Bump version: 0.24.0-beta.0 → 0.24.0

Bump version: 0.23.1-beta.2 → 0.24.0-beta.0
chore: upgrade lance to 0.30.0 (#2451 )
2025-12-23 05:19:58 +00:00 · 2025-06-20 05:46:05 +00:00 · 2025-06-20 05:46:04 +00:00 · 2025-06-20 11:27:20 +08:00 · 2025-06-19 08:51:48 -07:00 · 2025-06-19 10:38:34 +08:00
52 changed files with 1662 additions and 813 deletions
--- a/.bumpversion.toml
+++ b/.bumpversion.toml
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.20.0-beta.2"
+current_version = "0.20.1-beta.2"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
--- a/.github/workflows/npm-publish.yml
+++ b/.github/workflows/npm-publish.yml
@@ -539,10 +539,20 @@ jobs:
        # We need to deprecate the old package to avoid confusion.
        # Each time we publish a new version, it gets undeprecated.
        run: npm deprecate vectordb "Use @lancedb/lancedb instead."
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          ref: main
      - name: Update package-lock.json
-        run: bash ci/update_lockfiles.sh
+        run: |
+          git config user.name 'Lance Release'
+          git config user.email 'lance-dev@lancedb.com'
+          bash ci/update_lockfiles.sh
      - name: Push new commit
        uses: ad-m/github-push-action@master
+        with:
+          github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
+          branch: main
      - name: Notify Slack Action
        uses: ravsamhq/notify-slack-action@2.3.0
        if: ${{ always() }}
--- a/Cargo.lock
+++ b/Cargo.lock
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -21,14 +21,14 @@ categories = ["database-implementations"]
 rust-version = "1.78.0"

 [workspace.dependencies]
-lance = { "version" = "=0.29.0", "features" = ["dynamodb"] }
-lance-io = "=0.29.0"
-lance-index = "=0.29.0"
-lance-linalg = "=0.29.0"
-lance-table = "=0.29.0"
-lance-testing = "=0.29.0"
-lance-datafusion = "=0.29.0"
-lance-encoding = "=0.29.0"
+lance = { "version" = "=0.30.0", "features" = ["dynamodb"] }
+lance-io = "=0.30.0"
+lance-index = "=0.30.0"
+lance-linalg = "=0.30.0"
+lance-table = "=0.30.0"
+lance-testing = "=0.30.0"
+lance-datafusion = "=0.30.0"
+lance-encoding = "=0.30.0"
 # Note that this one does not include pyarrow
 arrow = { version = "55.1", optional = false }
 arrow-array = "55.1"
--- a/docs/src/guides/sql_querying.md
+++ b/docs/src/guides/sql_querying.md
@@ -42,6 +42,7 @@ duckdb.query("SELECT * FROM arrow_table")
 Have the required imports before doing any querying.

 === "Python"
+
    ```python
    --8<-- "python/python/tests/docs/test_guide_tables.py:import-lancedb"
    --8<-- "python/python/tests/docs/test_guide_tables.py:import-session-context"
@@ -51,6 +52,7 @@ Have the required imports before doing any querying.
 Register the table created with the Datafusion session context.

 === "Python"
+
    ```python
    --8<-- "python/python/tests/docs/test_guide_tables.py:lance_sql_basic"
    ```
--- a/docs/src/js/classes/BooleanQuery.md
+++ b/docs/src/js/classes/BooleanQuery.md
@@ -0,0 +1,53 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / BooleanQuery
+
+# Class: BooleanQuery
+
+Represents a full-text query interface.
+This interface defines the structure and behavior for full-text queries,
+including methods to retrieve the query type and convert the query to a dictionary format.
+
+## Implements
+
+- [`FullTextQuery`](../interfaces/FullTextQuery.md)
+
+## Constructors
+
+### new BooleanQuery()
+
+```ts
+new BooleanQuery(queries): BooleanQuery
+```
+
+Creates an instance of BooleanQuery.
+
+#### Parameters
+
+* **queries**: [[`Occur`](../enumerations/Occur.md), [`FullTextQuery`](../interfaces/FullTextQuery.md)][]
+    An array of (Occur, FullTextQuery objects) to combine.
+    Occur specifies whether the query must match, or should match.
+
+#### Returns
+
+[`BooleanQuery`](BooleanQuery.md)
+
+## Methods
+
+### queryType()
+
+```ts
+queryType(): FullTextQueryType
+```
+
+The type of the full-text query.
+
+#### Returns
+
+[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
+
+#### Implementation of
+
+[`FullTextQuery`](../interfaces/FullTextQuery.md).[`queryType`](../interfaces/FullTextQuery.md#querytype)
--- a/docs/src/js/classes/MatchQuery.md
+++ b/docs/src/js/classes/MatchQuery.md
@@ -40,6 +40,7 @@ Creates an instance of MatchQuery.
    - `boost`: The boost factor for the query (default is 1.0).
    - `fuzziness`: The fuzziness level for the query (default is 0).
    - `maxExpansions`: The maximum number of terms to consider for fuzzy matching (default is 50).
+    - `operator`: The logical operator to use for combining terms in the query (default is "OR").

 * **options.boost?**: `number`

@@ -47,6 +48,8 @@ Creates an instance of MatchQuery.

 * **options.maxExpansions?**: `number`

+* **options.operator?**: [`Operator`](../enumerations/Operator.md)
+
 #### Returns

 [`MatchQuery`](MatchQuery.md)
--- a/docs/src/js/classes/MultiMatchQuery.md
+++ b/docs/src/js/classes/MultiMatchQuery.md
@@ -38,9 +38,12 @@ Creates an instance of MultiMatchQuery.
 * **options?**
    Optional parameters for the multi-match query.
    - `boosts`: An array of boost factors for each column (default is 1.0 for all).
+    - `operator`: The logical operator to use for combining terms in the query (default is "OR").

 * **options.boosts?**: `number`[]

+* **options.operator?**: [`Operator`](../enumerations/Operator.md)
+
 #### Returns

 [`MultiMatchQuery`](MultiMatchQuery.md)
--- a/docs/src/js/classes/PhraseQuery.md
+++ b/docs/src/js/classes/PhraseQuery.md
@@ -19,7 +19,10 @@ including methods to retrieve the query type and convert the query to a dictiona
 ### new PhraseQuery()

 ```ts
-new PhraseQuery(query, column): PhraseQuery
+new PhraseQuery(
+   query,
+   column,
+   options?): PhraseQuery
 ```

 Creates an instance of `PhraseQuery`.
@@ -32,6 +35,12 @@ Creates an instance of `PhraseQuery`.
 * **column**: `string`
    The name of the column to search within.

+* **options?**
+    Optional parameters for the phrase query.
+    - `slop`: The maximum number of intervening unmatched positions allowed between words in the phrase (default is 0).
+
+* **options.slop?**: `number`
+
 #### Returns

 [`PhraseQuery`](PhraseQuery.md)
--- a/docs/src/js/enumerations/FullTextQueryType.md
+++ b/docs/src/js/enumerations/FullTextQueryType.md
@@ -15,6 +15,14 @@ Enum representing the types of full-text queries supported.

 ## Enumeration Members

+### Boolean
+
+```ts
+Boolean: "boolean";
+```
+
+***
+
 ### Boost

 ```ts
--- a/docs/src/js/enumerations/Occur.md
+++ b/docs/src/js/enumerations/Occur.md
@@ -0,0 +1,28 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / Occur
+
+# Enumeration: Occur
+
+Enum representing the occurrence of terms in full-text queries.
+
+- `Must`: The term must be present in the document.
+- `Should`: The term should contribute to the document score, but is not required.
+
+## Enumeration Members
+
+### Must
+
+```ts
+Must: "MUST";
+```
+
+***
+
+### Should
+
+```ts
+Should: "SHOULD";
+```
--- a/docs/src/js/enumerations/Operator.md
+++ b/docs/src/js/enumerations/Operator.md
@@ -0,0 +1,28 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / Operator
+
+# Enumeration: Operator
+
+Enum representing the logical operators used in full-text queries.
+
+- `And`: All terms must match.
+- `Or`: At least one term must match.
+
+## Enumeration Members
+
+### And
+
+```ts
+And: "AND";
+```
+
+***
+
+### Or
+
+```ts
+Or: "OR";
+```
--- a/docs/src/js/globals.md
+++ b/docs/src/js/globals.md
@@ -12,9 +12,12 @@
 ## Enumerations

 - [FullTextQueryType](enumerations/FullTextQueryType.md)
+- [Occur](enumerations/Occur.md)
+- [Operator](enumerations/Operator.md)

 ## Classes

+- [BooleanQuery](classes/BooleanQuery.md)
 - [BoostQuery](classes/BoostQuery.md)
 - [Connection](classes/Connection.md)
 - [Index](classes/Index.md)
--- a/docs/test/requirements.txt
+++ b/docs/test/requirements.txt
@@ -7,3 +7,4 @@ tantivy==0.20.1
 --extra-index-url https://download.pytorch.org/whl/cpu
 torch
 polars>=0.19, <=1.3.0
+datafusion
--- a/java/core/pom.xml
+++ b/java/core/pom.xml
@@ -8,7 +8,7 @@
    <parent>
        <groupId>com.lancedb</groupId>
        <artifactId>lancedb-parent</artifactId>
-        <version>0.20.0-beta.2</version>
+        <version>0.20.1-beta.2</version>
        <relativePath>../pom.xml</relativePath>
    </parent>

--- a/java/pom.xml
+++ b/java/pom.xml
@@ -6,7 +6,7 @@

    <groupId>com.lancedb</groupId>
    <artifactId>lancedb-parent</artifactId>
-    <version>0.20.0-beta.2</version>
+    <version>0.20.1-beta.2</version>
    <packaging>pom</packaging>

    <name>LanceDB Parent</name>
--- a/node/package-lock.json
+++ b/node/package-lock.json
@@ -1,12 +1,12 @@
 {
  "name": "vectordb",
-  "version": "0.20.0-beta.2",
+  "version": "0.20.1-beta.2",
  "lockfileVersion": 3,
  "requires": true,
  "packages": {
    "": {
      "name": "vectordb",
-      "version": "0.20.0-beta.2",
+      "version": "0.20.1-beta.2",
      "cpu": [
        "x64",
        "arm64"
@@ -52,11 +52,11 @@
        "uuid": "^9.0.0"
      },
      "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.20.0-beta.2",
-        "@lancedb/vectordb-darwin-x64": "0.20.0-beta.2",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.20.0-beta.2",
-        "@lancedb/vectordb-linux-x64-gnu": "0.20.0-beta.2",
-        "@lancedb/vectordb-win32-x64-msvc": "0.20.0-beta.2"
+        "@lancedb/vectordb-darwin-arm64": "0.20.1-beta.2",
+        "@lancedb/vectordb-darwin-x64": "0.20.1-beta.2",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.20.1-beta.2",
+        "@lancedb/vectordb-linux-x64-gnu": "0.20.1-beta.2",
+        "@lancedb/vectordb-win32-x64-msvc": "0.20.1-beta.2"
      },
      "peerDependencies": {
        "@apache-arrow/ts": "^14.0.2",
@@ -327,9 +327,9 @@
      }
    },
    "node_modules/@lancedb/vectordb-darwin-arm64": {
-      "version": "0.20.0-beta.2",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.20.0-beta.2.tgz",
-      "integrity": "sha512-H9PmJ/5KSvstVzR8Q7T22+eHRjJZ2ef3aA3gdFxXvoMi3xQ0MGIxz23HuKHGTRT4tfl1nNnpOPb2W7Na8etK9w==",
+      "version": "0.20.1-beta.2",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.20.1-beta.2.tgz",
+      "integrity": "sha512-mqi0yI+ZwBTydaDy1FRHAUZwrWS28u6tbHTe1s4uSrmERbVI6PfmoPR+NZWWAp6ZhlseSdl/+yeI4imk11rQSw==",
      "cpu": [
        "arm64"
      ],
@@ -339,9 +339,9 @@
      ]
    },
    "node_modules/@lancedb/vectordb-darwin-x64": {
-      "version": "0.20.0-beta.2",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.20.0-beta.2.tgz",
-      "integrity": "sha512-9AQkv4tIys+vg0cplZtSE48o61jd7EnmuMkUht+vLORL5/HAma84eAoU9lXHT7zAtPAQmL+98Bfvcsx7fJ6mVw==",
+      "version": "0.20.1-beta.2",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.20.1-beta.2.tgz",
+      "integrity": "sha512-m8EYYA8JZIeNsJqQsBDUMu6r31/u7FzpjonJ4Y+CjapVl6UdvI65KUkeL2dYrFao++RuIoaiqcm3e7gRgFZpXQ==",
      "cpu": [
        "x64"
      ],
@@ -351,9 +351,9 @@
      ]
    },
    "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.20.0-beta.2",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.20.0-beta.2.tgz",
-      "integrity": "sha512-eQWoJz2ePml7NyEInTBeakWx56+5c6r2p3F+iHC5tsLuznn6eFX90koXJunRxH1WXHDN48ECUlEmKypgfEmn4w==",
+      "version": "0.20.1-beta.2",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.20.1-beta.2.tgz",
+      "integrity": "sha512-3Og2+bk4GlWmMO1Yg2HBfeb5zrOMLaIHD7bEqQ4+6yw4IckAaV+ke05H0tyyqmOVrOQ0LpvtXgD7pPztjm9r9A==",
      "cpu": [
        "arm64"
      ],
@@ -363,9 +363,9 @@
      ]
    },
    "node_modules/@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.20.0-beta.2",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.20.0-beta.2.tgz",
-      "integrity": "sha512-/+84U+Dt07m8Jk0b8h+SvOzlrynITPP3SDBOlB+OonwmGSxirXhc8gkfNZctgXOJYKMyRIRSsMHP/QNjOp2ajA==",
+      "version": "0.20.1-beta.2",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.20.1-beta.2.tgz",
+      "integrity": "sha512-mwTQyA/FBoU/FkPuvCNBZG3y83gBN+iYoejehBH2HBkLUIcmlsDgSRZ1OQ+f9ijj12EMBCA11tBUPA9zhHzyrw==",
      "cpu": [
        "x64"
      ],
@@ -375,9 +375,9 @@
      ]
    },
    "node_modules/@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.20.0-beta.2",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.20.0-beta.2.tgz",
-      "integrity": "sha512-bgdunAPnknBh/5oO+vr6RXMr6wb3hHugNPXcIidxYMQvgFa8uhaAKtgYkAKuoyUReOYo8DGtVkZxNUUpZbF7/A==",
+      "version": "0.20.1-beta.2",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.20.1-beta.2.tgz",
+      "integrity": "sha512-VkjNpqhK3l3uHLLPmox+HrmKPMaZgV+qsGQWx0nfseGnSOEmXAWZWQFe0APVCQ9y0xTypQB0oH7eSOPZv2t4WQ==",
      "cpu": [
        "x64"
      ],
--- a/node/package.json
+++ b/node/package.json
@@ -1,6 +1,6 @@
 {
  "name": "vectordb",
-  "version": "0.20.0-beta.2",
+  "version": "0.20.1-beta.2",
  "description": " Serverless, low-latency vector database for AI applications",
  "private": false,
  "main": "dist/index.js",
@@ -89,10 +89,10 @@
    }
  },
  "optionalDependencies": {
-    "@lancedb/vectordb-darwin-x64": "0.20.0-beta.2",
-    "@lancedb/vectordb-darwin-arm64": "0.20.0-beta.2",
-    "@lancedb/vectordb-linux-x64-gnu": "0.20.0-beta.2",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.20.0-beta.2",
-    "@lancedb/vectordb-win32-x64-msvc": "0.20.0-beta.2"
+    "@lancedb/vectordb-darwin-x64": "0.20.1-beta.2",
+    "@lancedb/vectordb-darwin-arm64": "0.20.1-beta.2",
+    "@lancedb/vectordb-linux-x64-gnu": "0.20.1-beta.2",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.20.1-beta.2",
+    "@lancedb/vectordb-win32-x64-msvc": "0.20.1-beta.2"
  }
 }
--- a/nodejs/Cargo.toml
+++ b/nodejs/Cargo.toml
@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.20.0-beta.2"
+version = "0.20.1-beta.2"
 license.workspace = true
 description.workspace = true
 repository.workspace = true
--- a/nodejs/test/arrow.test.ts
+++ b/nodejs/test/arrow.test.ts
@@ -592,14 +592,14 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
        ).rejects.toThrow("column vector was missing");
      });

-      it("will provide a nice error if run twice", async function () {
+      it("will skip embedding application if already applied", async function () {
        const records = sampleRecords();
        const table = await convertToTable(records, dummyEmbeddingConfig);

        // fromTableToBuffer will try and apply the embeddings again
-        await expect(
-          fromTableToBuffer(table, dummyEmbeddingConfig),
-        ).rejects.toThrow("already existed");
+        // but should skip since the column already has non-null values
+        const result = await fromTableToBuffer(table, dummyEmbeddingConfig);
+        expect(result.byteLength).toBeGreaterThan(0);
      });
    });

--- a/nodejs/test/table.test.ts
+++ b/nodejs/test/table.test.ts
@@ -33,7 +33,12 @@ import {
  register,
 } from "../lancedb/embedding";
 import { Index } from "../lancedb/indices";
-import { instanceOfFullTextQuery } from "../lancedb/query";
+import {
+  BooleanQuery,
+  Occur,
+  Operator,
+  instanceOfFullTextQuery,
+} from "../lancedb/query";
 import exp = require("constants");

 describe.each([arrow15, arrow16, arrow17, arrow18])(
@@ -554,6 +559,32 @@ describe("When creating an index", () => {
    rst = await tbl.query().limit(2).offset(1).nearestTo(queryVec).toArrow();
    expect(rst.numRows).toBe(1);

+    // test nprobes
+    rst = await tbl.query().nearestTo(queryVec).limit(2).nprobes(50).toArrow();
+    expect(rst.numRows).toBe(2);
+    rst = await tbl
+      .query()
+      .nearestTo(queryVec)
+      .limit(2)
+      .minimumNprobes(15)
+      .toArrow();
+    expect(rst.numRows).toBe(2);
+    rst = await tbl
+      .query()
+      .nearestTo(queryVec)
+      .limit(2)
+      .minimumNprobes(10)
+      .maximumNprobes(20)
+      .toArrow();
+    expect(rst.numRows).toBe(2);
+
+    expect(() => tbl.query().nearestTo(queryVec).minimumNprobes(0)).toThrow(
+      "Invalid input, minimum_nprobes must be greater than 0",
+    );
+    expect(() => tbl.query().nearestTo(queryVec).maximumNprobes(5)).toThrow(
+      "Invalid input, maximum_nprobes must be greater than minimum_nprobes",
+    );
+
    await tbl.dropIndex("vec_idx");
    const indices2 = await tbl.listIndices();
    expect(indices2.length).toBe(0);
@@ -1531,6 +1562,18 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(

      const results = await table.search("hello").toArray();
      expect(results[0].text).toBe(data[0].text);
+
+      const results2 = await table
+        .search(new MatchQuery("hello world", "text"))
+        .toArray();
+      expect(results2.length).toBe(2);
+
+      const results3 = await table
+        .search(
+          new MatchQuery("hello world", "text", { operator: Operator.And }),
+        )
+        .toArray();
+      expect(results3.length).toBe(1);
    });

    test("full text search without lowercase", async () => {
@@ -1607,6 +1650,60 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
      expect(resultSet.has("fob")).toBe(true);
      expect(resultSet.has("fo")).toBe(true);
      expect(resultSet.has("food")).toBe(true);
+
+      const prefixResults = await table
+        .search(
+          new MatchQuery("foo", "text", { fuzziness: 3, prefixLength: 3 }),
+        )
+        .toArray();
+      expect(prefixResults.length).toBe(2);
+      const resultSet2 = new Set(prefixResults.map((r) => r.text));
+      expect(resultSet2.has("foo")).toBe(true);
+      expect(resultSet2.has("food")).toBe(true);
+    });
+
+    test("full text search boolean query", async () => {
+      const db = await connect(tmpDir.name);
+      const data = [
+        { text: "The cat and dog are playing" },
+        { text: "The cat is sleeping" },
+        { text: "The dog is barking" },
+        { text: "The dog chases the cat" },
+      ];
+      const table = await db.createTable("test", data);
+      await table.createIndex("text", {
+        config: Index.fts({ withPosition: false }),
+      });
+
+      const shouldResults = await table
+        .search(
+          new BooleanQuery([
+            [Occur.Should, new MatchQuery("cat", "text")],
+            [Occur.Should, new MatchQuery("dog", "text")],
+          ]),
+        )
+        .toArray();
+      expect(shouldResults.length).toBe(4);
+
+      const mustResults = await table
+        .search(
+          new BooleanQuery([
+            [Occur.Must, new MatchQuery("cat", "text")],
+            [Occur.Must, new MatchQuery("dog", "text")],
+          ]),
+        )
+        .toArray();
+      expect(mustResults.length).toBe(2);
+
+      const mustNotResults = await table
+        .search(
+          new BooleanQuery([
+            [Occur.Must, new MatchQuery("cat", "text")],
+            [Occur.MustNot, new MatchQuery("dog", "text")],
+          ]),
+        )
+        .toArray();
+      expect(mustNotResults.length).toBe(1);
    });

    test.each([
--- a/nodejs/lancedb/arrow.ts
+++ b/nodejs/lancedb/arrow.ts
@@ -417,7 +417,9 @@ function inferSchema(
        } else {
          const inferredType = inferType(value, path, opts);
          if (inferredType === undefined) {
-            throw new Error(`Failed to infer data type for field ${path.join(".")} at row ${rowI}. \
+            throw new Error(`Failed to infer data type for field ${path.join(
+              ".",
+            )} at row ${rowI}. \
                             Consider providing an explicit schema.`);
          }
          pathTree.set(path, inferredType);
@@ -799,11 +801,17 @@ async function applyEmbeddingsFromMetadata(
        `Cannot apply embedding function because the source column '${functionEntry.sourceColumn}' was not present in the data`,
      );
    }
+
+    // Check if destination column exists and handle accordingly
    if (columns[destColumn] !== undefined) {
-      throw new Error(
-        `Attempt to apply embeddings to table failed because column ${destColumn} already existed`,
-      );
+      const existingColumn = columns[destColumn];
+      // If the column exists but is all null, we can fill it with embeddings
+      if (existingColumn.nullCount !== existingColumn.length) {
+        // Column has non-null values, skip embedding application
+        continue;
+      }
    }
+
    if (table.batches.length > 1) {
      throw new Error(
        "Internal error: `makeArrowTable` unexpectedly created a table with more than one batch",
@@ -903,11 +911,23 @@ async function applyEmbeddings<T>(
      );
    }
  } else {
+    // Check if destination column exists and handle accordingly
    if (Object.prototype.hasOwnProperty.call(newColumns, destColumn)) {
-      throw new Error(
-        `Attempt to apply embeddings to table failed because column ${destColumn} already existed`,
-      );
+      const existingColumn = newColumns[destColumn];
+      // If the column exists but is all null, we can fill it with embeddings
+      if (existingColumn.nullCount !== existingColumn.length) {
+        // Column has non-null values, skip embedding application and return table as-is
+        let newTable = new ArrowTable(newColumns);
+        if (schema != null) {
+          newTable = alignTable(newTable, schema as Schema);
+        }
+        return new ArrowTable(
+          new Schema(newTable.schema.fields, schemaMetadata),
+          newTable.batches,
+        );
+      }
    }
+
    if (table.batches.length > 1) {
      throw new Error(
        "Internal error: `makeArrowTable` unexpectedly created a table with more than one batch",
--- a/nodejs/lancedb/index.ts
+++ b/nodejs/lancedb/index.ts
@@ -64,7 +64,10 @@ export {
  PhraseQuery,
  BoostQuery,
  MultiMatchQuery,
+  BooleanQuery,
  FullTextQueryType,
+  Operator,
+  Occur,
 } from "./query";

 export {
--- a/nodejs/lancedb/query.ts
+++ b/nodejs/lancedb/query.ts
@@ -448,6 +448,10 @@ export class VectorQuery extends QueryBase<NativeVectorQuery> {
   * For best results we recommend tuning this parameter with a benchmark against
   * your actual data to find the smallest possible value that will still give
   * you the desired recall.
+   *
+   * For more fine grained control over behavior when you have a very narrow filter
+   * you can use `minimumNprobes` and `maximumNprobes`.  This method sets both
+   * the minimum and maximum to the same value.
   */
  nprobes(nprobes: number): VectorQuery {
    super.doCall((inner) => inner.nprobes(nprobes));
@@ -455,6 +459,33 @@ export class VectorQuery extends QueryBase<NativeVectorQuery> {
    return this;
  }

+  /**
+   * Set the minimum number of probes used.
+   *
+   * This controls the minimum number of partitions that will be searched.  This
+   * parameter will impact every query against a vector index, regardless of the
+   * filter.  See `nprobes` for more details.  Higher values will increase recall
+   * but will also increase latency.
+   */
+  minimumNprobes(minimumNprobes: number): VectorQuery {
+    super.doCall((inner) => inner.minimumNprobes(minimumNprobes));
+    return this;
+  }
+
+  /**
+   * Set the maximum number of probes used.
+   *
+   * This controls the maximum number of partitions that will be searched.  If this
+   * number is greater than minimumNprobes then the excess partitions will _only_ be
+   * searched if we have not found enough results.  This can be useful when there is
+   * a narrow filter to allow these queries to spend more time searching and avoid
+   * potential false negatives.
+   */
+  maximumNprobes(maximumNprobes: number): VectorQuery {
+    super.doCall((inner) => inner.maximumNprobes(maximumNprobes));
+    return this;
+  }
+
  /*
   * Set the distance range to use
   *
@@ -762,6 +793,31 @@ export enum FullTextQueryType {
  MatchPhrase = "match_phrase",
  Boost = "boost",
  MultiMatch = "multi_match",
+  Boolean = "boolean",
+}
+
+/**
+ * Enum representing the logical operators used in full-text queries.
+ *
+ * - `And`: All terms must match.
+ * - `Or`: At least one term must match.
+ */
+export enum Operator {
+  And = "AND",
+  Or = "OR",
+}
+
+/**
+ * Enum representing the occurrence of terms in full-text queries.
+ *
+ * - `Must`: The term must be present in the document.
+ * - `Should`: The term should contribute to the document score, but is not required.
+ * - `MustNot`: The term must not be present in the document.
+ */
+export enum Occur {
+  Should = "SHOULD",
+  Must = "MUST",
+  MustNot = "MUST_NOT",
 }

 /**
@@ -791,6 +847,7 @@ export function instanceOfFullTextQuery(obj: any): obj is FullTextQuery {
 export class MatchQuery implements FullTextQuery {
  /** @ignore */
  public readonly inner: JsFullTextQuery;
+
  /**
   * Creates an instance of MatchQuery.
   *
@@ -800,6 +857,8 @@ export class MatchQuery implements FullTextQuery {
   *   - `boost`: The boost factor for the query (default is 1.0).
   *   - `fuzziness`: The fuzziness level for the query (default is 0).
   *   - `maxExpansions`: The maximum number of terms to consider for fuzzy matching (default is 50).
+   *   - `operator`: The logical operator to use for combining terms in the query (default is "OR").
+   *   - `prefixLength`: The number of beginning characters being unchanged for fuzzy matching.
   */
  constructor(
    query: string,
@@ -808,6 +867,8 @@ export class MatchQuery implements FullTextQuery {
      boost?: number;
      fuzziness?: number;
      maxExpansions?: number;
+      operator?: Operator;
+      prefixLength?: number;
    },
  ) {
    let fuzziness = options?.fuzziness;
@@ -820,6 +881,8 @@ export class MatchQuery implements FullTextQuery {
      options?.boost ?? 1.0,
      fuzziness,
      options?.maxExpansions ?? 50,
+      options?.operator ?? Operator.Or,
+      options?.prefixLength ?? 0,
    );
  }

@@ -836,9 +899,11 @@ export class PhraseQuery implements FullTextQuery {
   *
   * @param query - The phrase to search for in the specified column.
   * @param column - The name of the column to search within.
+   * @param options - Optional parameters for the phrase query.
+   *   - `slop`: The maximum number of intervening unmatched positions allowed between words in the phrase (default is 0).
   */
-  constructor(query: string, column: string) {
-    this.inner = JsFullTextQuery.phraseQuery(query, column);
+  constructor(query: string, column: string, options?: { slop?: number }) {
+    this.inner = JsFullTextQuery.phraseQuery(query, column, options?.slop ?? 0);
  }

  queryType(): FullTextQueryType {
@@ -889,18 +954,21 @@ export class MultiMatchQuery implements FullTextQuery {
   * @param columns - An array of column names to search within.
   * @param options - Optional parameters for the multi-match query.
   *  - `boosts`: An array of boost factors for each column (default is 1.0 for all).
+   *  - `operator`: The logical operator to use for combining terms in the query (default is "OR").
   */
  constructor(
    query: string,
    columns: string[],
    options?: {
      boosts?: number[];
+      operator?: Operator;
    },
  ) {
    this.inner = JsFullTextQuery.multiMatchQuery(
      query,
      columns,
      options?.boosts,
+      options?.operator ?? Operator.Or,
    );
  }

@@ -908,3 +976,23 @@ export class MultiMatchQuery implements FullTextQuery {
    return FullTextQueryType.MultiMatch;
  }
 }
+
+export class BooleanQuery implements FullTextQuery {
+  /** @ignore */
+  public readonly inner: JsFullTextQuery;
+  /**
+   * Creates an instance of BooleanQuery.
+   *
+   * @param queries - An array of (Occur, FullTextQuery objects) to combine.
+   * Occur specifies whether the query must match, or should match.
+   */
+  constructor(queries: [Occur, FullTextQuery][]) {
+    this.inner = JsFullTextQuery.booleanQuery(
+      queries.map(([occur, query]) => [occur, query.inner]),
+    );
+  }
+
+  queryType(): FullTextQueryType {
+    return FullTextQueryType.Boolean;
+  }
+}
--- a/nodejs/npm/darwin-arm64/package.json
+++ b/nodejs/npm/darwin-arm64/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.20.0-beta.2",
+	"version": "0.20.1-beta.2",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",
--- a/nodejs/npm/darwin-x64/package.json
+++ b/nodejs/npm/darwin-x64/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-x64",
-	"version": "0.20.0-beta.2",
+	"version": "0.20.1-beta.2",
 	"os": ["darwin"],
 	"cpu": ["x64"],
 	"main": "lancedb.darwin-x64.node",
--- a/nodejs/npm/linux-arm64-gnu/package.json
+++ b/nodejs/npm/linux-arm64-gnu/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.20.0-beta.2",
+	"version": "0.20.1-beta.2",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",
--- a/nodejs/npm/linux-arm64-musl/package.json
+++ b/nodejs/npm/linux-arm64-musl/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.20.0-beta.2",
+	"version": "0.20.1-beta.2",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-musl.node",
--- a/nodejs/npm/linux-x64-gnu/package.json
+++ b/nodejs/npm/linux-x64-gnu/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.20.0-beta.2",
+	"version": "0.20.1-beta.2",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",
--- a/nodejs/npm/linux-x64-musl/package.json
+++ b/nodejs/npm/linux-x64-musl/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.20.0-beta.2",
+	"version": "0.20.1-beta.2",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-musl.node",
--- a/nodejs/npm/win32-arm64-msvc/package.json
+++ b/nodejs/npm/win32-arm64-msvc/package.json
@@ -1,6 +1,6 @@
 {
  "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.20.0-beta.2",
+  "version": "0.20.1-beta.2",
  "os": [
    "win32"
  ],
--- a/nodejs/npm/win32-x64-msvc/package.json
+++ b/nodejs/npm/win32-x64-msvc/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.20.0-beta.2",
+	"version": "0.20.1-beta.2",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",
--- a/nodejs/package-lock.json
+++ b/nodejs/package-lock.json
@@ -1,12 +1,12 @@
 {
  "name": "@lancedb/lancedb",
-  "version": "0.20.0-beta.2",
+  "version": "0.20.1-beta.2",
  "lockfileVersion": 3,
  "requires": true,
  "packages": {
    "": {
      "name": "@lancedb/lancedb",
-      "version": "0.20.0-beta.2",
+      "version": "0.20.1-beta.2",
      "cpu": [
        "x64",
        "arm64"
--- a/nodejs/package.json
+++ b/nodejs/package.json
@@ -11,7 +11,7 @@
    "ann"
  ],
  "private": false,
-  "version": "0.20.0-beta.2",
+  "version": "0.20.1-beta.2",
  "main": "dist/index.js",
  "exports": {
    ".": "./dist/index.js",
--- a/nodejs/src/query.rs
+++ b/nodejs/src/query.rs
@@ -4,7 +4,8 @@
 use std::sync::Arc;

 use lancedb::index::scalar::{
-    BoostQuery, FtsQuery, FullTextSearchQuery, MatchQuery, MultiMatchQuery, PhraseQuery,
+    BooleanQuery, BoostQuery, FtsQuery, FullTextSearchQuery, MatchQuery, MultiMatchQuery, Occur,
+    Operator, PhraseQuery,
 };
 use lancedb::query::ExecutableQuery;
 use lancedb::query::Query as LanceDbQuery;
@@ -177,6 +178,31 @@ impl VectorQuery {
        self.inner = self.inner.clone().nprobes(nprobe as usize);
    }

+    #[napi]
+    pub fn minimum_nprobes(&mut self, minimum_nprobe: u32) -> napi::Result<()> {
+        self.inner = self
+            .inner
+            .clone()
+            .minimum_nprobes(minimum_nprobe as usize)
+            .default_error()?;
+        Ok(())
+    }
+
+    #[napi]
+    pub fn maximum_nprobes(&mut self, maximum_nprobes: u32) -> napi::Result<()> {
+        let maximum_nprobes = if maximum_nprobes == 0 {
+            None
+        } else {
+            Some(maximum_nprobes as usize)
+        };
+        self.inner = self
+            .inner
+            .clone()
+            .maximum_nprobes(maximum_nprobes)
+            .default_error()?;
+        Ok(())
+    }
+
    #[napi]
    pub fn distance_range(&mut self, lower_bound: Option<f64>, upper_bound: Option<f64>) {
        // napi doesn't support f32, so we have to convert to f32
@@ -308,6 +334,8 @@ impl JsFullTextQuery {
        boost: f64,
        fuzziness: Option<u32>,
        max_expansions: u32,
+        operator: String,
+        prefix_length: u32,
    ) -> napi::Result<Self> {
        Ok(Self {
            inner: MatchQuery::new(query)
@@ -315,14 +343,23 @@ impl JsFullTextQuery {
                .with_boost(boost as f32)
                .with_fuzziness(fuzziness)
                .with_max_expansions(max_expansions as usize)
+                .with_operator(
+                    Operator::try_from(operator.as_str()).map_err(|e| {
+                        napi::Error::from_reason(format!("Invalid operator: {}", e))
+                    })?,
+                )
+                .with_prefix_length(prefix_length)
                .into(),
        })
    }

    #[napi(factory)]
-    pub fn phrase_query(query: String, column: String) -> napi::Result<Self> {
+    pub fn phrase_query(query: String, column: String, slop: u32) -> napi::Result<Self> {
        Ok(Self {
-            inner: PhraseQuery::new(query).with_column(Some(column)).into(),
+            inner: PhraseQuery::new(query)
+                .with_column(Some(column))
+                .with_slop(slop)
+                .into(),
        })
    }

@@ -348,6 +385,7 @@ impl JsFullTextQuery {
        query: String,
        columns: Vec<String>,
        boosts: Option<Vec<f64>>,
+        operator: String,
    ) -> napi::Result<Self> {
        let q = match boosts {
            Some(boosts) => MultiMatchQuery::try_new(query, columns)
@@ -358,7 +396,37 @@ impl JsFullTextQuery {
            napi::Error::from_reason(format!("Failed to create multi match query: {}", e))
        })?;

-        Ok(Self { inner: q.into() })
+        let operator = Operator::try_from(operator.as_str()).map_err(|e| {
+            napi::Error::from_reason(format!("Invalid operator for multi match query: {}", e))
+        })?;
+
+        Ok(Self {
+            inner: q.with_operator(operator).into(),
+        })
+    }
+
+    #[napi(factory)]
+    pub fn boolean_query(queries: Vec<(String, &JsFullTextQuery)>) -> napi::Result<Self> {
+        let mut sub_queries = Vec::with_capacity(queries.len());
+        for (occur, q) in queries {
+            let occur = Occur::try_from(occur.as_str())
+                .map_err(|e| napi::Error::from_reason(e.to_string()))?;
+            sub_queries.push((occur, q.inner.clone()));
+        }
+        Ok(Self {
+            inner: BooleanQuery::new(sub_queries).into(),
+        })
+    }
+
+    #[napi(getter)]
+    pub fn query_type(&self) -> String {
+        match self.inner {
+            FtsQuery::Match(_) => "match".to_string(),
+            FtsQuery::Phrase(_) => "phrase".to_string(),
+            FtsQuery::Boost(_) => "boost".to_string(),
+            FtsQuery::MultiMatch(_) => "multi_match".to_string(),
+            FtsQuery::Boolean(_) => "boolean".to_string(),
+        }
    }
 }

--- a/python/.bumpversion.toml
+++ b/python/.bumpversion.toml
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.23.0"
+current_version = "0.24.0"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
--- a/python/Cargo.toml
+++ b/python/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.23.0"
+version = "0.24.0"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true
--- a/python/python/lancedb/_lancedb.pyi
+++ b/python/python/lancedb/_lancedb.pyi
@@ -143,6 +143,8 @@ class VectorQuery:
    def postfilter(self): ...
    def refine_factor(self, refine_factor: int): ...
    def nprobes(self, nprobes: int): ...
+    def minimum_nprobes(self, minimum_nprobes: int): ...
+    def maximum_nprobes(self, maximum_nprobes: int): ...
    def bypass_vector_index(self): ...
    def nearest_to_text(self, query: dict) -> HybridQuery: ...
    def to_query_request(self) -> PyQueryRequest: ...
@@ -158,6 +160,8 @@ class HybridQuery:
    def distance_type(self, distance_type: str): ...
    def refine_factor(self, refine_factor: int): ...
    def nprobes(self, nprobes: int): ...
+    def minimum_nprobes(self, minimum_nprobes: int): ...
+    def maximum_nprobes(self, maximum_nprobes: int): ...
    def bypass_vector_index(self): ...
    def to_vector_query(self) -> VectorQuery: ...
    def to_fts_query(self) -> FTSQuery: ...
@@ -165,23 +169,21 @@ class HybridQuery:
    def get_with_row_id(self) -> bool: ...
    def to_query_request(self) -> PyQueryRequest: ...

-class PyFullTextSearchQuery:
-    columns: Optional[List[str]]
-    query: str
-    limit: Optional[int]
-    wand_factor: Optional[float]
+class FullTextQuery:
+    pass

 class PyQueryRequest:
    limit: Optional[int]
    offset: Optional[int]
    filter: Optional[Union[str, bytes]]
-    full_text_search: Optional[PyFullTextSearchQuery]
+    full_text_search: Optional[FullTextQuery]
    select: Optional[Union[str, List[str]]]
    fast_search: Optional[bool]
    with_row_id: Optional[bool]
    column: Optional[str]
    query_vector: Optional[List[pa.Array]]
-    nprobes: Optional[int]
+    minimum_nprobes: Optional[int]
+    maximum_nprobes: Optional[int]
    lower_bound: Optional[float]
    upper_bound: Optional[float]
    ef: Optional[int]
--- a/python/python/lancedb/query.py
+++ b/python/python/lancedb/query.py
@@ -4,7 +4,6 @@
 from __future__ import annotations

 from abc import ABC, abstractmethod
-import abc
 from concurrent.futures import ThreadPoolExecutor
 from enum import Enum
 from datetime import timedelta
@@ -88,15 +87,28 @@ def ensure_vector_query(
        return val


-class FullTextQueryType(Enum):
+class FullTextQueryType(str, Enum):
    MATCH = "match"
    MATCH_PHRASE = "match_phrase"
    BOOST = "boost"
    MULTI_MATCH = "multi_match"
+    BOOLEAN = "boolean"


-class FullTextQuery(abc.ABC, pydantic.BaseModel):
-    @abc.abstractmethod
+class FullTextOperator(str, Enum):
+    AND = "AND"
+    OR = "OR"
+
+
+class Occur(str, Enum):
+    SHOULD = "SHOULD"
+    MUST = "MUST"
+    MUST_NOT = "MUST_NOT"
+
+
+@pydantic.dataclasses.dataclass
+class FullTextQuery(ABC):
+    @abstractmethod
    def query_type(self) -> FullTextQueryType:
        """
        Get the query type of the query.
@@ -106,193 +118,178 @@ class FullTextQuery(abc.ABC, pydantic.BaseModel):
        str
            The type of the query.
        """
+        pass

-    @abc.abstractmethod
-    def to_dict(self) -> dict:
+    def __and__(self, other: "FullTextQuery") -> "FullTextQuery":
        """
-        Convert the query to a dictionary.
-
-        Returns
-        -------
-        dict
-            The query as a dictionary.
-        """
-
-
-class MatchQuery(FullTextQuery):
-    query: str
-    column: str
-    boost: float = 1.0
-    fuzziness: int = 0
-    max_expansions: int = 50
-
-    def __init__(
-        self,
-        query: str,
-        column: str,
-        *,
-        boost: float = 1.0,
-        fuzziness: int = 0,
-        max_expansions: int = 50,
-    ):
-        """
-        Match query for full-text search.
+        Combine two queries with a logical AND operation.

        Parameters
        ----------
-        query : str
-            The query string to match against.
-        column : str
-            The name of the column to match against.
-        boost : float, default 1.0
-            The boost factor for the query.
-            The score of each matching document is multiplied by this value.
-        fuzziness : int, optional
-            The maximum edit distance for each term in the match query.
-            Defaults to 0 (exact match).
-            If None, fuzziness is applied automatically by the rules:
-                - 0 for terms with length <= 2
-                - 1 for terms with length <= 5
-                - 2 for terms with length > 5
-        max_expansions : int, optional
-            The maximum number of terms to consider for fuzzy matching.
-            Defaults to 50.
+        other : FullTextQuery
+            The other query to combine with.
+
+        Returns
+        -------
+        FullTextQuery
+            A new query that combines both queries with AND.
        """
-        super().__init__(
-            query=query,
-            column=column,
-            boost=boost,
-            fuzziness=fuzziness,
-            max_expansions=max_expansions,
-        )
+        return BooleanQuery([(Occur.MUST, self), (Occur.MUST, other)])
+
+    def __or__(self, other: "FullTextQuery") -> "FullTextQuery":
+        """
+        Combine two queries with a logical OR operation.
+
+        Parameters
+        ----------
+        other : FullTextQuery
+            The other query to combine with.
+
+        Returns
+        -------
+        FullTextQuery
+            A new query that combines both queries with OR.
+        """
+        return BooleanQuery([(Occur.SHOULD, self), (Occur.SHOULD, other)])
+
+
+@pydantic.dataclasses.dataclass
+class MatchQuery(FullTextQuery):
+    """
+    Match query for full-text search.
+
+    Parameters
+    ----------
+    query : str
+        The query string to match against.
+    column : str
+        The name of the column to match against.
+    boost : float, default 1.0
+        The boost factor for the query.
+        The score of each matching document is multiplied by this value.
+    fuzziness : int, optional
+        The maximum edit distance for each term in the match query.
+        Defaults to 0 (exact match).
+        If None, fuzziness is applied automatically by the rules:
+            - 0 for terms with length <= 2
+            - 1 for terms with length <= 5
+            - 2 for terms with length > 5
+    max_expansions : int, optional
+        The maximum number of terms to consider for fuzzy matching.
+        Defaults to 50.
+    operator : FullTextOperator, default OR
+        The operator to use for combining the query results.
+        Can be either `AND` or `OR`.
+        If `AND`, all terms in the query must match.
+        If `OR`, at least one term in the query must match.
+    prefix_length : int, optional
+        The number of beginning characters being unchanged for fuzzy matching.
+        This is useful to achieve prefix matching.
+    """
+
+    query: str
+    column: str
+    boost: float = pydantic.Field(1.0, kw_only=True)
+    fuzziness: int = pydantic.Field(0, kw_only=True)
+    max_expansions: int = pydantic.Field(50, kw_only=True)
+    operator: FullTextOperator = pydantic.Field(FullTextOperator.OR, kw_only=True)
+    prefix_length: int = pydantic.Field(0, kw_only=True)

    def query_type(self) -> FullTextQueryType:
        return FullTextQueryType.MATCH

-    def to_dict(self) -> dict:
-        return {
-            "match": {
-                self.column: {
-                    "query": self.query,
-                    "boost": self.boost,
-                    "fuzziness": self.fuzziness,
-                    "max_expansions": self.max_expansions,
-                }
-            }
-        }
-

+@pydantic.dataclasses.dataclass
 class PhraseQuery(FullTextQuery):
+    """
+    Phrase query for full-text search.
+
+    Parameters
+    ----------
+    query : str
+        The query string to match against.
+    column : str
+        The name of the column to match against.
+    """
+
    query: str
    column: str
-
-    def __init__(self, query: str, column: str):
-        """
-        Phrase query for full-text search.
-
-        Parameters
-        ----------
-        query : str
-            The query string to match against.
-        column : str
-            The name of the column to match against.
-        """
-        super().__init__(query=query, column=column)
+    slop: int = pydantic.Field(0, kw_only=True)

    def query_type(self) -> FullTextQueryType:
        return FullTextQueryType.MATCH_PHRASE

-    def to_dict(self) -> dict:
-        return {
-            "match_phrase": {
-                self.column: self.query,
-            }
-        }
-

+@pydantic.dataclasses.dataclass
 class BoostQuery(FullTextQuery):
+    """
+    Boost query for full-text search.
+
+    Parameters
+    ----------
+    positive : dict
+        The positive query object.
+    negative : dict
+        The negative query object.
+    negative_boost : float, default 0.5
+        The boost factor for the negative query.
+    """
+
    positive: FullTextQuery
    negative: FullTextQuery
-    negative_boost: float = 0.5
-
-    def __init__(
-        self,
-        positive: FullTextQuery,
-        negative: FullTextQuery,
-        *,
-        negative_boost: float = 0.5,
-    ):
-        """
-        Boost query for full-text search.
-
-        Parameters
-        ----------
-        positive : dict
-            The positive query object.
-        negative : dict
-            The negative query object.
-        negative_boost : float
-            The boost factor for the negative query.
-        """
-        super().__init__(
-            positive=positive, negative=negative, negative_boost=negative_boost
-        )
+    negative_boost: float = pydantic.Field(0.5, kw_only=True)

    def query_type(self) -> FullTextQueryType:
        return FullTextQueryType.BOOST

-    def to_dict(self) -> dict:
-        return {
-            "boost": {
-                "positive": self.positive.to_dict(),
-                "negative": self.negative.to_dict(),
-                "negative_boost": self.negative_boost,
-            }
-        }
-

+@pydantic.dataclasses.dataclass
 class MultiMatchQuery(FullTextQuery):
+    """
+    Multi-match query for full-text search.
+
+    Parameters
+    ----------
+    query : str | list[Query]
+        If a string, the query string to match against.
+    columns : list[str]
+        The list of columns to match against.
+    boosts : list[float], optional
+        The list of boost factors for each column. If not provided,
+        all columns will have the same boost factor.
+    operator : FullTextOperator, default OR
+        The operator to use for combining the query results.
+        Can be either `AND` or `OR`.
+        It would be applied to all columns individually.
+        For example, if the operator is `AND`,
+        then the query "hello world" is equal to
+        `match("hello AND world", column1) OR match("hello AND world", column2)`.
+    """
+
    query: str
    columns: list[str]
-    boosts: list[float]
-
-    def __init__(
-        self,
-        query: str,
-        columns: list[str],
-        *,
-        boosts: Optional[list[float]] = None,
-    ):
-        """
-        Multi-match query for full-text search.
-
-        Parameters
-        ----------
-        query : str
-            The query string to match against.
-
-        columns : list[str]
-            The list of columns to match against.
-
-        boosts : list[float], optional
-            The list of boost factors for each column. If not provided,
-            all columns will have the same boost factor.
-        """
-        if boosts is None:
-            boosts = [1.0] * len(columns)
-        super().__init__(query=query, columns=columns, boosts=boosts)
+    boosts: Optional[list[float]] = pydantic.Field(None, kw_only=True)
+    operator: FullTextOperator = pydantic.Field(FullTextOperator.OR, kw_only=True)

    def query_type(self) -> FullTextQueryType:
        return FullTextQueryType.MULTI_MATCH

-    def to_dict(self) -> dict:
-        return {
-            "multi_match": {
-                "query": self.query,
-                "columns": self.columns,
-                "boost": self.boosts,
-            }
-        }
+
+@pydantic.dataclasses.dataclass
+class BooleanQuery(FullTextQuery):
+    """
+    Boolean query for full-text search.
+
+    Parameters
+    ----------
+    queries : list[tuple(Occur, FullTextQuery)]
+        The list of queries with their occurrence requirements.
+    """
+
+    queries: list[tuple[Occur, FullTextQuery]]
+
+    def query_type(self) -> FullTextQueryType:
+        return FullTextQueryType.BOOLEAN


 class FullTextSearchQuery(pydantic.BaseModel):
@@ -445,8 +442,18 @@ class Query(pydantic.BaseModel):
    # which columns to return in the results
    columns: Optional[Union[List[str], Dict[str, str]]] = None

-    # number of IVF partitions to search
-    nprobes: Optional[int] = None
+    # minimum number of IVF partitions to search
+    #
+    # If None then a default value (20) will be used.
+    minimum_nprobes: Optional[int] = None
+
+    # maximum number of IVF partitions to search
+    #
+    # If None then a default value (20) will be used.
+    #
+    # If 0 then no limit will be applied and all partitions could be searched
+    # if needed to satisfy the limit.
+    maximum_nprobes: Optional[int] = None

    # lower bound for distance search
    lower_bound: Optional[float] = None
@@ -484,7 +491,8 @@ class Query(pydantic.BaseModel):
        query.vector_column = req.column
        query.vector = req.query_vector
        query.distance_type = req.distance_type
-        query.nprobes = req.nprobes
+        query.minimum_nprobes = req.minimum_nprobes
+        query.maximum_nprobes = req.maximum_nprobes
        query.lower_bound = req.lower_bound
        query.upper_bound = req.upper_bound
        query.ef = req.ef
@@ -493,10 +501,8 @@ class Query(pydantic.BaseModel):
        query.postfilter = req.postfilter
        if req.full_text_search is not None:
            query.full_text_query = FullTextSearchQuery(
-                columns=req.full_text_search.columns,
-                query=req.full_text_search.query,
-                limit=req.full_text_search.limit,
-                wand_factor=req.full_text_search.wand_factor,
+                columns=None,
+                query=req.full_text_search,
            )
        return query

@@ -1047,7 +1053,8 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
        super().__init__(table)
        self._query = query
        self._distance_type = None
-        self._nprobes = None
+        self._minimum_nprobes = None
+        self._maximum_nprobes = None
        self._lower_bound = None
        self._upper_bound = None
        self._refine_factor = None
@@ -1110,6 +1117,10 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
        See discussion in [Querying an ANN Index][querying-an-ann-index] for
        tuning advice.

+        This method sets both the minimum and maximum number of probes to the same
+        value. See `minimum_nprobes` and `maximum_nprobes` for more fine-grained
+        control.
+
        Parameters
        ----------
        nprobes: int
@@ -1120,7 +1131,36 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
        LanceVectorQueryBuilder
            The LanceQueryBuilder object.
        """
-        self._nprobes = nprobes
+        self._minimum_nprobes = nprobes
+        self._maximum_nprobes = nprobes
+        return self
+
+    def minimum_nprobes(self, minimum_nprobes: int) -> LanceVectorQueryBuilder:
+        """Set the minimum number of probes to use.
+
+        See `nprobes` for more details.
+
+        These partitions will be searched on every vector query and will increase recall
+        at the expense of latency.
+        """
+        self._minimum_nprobes = minimum_nprobes
+        return self
+
+    def maximum_nprobes(self, maximum_nprobes: int) -> LanceVectorQueryBuilder:
+        """Set the maximum number of probes to use.
+
+        See `nprobes` for more details.
+
+        If this value is greater than `minimum_nprobes` then the excess partitions
+        will be searched only if we have not found enough results.
+
+        This can be useful when there is a narrow filter to allow these queries to
+        spend more time searching and avoid potential false negatives.
+
+        If this value is 0 then no limit will be applied and all partitions could be
+        searched if needed to satisfy the limit.
+        """
+        self._maximum_nprobes = maximum_nprobes
        return self

    def distance_range(
@@ -1224,7 +1264,8 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
            limit=self._limit,
            distance_type=self._distance_type,
            columns=self._columns,
-            nprobes=self._nprobes,
+            minimum_nprobes=self._minimum_nprobes,
+            maximum_nprobes=self._maximum_nprobes,
            lower_bound=self._lower_bound,
            upper_bound=self._upper_bound,
            refine_factor=self._refine_factor,
@@ -1410,10 +1451,13 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):

        query = self._query
        if self._phrase_query:
-            raise NotImplementedError(
-                "Phrase query is not yet supported in Lance FTS. "
-                "Use tantivy-based index instead for now."
-            )
+            if isinstance(query, str):
+                if not query.startswith('"') or not query.endswith('"'):
+                    query = f'"{query}"'
+            elif isinstance(query, FullTextQuery) and not isinstance(
+                query, PhraseQuery
+            ):
+                raise TypeError("Please use PhraseQuery for phrase queries.")
        query = self.to_query_object()
        results = self._table._execute_query(query, timeout=timeout)
        results = results.read_all()
@@ -1588,7 +1632,8 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
        self._fts_columns = fts_columns
        self._norm = None
        self._reranker = None
-        self._nprobes = None
+        self._minimum_nprobes = None
+        self._maximum_nprobes = None
        self._refine_factor = None
        self._distance_type = None
        self._phrase_query = None
@@ -1820,7 +1865,24 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
        LanceHybridQueryBuilder
            The LanceHybridQueryBuilder object.
        """
-        self._nprobes = nprobes
+        self._minimum_nprobes = nprobes
+        self._maximum_nprobes = nprobes
+        return self
+
+    def minimum_nprobes(self, minimum_nprobes: int) -> LanceHybridQueryBuilder:
+        """Set the minimum number of probes to use.
+
+        See `nprobes` for more details.
+        """
+        self._minimum_nprobes = minimum_nprobes
+        return self
+
+    def maximum_nprobes(self, maximum_nprobes: int) -> LanceHybridQueryBuilder:
+        """Set the maximum number of probes to use.
+
+        See `nprobes` for more details.
+        """
+        self._maximum_nprobes = maximum_nprobes
        return self

    def distance_range(
@@ -2049,8 +2111,10 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
            self._fts_query.phrase_query(True)
        if self._distance_type:
            self._vector_query.metric(self._distance_type)
-        if self._nprobes:
-            self._vector_query.nprobes(self._nprobes)
+        if self._minimum_nprobes:
+            self._vector_query.minimum_nprobes(self._minimum_nprobes)
+        if self._maximum_nprobes is not None:
+            self._vector_query.maximum_nprobes(self._maximum_nprobes)
        if self._refine_factor:
            self._vector_query.refine_factor(self._refine_factor)
        if self._ef:
@@ -2513,7 +2577,7 @@ class AsyncQuery(AsyncQueryBase):
                self._inner.nearest_to_text({"query": query, "columns": columns})
            )
        # FullTextQuery object
-        return AsyncFTSQuery(self._inner.nearest_to_text({"query": query.to_dict()}))
+        return AsyncFTSQuery(self._inner.nearest_to_text({"query": query}))


 class AsyncFTSQuery(AsyncQueryBase):
@@ -2661,6 +2725,34 @@ class AsyncVectorQueryBase:
        self._inner.nprobes(nprobes)
        return self

+    def minimum_nprobes(self, minimum_nprobes: int) -> Self:
+        """Set the minimum number of probes to use.
+
+        See `nprobes` for more details.
+
+        These partitions will be searched on every indexed vector query and will
+        increase recall at the expense of latency.
+        """
+        self._inner.minimum_nprobes(minimum_nprobes)
+        return self
+
+    def maximum_nprobes(self, maximum_nprobes: int) -> Self:
+        """Set the maximum number of probes to use.
+
+        See `nprobes` for more details.
+
+        If this value is greater than `minimum_nprobes` then the excess partitions
+        will be searched only if we have not found enough results.
+
+        This can be useful when there is a narrow filter to allow these queries to
+        spend more time searching and avoid potential false negatives.
+
+        If this value is 0 then no limit will be applied and all partitions could be
+        searched if needed to satisfy the limit.
+        """
+        self._inner.maximum_nprobes(maximum_nprobes)
+        return self
+
    def distance_range(
        self, lower_bound: Optional[float] = None, upper_bound: Optional[float] = None
    ) -> Self:
@@ -2835,7 +2927,7 @@ class AsyncVectorQuery(AsyncQueryBase, AsyncVectorQueryBase):
                self._inner.nearest_to_text({"query": query, "columns": columns})
            )
        # FullTextQuery object
-        return AsyncHybridQuery(self._inner.nearest_to_text({"query": query.to_dict()}))
+        return AsyncHybridQuery(self._inner.nearest_to_text({"query": query}))

    async def to_batches(
        self,
--- a/python/python/lancedb/table.py
+++ b/python/python/lancedb/table.py
@@ -827,7 +827,7 @@ class Table(ABC):
        ordering_field_names: Optional[Union[str, List[str]]] = None,
        replace: bool = False,
        writer_heap_size: Optional[int] = 1024 * 1024 * 1024,
-        use_tantivy: bool = True,
+        use_tantivy: bool = False,
        tokenizer_name: Optional[str] = None,
        with_position: bool = False,
        # tokenizer configs:
@@ -864,7 +864,7 @@ class Table(ABC):
            The tokenizer to use for the index. Can be "raw", "default" or the 2 letter
            language code followed by "_stem". So for english it would be "en_stem".
            For available languages see: https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html
-        use_tantivy: bool, default True
+        use_tantivy: bool, default False
            If True, use the legacy full-text search implementation based on tantivy.
            If False, use the new full-text search implementation based on lance-index.
        with_position: bool, default False
@@ -1970,7 +1970,7 @@ class LanceTable(Table):
        ordering_field_names: Optional[Union[str, List[str]]] = None,
        replace: bool = False,
        writer_heap_size: Optional[int] = 1024 * 1024 * 1024,
-        use_tantivy: bool = True,
+        use_tantivy: bool = False,
        tokenizer_name: Optional[str] = None,
        with_position: bool = False,
        # tokenizer configs:
@@ -3637,8 +3637,10 @@ class AsyncTable:
            )
            if query.distance_type is not None:
                async_query = async_query.distance_type(query.distance_type)
-            if query.nprobes is not None:
-                async_query = async_query.nprobes(query.nprobes)
+            if query.minimum_nprobes is not None:
+                async_query = async_query.minimum_nprobes(query.minimum_nprobes)
+            if query.maximum_nprobes is not None:
+                async_query = async_query.maximum_nprobes(query.maximum_nprobes)
            if query.refine_factor is not None:
                async_query = async_query.refine_factor(query.refine_factor)
            if query.vector_column:
--- a/python/python/tests/docs/test_search.py
+++ b/python/python/tests/docs/test_search.py
@@ -6,7 +6,7 @@ import lancedb

 # --8<-- [end:import-lancedb]
 # --8<-- [start:import-numpy]
-from lancedb.query import BoostQuery, MatchQuery
+from lancedb.query import BooleanQuery, BoostQuery, MatchQuery, Occur
 import numpy as np
 import pyarrow as pa

@@ -191,6 +191,15 @@ def test_fts_fuzzy_query():
        "food",  # 1 insertion
    }

+    results = table.search(
+        MatchQuery("foo", "text", fuzziness=1, prefix_length=3)
+    ).to_pandas()
+    assert len(results) == 2
+    assert set(results["text"].to_list()) == {
+        "foo",
+        "food",
+    }
+

@pytest.mark.skipif(
    os.name == "nt", reason="Need to fix https://github.com/lancedb/lance/issues/3905"
@@ -240,6 +249,60 @@ def test_fts_boost_query():
    )


+@pytest.mark.skipif(
+    os.name == "nt", reason="Need to fix https://github.com/lancedb/lance/issues/3905"
+)
+def test_fts_boolean_query(tmp_path):
+    uri = tmp_path / "boolean-example"
+    db = lancedb.connect(uri)
+    table = db.create_table(
+        "my_table_fts_boolean",
+        data=[
+            {"text": "The cat and dog are playing"},
+            {"text": "The cat is sleeping"},
+            {"text": "The dog is barking"},
+            {"text": "The dog chases the cat"},
+        ],
+        mode="overwrite",
+    )
+    table.create_fts_index("text", use_tantivy=False, replace=True)
+
+    # SHOULD
+    results = table.search(
+        MatchQuery("cat", "text") | MatchQuery("dog", "text")
+    ).to_pandas()
+    assert len(results) == 4
+    assert set(results["text"].to_list()) == {
+        "The cat and dog are playing",
+        "The cat is sleeping",
+        "The dog is barking",
+        "The dog chases the cat",
+    }
+    # MUST
+    results = table.search(
+        MatchQuery("cat", "text") & MatchQuery("dog", "text")
+    ).to_pandas()
+    assert len(results) == 2
+    assert set(results["text"].to_list()) == {
+        "The cat and dog are playing",
+        "The dog chases the cat",
+    }
+
+    # MUST NOT
+    results = table.search(
+        BooleanQuery(
+            [
+                (Occur.MUST, MatchQuery("cat", "text")),
+                (Occur.MUST_NOT, MatchQuery("dog", "text")),
+            ]
+        )
+    ).to_pandas()
+    assert len(results) == 1
+    assert set(results["text"].to_list()) == {
+        "The cat is sleeping",
+    }
+
+
@pytest.mark.skipif(
    os.name == "nt", reason="Need to fix https://github.com/lancedb/lance/issues/3905"
 )
--- a/python/python/tests/test_fts.py
+++ b/python/python/tests/test_fts.py
@@ -215,6 +215,19 @@ def test_search_fts(table, use_tantivy):
        assert len(results) == 5
        assert len(results[0]) == 3  # id, text, _score

+        # Test boolean query
+        results = (
+            table.search(MatchQuery("puppy", "text") & MatchQuery("runs", "text"))
+            .select(["id", "text"])
+            .limit(5)
+            .to_list()
+        )
+        assert len(results) == 5
+        assert len(results[0]) == 3  # id, text, _score
+        for r in results:
+            assert "puppy" in r["text"]
+            assert "runs" in r["text"]
+

@pytest.mark.asyncio
 async def test_fts_select_async(async_table):
--- a/python/python/tests/test_query.py
+++ b/python/python/tests/test_query.py
@@ -25,6 +25,8 @@ from lancedb.query import (
    AsyncQueryBase,
    AsyncVectorQuery,
    LanceVectorQueryBuilder,
+    MatchQuery,
+    PhraseQuery,
    Query,
    FullTextSearchQuery,
 )
@@ -437,6 +439,33 @@ def test_query_builder_with_filter(table):
    assert all(np.array(rs[0]["vector"]) == [3, 4])


+def test_invalid_nprobes_sync(table):
+    with pytest.raises(ValueError, match="minimum_nprobes must be greater than 0"):
+        LanceVectorQueryBuilder(table, [0, 0], "vector").minimum_nprobes(0).to_list()
+    with pytest.raises(
+        ValueError, match="maximum_nprobes must be greater than minimum_nprobes"
+    ):
+        LanceVectorQueryBuilder(table, [0, 0], "vector").maximum_nprobes(5).to_list()
+    with pytest.raises(
+        ValueError, match="minimum_nprobes must be less or equal to maximum_nprobes"
+    ):
+        LanceVectorQueryBuilder(table, [0, 0], "vector").minimum_nprobes(100).to_list()
+
+
+@pytest.mark.asyncio
+async def test_invalid_nprobes_async(table_async: AsyncTable):
+    with pytest.raises(ValueError, match="minimum_nprobes must be greater than 0"):
+        await table_async.vector_search([0, 0]).minimum_nprobes(0).to_list()
+    with pytest.raises(
+        ValueError, match="maximum_nprobes must be greater than minimum_nprobes"
+    ):
+        await table_async.vector_search([0, 0]).maximum_nprobes(5).to_list()
+    with pytest.raises(
+        ValueError, match="minimum_nprobes must be less or equal to maximum_nprobes"
+    ):
+        await table_async.vector_search([0, 0]).minimum_nprobes(100).to_list()
+
+
 def test_query_builder_with_prefilter(table):
    df = (
        LanceVectorQueryBuilder(table, [0, 0], "vector")
@@ -583,6 +612,21 @@ async def test_query_async(table_async: AsyncTable):
        table_async.query().nearest_to(pa.array([1, 2])).nprobes(10),
        expected_num_rows=2,
    )
+    await check_query(
+        table_async.query().nearest_to(pa.array([1, 2])).minimum_nprobes(10),
+        expected_num_rows=2,
+    )
+    await check_query(
+        table_async.query().nearest_to(pa.array([1, 2])).maximum_nprobes(30),
+        expected_num_rows=2,
+    )
+    await check_query(
+        table_async.query()
+        .nearest_to(pa.array([1, 2]))
+        .minimum_nprobes(10)
+        .maximum_nprobes(20),
+        expected_num_rows=2,
+    )
    await check_query(
        table_async.query().nearest_to(pa.array([1, 2])).bypass_vector_index(),
        expected_num_rows=2,
@@ -909,7 +953,39 @@ def test_query_serialization_sync(table: lancedb.table.Table):

    q = table.search([5.0, 6.0]).nprobes(10).refine_factor(5).to_query_object()
    check_set_props(
-        q, vector_column="vector", vector=[5.0, 6.0], nprobes=10, refine_factor=5
+        q,
+        vector_column="vector",
+        vector=[5.0, 6.0],
+        minimum_nprobes=10,
+        maximum_nprobes=10,
+        refine_factor=5,
+    )
+
+    q = table.search([5.0, 6.0]).minimum_nprobes(10).to_query_object()
+    check_set_props(
+        q,
+        vector_column="vector",
+        vector=[5.0, 6.0],
+        minimum_nprobes=10,
+        maximum_nprobes=None,
+    )
+
+    q = table.search([5.0, 6.0]).nprobes(50).to_query_object()
+    check_set_props(
+        q,
+        vector_column="vector",
+        vector=[5.0, 6.0],
+        minimum_nprobes=50,
+        maximum_nprobes=50,
+    )
+
+    q = table.search([5.0, 6.0]).maximum_nprobes(10).to_query_object()
+    check_set_props(
+        q,
+        vector_column="vector",
+        vector=[5.0, 6.0],
+        maximum_nprobes=10,
+        minimum_nprobes=None,
    )

    q = table.search([5.0, 6.0]).distance_range(0.0, 1.0).to_query_object()
@@ -961,7 +1037,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
        limit=10,
        vector=sample_vector,
        postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
        with_row_id=False,
        bypass_vector_index=False,
    )
@@ -971,7 +1048,20 @@ async def test_query_serialization_async(table_async: AsyncTable):
        q,
        vector=sample_vector,
        postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
+        with_row_id=False,
+        bypass_vector_index=False,
+        limit=10,
+    )
+
+    q = (await table_async.search([5.0, 6.0])).nprobes(50).to_query_object()
+    check_set_props(
+        q,
+        vector=sample_vector,
+        postfilter=False,
+        minimum_nprobes=50,
+        maximum_nprobes=50,
        with_row_id=False,
        bypass_vector_index=False,
        limit=10,
@@ -990,7 +1080,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
        filter="id = 1",
        postfilter=True,
        vector=sample_vector,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
        with_row_id=False,
        bypass_vector_index=False,
    )
@@ -1004,7 +1095,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
    check_set_props(
        q,
        vector=sample_vector,
-        nprobes=10,
+        minimum_nprobes=10,
+        maximum_nprobes=10,
        refine_factor=5,
        postfilter=False,
        with_row_id=False,
@@ -1012,6 +1104,18 @@ async def test_query_serialization_async(table_async: AsyncTable):
        limit=10,
    )

+    q = (await table_async.search([5.0, 6.0])).minimum_nprobes(5).to_query_object()
+    check_set_props(
+        q,
+        vector=sample_vector,
+        minimum_nprobes=5,
+        maximum_nprobes=20,
+        postfilter=False,
+        with_row_id=False,
+        bypass_vector_index=False,
+        limit=10,
+    )
+
    q = (
        (await table_async.search([5.0, 6.0]))
        .distance_range(0.0, 1.0)
@@ -1023,7 +1127,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
        lower_bound=0.0,
        upper_bound=1.0,
        postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
        with_row_id=False,
        bypass_vector_index=False,
        limit=10,
@@ -1035,7 +1140,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
        distance_type="cosine",
        vector=sample_vector,
        postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
        with_row_id=False,
        bypass_vector_index=False,
        limit=10,
@@ -1047,7 +1153,8 @@ async def test_query_serialization_async(table_async: AsyncTable):
        ef=7,
        vector=sample_vector,
        postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
        with_row_id=False,
        bypass_vector_index=False,
        limit=10,
@@ -1059,24 +1166,34 @@ async def test_query_serialization_async(table_async: AsyncTable):
        bypass_vector_index=True,
        vector=sample_vector,
        postfilter=False,
-        nprobes=20,
+        minimum_nprobes=20,
+        maximum_nprobes=20,
        with_row_id=False,
        limit=10,
    )

    # FTS queries
-    q = (await table_async.search("foo")).limit(10).to_query_object()
+    match_query = MatchQuery("foo", "text")
+    q = (await table_async.search(match_query)).limit(10).to_query_object()
    check_set_props(
        q,
        limit=10,
-        full_text_query=FullTextSearchQuery(columns=[], query="foo"),
+        full_text_query=FullTextSearchQuery(columns=None, query=match_query),
        with_row_id=False,
    )

-    q = (await table_async.search("foo", query_type="fts")).to_query_object()
+    q = (await table_async.search(match_query)).to_query_object()
    check_set_props(
        q,
-        full_text_query=FullTextSearchQuery(columns=[], query="foo"),
+        full_text_query=FullTextSearchQuery(columns=None, query=match_query),
+        with_row_id=False,
+    )
+
+    phrase_query = PhraseQuery("foo", "text", slop=1)
+    q = (await table_async.search(phrase_query)).to_query_object()
+    check_set_props(
+        q,
+        full_text_query=FullTextSearchQuery(columns=None, query=phrase_query),
        with_row_id=False,
    )

--- a/python/python/tests/test_remote_db.py
+++ b/python/python/tests/test_remote_db.py
@@ -496,6 +496,8 @@ def test_query_sync_minimal():
            "ef": None,
            "vector": [1.0, 2.0, 3.0],
            "nprobes": 20,
+            "minimum_nprobes": 20,
+            "maximum_nprobes": 20,
            "version": None,
        }

@@ -536,6 +538,8 @@ def test_query_sync_maximal():
            "refine_factor": 10,
            "vector": [1.0, 2.0, 3.0],
            "nprobes": 5,
+            "minimum_nprobes": 5,
+            "maximum_nprobes": 5,
            "lower_bound": None,
            "upper_bound": None,
            "ef": None,
@@ -564,6 +568,66 @@ def test_query_sync_maximal():
        )


+def test_query_sync_nprobes():
+    def handler(body):
+        assert body == {
+            "distance_type": "l2",
+            "k": 10,
+            "prefilter": True,
+            "fast_search": True,
+            "vector_column": "vector2",
+            "refine_factor": None,
+            "lower_bound": None,
+            "upper_bound": None,
+            "ef": None,
+            "vector": [1.0, 2.0, 3.0],
+            "nprobes": 5,
+            "minimum_nprobes": 5,
+            "maximum_nprobes": 15,
+            "version": None,
+        }
+
+        return pa.table({"id": [1, 2, 3], "name": ["a", "b", "c"]})
+
+    with query_test_table(handler) as table:
+        (
+            table.search([1, 2, 3], vector_column_name="vector2", fast_search=True)
+            .minimum_nprobes(5)
+            .maximum_nprobes(15)
+            .to_list()
+        )
+
+
+def test_query_sync_no_max_nprobes():
+    def handler(body):
+        assert body == {
+            "distance_type": "l2",
+            "k": 10,
+            "prefilter": True,
+            "fast_search": True,
+            "vector_column": "vector2",
+            "refine_factor": None,
+            "lower_bound": None,
+            "upper_bound": None,
+            "ef": None,
+            "vector": [1.0, 2.0, 3.0],
+            "nprobes": 5,
+            "minimum_nprobes": 5,
+            "maximum_nprobes": 0,
+            "version": None,
+        }
+
+        return pa.table({"id": [1, 2, 3], "name": ["a", "b", "c"]})
+
+    with query_test_table(handler) as table:
+        (
+            table.search([1, 2, 3], vector_column_name="vector2", fast_search=True)
+            .minimum_nprobes(5)
+            .maximum_nprobes(0)
+            .to_list()
+        )
+
+
@pytest.mark.parametrize("server_version", [Version("0.1.0"), Version("0.2.0")])
 def test_query_sync_batch_queries(server_version):
    def handler(body):
@@ -666,6 +730,8 @@ def test_query_sync_hybrid():
                "refine_factor": None,
                "vector": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
                "nprobes": 20,
+                "minimum_nprobes": 20,
+                "maximum_nprobes": 20,
                "lower_bound": None,
                "upper_bound": None,
                "ef": None,
--- a/python/python/tests/test_s3.py
+++ b/python/python/tests/test_s3.py
@@ -245,7 +245,7 @@ def test_s3_dynamodb_sync(s3_bucket: str, commit_table: str, monkeypatch):
        NotImplementedError,
        match="Full-text search is only supported on the local filesystem",
    ):
-        table.create_fts_index("x")
+        table.create_fts_index("x", use_tantivy=True)

    # make sure list tables still works
    assert db.table_names() == ["test_ddb_sync"]
--- a/python/src/query.rs
+++ b/python/src/query.rs
@@ -9,15 +9,16 @@ use arrow::array::Array;
 use arrow::array::ArrayData;
 use arrow::pyarrow::FromPyArrow;
 use arrow::pyarrow::IntoPyArrow;
-use lancedb::index::scalar::{FtsQuery, FullTextSearchQuery, MatchQuery, PhraseQuery};
+use lancedb::index::scalar::{
+    BooleanQuery, BoostQuery, FtsQuery, FullTextSearchQuery, MatchQuery, MultiMatchQuery, Occur,
+    Operator, PhraseQuery,
+};
 use lancedb::query::QueryExecutionOptions;
 use lancedb::query::QueryFilter;
 use lancedb::query::{
    ExecutableQuery, Query as LanceDbQuery, QueryBase, Select, VectorQuery as LanceDbVectorQuery,
 };
 use lancedb::table::AnyQuery;
-use pyo3::exceptions::PyRuntimeError;
-use pyo3::exceptions::{PyNotImplementedError, PyValueError};
 use pyo3::prelude::{PyAnyMethods, PyDictMethods};
 use pyo3::pymethods;
 use pyo3::types::PyList;
@@ -27,30 +28,173 @@ use pyo3::IntoPyObject;
 use pyo3::PyAny;
 use pyo3::PyRef;
 use pyo3::PyResult;
+use pyo3::{exceptions::PyRuntimeError, FromPyObject};
+use pyo3::{
+    exceptions::{PyNotImplementedError, PyValueError},
+    intern,
+};
 use pyo3::{pyclass, PyErr};
 use pyo3_async_runtimes::tokio::future_into_py;

-use crate::arrow::RecordBatchStream;
-use crate::error::PythonErrorExt;
-use crate::util::{parse_distance_type, parse_fts_query};
+use crate::util::parse_distance_type;
+use crate::{arrow::RecordBatchStream, util::PyLanceDB};
+use crate::{error::PythonErrorExt, index::class_name};

-// Python representation of full text search parameters
-#[derive(Clone)]
-#[pyclass(get_all)]
-pub struct PyFullTextSearchQuery {
-    pub columns: Vec<String>,
-    pub query: String,
-    pub limit: Option<i64>,
-    pub wand_factor: Option<f32>,
+impl FromPyObject<'_> for PyLanceDB<FtsQuery> {
+    fn extract_bound(ob: &Bound<'_, PyAny>) -> PyResult<Self> {
+        match class_name(ob)?.as_str() {
+            "MatchQuery" => {
+                let query = ob.getattr("query")?.extract()?;
+                let column = ob.getattr("column")?.extract()?;
+                let boost = ob.getattr("boost")?.extract()?;
+                let fuzziness = ob.getattr("fuzziness")?.extract()?;
+                let max_expansions = ob.getattr("max_expansions")?.extract()?;
+                let operator = ob.getattr("operator")?.extract::<String>()?;
+                let prefix_length = ob.getattr("prefix_length")?.extract()?;
+
+                Ok(PyLanceDB(
+                    MatchQuery::new(query)
+                        .with_column(Some(column))
+                        .with_boost(boost)
+                        .with_fuzziness(fuzziness)
+                        .with_max_expansions(max_expansions)
+                        .with_operator(Operator::try_from(operator.as_str()).map_err(|e| {
+                            PyValueError::new_err(format!("Invalid operator: {}", e))
+                        })?)
+                        .with_prefix_length(prefix_length)
+                        .into(),
+                ))
+            }
+            "PhraseQuery" => {
+                let query = ob.getattr("query")?.extract()?;
+                let column = ob.getattr("column")?.extract()?;
+                let slop = ob.getattr("slop")?.extract()?;
+
+                Ok(PyLanceDB(
+                    PhraseQuery::new(query)
+                        .with_column(Some(column))
+                        .with_slop(slop)
+                        .into(),
+                ))
+            }
+            "BoostQuery" => {
+                let positive: PyLanceDB<FtsQuery> = ob.getattr("positive")?.extract()?;
+                let negative: PyLanceDB<FtsQuery> = ob.getattr("negative")?.extract()?;
+                let negative_boost = ob.getattr("negative_boost")?.extract()?;
+                Ok(PyLanceDB(
+                    BoostQuery::new(positive.0, negative.0, negative_boost).into(),
+                ))
+            }
+            "MultiMatchQuery" => {
+                let query = ob.getattr("query")?.extract()?;
+                let columns = ob.getattr("columns")?.extract()?;
+                let boosts: Option<Vec<f32>> = ob.getattr("boosts")?.extract()?;
+                let operator: String = ob.getattr("operator")?.extract()?;
+
+                let q = MultiMatchQuery::try_new(query, columns)
+                    .map_err(|e| PyValueError::new_err(format!("Invalid query: {}", e)))?;
+                let q = if let Some(boosts) = boosts {
+                    q.try_with_boosts(boosts)
+                        .map_err(|e| PyValueError::new_err(format!("Invalid boosts: {}", e)))?
+                } else {
+                    q
+                };
+
+                let op = Operator::try_from(operator.as_str())
+                    .map_err(|e| PyValueError::new_err(format!("Invalid operator: {}", e)))?;
+
+                Ok(PyLanceDB(q.with_operator(op).into()))
+            }
+            "BooleanQuery" => {
+                let queries: Vec<(String, PyLanceDB<FtsQuery>)> =
+                    ob.getattr("queries")?.extract()?;
+                let mut sub_queries = Vec::with_capacity(queries.len());
+                for (occur, q) in queries {
+                    let occur = Occur::try_from(occur.as_str())
+                        .map_err(|e| PyValueError::new_err(e.to_string()))?;
+                    sub_queries.push((occur, q.0));
+                }
+                Ok(PyLanceDB(BooleanQuery::new(sub_queries).into()))
+            }
+            name => Err(PyValueError::new_err(format!(
+                "Unsupported FTS query type: {}",
+                name
+            ))),
+        }
+    }
 }

-impl From<FullTextSearchQuery> for PyFullTextSearchQuery {
-    fn from(query: FullTextSearchQuery) -> Self {
-        Self {
-            columns: query.columns().into_iter().collect(),
-            query: query.query.query().to_owned(),
-            limit: query.limit,
-            wand_factor: query.wand_factor,
+impl<'py> IntoPyObject<'py> for PyLanceDB<FtsQuery> {
+    type Target = PyAny;
+    type Output = Bound<'py, Self::Target>;
+    type Error = PyErr;
+
+    fn into_pyobject(self, py: pyo3::Python<'py>) -> PyResult<Self::Output> {
+        let namespace = py
+            .import(intern!(py, "lancedb"))
+            .and_then(|m| m.getattr(intern!(py, "query")))
+            .expect("Failed to import namespace");
+
+        match self.0 {
+            FtsQuery::Match(query) => {
+                let kwargs = PyDict::new(py);
+                kwargs.set_item("boost", query.boost)?;
+                kwargs.set_item("fuzziness", query.fuzziness)?;
+                kwargs.set_item("max_expansions", query.max_expansions)?;
+                kwargs.set_item::<_, &str>("operator", query.operator.into())?;
+                kwargs.set_item("prefix_length", query.prefix_length)?;
+                namespace
+                    .getattr(intern!(py, "MatchQuery"))?
+                    .call((query.terms, query.column.unwrap()), Some(&kwargs))
+            }
+            FtsQuery::Phrase(query) => {
+                let kwargs = PyDict::new(py);
+                kwargs.set_item("slop", query.slop)?;
+                namespace
+                    .getattr(intern!(py, "PhraseQuery"))?
+                    .call((query.terms, query.column.unwrap()), Some(&kwargs))
+            }
+            FtsQuery::Boost(query) => {
+                let positive = PyLanceDB(query.positive.as_ref().clone()).into_pyobject(py)?;
+                let negative = PyLanceDB(query.negative.as_ref().clone()).into_pyobject(py)?;
+                let kwargs = PyDict::new(py);
+                kwargs.set_item("negative_boost", query.negative_boost)?;
+                namespace
+                    .getattr(intern!(py, "BoostQuery"))?
+                    .call((positive, negative), Some(&kwargs))
+            }
+            FtsQuery::MultiMatch(query) => {
+                let first = &query.match_queries[0];
+                let (columns, boosts): (Vec<_>, Vec<_>) = query
+                    .match_queries
+                    .iter()
+                    .map(|q| (q.column.as_ref().unwrap().clone(), q.boost))
+                    .unzip();
+                let kwargs = PyDict::new(py);
+                kwargs.set_item("boosts", boosts)?;
+                kwargs.set_item::<_, &str>("operator", first.operator.into())?;
+                namespace
+                    .getattr(intern!(py, "MultiMatchQuery"))?
+                    .call((first.terms.clone(), columns), Some(&kwargs))
+            }
+            FtsQuery::Boolean(query) => {
+                let mut queries: Vec<(&str, Bound<'py, PyAny>)> = Vec::with_capacity(
+                    query.should.len() + query.must.len() + query.must_not.len(),
+                );
+                for q in query.should {
+                    queries.push((Occur::Should.into(), PyLanceDB(q).into_pyobject(py)?));
+                }
+                for q in query.must {
+                    queries.push((Occur::Must.into(), PyLanceDB(q).into_pyobject(py)?));
+                }
+                for q in query.must_not {
+                    queries.push((Occur::MustNot.into(), PyLanceDB(q).into_pyobject(py)?));
+                }
+
+                namespace
+                    .getattr(intern!(py, "BooleanQuery"))?
+                    .call1((queries,))
+            }
        }
    }
 }
@@ -80,13 +224,16 @@ pub struct PyQueryRequest {
    pub limit: Option<usize>,
    pub offset: Option<usize>,
    pub filter: Option<PyQueryFilter>,
-    pub full_text_search: Option<PyFullTextSearchQuery>,
+    pub full_text_search: Option<PyLanceDB<FtsQuery>>,
    pub select: PySelect,
    pub fast_search: Option<bool>,
    pub with_row_id: Option<bool>,
    pub column: Option<String>,
    pub query_vector: Option<PyQueryVectors>,
-    pub nprobes: Option<usize>,
+    pub minimum_nprobes: Option<usize>,
+    // None means user did not set it and default shoud be used (currenty 20)
+    // Some(0) means user set it to None and there is no limit
+    pub maximum_nprobes: Option<usize>,
    pub lower_bound: Option<f32>,
    pub upper_bound: Option<f32>,
    pub ef: Option<usize>,
@@ -106,13 +253,14 @@ impl From<AnyQuery> for PyQueryRequest {
                filter: query_request.filter.map(PyQueryFilter),
                full_text_search: query_request
                    .full_text_search
-                    .map(PyFullTextSearchQuery::from),
+                    .map(|fts| PyLanceDB(fts.query)),
                select: PySelect(query_request.select),
                fast_search: Some(query_request.fast_search),
                with_row_id: Some(query_request.with_row_id),
                column: None,
                query_vector: None,
-                nprobes: None,
+                minimum_nprobes: None,
+                maximum_nprobes: None,
                lower_bound: None,
                upper_bound: None,
                ef: None,
@@ -132,7 +280,11 @@ impl From<AnyQuery> for PyQueryRequest {
                with_row_id: Some(vector_query.base.with_row_id),
                column: vector_query.column,
                query_vector: Some(PyQueryVectors(vector_query.query_vector)),
-                nprobes: Some(vector_query.nprobes),
+                minimum_nprobes: Some(vector_query.minimum_nprobes),
+                maximum_nprobes: match vector_query.maximum_nprobes {
+                    None => Some(0),
+                    Some(value) => Some(value),
+                },
                lower_bound: vector_query.lower_bound,
                upper_bound: vector_query.upper_bound,
                ef: vector_query.ef,
@@ -269,8 +421,8 @@ impl Query {
                }
            };
            let mut query = FullTextSearchQuery::new_query(query);
-            if let Some(cols) = columns {
-                if !cols.is_empty() {
+            match columns {
+                Some(cols) if !cols.is_empty() => {
                    query = query.with_columns(&cols).map_err(|e| {
                        PyValueError::new_err(format!(
                            "Failed to set full text search columns: {}",
@@ -278,15 +430,12 @@ impl Query {
                        ))
                    })?;
                }
+                _ => {}
            }
            query
-        } else if let Ok(query) = fts_query.downcast::<PyDict>() {
-            let query = parse_fts_query(query)?;
-            FullTextSearchQuery::new_query(query)
        } else {
-            return Err(PyValueError::new_err(
-                "query must be a string or a Query object",
-            ));
+            let query = fts_query.extract::<PyLanceDB<FtsQuery>>()?;
+            FullTextSearchQuery::new_query(query.0)
        };

        Ok(FTSQuery {
@@ -509,6 +658,29 @@ impl VectorQuery {
        self.inner = self.inner.clone().nprobes(nprobe as usize);
    }

+    pub fn minimum_nprobes(&mut self, minimum_nprobes: u32) -> PyResult<()> {
+        self.inner = self
+            .inner
+            .clone()
+            .minimum_nprobes(minimum_nprobes as usize)
+            .infer_error()?;
+        Ok(())
+    }
+
+    pub fn maximum_nprobes(&mut self, maximum_nprobes: u32) -> PyResult<()> {
+        let maximum_nprobes = if maximum_nprobes == 0 {
+            None
+        } else {
+            Some(maximum_nprobes as usize)
+        };
+        self.inner = self
+            .inner
+            .clone()
+            .maximum_nprobes(maximum_nprobes)
+            .infer_error()?;
+        Ok(())
+    }
+
    #[pyo3(signature = (lower_bound=None, upper_bound=None))]
    pub fn distance_range(&mut self, lower_bound: Option<f32>, upper_bound: Option<f32>) {
        self.inner = self.inner.clone().distance_range(lower_bound, upper_bound);
--- a/python/src/util.rs
+++ b/python/src/util.rs
@@ -3,15 +3,11 @@

 use std::sync::Mutex;

-use lancedb::index::scalar::{BoostQuery, FtsQuery, MatchQuery, MultiMatchQuery, PhraseQuery};
 use lancedb::DistanceType;
-use pyo3::prelude::{PyAnyMethods, PyDictMethods, PyListMethods};
-use pyo3::types::PyDict;
 use pyo3::{
    exceptions::{PyRuntimeError, PyValueError},
    pyfunction, PyResult,
 };
-use pyo3::{Bound, PyAny};

 /// A wrapper around a rust builder
 ///
@@ -64,116 +60,6 @@ pub fn validate_table_name(table_name: &str) -> PyResult<()> {
        .map_err(|e| PyValueError::new_err(e.to_string()))
 }

-pub fn parse_fts_query(query: &Bound<'_, PyDict>) -> PyResult<FtsQuery> {
-    let query_type = query.keys().get_item(0)?.extract::<String>()?;
-    let query_value = query
-        .get_item(&query_type)?
-        .ok_or(PyValueError::new_err(format!(
-            "Query type {} not found",
-            query_type
-        )))?;
-    let query_value = query_value.downcast::<PyDict>()?;
-
-    match query_type.as_str() {
-        "match" => {
-            let column = query_value.keys().get_item(0)?.extract::<String>()?;
-            let params = query_value
-                .get_item(&column)?
-                .ok_or(PyValueError::new_err(format!(
-                    "column {} not found",
-                    column
-                )))?;
-            let params = params.downcast::<PyDict>()?;
-
-            let query = params
-                .get_item("query")?
-                .ok_or(PyValueError::new_err("query not found"))?
-                .extract::<String>()?;
-            let boost = params
-                .get_item("boost")?
-                .ok_or(PyValueError::new_err("boost not found"))?
-                .extract::<f32>()?;
-            let fuzziness = params
-                .get_item("fuzziness")?
-                .ok_or(PyValueError::new_err("fuzziness not found"))?
-                .extract::<Option<u32>>()?;
-            let max_expansions = params
-                .get_item("max_expansions")?
-                .ok_or(PyValueError::new_err("max_expansions not found"))?
-                .extract::<usize>()?;
-
-            let query = MatchQuery::new(query)
-                .with_column(Some(column))
-                .with_boost(boost)
-                .with_fuzziness(fuzziness)
-                .with_max_expansions(max_expansions);
-            Ok(query.into())
-        }
-
-        "match_phrase" => {
-            let column = query_value.keys().get_item(0)?.extract::<String>()?;
-            let query = query_value
-                .get_item(&column)?
-                .ok_or(PyValueError::new_err(format!(
-                    "column {} not found",
-                    column
-                )))?
-                .extract::<String>()?;
-
-            let query = PhraseQuery::new(query).with_column(Some(column));
-            Ok(query.into())
-        }
-
-        "boost" => {
-            let positive: Bound<'_, PyAny> = query_value
-                .get_item("positive")?
-                .ok_or(PyValueError::new_err("positive not found"))?;
-            let positive = positive.downcast::<PyDict>()?;
-
-            let negative = query_value
-                .get_item("negative")?
-                .ok_or(PyValueError::new_err("negative not found"))?;
-            let negative = negative.downcast::<PyDict>()?;
-
-            let negative_boost = query_value
-                .get_item("negative_boost")?
-                .ok_or(PyValueError::new_err("negative_boost not found"))?
-                .extract::<f32>()?;
-
-            let positive_query = parse_fts_query(positive)?;
-            let negative_query = parse_fts_query(negative)?;
-            let query = BoostQuery::new(positive_query, negative_query, Some(negative_boost));
-
-            Ok(query.into())
-        }
-
-        "multi_match" => {
-            let query = query_value
-                .get_item("query")?
-                .ok_or(PyValueError::new_err("query not found"))?
-                .extract::<String>()?;
-
-            let columns = query_value
-                .get_item("columns")?
-                .ok_or(PyValueError::new_err("columns not found"))?
-                .extract::<Vec<String>>()?;
-
-            let boost = query_value
-                .get_item("boost")?
-                .ok_or(PyValueError::new_err("boost not found"))?
-                .extract::<Vec<f32>>()?;
-
-            let query = MultiMatchQuery::try_new(query, columns)
-                .and_then(|q| q.try_with_boosts(boost))
-                .map_err(|e| {
-                    PyValueError::new_err(format!("Error creating MultiMatchQuery: {}", e))
-                })?;
-            Ok(query.into())
-        }
-
-        _ => Err(PyValueError::new_err(format!(
-            "Unsupported query type: {}",
-            query_type
-        ))),
-    }
-}
+/// A wrapper around a LanceDB type to allow it to be used in Python
+#[derive(Debug, Clone)]
+pub struct PyLanceDB<T>(pub T);
--- a/rust/ffi/node/Cargo.toml
+++ b/rust/ffi/node/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-node"
-version = "0.20.0-beta.2"
+version = "0.20.1-beta.2"
 description = "Serverless, low-latency vector database for AI applications"
 license.workspace = true
 edition.workspace = true
--- a/rust/lancedb/Cargo.toml
+++ b/rust/lancedb/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.20.0-beta.2"
+version = "0.20.1-beta.2"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true
--- a/rust/lancedb/src/query.rs
+++ b/rust/lancedb/src/query.rs
@@ -796,8 +796,10 @@ pub struct VectorQueryRequest {
    pub column: Option<String>,
    /// The vector(s) to search for
    pub query_vector: Vec<Arc<dyn Array>>,
-    /// The number of partitions to search
-    pub nprobes: usize,
+    /// The minimum number of partitions to search
+    pub minimum_nprobes: usize,
+    /// The maximum number of partitions to search
+    pub maximum_nprobes: Option<usize>,
    /// The lower bound (inclusive) of the distance to search for.
    pub lower_bound: Option<f32>,
    /// The upper bound (exclusive) of the distance to search for.
@@ -819,7 +821,8 @@ impl Default for VectorQueryRequest {
            base: QueryRequest::default(),
            column: None,
            query_vector: Vec::new(),
-            nprobes: 20,
+            minimum_nprobes: 20,
+            maximum_nprobes: Some(20),
            lower_bound: None,
            upper_bound: None,
            ef: None,
@@ -925,11 +928,75 @@ impl VectorQuery {
    /// For best results we recommend tuning this parameter with a benchmark against
    /// your actual data to find the smallest possible value that will still give
    /// you the desired recall.
+    ///
+    /// This method sets both the minimum and maximum number of partitions to search.
+    /// For more fine-grained control see [`VectorQuery::minimum_nprobes`] and
+    /// [`VectorQuery::maximum_nprobes`].
    pub fn nprobes(mut self, nprobes: usize) -> Self {
-        self.request.nprobes = nprobes;
+        self.request.minimum_nprobes = nprobes;
+        self.request.maximum_nprobes = Some(nprobes);
        self
    }

+    /// Set the minimum number of partitions to search
+    ///
+    /// This argument is only used when the vector column has an IVF PQ index.
+    /// If there is no index then this value is ignored.
+    ///
+    /// See [`VectorQuery::nprobes`] for more details.
+    ///
+    /// These partitions will be searched on every indexed vector query.
+    ///
+    /// Will return an error if the value is not greater than 0 or if maximum_nprobes
+    /// has been set and is less than the minimum_nprobes.
+    pub fn minimum_nprobes(mut self, minimum_nprobes: usize) -> Result<Self> {
+        if minimum_nprobes == 0 {
+            return Err(Error::InvalidInput {
+                message: "minimum_nprobes must be greater than 0".to_string(),
+            });
+        }
+        if let Some(maximum_nprobes) = self.request.maximum_nprobes {
+            if minimum_nprobes > maximum_nprobes {
+                return Err(Error::InvalidInput {
+                    message: "minimum_nprobes must be less or equal to maximum_nprobes".to_string(),
+                });
+            }
+        }
+        self.request.minimum_nprobes = minimum_nprobes;
+        Ok(self)
+    }
+
+    /// Set the maximum number of partitions to search
+    ///
+    /// This argument is only used when the vector column has an IVF PQ index.
+    /// If there is no index then this value is ignored.
+    ///
+    /// See [`VectorQuery::nprobes`] for more details.
+    ///
+    /// If this value is greater than minimum_nprobes then the excess partitions will
+    /// only be searched if the initial search does not return enough results.
+    ///
+    /// This can be useful when there is a narrow filter to allow these queries to
+    /// spend more time searching and avoid potential false negatives.
+    ///
+    /// Set to None to search all partitions, if needed, to satsify the limit
+    pub fn maximum_nprobes(mut self, maximum_nprobes: Option<usize>) -> Result<Self> {
+        if let Some(maximum_nprobes) = maximum_nprobes {
+            if maximum_nprobes == 0 {
+                return Err(Error::InvalidInput {
+                    message: "maximum_nprobes must be greater than 0".to_string(),
+                });
+            }
+            if maximum_nprobes < self.request.minimum_nprobes {
+                return Err(Error::InvalidInput {
+                    message: "maximum_nprobes must be greater than minimum_nprobes".to_string(),
+                });
+            }
+        }
+        self.request.maximum_nprobes = maximum_nprobes;
+        Ok(self)
+    }
+
    /// Set the distance range for vector search,
    /// only rows with distances in the range [lower_bound, upper_bound) will be returned
    pub fn distance_range(mut self, lower_bound: Option<f32>, upper_bound: Option<f32>) -> Self {
@@ -1208,7 +1275,8 @@ mod tests {
        );
        assert_eq!(query.request.base.limit.unwrap(), 100);
        assert_eq!(query.request.base.offset.unwrap(), 1);
-        assert_eq!(query.request.nprobes, 1000);
+        assert_eq!(query.request.minimum_nprobes, 1000);
+        assert_eq!(query.request.maximum_nprobes, Some(1000));
        assert!(query.request.use_index);
        assert_eq!(query.request.distance_type, Some(DistanceType::Cosine));
        assert_eq!(query.request.refine_factor, Some(999));
--- a/rust/lancedb/src/remote/table.rs
+++ b/rust/lancedb/src/remote/table.rs
@@ -32,6 +32,7 @@ use lance::dataset::{ColumnAlteration, NewColumnTransform, Version};
 use lance_datafusion::exec::{execute_plan, OneShotExec};
 use reqwest::{RequestBuilder, Response};
 use serde::{Deserialize, Serialize};
+use serde_json::Number;
 use std::collections::HashMap;
 use std::io::Cursor;
 use std::pin::Pin;
@@ -438,7 +439,18 @@ impl<S: HttpSend> RemoteTable<S> {

        // Apply general parameters, before we dispatch based on number of query vectors.
        body["distance_type"] = serde_json::json!(query.distance_type.unwrap_or_default());
-        body["nprobes"] = query.nprobes.into();
+        // In 0.23.1 we migrated from `nprobes` to `minimum_nprobes` and `maximum_nprobes`.
+        // Old client / new server: since minimum_nprobes is missing, fallback to nprobes
+        // New client / old server: old server will only see nprobes, make sure to set both
+        //                          nprobes and minimum_nprobes
+        // New client / new server: since minimum_nprobes is present, server can ignore nprobes
+        body["nprobes"] = query.minimum_nprobes.into();
+        body["minimum_nprobes"] = query.minimum_nprobes.into();
+        if let Some(maximum_nprobes) = query.maximum_nprobes {
+            body["maximum_nprobes"] = maximum_nprobes.into();
+        } else {
+            body["maximum_nprobes"] = serde_json::Value::Number(Number::from_u128(0).unwrap())
+        }
        body["lower_bound"] = query.lower_bound.into();
        body["upper_bound"] = query.upper_bound.into();
        body["ef"] = query.ef.into();
@@ -2075,6 +2087,8 @@ mod tests {
                "prefilter": true,
                "distance_type": "l2",
                "nprobes": 20,
+                "minimum_nprobes": 20,
+                "maximum_nprobes": 20,
                "lower_bound": Option::<f32>::None,
                "upper_bound": Option::<f32>::None,
                "k": 10,
@@ -2175,6 +2189,8 @@ mod tests {
                "bypass_vector_index": true,
                "columns": ["a", "b"],
                "nprobes": 12,
+                "minimum_nprobes": 12,
+                "maximum_nprobes": 12,
                "lower_bound": Option::<f32>::None,
                "upper_bound": Option::<f32>::None,
                "ef": Option::<usize>::None,
@@ -2302,6 +2318,7 @@ mod tests {
                                "fuzziness": 0,
                                "max_expansions": 50,
                                "operator": "Or",
+                                "prefix_length": 0,
                            },
                        }
                    },
--- a/rust/lancedb/src/table.rs
+++ b/rust/lancedb/src/table.rs
@@ -2354,12 +2354,15 @@ impl BaseTable for NativeTable {
                    query.base.limit.unwrap_or(DEFAULT_TOP_K),
                )?;
            }
+            scanner.minimum_nprobes(query.minimum_nprobes);
+            if let Some(maximum_nprobes) = query.maximum_nprobes {
+                scanner.maximum_nprobes(maximum_nprobes);
+            }
        }
        scanner.limit(
            query.base.limit.map(|limit| limit as i64),
            query.base.offset.map(|offset| offset as i64),
        )?;
-        scanner.nprobs(query.nprobes);
        if let Some(ef) = query.ef {
            scanner.ef(ef);
        }
Author	SHA1	Message	Date
Lance Release	c625b6f2b2	Bump version: 0.24.0-beta.0 → 0.24.0	2025-06-20 05:46:05 +00:00
Lance Release	bec8fe6547	Bump version: 0.23.1-beta.2 → 0.24.0-beta.0	2025-06-20 05:46:04 +00:00
BubbleCal	dc1150c011	chore: upgrade lance to 0.30.0 (#2451 ) lance [release details](https://github.com/lancedb/lance/releases/tag/v0.30.0) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - Chores - Updated dependency specifications to use exact version numbers instead of referencing a git repository and tag. <!-- end of auto-generated comment: release notes by coderabbit.ai --> Signed-off-by: BubbleCal <bubble-cal@outlook.com>	2025-06-20 11:27:20 +08:00
Will Jones	afaefc6264	ci: fix package lock again (#2449 ) We are able to push commits over here: `cb7293e073/.github/workflows/make-release-commit.yml (L88-L95)` So I think it's safe to assume this will work. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - Chores - Updated workflow configuration to improve authentication and branch targeting for automated release processes. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-06-19 08:51:48 -07:00
BubbleCal	cb70ff8cee	feat!: switch default FTS to native lance FTS (#2428 ) This switches the default FTS to native lance FTS for Python sync table API, the other APIs have switched to native implementation already <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - New Features - The default behavior for creating a full-text search index now uses the new implementation rather than the legacy one. - Bug Fixes - Improved handling and error messages for phrase queries in full-text search. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: BubbleCal <bubble-cal@outlook.com>	2025-06-19 10:38:34 +08:00
BubbleCal	cbb5a841b1	feat: support prefix matching and must_not clause (#2441 )	2025-06-19 10:32:32 +08:00
Lance Release	c72f6770fd	Bump version: 0.20.1-beta.1 → 0.20.1-beta.2	2025-06-18 23:33:57 +00:00
Lance Release	e5a80a5e86	Bump version: 0.23.1-beta.1 → 0.23.1-beta.2	2025-06-18 23:33:05 +00:00
Will Jones	8d0a7fad1f	ci: try again to fix node lockfiles (#2445 ) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - Chores - Updated the release workflow to explicitly check out the main branch during the publishing process. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-06-18 14:45:39 -07:00
LuQQiu	b80d4d0134	chore: update Lance to v0.30.0-beta.1 (#2444 ) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - Chores - Updated internal dependencies for improved stability and compatibility. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-06-18 14:15:39 -07:00
satya-nutella	9645fe52c2	fix: improve error handling and embedding logic in arrow.ts (#2433 ) - Enhanced error messages for schema inference failures to suggest providing an explicit schema. - Updated embedding application logic to check for existing destination columns, allowing for filling embeddings in columns that are all null. - Added comments for clarity on handling existing columns during embedding application. Fixes https://github.com/lancedb/lancedb/issues/2183 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit ## Summary by CodeRabbit - Bug Fixes - Improved error messages for schema inference to enhance readability. - Prevented redundant embedding application by skipping columns that already contain data, avoiding unnecessary errors and computations. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-06-18 12:45:11 -07:00
Lance Release	b77314168d	Bump version: 0.20.1-beta.0 → 0.20.1-beta.1	2025-06-17 23:22:50 +00:00
Lance Release	e08d45e090	Bump version: 0.23.1-beta.0 → 0.23.1-beta.1	2025-06-17 23:22:00 +00:00
Will Jones	2e3ddb8382	ci: fix lockfile failure for vectordb node (#2443 ) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - Chores - Updated release workflow to set a specific Git user name and email for automated commits during the package publishing process. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-06-17 15:14:11 -07:00
Wyatt Alt	627ca4c810	chore: update lance to v0.29.1-beta.2 (#2442 ) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - Chores - Updated internal dependencies to use a newer version of the Lance library. - New Features - Added support for a new query occurrence type labeled "MUST NOT" in search filters. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-06-17 14:02:13 -07:00
Lance Release	f8dae4ffe9	Bump version: 0.20.0 → 0.20.1-beta.0	2025-06-16 16:30:14 +00:00
Lance Release	9eb6119468	Bump version: 0.23.0 → 0.23.1-beta.0	2025-06-16 16:29:22 +00:00
Weston Pace	59b57e30ed	feat: add maximum and minimum nprobes properties (#2430 ) This exposes the maximum_nprobes and minimum_nprobes feature that was added in https://github.com/lancedb/lance/pull/3903 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - New Features - Added support for specifying minimum and maximum probe counts in vector search queries, allowing finer control over search behavior. - Users can now independently set minimum and maximum probes for vector and hybrid queries via new methods and parameters in Python, Node.js, and Rust APIs. - Bug Fixes - Improved parameter validation to ensure correct usage of minimum and maximum probe values. - Tests - Expanded test coverage to validate correct handling, serialization, and error cases for the new probe parameters. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-06-13 15:18:29 -07:00
BubbleCal	fec8d58f06	feat: support a bunch or FTS features in JS SDK (#2431 ) - operator for match query - slop for phrase query - boolean query <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - New Features - Introduced support for boolean full-text search queries with AND/OR logic and occurrence conditions. - Added operator options for match and multi-match queries to control term combination logic. - Enabled phrase queries to specify proximity (slop) for flexible phrase matching. - Added new enumerations (`Operator`, `Occur`) and the `BooleanQuery` class for enhanced query expressiveness. - Bug Fixes - Improved validation and error handling for invalid operator and occurrence inputs in full-text queries. - Tests - Expanded test coverage with new cases for boolean queries and operator-based full-text searches. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: BubbleCal <bubble-cal@outlook.com>	2025-06-12 17:04:19 +08:00
BubbleCal	84ded9d678	feat: support new FTS features in python SDK (#2411 ) - AND operator - phrase query slop param - boolean query <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - New Features - Added support for combining full-text search queries using AND/OR operators, enabling more flexible query composition. - Introduced new query types and parameters, including boolean queries, operator selection, occurrence constraints, and phrase slop for advanced search scenarios. - Enhanced asynchronous search to accept rich full-text query objects directly. - Bug Fixes - Improved handling and validation of full-text search queries in both synchronous and asynchronous search operations. - Tests - Updated and expanded tests to cover new full-text query types and their usage in search functions. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: BubbleCal <bubble-cal@outlook.com>	2025-06-06 14:33:46 +08:00
Wyatt Alt	65696d9713	chore: update lance in lancedb (#2424 ) This updates lance to v0.29.1-beta.1. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - Chores - Updated workspace dependencies for improved consistency and reliability. No changes to user-facing functionality. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-06-04 19:06:51 -07:00
Will Jones	e2f2ea32e4	ci: fix vectordb release (#2422 ) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - Chores - Updated the release workflow to include an additional step for improved process reliability. No changes to user-facing functionality. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-06-04 17:06:02 -07:00
Lance Release	d5f2eca754	Bump version: 0.20.0-beta.3 → 0.20.0	2025-06-04 21:08:31 +00:00
Lance Release	7fa455a8a5	Bump version: 0.20.0-beta.2 → 0.20.0-beta.3	2025-06-04 21:07:59 +00:00