok(())
+    }
+
+    fn time_travel_version(&self) -> Option<u64> {
+        match self {
+            Self::Latest { .. } => None,
+            Self::TimeTravel { version, .. } => Some(*version),
+        }
+    }
+
     fn set_latest(&mut self, dataset: Dataset) {
         match self {
             Self::Latest {
@@ -106,23 +133,6 @@ impl DatasetConsistencyWrapper {
         })))
     }
 
-    /// Create a new wrapper in the time travel mode.
-    pub fn new_time_travel(dataset: Dataset, version: u64) -> Self {
-        Self(Arc::new(RwLock::new(DatasetRef::TimeTravel {
-            dataset,
-            version,
-        })))
-    }
-
-    /// Create an independent copy of self.
-    ///
-    /// Unlike Clone, this will track versions independently of the original wrapper and
-    /// will be tied to a different RwLock.
-    pub async fn duplicate(&self) -> Self {
-        let ds_ref = self.0.read().await;
-        Self(Arc::new(RwLock::new((*ds_ref).clone())))
-    }
-
     /// Get an immutable reference to the dataset.
     pub async fn get(&self) -> Result<DatasetReadGuard<'_>> {
         self.ensure_up_to_date().await?;
@@ -132,7 +142,19 @@ impl DatasetConsistencyWrapper {
     }
 
     /// Get a mutable reference to the dataset.
+    ///
+    /// If the dataset is in time travel mode this will fail
     pub async fn get_mut(&self) -> Result<DatasetWriteGuard<'_>> {
+        self.ensure_mutable().await?;
+        self.ensure_up_to_date().await?;
+        Ok(DatasetWriteGuard {
+            guard: self.0.write().await,
+        })
+    }
+
+    /// Get a mutable reference to the dataset without requiring the
+    /// dataset to be in a Latest mode.
+    pub async fn get_mut_unchecked(&self) -> Result<DatasetWriteGuard<'_>> {
         self.ensure_up_to_date().await?;
         Ok(DatasetWriteGuard {
             guard: self.0.write().await,
@@ -140,7 +162,7 @@ impl DatasetConsistencyWrapper {
     }
 
     /// Convert into a wrapper in latest version mode
-    pub async fn as_latest(&mut self, read_consistency_interval: Option<Duration>) -> Result<()> {
+    pub async fn as_latest(&self, read_consistency_interval: Option<Duration>) -> Result<()> {
         self.0
             .write()
             .await
@@ -148,6 +170,10 @@ impl DatasetConsistencyWrapper {
             .await
     }
 
+    pub async fn as_time_travel(&self, target_version: u64) -> Result<()> {
+        self.0.write().await.as_time_travel(target_version).await
+    }
+
     /// Provide a known latest version of the dataset.
     ///
     /// This is usually done after some write operation, which inherently will
@@ -160,6 +186,22 @@ impl DatasetConsistencyWrapper {
         self.0.write().await.reload().await
     }
 
+    /// Returns the version, if in time travel mode, or None otherwise
+    pub async fn time_travel_version(&self) -> Option<u64> {
+        self.0.read().await.time_travel_version()
+    }
+
+    pub async fn ensure_mutable(&self) -> Result<()> {
+        let dataset_ref = self.0.read().await;
+        match &*dataset_ref {
+            DatasetRef::Latest { .. } => Ok(()),
+            DatasetRef::TimeTravel { .. } => Err(crate::Error::InvalidInput {
+                message: "table cannot be modified when a specific version is checked out"
+                    .to_string(),
+            }),
+        }
+    }
+
     async fn is_up_to_date(&self) -> Result<bool> {
         let dataset_ref = self.0.read().await;
         match &*dataset_ref {

rust/lancedb/src/table/merge.rs

@@ -98,6 +98,6 @@ impl MergeInsertBuilder {
     ///
     /// Nothing is returned but the [`super::Table`] is updated
     pub async fn execute(self, new_data: Box<dyn RecordBatchReader + Send>) -> Result<()> {
-        self.table.clone().do_merge_insert(self, new_data).await
+        self.table.clone().merge_insert(self, new_data).await
     }
 }