Bump version: 0.21.2-beta.2 → 0.21.2

Bump version: 0.21.2-beta.1 → 0.21.2-beta.2
Bump version: 0.18.2-beta.0 → 0.18.2-beta.1
2025-12-23 13:29:57 +00:00 · 2025-03-26 16:27:05 +00:00 · 2025-03-26 16:27:05 +00:00 · 2025-03-26 16:24:17 +00:00 · 2025-03-26 16:23:37 +00:00 · 2025-03-26 09:19:48 -07:00
89 changed files with 1067 additions and 4349 deletions
--- a/.bumpversion.toml
+++ b/.bumpversion.toml
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.19.0-beta.7"
+current_version = "0.18.2-beta.1"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
--- a/.github/workflows/java-publish.yml
+++ b/.github/workflows/java-publish.yml
@@ -43,7 +43,7 @@ jobs:
      - uses: Swatinem/rust-cache@v2
      - uses: actions-rust-lang/setup-rust-toolchain@v1
        with:
-          toolchain: "1.81.0"
+          toolchain: "1.79.0"
          cache-workspaces: "./java/core/lancedb-jni"
          # Disable full debug symbol generation to speed up CI build and keep memory down
          # "1" means line tables only, which is useful for panic tracebacks.
@@ -97,7 +97,7 @@ jobs:
      - name: Dry run
        if: github.event_name == 'pull_request'
        run: |
-          mvn --batch-mode -DskipTests -Drust.release.build=true package
+          mvn --batch-mode -DskipTests package
      - name: Set github
        run: |
          git config --global user.email "LanceDB Github Runner"
@@ -108,7 +108,7 @@ jobs:
          echo "use-agent" >> ~/.gnupg/gpg.conf
          echo "pinentry-mode loopback" >> ~/.gnupg/gpg.conf
          export GPG_TTY=$(tty)
-          mvn --batch-mode -DskipTests -Drust.release.build=true -DpushChanges=false -Dgpg.passphrase=${{ secrets.GPG_PASSPHRASE }} deploy -P deploy-to-ossrh
+          mvn --batch-mode -DskipTests -DpushChanges=false -Dgpg.passphrase=${{ secrets.GPG_PASSPHRASE }} deploy -P deploy-to-ossrh
        env:
          SONATYPE_USER: ${{ secrets.SONATYPE_USER }}
          SONATYPE_TOKEN: ${{ secrets.SONATYPE_TOKEN }}
--- a/.github/workflows/npm-publish.yml
+++ b/.github/workflows/npm-publish.yml
@@ -18,7 +18,6 @@ on:
    # This should trigger a dry run (we skip the final publish step)
    paths:
      - .github/workflows/npm-publish.yml
-      - Cargo.toml # Change in dependency frequently breaks builds

 concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}
@@ -131,24 +130,29 @@ jobs:
              set -e &&
              apt-get update &&
              apt-get install -y protobuf-compiler pkg-config
-          - target: x86_64-unknown-linux-musl
-            # This one seems to need some extra memory
-            host: ubuntu-2404-8x-x64
-            # https://github.com/napi-rs/napi-rs/blob/main/alpine.Dockerfile
-            docker: ghcr.io/napi-rs/napi-rs/nodejs-rust:lts-alpine
-            features: fp16kernels
-            pre_build: |-
-              set -e &&
-              apk add protobuf-dev curl &&
-              ln -s /usr/lib/gcc/x86_64-alpine-linux-musl/14.2.0/crtbeginS.o /usr/lib/crtbeginS.o &&
-              ln -s /usr/lib/libgcc_s.so /usr/lib/libgcc.so &&
-              CC=gcc &&
-              CXX=g++
+
+          # TODO: re-enable x64 musl builds. I could not figure out why, but it
+          # consistently made GHA runners non-responsive at the end of build. Example:
+          # https://github.com/lancedb/lancedb/actions/runs/13980431071/job/39144319470?pr=2250
+
+          # - target: x86_64-unknown-linux-musl
+          #   # This one seems to need some extra memory
+          #   host: ubuntu-2404-8x-x64
+          #   # https://github.com/napi-rs/napi-rs/blob/main/alpine.Dockerfile
+          #   docker: ghcr.io/napi-rs/napi-rs/nodejs-rust:lts-alpine
+          #   features: ","
+          #   pre_build: |-
+          #     set -e &&
+          #     apk add protobuf-dev curl &&
+          #     ln -s /usr/lib/gcc/x86_64-alpine-linux-musl/14.2.0/crtbeginS.o /usr/lib/crtbeginS.o &&
+          #     ln -s /usr/lib/libgcc_s.so /usr/lib/libgcc.so
+
          - target: aarch64-unknown-linux-gnu
            host: ubuntu-2404-8x-x64
            # https://github.com/napi-rs/napi-rs/blob/main/debian-aarch64.Dockerfile
            docker: ghcr.io/napi-rs/napi-rs/nodejs-rust:lts-debian-aarch64
-            features: "fp16kernels"
+            # TODO: enable fp16kernels after https://github.com/lancedb/lance/pull/3559
+            features: ","
            pre_build: |-
              set -e &&
              apt-get update &&
@@ -166,8 +170,8 @@ jobs:
              set -e &&
              apk add protobuf-dev &&
              rustup target add aarch64-unknown-linux-musl &&
-              export CC_aarch64_unknown_linux_musl=aarch64-linux-musl-gcc &&
-              export CXX_aarch64_unknown_linux_musl=aarch64-linux-musl-g++
+              export CC="/aarch64-linux-musl-cross/bin/aarch64-linux-musl-gcc" &&
+              export CXX="/aarch64-linux-musl-cross/bin/aarch64-linux-musl-g++"
    name: build - ${{ matrix.settings.target }}
    runs-on: ${{ matrix.settings.host }}
    defaults:
@@ -531,12 +535,6 @@ jobs:
          for filename in *.tgz; do
            npm publish $PUBLISH_ARGS $filename
          done
-      - name: Deprecate
-        env:
-          NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
-        # 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: Notify Slack Action
        uses: ravsamhq/notify-slack-action@2.3.0
        if: ${{ always() }}
--- a/.github/workflows/pypi-publish.yml
+++ b/.github/workflows/pypi-publish.yml
@@ -8,7 +8,6 @@ on:
    # This should trigger a dry run (we skip the final publish step)
    paths:
      - .github/workflows/pypi-publish.yml
-      - Cargo.toml # Change in dependency frequently breaks builds

 jobs:
  linux:
--- a/Cargo.lock
+++ b/Cargo.lock
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -21,16 +21,16 @@ categories = ["database-implementations"]
 rust-version = "1.78.0"

 [workspace.dependencies]
-lance = { "version" = "=0.26.0", "features" = [
+lance = { "version" = "=0.25.0", "features" = [
    "dynamodb",
-], tag = "v0.26.0-beta.1", git = "https://github.com/lancedb/lance" }
-lance-io = { version = "=0.26.0", tag = "v0.26.0-beta.1", git = "https://github.com/lancedb/lance" }
-lance-index = { version = "=0.26.0", tag = "v0.26.0-beta.1", git = "https://github.com/lancedb/lance" }
-lance-linalg = { version = "=0.26.0", tag = "v0.26.0-beta.1", git = "https://github.com/lancedb/lance" }
-lance-table = { version = "=0.26.0", tag = "v0.26.0-beta.1", git = "https://github.com/lancedb/lance" }
-lance-testing = { version = "=0.26.0", tag = "v0.26.0-beta.1", git = "https://github.com/lancedb/lance" }
-lance-datafusion = { version = "=0.26.0", tag = "v0.26.0-beta.1", git = "https://github.com/lancedb/lance" }
-lance-encoding = { version = "=0.26.0", tag = "v0.26.0-beta.1", git = "https://github.com/lancedb/lance" }
+] }
+lance-io = { version = "=0.25.0" }
+lance-index = { version = "=0.25.0" }
+lance-linalg = { version = "=0.25.0" }
+lance-table = { version = "=0.25.0" }
+lance-testing = { version = "=0.25.0" }
+lance-datafusion = { version = "=0.25.0" }
+lance-encoding = { version = "=0.25.0" }
 # Note that this one does not include pyarrow
 arrow = { version = "54.1", optional = false }
 arrow-array = "54.1"
@@ -41,12 +41,12 @@ arrow-schema = "54.1"
 arrow-arith = "54.1"
 arrow-cast = "54.1"
 async-trait = "0"
-datafusion = { version = "46.0", default-features = false }
-datafusion-catalog = "46.0"
-datafusion-common = { version = "46.0", default-features = false }
-datafusion-execution = "46.0"
-datafusion-expr = "46.0"
-datafusion-physical-plan = "46.0"
+datafusion = { version = "45.0", default-features = false }
+datafusion-catalog = "45.0"
+datafusion-common = { version = "45.0", default-features = false }
+datafusion-execution = "45.0"
+datafusion-expr = "45.0"
+datafusion-physical-plan = "45.0"
 env_logger = "0.11"
 half = { "version" = "=2.4.1", default-features = false, features = [
    "num-traits",
--- a/docs/README.md
+++ b/docs/README.md
@@ -2,7 +2,7 @@

 LanceDB docs are deployed to https://lancedb.github.io/lancedb/.

-Docs is built and deployed automatically by [Github Actions](../.github/workflows/docs.yml)
+Docs is built and deployed automatically by [Github Actions](.github/workflows/docs.yml)
 whenever a commit is pushed to the `main` branch. So it is possible for the docs to show
 unreleased features.

--- a/docs/src/guides/storage.md
+++ b/docs/src/guides/storage.md
@@ -342,7 +342,7 @@ For **read and write access**, LanceDB will need a policy such as:
            "Action": [
              "s3:PutObject",
              "s3:GetObject",
-              "s3:DeleteObject"
+              "s3:DeleteObject",
            ],
            "Resource": "arn:aws:s3:::<bucket>/<prefix>/*"
        },
@@ -374,7 +374,7 @@ For **read-only access**, LanceDB will need a policy such as:
        {
            "Effect": "Allow",
            "Action": [
-              "s3:GetObject"
+              "s3:GetObject",
            ],
            "Resource": "arn:aws:s3:::<bucket>/<prefix>/*"
        },
--- a/docs/src/js/classes/BoostQuery.md
+++ b/docs/src/js/classes/BoostQuery.md
@@ -1,67 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / BoostQuery
-
-# Class: BoostQuery
-
-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 BoostQuery()
-
-```ts
-new BoostQuery(
-   positive,
-   negative,
-   options?): BoostQuery
-```
-
-Creates an instance of BoostQuery.
-The boost returns documents that match the positive query,
-but penalizes those that match the negative query.
-the penalty is controlled by the `negativeBoost` parameter.
-
-#### Parameters
-
-* **positive**: [`FullTextQuery`](../interfaces/FullTextQuery.md)
-    The positive query that boosts the relevance score.
-
-* **negative**: [`FullTextQuery`](../interfaces/FullTextQuery.md)
-    The negative query that reduces the relevance score.
-
-* **options?**
-    Optional parameters for the boost query.
-    - `negativeBoost`: The boost factor for the negative query (default is 0.0).
-
-* **options.negativeBoost?**: `number`
-
-#### Returns
-
-[`BoostQuery`](BoostQuery.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
@@ -1,70 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / MatchQuery
-
-# Class: MatchQuery
-
-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 MatchQuery()
-
-```ts
-new MatchQuery(
-   query,
-   column,
-   options?): MatchQuery
-```
-
-Creates an instance of MatchQuery.
-
-#### Parameters
-
-* **query**: `string`
-    The text query to search for.
-
-* **column**: `string`
-    The name of the column to search within.
-
-* **options?**
-    Optional parameters for the match query.
-    - `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).
-
-* **options.boost?**: `number`
-
-* **options.fuzziness?**: `number`
-
-* **options.maxExpansions?**: `number`
-
-#### Returns
-
-[`MatchQuery`](MatchQuery.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/MultiMatchQuery.md
+++ b/docs/src/js/classes/MultiMatchQuery.md
@@ -1,64 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / MultiMatchQuery
-
-# Class: MultiMatchQuery
-
-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 MultiMatchQuery()
-
-```ts
-new MultiMatchQuery(
-   query,
-   columns,
-   options?): MultiMatchQuery
-```
-
-Creates an instance of MultiMatchQuery.
-
-#### Parameters
-
-* **query**: `string`
-    The text query to search for across multiple columns.
-
-* **columns**: `string`[]
-    An array of column names to search within.
-
-* **options?**
-    Optional parameters for the multi-match query.
-    - `boosts`: An array of boost factors for each column (default is 1.0 for all).
-
-* **options.boosts?**: `number`[]
-
-#### Returns
-
-[`MultiMatchQuery`](MultiMatchQuery.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/PhraseQuery.md
+++ b/docs/src/js/classes/PhraseQuery.md
@@ -1,55 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / PhraseQuery
-
-# Class: PhraseQuery
-
-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 PhraseQuery()
-
-```ts
-new PhraseQuery(query, column): PhraseQuery
-```
-
-Creates an instance of `PhraseQuery`.
-
-#### Parameters
-
-* **query**: `string`
-    The phrase to search for in the specified column.
-
-* **column**: `string`
-    The name of the column to search within.
-
-#### Returns
-
-[`PhraseQuery`](PhraseQuery.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/Query.md
+++ b/docs/src/js/classes/Query.md
@@ -30,53 +30,6 @@ protected inner: Query | Promise<Query>;

 ## Methods

-### analyzePlan()
-
-```ts
-analyzePlan(): Promise<string>
-```
-
-Executes the query and returns the physical query plan annotated with runtime metrics.
-
-This is useful for debugging and performance analysis, as it shows how the query was executed
-and includes metrics such as elapsed time, rows processed, and I/O statistics.
-
-#### Returns
-
-`Promise`&lt;`string`&gt;
-
-A query execution plan with runtime metrics for each step.
-
-#### Example
-
-```ts
-import * as lancedb from "@lancedb/lancedb"
-
-const db = await lancedb.connect("./.lancedb");
-const table = await db.createTable("my_table", [
-  { vector: [1.1, 0.9], id: "1" },
-]);
-
-const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
-
-Example output (with runtime metrics inlined):
-AnalyzeExec verbose=true, metrics=[]
- ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
-  Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
-   CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
-    GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
-     FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
-      SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
-       KNNVectorDistance: metric=l2, metrics=[output_rows=1, elapsed_compute=114.333µs, output_batches=1]
-        LanceScan: uri=/path/to/data, projection=[vector], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1, elapsed_compute=103.626µs, bytes_read=549, iops=2, requests=2]
-```
-
-#### Inherited from
-
-[`QueryBase`](QueryBase.md).[`analyzePlan`](QueryBase.md#analyzeplan)
-
-***
-
 ### execute()

 ```ts
@@ -206,7 +159,7 @@ fullTextSearch(query, options?): this

 #### Parameters

-* **query**: `string` \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
+* **query**: `string`

 * **options?**: `Partial`&lt;[`FullTextSearchOptions`](../interfaces/FullTextSearchOptions.md)&gt;

@@ -309,7 +262,7 @@ nearestToText(query, columns?): Query

 #### Parameters

-* **query**: `string` \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
+* **query**: `string`

 * **columns?**: `string`[]

--- a/docs/src/js/classes/QueryBase.md
+++ b/docs/src/js/classes/QueryBase.md
@@ -36,49 +36,6 @@ protected inner: NativeQueryType | Promise<NativeQueryType>;

 ## Methods

-### analyzePlan()
-
-```ts
-analyzePlan(): Promise<string>
-```
-
-Executes the query and returns the physical query plan annotated with runtime metrics.
-
-This is useful for debugging and performance analysis, as it shows how the query was executed
-and includes metrics such as elapsed time, rows processed, and I/O statistics.
-
-#### Returns
-
-`Promise`&lt;`string`&gt;
-
-A query execution plan with runtime metrics for each step.
-
-#### Example
-
-```ts
-import * as lancedb from "@lancedb/lancedb"
-
-const db = await lancedb.connect("./.lancedb");
-const table = await db.createTable("my_table", [
-  { vector: [1.1, 0.9], id: "1" },
-]);
-
-const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
-
-Example output (with runtime metrics inlined):
-AnalyzeExec verbose=true, metrics=[]
- ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
-  Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
-   CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
-    GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
-     FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
-      SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
-       KNNVectorDistance: metric=l2, metrics=[output_rows=1, elapsed_compute=114.333µs, output_batches=1]
-        LanceScan: uri=/path/to/data, projection=[vector], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1, elapsed_compute=103.626µs, bytes_read=549, iops=2, requests=2]
-```
-
-***
-
 ### execute()

 ```ts
@@ -192,7 +149,7 @@ fullTextSearch(query, options?): this

 #### Parameters

-* **query**: `string` \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
+* **query**: `string`

 * **options?**: `Partial`&lt;[`FullTextSearchOptions`](../interfaces/FullTextSearchOptions.md)&gt;

--- a/docs/src/js/classes/Table.md
+++ b/docs/src/js/classes/Table.md
@@ -454,28 +454,6 @@ Modeled after ``VACUUM`` in PostgreSQL.

 ***

-### prewarmIndex()
-
-```ts
-abstract prewarmIndex(name): Promise<void>
-```
-
-Prewarm an index in the table.
-
-#### Parameters
-
-* **name**: `string`
-    The name of the index.
-    This will load the index into memory.  This may reduce the cold-start time for
-    future queries.  If the index does not fit in the cache then this call may be
-    wasteful.
-
-#### Returns
-
-`Promise`&lt;`void`&gt;
-
-***
-
 ### query()

 ```ts
@@ -597,7 +575,7 @@ of the given query

 #### Parameters

-* **query**: `string` \| [`IntoVector`](../type-aliases/IntoVector.md) \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
+* **query**: `string` \| [`IntoVector`](../type-aliases/IntoVector.md)
    the query, a vector or string

 * **queryType?**: `string`
--- a/docs/src/js/classes/VectorQuery.md
+++ b/docs/src/js/classes/VectorQuery.md
@@ -48,53 +48,6 @@ addQueryVector(vector): VectorQuery

 ***

-### analyzePlan()
-
-```ts
-analyzePlan(): Promise<string>
-```
-
-Executes the query and returns the physical query plan annotated with runtime metrics.
-
-This is useful for debugging and performance analysis, as it shows how the query was executed
-and includes metrics such as elapsed time, rows processed, and I/O statistics.
-
-#### Returns
-
-`Promise`&lt;`string`&gt;
-
-A query execution plan with runtime metrics for each step.
-
-#### Example
-
-```ts
-import * as lancedb from "@lancedb/lancedb"
-
-const db = await lancedb.connect("./.lancedb");
-const table = await db.createTable("my_table", [
-  { vector: [1.1, 0.9], id: "1" },
-]);
-
-const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
-
-Example output (with runtime metrics inlined):
-AnalyzeExec verbose=true, metrics=[]
- ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
-  Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
-   CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
-    GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
-     FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
-      SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
-       KNNVectorDistance: metric=l2, metrics=[output_rows=1, elapsed_compute=114.333µs, output_batches=1]
-        LanceScan: uri=/path/to/data, projection=[vector], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1, elapsed_compute=103.626µs, bytes_read=549, iops=2, requests=2]
-```
-
-#### Inherited from
-
-[`QueryBase`](QueryBase.md).[`analyzePlan`](QueryBase.md#analyzeplan)
-
-***
-
 ### bypassVectorIndex()

 ```ts
@@ -347,7 +300,7 @@ fullTextSearch(query, options?): this

 #### Parameters

-* **query**: `string` \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
+* **query**: `string`

 * **options?**: `Partial`&lt;[`FullTextSearchOptions`](../interfaces/FullTextSearchOptions.md)&gt;

--- a/docs/src/js/enumerations/FullTextQueryType.md
+++ b/docs/src/js/enumerations/FullTextQueryType.md
@@ -1,46 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / FullTextQueryType
-
-# Enumeration: FullTextQueryType
-
-Enum representing the types of full-text queries supported.
-
- `Match`: Performs a full-text search for terms in the query string.
- `MatchPhrase`: Searches for an exact phrase match in the text.
- `Boost`: Boosts the relevance score of specific terms in the query.
- `MultiMatch`: Searches across multiple fields for the query terms.
-
-## Enumeration Members
-
-### Boost
-
-```ts
-Boost: "boost";
-```
-
-***
-
-### Match
-
-```ts
-Match: "match";
-```
-
-***
-
-### MatchPhrase
-
-```ts
-MatchPhrase: "match_phrase";
-```
-
-***
-
-### MultiMatch
-
-```ts
-MultiMatch: "multi_match";
-```
--- a/docs/src/js/globals.md
+++ b/docs/src/js/globals.md
@@ -9,20 +9,12 @@
 - [embedding](namespaces/embedding/README.md)
 - [rerankers](namespaces/rerankers/README.md)

-## Enumerations
-
- [FullTextQueryType](enumerations/FullTextQueryType.md)
-
 ## Classes

- [BoostQuery](classes/BoostQuery.md)
 - [Connection](classes/Connection.md)
 - [Index](classes/Index.md)
 - [MakeArrowTableOptions](classes/MakeArrowTableOptions.md)
- [MatchQuery](classes/MatchQuery.md)
 - [MergeInsertBuilder](classes/MergeInsertBuilder.md)
- [MultiMatchQuery](classes/MultiMatchQuery.md)
- [PhraseQuery](classes/PhraseQuery.md)
 - [Query](classes/Query.md)
 - [QueryBase](classes/QueryBase.md)
 - [RecordBatchIterator](classes/RecordBatchIterator.md)
@@ -41,7 +33,6 @@
 - [CreateTableOptions](interfaces/CreateTableOptions.md)
 - [ExecutableQuery](interfaces/ExecutableQuery.md)
 - [FtsOptions](interfaces/FtsOptions.md)
- [FullTextQuery](interfaces/FullTextQuery.md)
 - [FullTextSearchOptions](interfaces/FullTextSearchOptions.md)
 - [HnswPqOptions](interfaces/HnswPqOptions.md)
 - [HnswSqOptions](interfaces/HnswSqOptions.md)
--- a/docs/src/js/interfaces/FullTextQuery.md
+++ b/docs/src/js/interfaces/FullTextQuery.md
@@ -1,25 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / FullTextQuery
-
-# Interface: FullTextQuery
-
-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.
-
-## Methods
-
-### queryType()
-
-```ts
-queryType(): FullTextQueryType
-```
-
-The type of the full-text query.
-
-#### Returns
-
-[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
--- a/docs/src/js/interfaces/QueryExecutionOptions.md
+++ b/docs/src/js/interfaces/QueryExecutionOptions.md
@@ -20,13 +20,3 @@ The maximum number of rows to return in a single batch

 Batches may have fewer rows if the underlying data is stored
 in smaller chunks.
-
-***
-
-### timeoutMs?
-
-```ts
-optional timeoutMs: number;
-```
-
-Timeout for query execution in milliseconds
--- a/docs/src/troubleshooting.md
+++ b/docs/src/troubleshooting.md
@@ -35,9 +35,3 @@ print the resolved query plan. You can use the `explain_plan` method to do this:
 * Python Sync: [LanceQueryBuilder.explain_plan][lancedb.query.LanceQueryBuilder.explain_plan]
 * Python Async: [AsyncQueryBase.explain_plan][lancedb.query.AsyncQueryBase.explain_plan]
 * Node @lancedb/lancedb: [LanceQueryBuilder.explainPlan](/lancedb/js/classes/QueryBase/#explainplan)
-
-To understand how a query was actually executed—including metrics like execution time, number of rows processed, I/O stats, and more—use the analyze_plan method. This executes the query and returns a physical execution plan annotated with runtime metrics, making it especially helpful for performance tuning and debugging.
-
-* Python Sync: [LanceQueryBuilder.analyze_plan][lancedb.query.LanceQueryBuilder.analyze_plan]
-* Python Async: [AsyncQueryBase.analyze_plan][lancedb.query.AsyncQueryBase.analyze_plan]
-* Node @lancedb/lancedb: [LanceQueryBuilder.analyzePlan](/lancedb/js/classes/QueryBase/#analyzePlan)
--- a/java/core/pom.xml
+++ b/java/core/pom.xml
@@ -8,16 +8,13 @@
    <parent>
        <groupId>com.lancedb</groupId>
        <artifactId>lancedb-parent</artifactId>
-        <version>0.19.0-beta.7</version>
+        <version>0.18.2-beta.1</version>
        <relativePath>../pom.xml</relativePath>
    </parent>

    <artifactId>lancedb-core</artifactId>
    <name>LanceDB Core</name>
    <packaging>jar</packaging>
-    <properties>
-        <rust.release.build>false</rust.release.build>
-    </properties>

    <dependencies>
        <dependency>
@@ -71,7 +68,7 @@
                                </goals>
                                <configuration>
                                    <path>lancedb-jni</path>
-                                    <release>${rust.release.build}</release>
+                                    <release>true</release>
                                    <!-- Copy native libraries to target/classes for runtime access -->
                                    <copyTo>${project.build.directory}/classes/nativelib</copyTo>
                                    <copyWithPlatformDir>true</copyWithPlatformDir>
--- a/java/core/src/main/java/com/lancedb/lancedb/Connection.java
+++ b/java/core/src/main/java/com/lancedb/lancedb/Connection.java
@@ -1,25 +1,16 @@
-/*
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
 package com.lancedb.lancedb;

 import io.questdb.jar.jni.JarJniLoader;
-
 import java.io.Closeable;
 import java.util.List;
 import java.util.Optional;

-/** Represents LanceDB database. */
+/**
+ * Represents LanceDB database.
+ */
 public class Connection implements Closeable {
  static {
    JarJniLoader.loadLib(Connection.class, "/nativelib", "lancedb_jni");
@@ -27,11 +18,14 @@ public class Connection implements Closeable {

  private long nativeConnectionHandle;

-  /** Connect to a LanceDB instance. */
+  /**
+   * Connect to a LanceDB instance.
+   */
  public static native Connection connect(String uri);

  /**
-   * Get the names of all tables in the database. The names are sorted in ascending order.
+   * Get the names of all tables in the database. The names are sorted in
+   * ascending order.
   *
   * @return the table names
   */
@@ -40,7 +34,8 @@ public class Connection implements Closeable {
  }

  /**
-   * Get the names of filtered tables in the database. The names are sorted in ascending order.
+   * Get the names of filtered tables in the database. The names are sorted in
+   * ascending order.
   *
   * @param limit The number of results to return.
   * @return the table names
@@ -50,11 +45,12 @@ public class Connection implements Closeable {
  }

  /**
-   * Get the names of filtered tables in the database. The names are sorted in ascending order.
+   * Get the names of filtered tables in the database. The names are sorted in
+   * ascending order.
   *
   * @param startAfter If present, only return names that come lexicographically after the supplied
-   *     value. This can be combined with limit to implement pagination by setting this to the last
-   *     table name from the previous page.
+   *                   value. This can be combined with limit to implement pagination
+   *                   by setting this to the last table name from the previous page.
   * @return the table names
   */
  public List<String> tableNames(String startAfter) {
@@ -62,11 +58,12 @@ public class Connection implements Closeable {
  }

  /**
-   * Get the names of filtered tables in the database. The names are sorted in ascending order.
+   * Get the names of filtered tables in the database. The names are sorted in
+   * ascending order.
   *
   * @param startAfter If present, only return names that come lexicographically after the supplied
-   *     value. This can be combined with limit to implement pagination by setting this to the last
-   *     table name from the previous page.
+   *                   value. This can be combined with limit to implement pagination
+   *                   by setting this to the last table name from the previous page.
   * @param limit The number of results to return.
   * @return the table names
   */
@@ -75,19 +72,22 @@ public class Connection implements Closeable {
  }

  /**
-   * Get the names of filtered tables in the database. The names are sorted in ascending order.
+   * Get the names of filtered tables in the database. The names are sorted in
+   * ascending order.
   *
   * @param startAfter If present, only return names that come lexicographically after the supplied
-   *     value. This can be combined with limit to implement pagination by setting this to the last
-   *     table name from the previous page.
+   *                   value. This can be combined with limit to implement pagination
+   *                   by setting this to the last table name from the previous page.
   * @param limit The number of results to return.
   * @return the table names
   */
-  public native List<String> tableNames(Optional<String> startAfter, Optional<Integer> limit);
+  public native List<String> tableNames(
+      Optional<String> startAfter, Optional<Integer> limit);

  /**
-   * Closes this connection and releases any system resources associated with it. If the connection
-   * is already closed, then invoking this method has no effect.
+   * Closes this connection and releases any system resources associated with it. If
+   * the connection is
+   * already closed, then invoking this method has no effect.
   */
  @Override
  public void close() {
@@ -98,7 +98,8 @@ public class Connection implements Closeable {
  }

  /**
-   * Native method to release the Lance connection resources associated with the given handle.
+   * Native method to release the Lance connection resources associated with the
+   * given handle.
   *
   * @param handle The native handle to the connection resource.
   */
--- a/java/core/src/test/java/com/lancedb/lancedb/ConnectionTest.java
+++ b/java/core/src/test/java/com/lancedb/lancedb/ConnectionTest.java
@@ -1,35 +1,27 @@
-/*
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- * Unless required by applicable law or agreed to in writing, software
- * distributed under the License is distributed on an "AS IS" BASIS,
- * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- * See the License for the specific language governing permissions and
- * limitations under the License.
- */
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
 package com.lancedb.lancedb;

-import org.junit.jupiter.api.BeforeAll;
-import org.junit.jupiter.api.Test;
-import org.junit.jupiter.api.io.TempDir;
-
-import java.net.URL;
-import java.nio.file.Path;
-import java.util.List;
-
 import static org.junit.jupiter.api.Assertions.assertEquals;
 import static org.junit.jupiter.api.Assertions.assertTrue;

+import java.nio.file.Path;
+import java.util.List;
+import java.net.URL;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+
 public class ConnectionTest {
  private static final String[] TABLE_NAMES = {
-    "dataset_version", "new_empty_dataset", "test", "write_stream"
+      "dataset_version",
+      "new_empty_dataset",
+      "test",
+      "write_stream"
  };

-  @TempDir static Path tempDir; // Temporary directory for the tests
+  @TempDir
+  static Path tempDir; // Temporary directory for the tests
  private static URL lanceDbURL;

  @BeforeAll
@@ -61,21 +53,18 @@ public class ConnectionTest {
  @Test
  void tableNamesStartAfter() {
    try (Connection conn = Connection.connect(lanceDbURL.toString())) {
-      assertTableNamesStartAfter(
-          conn, TABLE_NAMES[0], 3, TABLE_NAMES[1], TABLE_NAMES[2], TABLE_NAMES[3]);
+      assertTableNamesStartAfter(conn, TABLE_NAMES[0], 3, TABLE_NAMES[1], TABLE_NAMES[2], TABLE_NAMES[3]);
      assertTableNamesStartAfter(conn, TABLE_NAMES[1], 2, TABLE_NAMES[2], TABLE_NAMES[3]);
      assertTableNamesStartAfter(conn, TABLE_NAMES[2], 1, TABLE_NAMES[3]);
      assertTableNamesStartAfter(conn, TABLE_NAMES[3], 0);
-      assertTableNamesStartAfter(
-          conn, "a_dataset", 4, TABLE_NAMES[0], TABLE_NAMES[1], TABLE_NAMES[2], TABLE_NAMES[3]);
+      assertTableNamesStartAfter(conn, "a_dataset", 4, TABLE_NAMES[0], TABLE_NAMES[1], TABLE_NAMES[2], TABLE_NAMES[3]);
      assertTableNamesStartAfter(conn, "o_dataset", 2, TABLE_NAMES[2], TABLE_NAMES[3]);
      assertTableNamesStartAfter(conn, "v_dataset", 1, TABLE_NAMES[3]);
      assertTableNamesStartAfter(conn, "z_dataset", 0);
    }
  }

-  private void assertTableNamesStartAfter(
-      Connection conn, String startAfter, int expectedSize, String... expectedNames) {
+  private void assertTableNamesStartAfter(Connection conn, String startAfter, int expectedSize, String... expectedNames) {
    List<String> tableNames = conn.tableNames(startAfter);
    assertEquals(expectedSize, tableNames.size());
    for (int i = 0; i < expectedNames.length; i++) {
@@ -85,7 +74,7 @@ public class ConnectionTest {

  @Test
  void tableNamesLimit() {
-    try (Connection conn = Connection.connect(lanceDbURL.toString())) {
+      try (Connection conn = Connection.connect(lanceDbURL.toString())) {
      for (int i = 0; i <= TABLE_NAMES.length; i++) {
        List<String> tableNames = conn.tableNames(i);
        assertEquals(i, tableNames.size());
--- a/java/pom.xml
+++ b/java/pom.xml
@@ -6,7 +6,7 @@

    <groupId>com.lancedb</groupId>
    <artifactId>lancedb-parent</artifactId>
-    <version>0.19.0-beta.7</version>
+    <version>0.18.2-beta.1</version>
    <packaging>pom</packaging>

    <name>LanceDB Parent</name>
@@ -29,25 +29,6 @@
    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <arrow.version>15.0.0</arrow.version>
-        <spotless.skip>false</spotless.skip>
-        <spotless.version>2.30.0</spotless.version>
-        <spotless.java.googlejavaformat.version>1.7</spotless.java.googlejavaformat.version>
-        <spotless.delimiter>package</spotless.delimiter>
-        <spotless.license.header>
-            /*
-            * Licensed under the Apache License, Version 2.0 (the "License");
-            * you may not use this file except in compliance with the License.
-            * You may obtain a copy of the License at
-            *
-            *     http://www.apache.org/licenses/LICENSE-2.0
-            *
-            * Unless required by applicable law or agreed to in writing, software
-            * distributed under the License is distributed on an "AS IS" BASIS,
-            * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-            * See the License for the specific language governing permissions and
-            * limitations under the License.
-            */
-        </spotless.license.header>
    </properties>

    <modules>
@@ -146,8 +127,7 @@
                <configuration>
                    <configLocation>google_checks.xml</configLocation>
                    <consoleOutput>true</consoleOutput>
-                    <failsOnError>false</failsOnError>
-                    <failOnViolation>false</failOnViolation>
+                    <failsOnError>true</failsOnError>
                    <violationSeverity>warning</violationSeverity>
                    <linkXRef>false</linkXRef>
                </configuration>
@@ -161,10 +141,6 @@
                    </execution>
                </executions>
            </plugin>
-            <plugin>
-                <groupId>com.diffplug.spotless</groupId>
-                <artifactId>spotless-maven-plugin</artifactId>
-            </plugin>
        </plugins>
        <pluginManagement>
            <plugins>
@@ -203,54 +179,6 @@
                    <artifactId>maven-install-plugin</artifactId>
                    <version>2.5.2</version>
                </plugin>
-                <plugin>
-                    <groupId>com.diffplug.spotless</groupId>
-                    <artifactId>spotless-maven-plugin</artifactId>
-                    <version>${spotless.version}</version>
-                    <configuration>
-                        <skip>${spotless.skip}</skip>
-                        <upToDateChecking>
-                            <enabled>true</enabled>
-                        </upToDateChecking>
-                        <java>
-                            <includes>
-                                <include>src/main/java/**/*.java</include>
-                                <include>src/test/java/**/*.java</include>
-                            </includes>
-                            <googleJavaFormat>
-                                <version>${spotless.java.googlejavaformat.version}</version>
-                                <style>GOOGLE</style>
-                            </googleJavaFormat>
-
-                            <importOrder>
-                                <order>com.lancedb.lance,,javax,java,\#</order>
-                            </importOrder>
-
-                            <removeUnusedImports />
-                        </java>
-                        <scala>
-                            <includes>
-                                <include>src/main/scala/**/*.scala</include>
-                                <include>src/main/scala-*/**/*.scala</include>
-                                <include>src/test/scala/**/*.scala</include>
-                                <include>src/test/scala-*/**/*.scala</include>
-                            </includes>
-                        </scala>
-                        <licenseHeader>
-                            <content>${spotless.license.header}</content>
-                            <delimiter>${spotless.delimiter}</delimiter>
-                        </licenseHeader>
-                    </configuration>
-                    <executions>
-                        <execution>
-                            <id>spotless-check</id>
-                            <phase>validate</phase>
-                            <goals>
-                                <goal>apply</goal>
-                            </goals>
-                        </execution>
-                    </executions>
-                </plugin>
            </plugins>
        </pluginManagement>
    </build>
--- a/node/package-lock.json
+++ b/node/package-lock.json
@@ -1,12 +1,12 @@
 {
  "name": "vectordb",
-  "version": "0.19.0-beta.7",
+  "version": "0.18.2-beta.0",
  "lockfileVersion": 3,
  "requires": true,
  "packages": {
    "": {
      "name": "vectordb",
-      "version": "0.19.0-beta.7",
+      "version": "0.18.2-beta.0",
      "cpu": [
        "x64",
        "arm64"
@@ -52,11 +52,11 @@
        "uuid": "^9.0.0"
      },
      "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.19.0-beta.7",
-        "@lancedb/vectordb-darwin-x64": "0.19.0-beta.7",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.19.0-beta.7",
-        "@lancedb/vectordb-linux-x64-gnu": "0.19.0-beta.7",
-        "@lancedb/vectordb-win32-x64-msvc": "0.19.0-beta.7"
+        "@lancedb/vectordb-darwin-arm64": "0.18.2-beta.0",
+        "@lancedb/vectordb-darwin-x64": "0.18.2-beta.0",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.18.2-beta.0",
+        "@lancedb/vectordb-linux-x64-gnu": "0.18.2-beta.0",
+        "@lancedb/vectordb-win32-x64-msvc": "0.18.2-beta.0"
      },
      "peerDependencies": {
        "@apache-arrow/ts": "^14.0.2",
@@ -327,9 +327,9 @@
      }
    },
    "node_modules/@lancedb/vectordb-darwin-arm64": {
-      "version": "0.19.0-beta.7",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.19.0-beta.7.tgz",
-      "integrity": "sha512-HpbVKw4Vs+mPv7uPwaK7ilJlGrGdjOrNlC2mSkMCj0OlEwGRVcEcrSyijI7LXQH7ybEgNnDhSds5TuzBV26SGg==",
+      "version": "0.18.2-beta.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.18.2-beta.0.tgz",
+      "integrity": "sha512-FzIcElkS6R5I5kU1S5m7yLVTB1Duv1XcmZQtVmYl/JjNlfxS1WTtMzdzMqSBFohDcgU2Tkc5+1FpK1B94dUUbg==",
      "cpu": [
        "arm64"
      ],
@@ -340,9 +340,9 @@
      ]
    },
    "node_modules/@lancedb/vectordb-darwin-x64": {
-      "version": "0.19.0-beta.7",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.19.0-beta.7.tgz",
-      "integrity": "sha512-x3X7nqIYVZtxaa0uZUk/M99vKvDinZ5G0+8k2NqZ696YXGWKGyRxR6k8ZzKYCoCTSuYXnBftgKoIlwJGtNt8Bw==",
+      "version": "0.18.2-beta.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.18.2-beta.0.tgz",
+      "integrity": "sha512-jv+XludfLNBDm1DjdqyghwDMtd4E+ygwycQpkpK72wyZSh6Qytrgq+4dNi/zCZ3UChFLbKbIxrVxv9yENQn2Pg==",
      "cpu": [
        "x64"
      ],
@@ -353,9 +353,9 @@
      ]
    },
    "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.19.0-beta.7",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.19.0-beta.7.tgz",
-      "integrity": "sha512-Vwj0HI3+b4NgXKf+5+W/GfLBCGoQMBGM47vA/ts1dpe/PxraOQYPDv67I5kbXkCQKwhal7b0iZx/PbMu0JZPyw==",
+      "version": "0.18.2-beta.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.18.2-beta.0.tgz",
+      "integrity": "sha512-8/fBpbNYhhpetf/pZv0DyPnQkeAbsiICMyCoRiNu5auvQK4AsGF1XvLWrDi68u9F0GysBKvuatYuGqa/yh+Anw==",
      "cpu": [
        "arm64"
      ],
@@ -366,9 +366,9 @@
      ]
    },
    "node_modules/@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.19.0-beta.7",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.19.0-beta.7.tgz",
-      "integrity": "sha512-Dx2B6UWQei9D7Rt+MgHWqPTYtEK2w3EgsNb5ENEWUTZxH7lD/CV7Sw0JMK5LDG209fFcpXFerveF6J8ZC8uGBQ==",
+      "version": "0.18.2-beta.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.18.2-beta.0.tgz",
+      "integrity": "sha512-7a1Kc/2V2ff4HlLzXyXVdK0Z0VIFUt50v2SBRdlcycJ0NLW9ZqV+9UjB/NAOwMXVgYd7d3rKjACGkQzkpvcyeg==",
      "cpu": [
        "x64"
      ],
@@ -379,9 +379,9 @@
      ]
    },
    "node_modules/@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.19.0-beta.7",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.19.0-beta.7.tgz",
-      "integrity": "sha512-F5LZGa+gkUH1TgsWZWLLAMejwXFIWdash7+85ip4k2M0ThyqLF/dtlldOvteUEd5+flxihGjHg6TUtnSY8XBFA==",
+      "version": "0.18.2-beta.0",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.18.2-beta.0.tgz",
+      "integrity": "sha512-EeCiSf2RtJMESnkIca28GI6rAStYj2q9sVIyNCXpmIZSkJVpfQ3iswHGAbHrEfaPl0J1Re9cnRHLLuqkumwiIQ==",
      "cpu": [
        "x64"
      ],
@@ -1184,10 +1184,9 @@
      }
    },
    "node_modules/axios": {
-      "version": "1.8.4",
-      "resolved": "https://registry.npmjs.org/axios/-/axios-1.8.4.tgz",
-      "integrity": "sha512-eBSYY4Y68NNlHbHBMdeDmKNtDgXWhQsJcGqzO3iLUM0GraQFSS9cVgPX5I9b3lbdFKyYoAEGAZF1DwhTaljNAw==",
-      "license": "MIT",
+      "version": "1.7.7",
+      "resolved": "https://registry.npmjs.org/axios/-/axios-1.7.7.tgz",
+      "integrity": "sha512-S4kL7XrjgBmvdGut0sN3yJxqYzrDOnivkBiN0OFs6hLiUam3UPvswUo0kqGyhqUZGEOytHyumEdXsAkgCOUf3Q==",
      "dependencies": {
        "follow-redirects": "^1.15.6",
        "form-data": "^4.0.0",
--- a/node/package.json
+++ b/node/package.json
@@ -1,6 +1,6 @@
 {
  "name": "vectordb",
-  "version": "0.19.0-beta.7",
+  "version": "0.18.2-beta.1",
  "description": " Serverless, low-latency vector database for AI applications",
  "private": false,
  "main": "dist/index.js",
@@ -89,10 +89,10 @@
    }
  },
  "optionalDependencies": {
-    "@lancedb/vectordb-darwin-x64": "0.19.0-beta.7",
-    "@lancedb/vectordb-darwin-arm64": "0.19.0-beta.7",
-    "@lancedb/vectordb-linux-x64-gnu": "0.19.0-beta.7",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.19.0-beta.7",
-    "@lancedb/vectordb-win32-x64-msvc": "0.19.0-beta.7"
+    "@lancedb/vectordb-darwin-x64": "0.18.2-beta.1",
+    "@lancedb/vectordb-darwin-arm64": "0.18.2-beta.1",
+    "@lancedb/vectordb-linux-x64-gnu": "0.18.2-beta.1",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.18.2-beta.1",
+    "@lancedb/vectordb-win32-x64-msvc": "0.18.2-beta.1"
  }
 }
--- a/nodejs/Cargo.toml
+++ b/nodejs/Cargo.toml
@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.19.0-beta.7"
+version = "0.18.2-beta.1"
 license.workspace = true
 description.workspace = true
 repository.workspace = true
--- a/nodejs/test/table.test.ts
+++ b/nodejs/test/table.test.ts
@@ -10,7 +10,7 @@ import * as arrow16 from "apache-arrow-16";
 import * as arrow17 from "apache-arrow-17";
 import * as arrow18 from "apache-arrow-18";

-import { MatchQuery, PhraseQuery, Table, connect } from "../lancedb";
+import { Table, connect } from "../lancedb";
 import {
  Table as ArrowTable,
  Field,
@@ -33,7 +33,6 @@ import {
  register,
 } from "../lancedb/embedding";
 import { Index } from "../lancedb/indices";
-import { instanceOfFullTextQuery } from "../lancedb/query";

 describe.each([arrow15, arrow16, arrow17, arrow18])(
  "Given a table",
@@ -634,23 +633,6 @@ describe("When creating an index", () => {
    expect(plan2).not.toMatch("LanceScan");
  });

-  it("should be able to run analyze plan", async () => {
-    await tbl.createIndex("vec");
-    await tbl.add([
-      {
-        id: 300,
-        vec: Array(32)
-          .fill(1)
-          .map(() => Math.random()),
-        tags: [],
-      },
-    ]);
-
-    const plan = await tbl.query().nearestTo(queryVec).analyzePlan();
-    expect(plan).toMatch("AnalyzeExec");
-    expect(plan).toMatch("metrics=");
-  });
-
  it("should be able to query with row id", async () => {
    const results = await tbl
      .query()
@@ -868,44 +850,6 @@ describe("When creating an index", () => {
  });
 });

-describe("When querying a table", () => {
-  let tmpDir: tmp.DirResult;
-  beforeEach(() => {
-    tmpDir = tmp.dirSync({ unsafeCleanup: true });
-  });
-  afterEach(() => tmpDir.removeCallback());
-
-  it("should throw an error when timeout is reached", async () => {
-    const db = await connect(tmpDir.name);
-    const data = makeArrowTable([
-      { text: "a", vector: [0.1, 0.2] },
-      { text: "b", vector: [0.3, 0.4] },
-    ]);
-    const table = await db.createTable("test", data);
-    await table.createIndex("text", { config: Index.fts() });
-
-    await expect(
-      table.query().where("text != 'a'").toArray({ timeoutMs: 0 }),
-    ).rejects.toThrow("Query timeout");
-
-    await expect(
-      table.query().nearestTo([0.0, 0.0]).toArrow({ timeoutMs: 0 }),
-    ).rejects.toThrow("Query timeout");
-
-    await expect(
-      table.search("a", "fts").toArray({ timeoutMs: 0 }),
-    ).rejects.toThrow("Query timeout");
-
-    await expect(
-      table
-        .query()
-        .nearestToText("a")
-        .nearestTo([0.0, 0.0])
-        .toArrow({ timeoutMs: 0 }),
-    ).rejects.toThrow("Query timeout");
-  });
-});
-
 describe("Read consistency interval", () => {
  let tmpDir: tmp.DirResult;
  beforeEach(() => {
@@ -1303,56 +1247,6 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(

      const results = await table.search("hello").toArray();
      expect(results[0].text).toBe(data[0].text);
-
-      const query = new MatchQuery("goodbye", "text");
-      expect(instanceOfFullTextQuery(query)).toBe(true);
-      const results2 = await table
-        .search(new MatchQuery("goodbye", "text"))
-        .toArray();
-      expect(results2[0].text).toBe(data[1].text);
-    });
-
-    test("prewarm full text search index", async () => {
-      const db = await connect(tmpDir.name);
-      const data = [
-        { text: ["lance database", "the", "search"], vector: [0.1, 0.2, 0.3] },
-        { text: ["lance database"], vector: [0.4, 0.5, 0.6] },
-        { text: ["lance", "search"], vector: [0.7, 0.8, 0.9] },
-        { text: ["database", "search"], vector: [1.0, 1.1, 1.2] },
-        { text: ["unrelated", "doc"], vector: [1.3, 1.4, 1.5] },
-      ];
-      const table = await db.createTable("test", data);
-      await table.createIndex("text", {
-        config: Index.fts(),
-      });
-
-      // For the moment, we just confirm we can call prewarmIndex without error
-      // and still search it afterwards
-      await table.prewarmIndex("text_idx");
-
-      const results = await table.search("lance").toArray();
-      expect(results.length).toBe(3);
-    });
-
-    test("full text index on list", async () => {
-      const db = await connect(tmpDir.name);
-      const data = [
-        { text: ["lance database", "the", "search"], vector: [0.1, 0.2, 0.3] },
-        { text: ["lance database"], vector: [0.4, 0.5, 0.6] },
-        { text: ["lance", "search"], vector: [0.7, 0.8, 0.9] },
-        { text: ["database", "search"], vector: [1.0, 1.1, 1.2] },
-        { text: ["unrelated", "doc"], vector: [1.3, 1.4, 1.5] },
-      ];
-      const table = await db.createTable("test", data);
-      await table.createIndex("text", {
-        config: Index.fts(),
-      });
-
-      const results = await table.search("lance").toArray();
-      expect(results.length).toBe(3);
-
-      const results2 = await table.search('"lance database"').toArray();
-      expect(results2.length).toBe(2);
    });

    test("full text search without positions", async () => {
@@ -1405,43 +1299,6 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
      expect(results.length).toBe(2);
      const phraseResults = await table.search('"hello world"').toArray();
      expect(phraseResults.length).toBe(1);
-      const phraseResults2 = await table
-        .search(new PhraseQuery("hello world", "text"))
-        .toArray();
-      expect(phraseResults2.length).toBe(1);
-    });
-
-    test("full text search fuzzy query", async () => {
-      const db = await connect(tmpDir.name);
-      const data = [
-        { text: "fa", vector: [0.1, 0.2, 0.3] },
-        { text: "fo", vector: [0.4, 0.5, 0.6] },
-        { text: "fob", vector: [0.4, 0.5, 0.6] },
-        { text: "focus", vector: [0.4, 0.5, 0.6] },
-        { text: "foo", vector: [0.4, 0.5, 0.6] },
-        { text: "food", vector: [0.4, 0.5, 0.6] },
-        { text: "foul", vector: [0.4, 0.5, 0.6] },
-      ];
-      const table = await db.createTable("test", data);
-      await table.createIndex("text", {
-        config: Index.fts(),
-      });
-
-      const results = await table
-        .search(new MatchQuery("foo", "text"))
-        .toArray();
-      expect(results.length).toBe(1);
-      expect(results[0].text).toBe("foo");
-
-      const fuzzyResults = await table
-        .search(new MatchQuery("foo", "text", { fuzziness: 1 }))
-        .toArray();
-      expect(fuzzyResults.length).toBe(4);
-      const resultSet = new Set(fuzzyResults.map((r) => r.text));
-      expect(resultSet.has("foo")).toBe(true);
-      expect(resultSet.has("fob")).toBe(true);
-      expect(resultSet.has("fo")).toBe(true);
-      expect(resultSet.has("food")).toBe(true);
    });

    test.each([
@@ -1489,30 +1346,6 @@ describe("when calling explainPlan", () => {
  });
 });

-describe("when calling analyzePlan", () => {
-  let tmpDir: tmp.DirResult;
-  let table: Table;
-  let queryVec: number[];
-  beforeEach(async () => {
-    tmpDir = tmp.dirSync({ unsafeCleanup: true });
-    const con = await connect(tmpDir.name);
-    table = await con.createTable("vectors", [{ id: 1, vector: [1.1, 0.9] }]);
-  });
-
-  afterEach(() => {
-    tmpDir.removeCallback();
-  });
-
-  it("retrieves runtime metrics", async () => {
-    queryVec = Array(2)
-      .fill(1)
-      .map(() => Math.random());
-    const plan = await table.query().nearestTo(queryVec).analyzePlan();
-    console.log("Query Plan:\n", plan); // <--- Print the plan
-    expect(plan).toMatch("AnalyzeExec");
-  });
-});
-
 describe("column name options", () => {
  let tmpDir: tmp.DirResult;
  let table: Table;
--- a/nodejs/lancedb/index.ts
+++ b/nodejs/lancedb/index.ts
@@ -47,12 +47,6 @@ export {
  QueryExecutionOptions,
  FullTextSearchOptions,
  RecordBatchIterator,
-  FullTextQuery,
-  MatchQuery,
-  PhraseQuery,
-  BoostQuery,
-  MultiMatchQuery,
-  FullTextQueryType,
 } from "./query";

 export {
--- a/nodejs/lancedb/query.ts
+++ b/nodejs/lancedb/query.ts
@@ -11,14 +11,12 @@ import {
 } from "./arrow";
 import { type IvfPqOptions } from "./indices";
 import {
-  JsFullTextQuery,
  RecordBatchIterator as NativeBatchIterator,
  Query as NativeQuery,
  Table as NativeTable,
  VectorQuery as NativeVectorQuery,
 } from "./native";
 import { Reranker } from "./rerankers";
-
 export class RecordBatchIterator implements AsyncIterator<RecordBatch> {
  private promisedInner?: Promise<NativeBatchIterator>;
  private inner?: NativeBatchIterator;
@@ -64,7 +62,7 @@ class RecordBatchIterable<
  // biome-ignore lint/suspicious/noExplicitAny: skip
  [Symbol.asyncIterator](): AsyncIterator<RecordBatch<any>, any, undefined> {
    return new RecordBatchIterator(
-      this.inner.execute(this.options?.maxBatchLength, this.options?.timeoutMs),
+      this.inner.execute(this.options?.maxBatchLength),
    );
  }
 }
@@ -80,11 +78,6 @@ export interface QueryExecutionOptions {
   * in smaller chunks.
   */
  maxBatchLength?: number;
-
-  /**
-   * Timeout for query execution in milliseconds
-   */
-  timeoutMs?: number;
 }

 /**
@@ -159,7 +152,7 @@ export class QueryBase<NativeQueryType extends NativeQuery | NativeVectorQuery>
  }

  fullTextSearch(
-    query: string | FullTextQuery,
+    query: string,
    options?: Partial<FullTextSearchOptions>,
  ): this {
    let columns: string[] | null = null;
@@ -171,16 +164,9 @@ export class QueryBase<NativeQueryType extends NativeQuery | NativeVectorQuery>
      }
    }

-    this.doCall((inner: NativeQueryType) => {
-      if (typeof query === "string") {
-        inner.fullTextSearch({
-          query: query,
-          columns: columns,
-        });
-      } else {
-        inner.fullTextSearch({ query: query.inner });
-      }
-    });
+    this.doCall((inner: NativeQueryType) =>
+      inner.fullTextSearch(query, columns),
+    );
    return this;
  }

@@ -287,11 +273,9 @@ export class QueryBase<NativeQueryType extends NativeQuery | NativeVectorQuery>
    options?: Partial<QueryExecutionOptions>,
  ): Promise<NativeBatchIterator> {
    if (this.inner instanceof Promise) {
-      return this.inner.then((inner) =>
-        inner.execute(options?.maxBatchLength, options?.timeoutMs),
-      );
+      return this.inner.then((inner) => inner.execute(options?.maxBatchLength));
    } else {
-      return this.inner.execute(options?.maxBatchLength, options?.timeoutMs);
+      return this.inner.execute(options?.maxBatchLength);
    }
  }

@@ -364,43 +348,6 @@ export class QueryBase<NativeQueryType extends NativeQuery | NativeVectorQuery>
      return this.inner.explainPlan(verbose);
    }
  }
-
-  /**
-   * Executes the query and returns the physical query plan annotated with runtime metrics.
-   *
-   * This is useful for debugging and performance analysis, as it shows how the query was executed
-   * and includes metrics such as elapsed time, rows processed, and I/O statistics.
-   *
-   * @example
-   * import * as lancedb from "@lancedb/lancedb"
-   *
-   * const db = await lancedb.connect("./.lancedb");
-   * const table = await db.createTable("my_table", [
-   *   { vector: [1.1, 0.9], id: "1" },
-   * ]);
-   *
-   * const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
-   *
-   * Example output (with runtime metrics inlined):
-   * AnalyzeExec verbose=true, metrics=[]
-   *  ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
-   *   Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
-   *    CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
-   *     GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
-   *      FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
-   *       SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
-   *        KNNVectorDistance: metric=l2, metrics=[output_rows=1, elapsed_compute=114.333µs, output_batches=1]
-   *         LanceScan: uri=/path/to/data, projection=[vector], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1, elapsed_compute=103.626µs, bytes_read=549, iops=2, requests=2]
-   *
-   * @returns A query execution plan with runtime metrics for each step.
-   */
-  async analyzePlan(): Promise<string> {
-    if (this.inner instanceof Promise) {
-      return this.inner.then((inner) => inner.analyzePlan());
-    } else {
-      return this.inner.analyzePlan();
-    }
-  }
 }

 /**
@@ -734,177 +681,8 @@ export class Query extends QueryBase<NativeQuery> {
    }
  }

-  nearestToText(query: string | FullTextQuery, columns?: string[]): Query {
-    this.doCall((inner) => {
-      if (typeof query === "string") {
-        inner.fullTextSearch({
-          query: query,
-          columns: columns,
-        });
-      } else {
-        inner.fullTextSearch({ query: query.inner });
-      }
-    });
+  nearestToText(query: string, columns?: string[]): Query {
+    this.doCall((inner) => inner.fullTextSearch(query, columns));
    return this;
  }
 }
-
-/**
- * Enum representing the types of full-text queries supported.
- *
- * - `Match`: Performs a full-text search for terms in the query string.
- * - `MatchPhrase`: Searches for an exact phrase match in the text.
- * - `Boost`: Boosts the relevance score of specific terms in the query.
- * - `MultiMatch`: Searches across multiple fields for the query terms.
- */
-export enum FullTextQueryType {
-  Match = "match",
-  MatchPhrase = "match_phrase",
-  Boost = "boost",
-  MultiMatch = "multi_match",
-}
-
-/**
- * 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.
- */
-export interface FullTextQuery {
-  /**
-   * Returns the inner query object.
-   * This is the underlying query object used by the database engine.
-   * @ignore
-   */
-  inner: JsFullTextQuery;
-
-  /**
-   * The type of the full-text query.
-   */
-  queryType(): FullTextQueryType;
-}
-
-// biome-ignore lint/suspicious/noExplicitAny: we want any here
-export function instanceOfFullTextQuery(obj: any): obj is FullTextQuery {
-  return obj != null && obj.inner instanceof JsFullTextQuery;
-}
-
-export class MatchQuery implements FullTextQuery {
-  /** @ignore */
-  public readonly inner: JsFullTextQuery;
-  /**
-   * Creates an instance of MatchQuery.
-   *
-   * @param query - The text query to search for.
-   * @param column - The name of the column to search within.
-   * @param options - Optional parameters for the match query.
-   *   - `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).
-   */
-  constructor(
-    query: string,
-    column: string,
-    options?: {
-      boost?: number;
-      fuzziness?: number;
-      maxExpansions?: number;
-    },
-  ) {
-    let fuzziness = options?.fuzziness;
-    if (fuzziness === undefined) {
-      fuzziness = 0;
-    }
-    this.inner = JsFullTextQuery.matchQuery(
-      query,
-      column,
-      options?.boost ?? 1.0,
-      fuzziness,
-      options?.maxExpansions ?? 50,
-    );
-  }
-
-  queryType(): FullTextQueryType {
-    return FullTextQueryType.Match;
-  }
-}
-
-export class PhraseQuery implements FullTextQuery {
-  /** @ignore */
-  public readonly inner: JsFullTextQuery;
-  /**
-   * Creates an instance of `PhraseQuery`.
-   *
-   * @param query - The phrase to search for in the specified column.
-   * @param column - The name of the column to search within.
-   */
-  constructor(query: string, column: string) {
-    this.inner = JsFullTextQuery.phraseQuery(query, column);
-  }
-
-  queryType(): FullTextQueryType {
-    return FullTextQueryType.MatchPhrase;
-  }
-}
-
-export class BoostQuery implements FullTextQuery {
-  /** @ignore */
-  public readonly inner: JsFullTextQuery;
-  /**
-   * Creates an instance of BoostQuery.
-   * The boost returns documents that match the positive query,
-   * but penalizes those that match the negative query.
-   * the penalty is controlled by the `negativeBoost` parameter.
-   *
-   * @param positive - The positive query that boosts the relevance score.
-   * @param negative - The negative query that reduces the relevance score.
-   * @param options - Optional parameters for the boost query.
-   *  - `negativeBoost`: The boost factor for the negative query (default is 0.0).
-   */
-  constructor(
-    positive: FullTextQuery,
-    negative: FullTextQuery,
-    options?: {
-      negativeBoost?: number;
-    },
-  ) {
-    this.inner = JsFullTextQuery.boostQuery(
-      positive.inner,
-      negative.inner,
-      options?.negativeBoost,
-    );
-  }
-
-  queryType(): FullTextQueryType {
-    return FullTextQueryType.Boost;
-  }
-}
-
-export class MultiMatchQuery implements FullTextQuery {
-  /** @ignore */
-  public readonly inner: JsFullTextQuery;
-  /**
-   * Creates an instance of MultiMatchQuery.
-   *
-   * @param query - The text query to search for across multiple columns.
-   * @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).
-   */
-  constructor(
-    query: string,
-    columns: string[],
-    options?: {
-      boosts?: number[];
-    },
-  ) {
-    this.inner = JsFullTextQuery.multiMatchQuery(
-      query,
-      columns,
-      options?.boosts,
-    );
-  }
-
-  queryType(): FullTextQueryType {
-    return FullTextQueryType.MultiMatch;
-  }
-}
--- a/nodejs/lancedb/table.ts
+++ b/nodejs/lancedb/table.ts
@@ -22,12 +22,7 @@ import {
  OptimizeStats,
  Table as _NativeTable,
 } from "./native";
-import {
-  FullTextQuery,
-  Query,
-  VectorQuery,
-  instanceOfFullTextQuery,
-} from "./query";
+import { Query, VectorQuery } from "./query";
 import { sanitizeType } from "./sanitize";
 import { IntoSql, toSQL } from "./util";
 export { IndexConfig } from "./native";
@@ -235,17 +230,6 @@ export abstract class Table {
   */
  abstract dropIndex(name: string): Promise<void>;

-  /**
-   * Prewarm an index in the table.
-   *
-   * @param name The name of the index.
-   *
-   * This will load the index into memory.  This may reduce the cold-start time for
-   * future queries.  If the index does not fit in the cache then this call may be
-   * wasteful.
-   */
-  abstract prewarmIndex(name: string): Promise<void>;
-
  /**
   * Create a {@link Query} Builder.
   *
@@ -310,7 +294,7 @@ export abstract class Table {
   * if the query is a string and no embedding function is defined, it will be treated as a full text search query
   */
  abstract search(
-    query: string | IntoVector | FullTextQuery,
+    query: string | IntoVector,
    queryType?: string,
    ftsColumns?: string | string[],
  ): VectorQuery | Query;
@@ -576,20 +560,16 @@ export class LocalTable extends Table {
    await this.inner.dropIndex(name);
  }

-  async prewarmIndex(name: string): Promise<void> {
-    await this.inner.prewarmIndex(name);
-  }
-
  query(): Query {
    return new Query(this.inner);
  }

  search(
-    query: string | IntoVector | FullTextQuery,
+    query: string | IntoVector,
    queryType: string = "auto",
    ftsColumns?: string | string[],
  ): VectorQuery | Query {
-    if (typeof query !== "string" && !instanceOfFullTextQuery(query)) {
+    if (typeof query !== "string") {
      if (queryType === "fts") {
        throw new Error("Cannot perform full text search on a vector query");
      }
@@ -605,10 +585,7 @@ export class LocalTable extends Table {

    // The query type is auto or vector
    // fall back to full text search if no embedding functions are defined and the query is a string
-    if (
-      queryType === "auto" &&
-      (getRegistry().length() === 0 || instanceOfFullTextQuery(query))
-    ) {
+    if (queryType === "auto" && getRegistry().length() === 0) {
      return this.query().fullTextSearch(query, {
        columns: ftsColumns,
      });
--- a/nodejs/npm/darwin-arm64/package.json
+++ b/nodejs/npm/darwin-arm64/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.19.0-beta.7",
+	"version": "0.18.2-beta.1",
 	"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": "0.19.0-beta.7",
+	"version": "0.18.2-beta.1",
 	"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": "0.19.0-beta.7",
+	"version": "0.18.2-beta.1",
 	"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": "0.19.0-beta.7",
+	"version": "0.18.2-beta.1",
 	"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": "0.19.0-beta.7",
+	"version": "0.18.2-beta.1",
 	"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": "0.19.0-beta.7",
+	"version": "0.18.2-beta.1",
 	"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": "0.19.0-beta.7",
+  "version": "0.18.2-beta.1",
  "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": "0.19.0-beta.7",
+	"version": "0.18.2-beta.1",
 	"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": "0.19.0-beta.7",
+  "version": "0.18.2-beta.0",
  "lockfileVersion": 3,
  "requires": true,
  "packages": {
    "": {
      "name": "@lancedb/lancedb",
-      "version": "0.19.0-beta.7",
+      "version": "0.18.2-beta.0",
      "cpu": [
        "x64",
        "arm64"
@@ -2304,20 +2304,89 @@
      }
    },
    "node_modules/@babel/code-frame": {
-      "version": "7.26.2",
-      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.26.2.tgz",
-      "integrity": "sha512-RJlIHRueQgwWitWgF8OdFYGZX328Ax5BCemNGlqHfplnRT9ESi8JkFlvaVYbS+UubVY6dpv87Fs2u5M29iNFVQ==",
+      "version": "7.23.5",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.23.5.tgz",
+      "integrity": "sha512-CgH3s1a96LipHCmSUmYFPwY7MNx8C3avkq7i4Wl3cfa662ldtUe4VM1TPXX70pfmrlWTb6jLqTYrZyT2ZTJBgA==",
      "dev": true,
-      "license": "MIT",
      "dependencies": {
-        "@babel/helper-validator-identifier": "^7.25.9",
-        "js-tokens": "^4.0.0",
-        "picocolors": "^1.0.0"
+        "@babel/highlight": "^7.23.4",
+        "chalk": "^2.4.2"
      },
      "engines": {
        "node": ">=6.9.0"
      }
    },
+    "node_modules/@babel/code-frame/node_modules/ansi-styles": {
+      "version": "3.2.1",
+      "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-3.2.1.tgz",
+      "integrity": "sha512-VT0ZI6kZRdTh8YyJw3SMbYm/u+NqfsAxEpWO0Pf9sq8/e94WxxOpPKx9FR1FlyCtOVDNOQ+8ntlqFxiRc+r5qA==",
+      "dev": true,
+      "dependencies": {
+        "color-convert": "^1.9.0"
+      },
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/chalk": {
+      "version": "2.4.2",
+      "resolved": "https://registry.npmjs.org/chalk/-/chalk-2.4.2.tgz",
+      "integrity": "sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ==",
+      "dev": true,
+      "dependencies": {
+        "ansi-styles": "^3.2.1",
+        "escape-string-regexp": "^1.0.5",
+        "supports-color": "^5.3.0"
+      },
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/color-convert": {
+      "version": "1.9.3",
+      "resolved": "https://registry.npmjs.org/color-convert/-/color-convert-1.9.3.tgz",
+      "integrity": "sha512-QfAUtd+vFdAtFQcC8CCyYt1fYWxSqAiK2cSD6zDB8N3cpsEBAvRxp9zOGg6G/SHHJYAT88/az/IuDGALsNVbGg==",
+      "dev": true,
+      "dependencies": {
+        "color-name": "1.1.3"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/color-name": {
+      "version": "1.1.3",
+      "resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.3.tgz",
+      "integrity": "sha512-72fSenhMw2HZMTVHeCA9KCmpEIbzWiQsjN+BHcBbS9vr1mtt+vJjPdksIBNUmKAW8TFUDPJK5SUU3QhE9NEXDw==",
+      "dev": true
+    },
+    "node_modules/@babel/code-frame/node_modules/escape-string-regexp": {
+      "version": "1.0.5",
+      "resolved": "https://registry.npmjs.org/escape-string-regexp/-/escape-string-regexp-1.0.5.tgz",
+      "integrity": "sha512-vbRorB5FUQWvla16U8R/qgaFIya2qGzwDrNmCZuYKrbdSUMG6I1ZCGQRefkRVhuOkIGVne7BQ35DSfo1qvJqFg==",
+      "dev": true,
+      "engines": {
+        "node": ">=0.8.0"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/has-flag": {
+      "version": "3.0.0",
+      "resolved": "https://registry.npmjs.org/has-flag/-/has-flag-3.0.0.tgz",
+      "integrity": "sha512-sKJf1+ceQBr4SMkvQnBDNDtf4TXpVhVGateu0t918bl30FnbE2m4vNLX+VWe/dpjlb+HugGYzW7uQXH98HPEYw==",
+      "dev": true,
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/@babel/code-frame/node_modules/supports-color": {
+      "version": "5.5.0",
+      "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-5.5.0.tgz",
+      "integrity": "sha512-QjVjwdXIt408MIiAqCX4oUKsgU2EqAGzs2Ppkm4aQYbjm+ZEWEcW4SfFNTr4uMNZma0ey4f5lgLrkB0aX0QMow==",
+      "dev": true,
+      "dependencies": {
+        "has-flag": "^3.0.0"
+      },
+      "engines": {
+        "node": ">=4"
+      }
+    },
    "node_modules/@babel/compat-data": {
      "version": "7.23.5",
      "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.23.5.tgz",
@@ -2520,21 +2589,19 @@
      }
    },
    "node_modules/@babel/helper-string-parser": {
-      "version": "7.25.9",
-      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.25.9.tgz",
-      "integrity": "sha512-4A/SCr/2KLd5jrtOMFzaKjVtAei3+2r/NChoBNoZ3EyP/+GlhoaEGoWOZUmFmoITP7zOJyHIMm+DYRd8o3PvHA==",
+      "version": "7.23.4",
+      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.23.4.tgz",
+      "integrity": "sha512-803gmbQdqwdf4olxrX4AJyFBV/RTr3rSmOj0rKwesmzlfhYNDEs+/iOcznzpNWlJlIlTJC2QfPFcHB6DlzdVLQ==",
      "dev": true,
-      "license": "MIT",
      "engines": {
        "node": ">=6.9.0"
      }
    },
    "node_modules/@babel/helper-validator-identifier": {
-      "version": "7.25.9",
-      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.25.9.tgz",
-      "integrity": "sha512-Ed61U6XJc3CVRfkERJWDz4dJwKe7iLmmJsbOGu9wSloNSFttHV0I8g6UAgb7qnK5ly5bGLPd4oXZlxCdANBOWQ==",
+      "version": "7.22.20",
+      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.22.20.tgz",
+      "integrity": "sha512-Y4OZ+ytlatR8AI+8KZfKuL5urKp7qey08ha31L8b3BwewJAoJamTzyvxPR/5D+KkdJCGPq/+8TukHBlY10FX9A==",
      "dev": true,
-      "license": "MIT",
      "engines": {
        "node": ">=6.9.0"
      }
@@ -2549,28 +2616,109 @@
      }
    },
    "node_modules/@babel/helpers": {
-      "version": "7.27.0",
-      "resolved": "https://registry.npmjs.org/@babel/helpers/-/helpers-7.27.0.tgz",
-      "integrity": "sha512-U5eyP/CTFPuNE3qk+WZMxFkp/4zUzdceQlfzf7DdGdhp+Fezd7HD+i8Y24ZuTMKX3wQBld449jijbGq6OdGNQg==",
+      "version": "7.23.8",
+      "resolved": "https://registry.npmjs.org/@babel/helpers/-/helpers-7.23.8.tgz",
+      "integrity": "sha512-KDqYz4PiOWvDFrdHLPhKtCThtIcKVy6avWD2oG4GEvyQ+XDZwHD4YQd+H2vNMnq2rkdxsDkU82T+Vk8U/WXHRQ==",
      "dev": true,
-      "license": "MIT",
      "dependencies": {
-        "@babel/template": "^7.27.0",
-        "@babel/types": "^7.27.0"
+        "@babel/template": "^7.22.15",
+        "@babel/traverse": "^7.23.7",
+        "@babel/types": "^7.23.6"
      },
      "engines": {
        "node": ">=6.9.0"
      }
    },
-    "node_modules/@babel/parser": {
-      "version": "7.27.0",
-      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.27.0.tgz",
-      "integrity": "sha512-iaepho73/2Pz7w2eMS0Q5f83+0RKI7i4xmiYeBmDzfRVbQtTOG7Ts0S4HzJVsTMGI9keU8rNfuZr8DKfSt7Yyg==",
+    "node_modules/@babel/highlight": {
+      "version": "7.23.4",
+      "resolved": "https://registry.npmjs.org/@babel/highlight/-/highlight-7.23.4.tgz",
+      "integrity": "sha512-acGdbYSfp2WheJoJm/EBBBLh/ID8KDc64ISZ9DYtBmC8/Q204PZJLHyzeB5qMzJ5trcOkybd78M4x2KWsUq++A==",
      "dev": true,
-      "license": "MIT",
      "dependencies": {
-        "@babel/types": "^7.27.0"
+        "@babel/helper-validator-identifier": "^7.22.20",
+        "chalk": "^2.4.2",
+        "js-tokens": "^4.0.0"
      },
+      "engines": {
+        "node": ">=6.9.0"
+      }
+    },
+    "node_modules/@babel/highlight/node_modules/ansi-styles": {
+      "version": "3.2.1",
+      "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-3.2.1.tgz",
+      "integrity": "sha512-VT0ZI6kZRdTh8YyJw3SMbYm/u+NqfsAxEpWO0Pf9sq8/e94WxxOpPKx9FR1FlyCtOVDNOQ+8ntlqFxiRc+r5qA==",
+      "dev": true,
+      "dependencies": {
+        "color-convert": "^1.9.0"
+      },
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/@babel/highlight/node_modules/chalk": {
+      "version": "2.4.2",
+      "resolved": "https://registry.npmjs.org/chalk/-/chalk-2.4.2.tgz",
+      "integrity": "sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ==",
+      "dev": true,
+      "dependencies": {
+        "ansi-styles": "^3.2.1",
+        "escape-string-regexp": "^1.0.5",
+        "supports-color": "^5.3.0"
+      },
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/@babel/highlight/node_modules/color-convert": {
+      "version": "1.9.3",
+      "resolved": "https://registry.npmjs.org/color-convert/-/color-convert-1.9.3.tgz",
+      "integrity": "sha512-QfAUtd+vFdAtFQcC8CCyYt1fYWxSqAiK2cSD6zDB8N3cpsEBAvRxp9zOGg6G/SHHJYAT88/az/IuDGALsNVbGg==",
+      "dev": true,
+      "dependencies": {
+        "color-name": "1.1.3"
+      }
+    },
+    "node_modules/@babel/highlight/node_modules/color-name": {
+      "version": "1.1.3",
+      "resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.3.tgz",
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+      "dev": true
+    },
+    "node_modules/@babel/highlight/node_modules/escape-string-regexp": {
+      "version": "1.0.5",
+      "resolved": "https://registry.npmjs.org/escape-string-regexp/-/escape-string-regexp-1.0.5.tgz",
+      "integrity": "sha512-vbRorB5FUQWvla16U8R/qgaFIya2qGzwDrNmCZuYKrbdSUMG6I1ZCGQRefkRVhuOkIGVne7BQ35DSfo1qvJqFg==",
+      "dev": true,
+      "engines": {
+        "node": ">=0.8.0"
+      }
+    },
+    "node_modules/@babel/highlight/node_modules/has-flag": {
+      "version": "3.0.0",
+      "resolved": "https://registry.npmjs.org/has-flag/-/has-flag-3.0.0.tgz",
+      "integrity": "sha512-sKJf1+ceQBr4SMkvQnBDNDtf4TXpVhVGateu0t918bl30FnbE2m4vNLX+VWe/dpjlb+HugGYzW7uQXH98HPEYw==",
+      "dev": true,
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/@babel/highlight/node_modules/supports-color": {
+      "version": "5.5.0",
+      "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-5.5.0.tgz",
+      "integrity": "sha512-QjVjwdXIt408MIiAqCX4oUKsgU2EqAGzs2Ppkm4aQYbjm+ZEWEcW4SfFNTr4uMNZma0ey4f5lgLrkB0aX0QMow==",
+      "dev": true,
+      "dependencies": {
+        "has-flag": "^3.0.0"
+      },
+      "engines": {
+        "node": ">=4"
+      }
+    },
+    "node_modules/@babel/parser": {
+      "version": "7.23.6",
+      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.23.6.tgz",
+      "integrity": "sha512-Z2uID7YJ7oNvAI20O9X0bblw7Qqs8Q2hFy0R9tAfnfLkp5MW0UH9eUvnDSnFwKZ0AvgS1ucqR4KzvVHgnke1VQ==",
+      "dev": true,
      "bin": {
        "parser": "bin/babel-parser.js"
      },
@@ -2756,15 +2904,14 @@
      }
    },
    "node_modules/@babel/template": {
-      "version": "7.27.0",
-      "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.27.0.tgz",
-      "integrity": "sha512-2ncevenBqXI6qRMukPlXwHKHchC7RyMuu4xv5JBXRfOGVcTy1mXCD12qrp7Jsoxll1EV3+9sE4GugBVRjT2jFA==",
+      "version": "7.22.15",
+      "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.22.15.tgz",
+      "integrity": "sha512-QPErUVm4uyJa60rkI73qneDacvdvzxshT3kksGqlGWYdOTIUOwJ7RDUL8sGqslY1uXWSL6xMFKEXDS3ox2uF0w==",
      "dev": true,
-      "license": "MIT",
      "dependencies": {
-        "@babel/code-frame": "^7.26.2",
-        "@babel/parser": "^7.27.0",
-        "@babel/types": "^7.27.0"
+        "@babel/code-frame": "^7.22.13",
+        "@babel/parser": "^7.22.15",
+        "@babel/types": "^7.22.15"
      },
      "engines": {
        "node": ">=6.9.0"
@@ -2801,14 +2948,14 @@
      }
    },
    "node_modules/@babel/types": {
-      "version": "7.27.0",
-      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.27.0.tgz",
-      "integrity": "sha512-H45s8fVLYjbhFH62dIJ3WtmJ6RSPt/3DRO0ZcT2SUiYiQyz3BLVb9ADEnLl91m74aQPS3AzzeajZHYOalWe3bg==",
+      "version": "7.23.6",
+      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.23.6.tgz",
+      "integrity": "sha512-+uarb83brBzPKN38NX1MkB6vb6+mwvR6amUulqAE7ccQw1pEl+bCia9TbdG1lsnFP7lZySvUn37CHyXQdfTwzg==",
      "dev": true,
-      "license": "MIT",
      "dependencies": {
-        "@babel/helper-string-parser": "^7.25.9",
-        "@babel/helper-validator-identifier": "^7.25.9"
+        "@babel/helper-string-parser": "^7.23.4",
+        "@babel/helper-validator-identifier": "^7.22.20",
+        "to-fast-properties": "^2.0.0"
      },
      "engines": {
        "node": ">=6.9.0"
@@ -5403,11 +5550,10 @@
      "devOptional": true
    },
    "node_modules/axios": {
-      "version": "1.8.4",
-      "resolved": "https://registry.npmjs.org/axios/-/axios-1.8.4.tgz",
-      "integrity": "sha512-eBSYY4Y68NNlHbHBMdeDmKNtDgXWhQsJcGqzO3iLUM0GraQFSS9cVgPX5I9b3lbdFKyYoAEGAZF1DwhTaljNAw==",
+      "version": "1.7.7",
+      "resolved": "https://registry.npmjs.org/axios/-/axios-1.7.7.tgz",
+      "integrity": "sha512-S4kL7XrjgBmvdGut0sN3yJxqYzrDOnivkBiN0OFs6hLiUam3UPvswUo0kqGyhqUZGEOytHyumEdXsAkgCOUf3Q==",
      "dev": true,
-      "license": "MIT",
      "dependencies": {
        "follow-redirects": "^1.15.6",
        "form-data": "^4.0.0",
@@ -7723,8 +7869,7 @@
      "version": "4.0.0",
      "resolved": "https://registry.npmjs.org/js-tokens/-/js-tokens-4.0.0.tgz",
      "integrity": "sha512-RdJUflcE3cUzKiMqQgsCu06FPu9UdIJO0beYbPhHN4k6apgJtifcoCtT9bcxOpYBtpD2kCM6Sbzg4CausW/PKQ==",
-      "dev": true,
-      "license": "MIT"
+      "dev": true
    },
    "node_modules/js-yaml": {
      "version": "3.14.1",
@@ -9215,6 +9360,15 @@
      "integrity": "sha512-3f0uOEAQwIqGuWW2MVzYg8fV/QNnc/IpuJNG837rLuczAaLVHslWHZQj4IGiEl5Hs3kkbhwL9Ab7Hrsmuj+Smw==",
      "dev": true
    },
+    "node_modules/to-fast-properties": {
+      "version": "2.0.0",
+      "resolved": "https://registry.npmjs.org/to-fast-properties/-/to-fast-properties-2.0.0.tgz",
+      "integrity": "sha512-/OaKK0xYrs3DmxRYqL/yDc+FxFUVYhDlXMhRmv3z915w2HF1tnN1omB354j8VUGO/hbRzyD6Y3sA7v7GS/ceog==",
+      "dev": true,
+      "engines": {
+        "node": ">=4"
+      }
+    },
    "node_modules/to-regex-range": {
      "version": "5.0.1",
      "resolved": "https://registry.npmjs.org/to-regex-range/-/to-regex-range-5.0.1.tgz",
--- a/nodejs/package.json
+++ b/nodejs/package.json
@@ -11,7 +11,7 @@
    "ann"
  ],
  "private": false,
-  "version": "0.19.0-beta.7",
+  "version": "0.18.2-beta.1",
  "main": "dist/index.js",
  "exports": {
    ".": "./dist/index.js",
@@ -29,7 +29,6 @@
        "aarch64-apple-darwin",
        "x86_64-unknown-linux-gnu",
        "aarch64-unknown-linux-gnu",
-        "x86_64-unknown-linux-musl",
        "aarch64-unknown-linux-musl",
        "x86_64-pc-windows-msvc",
        "aarch64-pc-windows-msvc"
--- a/nodejs/src/query.rs
+++ b/nodejs/src/query.rs
@@ -3,9 +3,7 @@

 use std::sync::Arc;

-use lancedb::index::scalar::{
-    BoostQuery, FtsQuery, FullTextSearchQuery, MatchQuery, MultiMatchQuery, PhraseQuery,
-};
+use lancedb::index::scalar::FullTextSearchQuery;
 use lancedb::query::ExecutableQuery;
 use lancedb::query::Query as LanceDbQuery;
 use lancedb::query::QueryBase;
@@ -40,10 +38,9 @@ impl Query {
    }

    #[napi]
-    pub fn full_text_search(&mut self, query: napi::JsObject) -> napi::Result<()> {
-        let query = parse_fts_query(query)?;
+    pub fn full_text_search(&mut self, query: String, columns: Option<Vec<String>>) {
+        let query = FullTextSearchQuery::new(query).columns(columns);
        self.inner = self.inner.clone().full_text_search(query);
-        Ok(())
    }

    #[napi]
@@ -90,15 +87,11 @@ impl Query {
    pub async fn execute(
        &self,
        max_batch_length: Option<u32>,
-        timeout_ms: Option<u32>,
    ) -> napi::Result<RecordBatchIterator> {
        let mut execution_opts = QueryExecutionOptions::default();
        if let Some(max_batch_length) = max_batch_length {
            execution_opts.max_batch_length = max_batch_length;
        }
-        if let Some(timeout_ms) = timeout_ms {
-            execution_opts.timeout = Some(std::time::Duration::from_millis(timeout_ms as u64))
-        }
        let inner_stream = self
            .inner
            .execute_with_options(execution_opts)
@@ -121,16 +114,6 @@ impl Query {
            ))
        })
    }
-
-    #[napi(catch_unwind)]
-    pub async fn analyze_plan(&self) -> napi::Result<String> {
-        self.inner.analyze_plan().await.map_err(|e| {
-            napi::Error::from_reason(format!(
-                "Failed to execute analyze plan: {}",
-                convert_error(&e)
-            ))
-        })
-    }
 }

 #[napi]
@@ -202,10 +185,9 @@ impl VectorQuery {
    }

    #[napi]
-    pub fn full_text_search(&mut self, query: napi::JsObject) -> napi::Result<()> {
-        let query = parse_fts_query(query)?;
+    pub fn full_text_search(&mut self, query: String, columns: Option<Vec<String>>) {
+        let query = FullTextSearchQuery::new(query).columns(columns);
        self.inner = self.inner.clone().full_text_search(query);
-        Ok(())
    }

    #[napi]
@@ -250,15 +232,11 @@ impl VectorQuery {
    pub async fn execute(
        &self,
        max_batch_length: Option<u32>,
-        timeout_ms: Option<u32>,
    ) -> napi::Result<RecordBatchIterator> {
        let mut execution_opts = QueryExecutionOptions::default();
        if let Some(max_batch_length) = max_batch_length {
            execution_opts.max_batch_length = max_batch_length;
        }
-        if let Some(timeout_ms) = timeout_ms {
-            execution_opts.timeout = Some(std::time::Duration::from_millis(timeout_ms as u64))
-        }
        let inner_stream = self
            .inner
            .execute_with_options(execution_opts)
@@ -281,127 +259,4 @@ impl VectorQuery {
            ))
        })
    }
-
-    #[napi(catch_unwind)]
-    pub async fn analyze_plan(&self) -> napi::Result<String> {
-        self.inner.analyze_plan().await.map_err(|e| {
-            napi::Error::from_reason(format!(
-                "Failed to execute analyze plan: {}",
-                convert_error(&e)
-            ))
-        })
-    }
-}
-
-#[napi]
-#[derive(Debug, Clone)]
-pub struct JsFullTextQuery {
-    pub(crate) inner: FtsQuery,
-}
-
-#[napi]
-impl JsFullTextQuery {
-    #[napi(factory)]
-    pub fn match_query(
-        query: String,
-        column: String,
-        boost: f64,
-        fuzziness: Option<u32>,
-        max_expansions: u32,
-    ) -> napi::Result<Self> {
-        Ok(Self {
-            inner: MatchQuery::new(query)
-                .with_column(Some(column))
-                .with_boost(boost as f32)
-                .with_fuzziness(fuzziness)
-                .with_max_expansions(max_expansions as usize)
-                .into(),
-        })
-    }
-
-    #[napi(factory)]
-    pub fn phrase_query(query: String, column: String) -> napi::Result<Self> {
-        Ok(Self {
-            inner: PhraseQuery::new(query).with_column(Some(column)).into(),
-        })
-    }
-
-    #[napi(factory)]
-    #[allow(clippy::use_self)] // NAPI doesn't allow Self here but clippy reports it
-    pub fn boost_query(
-        positive: &JsFullTextQuery,
-        negative: &JsFullTextQuery,
-        negative_boost: Option<f64>,
-    ) -> napi::Result<Self> {
-        Ok(Self {
-            inner: BoostQuery::new(
-                positive.inner.clone(),
-                negative.inner.clone(),
-                negative_boost.map(|v| v as f32),
-            )
-            .into(),
-        })
-    }
-
-    #[napi(factory)]
-    pub fn multi_match_query(
-        query: String,
-        columns: Vec<String>,
-        boosts: Option<Vec<f64>>,
-    ) -> napi::Result<Self> {
-        let q = match boosts {
-            Some(boosts) => MultiMatchQuery::try_new(query, columns)
-                .and_then(|q| q.try_with_boosts(boosts.into_iter().map(|v| v as f32).collect())),
-            None => MultiMatchQuery::try_new(query, columns),
-        }
-        .map_err(|e| {
-            napi::Error::from_reason(format!("Failed to create multi match query: {}", e))
-        })?;
-
-        Ok(Self { inner: q.into() })
-    }
-}
-
-fn parse_fts_query(query: napi::JsObject) -> napi::Result<FullTextSearchQuery> {
-    if let Ok(Some(query)) = query.get::<_, &JsFullTextQuery>("query") {
-        Ok(FullTextSearchQuery::new_query(query.inner.clone()))
-    } else if let Ok(Some(query_text)) = query.get::<_, String>("query") {
-        let mut query_text = query_text;
-        let columns = query.get::<_, Option<Vec<String>>>("columns")?.flatten();
-
-        let is_phrase =
-            query_text.len() >= 2 && query_text.starts_with('"') && query_text.ends_with('"');
-        let is_multi_match = columns.as_ref().map(|cols| cols.len() > 1).unwrap_or(false);
-
-        if is_phrase {
-            // Remove the surrounding quotes for phrase queries
-            query_text = query_text[1..query_text.len() - 1].to_string();
-        }
-
-        let query: FtsQuery = match (is_phrase, is_multi_match) {
-            (false, _) => MatchQuery::new(query_text).into(),
-            (true, false) => PhraseQuery::new(query_text).into(),
-            (true, true) => {
-                return Err(napi::Error::from_reason(
-                    "Phrase queries cannot be used with multiple columns.",
-                ));
-            }
-        };
-        let mut query = FullTextSearchQuery::new_query(query);
-        if let Some(cols) = columns {
-            if !cols.is_empty() {
-                query = query.with_columns(&cols).map_err(|e| {
-                    napi::Error::from_reason(format!(
-                        "Failed to set full text search columns: {}",
-                        e
-                    ))
-                })?;
-            }
-        }
-        Ok(query)
-    } else {
-        Err(napi::Error::from_reason(
-            "Invalid full text search query object".to_string(),
-        ))
-    }
 }
--- a/nodejs/src/table.rs
+++ b/nodejs/src/table.rs
@@ -132,14 +132,6 @@ impl Table {
            .default_error()
    }

-    #[napi(catch_unwind)]
-    pub async fn prewarm_index(&self, index_name: String) -> napi::Result<()> {
-        self.inner_ref()?
-            .prewarm_index(&index_name)
-            .await
-            .default_error()
-    }
-
    #[napi(catch_unwind)]
    pub async fn update(
        &self,
--- a/python/.bumpversion.toml
+++ b/python/.bumpversion.toml
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.22.0-beta.8"
+current_version = "0.21.2"
 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 = "0.22.0-beta.8"
+version = "0.21.2"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -4,12 +4,11 @@ name = "lancedb"
 dynamic = ["version"]
 dependencies = [
    "deprecation",
-    "numpy",
-    "overrides>=0.7",
-    "packaging",
+    "tqdm>=4.27.0",
    "pyarrow>=14",
    "pydantic>=1.10",
-    "tqdm>=4.27.0",
+    "packaging",
+    "overrides>=0.7",
 ]
 description = "lancedb"
 authors = [{ name = "LanceDB Devs", email = "dev@lancedb.com" }]
@@ -43,9 +42,6 @@ classifiers = [
 repository = "https://github.com/lancedb/lancedb"

 [project.optional-dependencies]
-pylance = [
-    "pylance>=0.25",
-]
 tests = [
    "aiohttp",
    "boto3",
@@ -58,8 +54,7 @@ tests = [
    "polars>=0.19, <=1.3.0",
    "tantivy",
    "pyarrow-stubs",
-    "pylance>=0.25",
-    "requests",
+    "pylance>=0.23.2",
 ]
 dev = [
    "ruff",
--- a/python/python/lancedb/_lancedb.pyi
+++ b/python/python/lancedb/_lancedb.pyi
@@ -1,4 +1,3 @@
-from datetime import timedelta
 from typing import Dict, List, Optional, Tuple, Any, Union, Literal

 import pyarrow as pa
@@ -49,11 +48,10 @@ class Table:
    async def version(self) -> int: ...
    async def checkout(self, version: int): ...
    async def checkout_latest(self): ...
-    async def restore(self, version: Optional[int] = None): ...
+    async def restore(self): ...
    async def list_indices(self) -> list[IndexConfig]: ...
    async def delete(self, filter: str): ...
    async def add_columns(self, columns: list[tuple[str, str]]) -> None: ...
-    async def add_columns_with_schema(self, schema: pa.Schema) -> None: ...
    async def alter_columns(self, columns: list[dict[str, Any]]) -> None: ...
    async def optimize(
        self,
@@ -95,11 +93,7 @@ class Query:
    def postfilter(self): ...
    def nearest_to(self, query_vec: pa.Array) -> VectorQuery: ...
    def nearest_to_text(self, query: dict) -> FTSQuery: ...
-    async def execute(
-        self, max_batch_length: Optional[int], timeout: Optional[timedelta]
-    ) -> RecordBatchStream: ...
-    async def explain_plan(self, verbose: Optional[bool]) -> str: ...
-    async def analyze_plan(self) -> str: ...
+    async def execute(self, max_batch_length: Optional[int]) -> RecordBatchStream: ...
    def to_query_request(self) -> PyQueryRequest: ...

 class FTSQuery:
@@ -113,9 +107,8 @@ class FTSQuery:
    def get_query(self) -> str: ...
    def add_query_vector(self, query_vec: pa.Array) -> None: ...
    def nearest_to(self, query_vec: pa.Array) -> HybridQuery: ...
-    async def execute(
-        self, max_batch_length: Optional[int], timeout: Optional[timedelta]
-    ) -> RecordBatchStream: ...
+    async def execute(self, max_batch_length: Optional[int]) -> RecordBatchStream: ...
+    async def explain_plan(self) -> str: ...
    def to_query_request(self) -> PyQueryRequest: ...

 class VectorQuery:
--- a/python/python/lancedb/embeddings/voyageai.py
+++ b/python/python/lancedb/embeddings/voyageai.py
@@ -1,12 +1,9 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
-import base64
-import os
-from typing import ClassVar, TYPE_CHECKING, List, Union, Any

-from pathlib import Path
-from urllib.parse import urlparse
-from io import BytesIO
+
+import os
+from typing import ClassVar, TYPE_CHECKING, List, Union

 import numpy as np
 import pyarrow as pa
@@ -14,100 +11,12 @@ import pyarrow as pa
 from ..util import attempt_import_or_raise
 from .base import EmbeddingFunction
 from .registry import register
-from .utils import api_key_not_found_help, IMAGES, TEXT
+from .utils import api_key_not_found_help, IMAGES

 if TYPE_CHECKING:
    import PIL


-def is_valid_url(text):
-    try:
-        parsed = urlparse(text)
-        return bool(parsed.scheme) and bool(parsed.netloc)
-    except Exception:
-        return False
-
-
-def transform_input(input_data: Union[str, bytes, Path]):
-    PIL = attempt_import_or_raise("PIL", "pillow")
-    if isinstance(input_data, str):
-        if is_valid_url(input_data):
-            content = {"type": "image_url", "image_url": input_data}
-        else:
-            content = {"type": "text", "text": input_data}
-    elif isinstance(input_data, PIL.Image.Image):
-        buffered = BytesIO()
-        input_data.save(buffered, format="JPEG")
-        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-        content = {
-            "type": "image_base64",
-            "image_base64": "data:image/jpeg;base64," + img_str,
-        }
-    elif isinstance(input_data, bytes):
-        img = PIL.Image.open(BytesIO(input_data))
-        buffered = BytesIO()
-        img.save(buffered, format="JPEG")
-        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-        content = {
-            "type": "image_base64",
-            "image_base64": "data:image/jpeg;base64," + img_str,
-        }
-    elif isinstance(input_data, Path):
-        img = PIL.Image.open(input_data)
-        buffered = BytesIO()
-        img.save(buffered, format="JPEG")
-        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-        content = {
-            "type": "image_base64",
-            "image_base64": "data:image/jpeg;base64," + img_str,
-        }
-    else:
-        raise ValueError("Each input should be either str, bytes, Path or Image.")
-
-    return {"content": [content]}
-
-
-def sanitize_multimodal_input(inputs: Union[TEXT, IMAGES]) -> List[Any]:
-    """
-    Sanitize the input to the embedding function.
-    """
-    PIL = attempt_import_or_raise("PIL", "pillow")
-    if isinstance(inputs, (str, bytes, Path, PIL.Image.Image)):
-        inputs = [inputs]
-    elif isinstance(inputs, pa.Array):
-        inputs = inputs.to_pylist()
-    elif isinstance(inputs, pa.ChunkedArray):
-        inputs = inputs.combine_chunks().to_pylist()
-    else:
-        raise ValueError(
-            f"Input type {type(inputs)} not allowed with multimodal model."
-        )
-
-    if not all(isinstance(x, (str, bytes, Path, PIL.Image.Image)) for x in inputs):
-        raise ValueError("Each input should be either str, bytes, Path or Image.")
-
-    return [transform_input(i) for i in inputs]
-
-
-def sanitize_text_input(inputs: TEXT) -> List[str]:
-    """
-    Sanitize the input to the embedding function.
-    """
-    if isinstance(inputs, str):
-        inputs = [inputs]
-    elif isinstance(inputs, pa.Array):
-        inputs = inputs.to_pylist()
-    elif isinstance(inputs, pa.ChunkedArray):
-        inputs = inputs.combine_chunks().to_pylist()
-    else:
-        raise ValueError(f"Input type {type(inputs)} not allowed with text model.")
-
-    if not all(isinstance(x, str) for x in inputs):
-        raise ValueError("Each input should be str.")
-
-    return inputs
-
-
@register("voyageai")
 class VoyageAIEmbeddingFunction(EmbeddingFunction):
    """
@@ -165,11 +74,6 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
    ]
    multimodal_embedding_models: list = ["voyage-multimodal-3"]

-    def _is_multimodal_model(self, model_name: str):
-        return (
-            model_name in self.multimodal_embedding_models or "multimodal" in model_name
-        )
-
    def ndims(self):
        if self.name == "voyage-3-lite":
            return 512
@@ -181,12 +85,55 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
            "voyage-finance-2",
            "voyage-multilingual-2",
            "voyage-law-2",
-            "voyage-multimodal-3",
        ]:
            return 1024
        else:
            raise ValueError(f"Model {self.name} not supported")

+    def sanitize_input(self, images: IMAGES) -> Union[List[bytes], np.ndarray]:
+        """
+        Sanitize the input to the embedding function.
+        """
+        if isinstance(images, (str, bytes)):
+            images = [images]
+        elif isinstance(images, pa.Array):
+            images = images.to_pylist()
+        elif isinstance(images, pa.ChunkedArray):
+            images = images.combine_chunks().to_pylist()
+        return images
+
+    def generate_text_embeddings(self, text: str, **kwargs) -> np.ndarray:
+        """
+        Get the embeddings for the given texts
+
+        Parameters
+        ----------
+        texts: list[str] or np.ndarray (of str)
+            The texts to embed
+        input_type: Optional[str]
+
+        truncation: Optional[bool]
+        """
+        client = VoyageAIEmbeddingFunction._get_client()
+        if self.name in self.text_embedding_models:
+            rs = client.embed(texts=[text], model=self.name, **kwargs)
+        elif self.name in self.multimodal_embedding_models:
+            rs = client.multimodal_embed(inputs=[[text]], model=self.name, **kwargs)
+        else:
+            raise ValueError(
+                f"Model {self.name} not supported to generate text embeddings"
+            )
+
+        return rs.embeddings[0]
+
+    def generate_image_embedding(
+        self, image: "PIL.Image.Image", **kwargs
+    ) -> np.ndarray:
+        rs = VoyageAIEmbeddingFunction._get_client().multimodal_embed(
+            inputs=[[image]], model=self.name, **kwargs
+        )
+        return rs.embeddings[0]
+
    def compute_query_embeddings(
        self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
    ) -> List[np.ndarray]:
@@ -197,52 +144,23 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
        ----------
        query : Union[str, PIL.Image.Image]
            The query to embed. A query can be either text or an image.
-
-        Returns
-        -------
-            List[np.array]: the list of embeddings
        """
-        client = VoyageAIEmbeddingFunction._get_client()
-        if self._is_multimodal_model(self.name):
-            result = client.multimodal_embed(
-                inputs=[[query]], model=self.name, input_type="query", **kwargs
-            )
+        if isinstance(query, str):
+            return [self.generate_text_embeddings(query, input_type="query")]
        else:
-            result = client.embed(
-                texts=[query], model=self.name, input_type="query", **kwargs
-            )
-
-        return [result.embeddings[0]]
+            PIL = attempt_import_or_raise("PIL", "pillow")
+            if isinstance(query, PIL.Image.Image):
+                return [self.generate_image_embedding(query, input_type="query")]
+            else:
+                raise TypeError("Only text PIL images supported as query")

    def compute_source_embeddings(
-        self, inputs: Union[TEXT, IMAGES], *args, **kwargs
+        self, images: IMAGES, *args, **kwargs
    ) -> List[np.array]:
-        """
-        Compute the embeddings for the inputs
-
-        Parameters
-        ----------
-        inputs : Union[TEXT, IMAGES]
-            The inputs to embed. The input can be either str, bytes, Path (to an image),
-            PIL.Image or list of these.
-
-        Returns
-        -------
-            List[np.array]: the list of embeddings
-        """
-        client = VoyageAIEmbeddingFunction._get_client()
-        if self._is_multimodal_model(self.name):
-            inputs = sanitize_multimodal_input(inputs)
-            result = client.multimodal_embed(
-                inputs=inputs, model=self.name, input_type="document", **kwargs
-            )
-        else:
-            inputs = sanitize_text_input(inputs)
-            result = client.embed(
-                texts=inputs, model=self.name, input_type="document", **kwargs
-            )
-
-        return result.embeddings
+        images = self.sanitize_input(images)
+        return [
+            self.generate_image_embedding(img, input_type="document") for img in images
+        ]

    @staticmethod
    def _get_client():
--- a/python/python/lancedb/query.py
+++ b/python/python/lancedb/query.py
@@ -4,10 +4,7 @@
 from __future__ import annotations

 from abc import ABC, abstractmethod
-import abc
 from concurrent.futures import ThreadPoolExecutor
-from enum import Enum
-from datetime import timedelta
 from typing import (
    TYPE_CHECKING,
    Dict,
@@ -86,213 +83,6 @@ def ensure_vector_query(
        return val


-class FullTextQueryType(Enum):
-    MATCH = "match"
-    MATCH_PHRASE = "match_phrase"
-    BOOST = "boost"
-    MULTI_MATCH = "multi_match"
-
-
-class FullTextQuery(abc.ABC, pydantic.BaseModel):
-    @abc.abstractmethod
-    def query_type(self) -> FullTextQueryType:
-        """
-        Get the query type of the query.
-
-        Returns
-        -------
-        str
-            The type of the query.
-        """
-
-    @abc.abstractmethod
-    def to_dict(self) -> dict:
-        """
-        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.
-
-        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.
-        """
-        super().__init__(
-            query=query,
-            column=column,
-            boost=boost,
-            fuzziness=fuzziness,
-            max_expansions=max_expansions,
-        )
-
-    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,
-                }
-            }
-        }
-
-
-class PhraseQuery(FullTextQuery):
-    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)
-
-    def query_type(self) -> FullTextQueryType:
-        return FullTextQueryType.MATCH_PHRASE
-
-    def to_dict(self) -> dict:
-        return {
-            "match_phrase": {
-                self.column: self.query,
-            }
-        }
-
-
-class BoostQuery(FullTextQuery):
-    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
-        )
-
-    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,
-            }
-        }
-
-
-class MultiMatchQuery(FullTextQuery):
-    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)
-
-    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,
-            }
-        }
-
-
 class FullTextSearchQuery(pydantic.BaseModel):
    """A LanceDB Full Text Search Query

@@ -302,13 +92,18 @@ class FullTextSearchQuery(pydantic.BaseModel):
        The columns to search

        If None, then the table should select the column automatically.
-    query: str | FullTextQuery
-        If a string, it is treated as a MatchQuery.
-        If a FullTextQuery object, it is used directly.
+    query: str
+        The query to search for
+    limit: Optional[int] = None
+        The limit on the number of results to return
+    wand_factor: Optional[float] = None
+        The wand factor to use for the search
    """

    columns: Optional[List[str]] = None
-    query: Union[str, FullTextQuery]
+    query: str
+    limit: Optional[int] = None
+    wand_factor: Optional[float] = None


 class Query(pydantic.BaseModel):
@@ -562,7 +357,7 @@ class LanceQueryBuilder(ABC):
                table, query, vector_column_name, fts_columns=fts_columns
            )

-        if isinstance(query, (str, FullTextQuery)):
+        if isinstance(query, str):
            # fts
            return LanceFtsQueryBuilder(
                table,
@@ -587,10 +382,8 @@ class LanceQueryBuilder(ABC):
        # If query_type is fts, then query must be a string.
        # otherwise raise TypeError
        if query_type == "fts":
-            if not isinstance(query, (str, FullTextQuery)):
-                raise TypeError(
-                    f"'fts' query must be a string or FullTextQuery: {type(query)}"
-                )
+            if not isinstance(query, str):
+                raise TypeError(f"'fts' queries must be a string: {type(query)}")
            return query, query_type
        elif query_type == "vector":
            query = cls._query_to_vector(table, query, vector_column_name)
@@ -651,12 +444,7 @@ class LanceQueryBuilder(ABC):
        """
        return self.to_pandas()

-    def to_pandas(
-        self,
-        flatten: Optional[Union[int, bool]] = None,
-        *,
-        timeout: Optional[timedelta] = None,
-    ) -> "pd.DataFrame":
+    def to_pandas(self, flatten: Optional[Union[int, bool]] = None) -> "pd.DataFrame":
        """
        Execute the query and return the results as a pandas DataFrame.
        In addition to the selected columns, LanceDB also returns a vector
@@ -670,15 +458,12 @@ class LanceQueryBuilder(ABC):
            If flatten is an integer, flatten the nested columns up to the
            specified depth.
            If unspecified, do not flatten the nested columns.
-        timeout: Optional[timedelta]
-            The maximum time to wait for the query to complete.
-            If None, wait indefinitely.
        """
-        tbl = flatten_columns(self.to_arrow(timeout=timeout), flatten)
+        tbl = flatten_columns(self.to_arrow(), flatten)
        return tbl.to_pandas()

    @abstractmethod
-    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
+    def to_arrow(self) -> pa.Table:
        """
        Execute the query and return the results as an
        [Apache Arrow Table](https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table).
@@ -686,65 +471,34 @@ class LanceQueryBuilder(ABC):
        In addition to the selected columns, LanceDB also returns a vector
        and also the "_distance" column which is the distance between the query
        vector and the returned vectors.
-
-        Parameters
-        ----------
-        timeout: Optional[timedelta]
-            The maximum time to wait for the query to complete.
-            If None, wait indefinitely.
        """
        raise NotImplementedError

    @abstractmethod
-    def to_batches(
-        self,
-        /,
-        batch_size: Optional[int] = None,
-        *,
-        timeout: Optional[timedelta] = None,
-    ) -> pa.RecordBatchReader:
+    def to_batches(self, /, batch_size: Optional[int] = None) -> pa.RecordBatchReader:
        """
        Execute the query and return the results as a pyarrow
        [RecordBatchReader](https://arrow.apache.org/docs/python/generated/pyarrow.RecordBatchReader.html)
-
-        Parameters
-        ----------
-        batch_size: int
-            The maximum number of selected records in a RecordBatch object.
-        timeout: Optional[timedelta]
-            The maximum time to wait for the query to complete.
-            If None, wait indefinitely.
        """
        raise NotImplementedError

-    def to_list(self, *, timeout: Optional[timedelta] = None) -> List[dict]:
+    def to_list(self) -> List[dict]:
        """
        Execute the query and return the results as a list of dictionaries.

        Each list entry is a dictionary with the selected column names as keys,
        or all table columns if `select` is not called. The vector and the "_distance"
        fields are returned whether or not they're explicitly selected.
-
-        Parameters
-        ----------
-        timeout: Optional[timedelta]
-            The maximum time to wait for the query to complete.
-            If None, wait indefinitely.
        """
-        return self.to_arrow(timeout=timeout).to_pylist()
+        return self.to_arrow().to_pylist()

-    def to_pydantic(
-        self, model: Type[LanceModel], *, timeout: Optional[timedelta] = None
-    ) -> List[LanceModel]:
+    def to_pydantic(self, model: Type[LanceModel]) -> List[LanceModel]:
        """Return the table as a list of pydantic models.

        Parameters
        ----------
        model: Type[LanceModel]
            The pydantic model to use.
-        timeout: Optional[timedelta]
-            The maximum time to wait for the query to complete.
-            If None, wait indefinitely.

        Returns
        -------
@@ -752,25 +506,19 @@ class LanceQueryBuilder(ABC):
        """
        return [
            model(**{k: v for k, v in row.items() if k in model.field_names()})
-            for row in self.to_arrow(timeout=timeout).to_pylist()
+            for row in self.to_arrow().to_pylist()
        ]

-    def to_polars(self, *, timeout: Optional[timedelta] = None) -> "pl.DataFrame":
+    def to_polars(self) -> "pl.DataFrame":
        """
        Execute the query and return the results as a Polars DataFrame.
        In addition to the selected columns, LanceDB also returns a vector
        and also the "_distance" column which is the distance between the query
        vector and the returned vector.
-
-        Parameters
-        ----------
-        timeout: Optional[timedelta]
-            The maximum time to wait for the query to complete.
-            If None, wait indefinitely.
        """
        import polars as pl

-        return pl.from_arrow(self.to_arrow(timeout=timeout))
+        return pl.from_arrow(self.to_arrow())

    def limit(self, limit: Union[int, None]) -> Self:
        """Set the maximum number of results to return.
@@ -909,45 +657,7 @@ class LanceQueryBuilder(ABC):
        -------
        plan : str
        """  # noqa: E501
-        return self._table._explain_plan(self.to_query_object(), verbose=verbose)
-
-    def analyze_plan(self) -> str:
-        """
-        Run the query and return its execution plan with runtime metrics.
-
-        This returns detailed metrics for each step, such as elapsed time,
-        rows processed, bytes read, and I/O stats. It is useful for debugging
-        and performance tuning.
-
-        Examples
-        --------
-        >>> import lancedb
-        >>> db = lancedb.connect("./.lancedb")
-        >>> table = db.create_table("my_table", [{"vector": [99.0, 99]}])
-        >>> query = [100, 100]
-        >>> plan = table.search(query).analyze_plan()
-        >>> print(plan)  # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
-        AnalyzeExec verbose=true, metrics=[]
-          ProjectionExec: expr=[...], metrics=[...]
-            GlobalLimitExec: skip=0, fetch=10, metrics=[...]
-              FilterExec: _distance@2 IS NOT NULL,
-              metrics=[output_rows=..., elapsed_compute=...]
-                SortExec: TopK(fetch=10), expr=[...],
-                preserve_partitioning=[...],
-                metrics=[output_rows=..., elapsed_compute=..., row_replacements=...]
-                  KNNVectorDistance: metric=l2,
-                  metrics=[output_rows=..., elapsed_compute=..., output_batches=...]
-                    LanceScan: uri=..., projection=[vector], row_id=true,
-                    row_addr=false, ordered=false,
-                    metrics=[output_rows=..., elapsed_compute=...,
-                    bytes_read=..., iops=..., requests=...]
-
-        Returns
-        -------
-        plan : str
-            The physical query execution plan with runtime metrics.
-        """
-        return self._table._analyze_plan(self.to_query_object())
+        return self._table._explain_plan(self.to_query_object())

    def vector(self, vector: Union[np.ndarray, list]) -> Self:
        """Set the vector to search for.
@@ -964,14 +674,13 @@ class LanceQueryBuilder(ABC):
        """
        raise NotImplementedError

-    def text(self, text: str | FullTextQuery) -> Self:
+    def text(self, text: str) -> Self:
        """Set the text to search for.

        Parameters
        ----------
-        text: str | FullTextQuery
-            If a string, it is treated as a MatchQuery.
-            If a FullTextQuery object, it is used directly.
+        text: str
+            The text to search for.

        Returns
        -------
@@ -1185,7 +894,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
        self._refine_factor = refine_factor
        return self

-    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
+    def to_arrow(self) -> pa.Table:
        """
        Execute the query and return the results as an
        [Apache Arrow Table](https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table).
@@ -1193,14 +902,8 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
        In addition to the selected columns, LanceDB also returns a vector
        and also the "_distance" column which is the distance between the query
        vector and the returned vectors.
-
-        Parameters
-        ----------
-        timeout: Optional[timedelta]
-            The maximum time to wait for the query to complete.
-            If None, wait indefinitely.
        """
-        return self.to_batches(timeout=timeout).read_all()
+        return self.to_batches().read_all()

    def to_query_object(self) -> Query:
        """
@@ -1230,13 +933,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
            bypass_vector_index=self._bypass_vector_index,
        )

-    def to_batches(
-        self,
-        /,
-        batch_size: Optional[int] = None,
-        *,
-        timeout: Optional[timedelta] = None,
-    ) -> pa.RecordBatchReader:
+    def to_batches(self, /, batch_size: Optional[int] = None) -> pa.RecordBatchReader:
        """
        Execute the query and return the result as a RecordBatchReader object.

@@ -1244,9 +941,6 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
        ----------
        batch_size: int
            The maximum number of selected records in a RecordBatch object.
-        timeout: timedelta, default None
-            The maximum time to wait for the query to complete.
-            If None, wait indefinitely.

        Returns
        -------
@@ -1256,9 +950,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
        if isinstance(vector[0], np.ndarray):
            vector = [v.tolist() for v in vector]
        query = self.to_query_object()
-        result_set = self._table._execute_query(
-            query, batch_size=batch_size, timeout=timeout
-        )
+        result_set = self._table._execute_query(query, batch_size)
        if self._reranker is not None:
            rs_table = result_set.read_all()
            result_set = self._reranker.rerank_vector(self._str_query, rs_table)
@@ -1354,7 +1046,7 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
    def __init__(
        self,
        table: "Table",
-        query: str | FullTextQuery,
+        query: str,
        ordering_field_name: Optional[str] = None,
        fts_columns: Optional[Union[str, List[str]]] = None,
    ):
@@ -1397,7 +1089,7 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
            offset=self._offset,
        )

-    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
+    def to_arrow(self) -> pa.Table:
        path, fs, exist = self._table._get_fts_index_path()
        if exist:
            return self.tantivy_to_arrow()
@@ -1409,16 +1101,14 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
                "Use tantivy-based index instead for now."
            )
        query = self.to_query_object()
-        results = self._table._execute_query(query, timeout=timeout)
+        results = self._table._execute_query(query)
        results = results.read_all()
        if self._reranker is not None:
            results = self._reranker.rerank_fts(self._query, results)
            check_reranker_result(results)
        return results

-    def to_batches(
-        self, /, batch_size: Optional[int] = None, timeout: Optional[timedelta] = None
-    ):
+    def to_batches(self, /, batch_size: Optional[int] = None):
        raise NotImplementedError("to_batches on an FTS query")

    def tantivy_to_arrow(self) -> pa.Table:
@@ -1523,8 +1213,8 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):


 class LanceEmptyQueryBuilder(LanceQueryBuilder):
-    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
-        return self.to_batches(timeout=timeout).read_all()
+    def to_arrow(self) -> pa.Table:
+        return self.to_batches().read_all()

    def to_query_object(self) -> Query:
        return Query(
@@ -1535,11 +1225,9 @@ class LanceEmptyQueryBuilder(LanceQueryBuilder):
            offset=self._offset,
        )

-    def to_batches(
-        self, /, batch_size: Optional[int] = None, timeout: Optional[timedelta] = None
-    ) -> pa.RecordBatchReader:
+    def to_batches(self, /, batch_size: Optional[int] = None) -> pa.RecordBatchReader:
        query = self.to_query_object()
-        return self._table._execute_query(query, batch_size=batch_size, timeout=timeout)
+        return self._table._execute_query(query, batch_size)

    def rerank(self, reranker: Reranker) -> LanceEmptyQueryBuilder:
        """Rerank the results using the specified reranker.
@@ -1572,7 +1260,7 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
    def __init__(
        self,
        table: "Table",
-        query: Optional[Union[str, FullTextQuery]] = None,
+        query: Optional[str] = None,
        vector_column: Optional[str] = None,
        fts_columns: Optional[Union[str, List[str]]] = None,
    ):
@@ -1602,8 +1290,8 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
        text_query = text or query
        if text_query is None:
            raise ValueError("Text query must be provided for hybrid search.")
-        if not isinstance(text_query, (str, FullTextQuery)):
-            raise ValueError("Text query must be a string or FullTextQuery")
+        if not isinstance(text_query, str):
+            raise ValueError("Text query must be a string")

        return vector_query, text_query

@@ -1627,7 +1315,7 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
    def to_query_object(self) -> Query:
        raise NotImplementedError("to_query_object not yet supported on a hybrid query")

-    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
+    def to_arrow(self) -> pa.Table:
        vector_query, fts_query = self._validate_query(
            self._query, self._vector, self._text
        )
@@ -1670,11 +1358,9 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
            self._reranker = RRFReranker()

        with ThreadPoolExecutor() as executor:
-            fts_future = executor.submit(
-                self._fts_query.with_row_id(True).to_arrow, timeout=timeout
-            )
+            fts_future = executor.submit(self._fts_query.with_row_id(True).to_arrow)
            vector_future = executor.submit(
-                self._vector_query.with_row_id(True).to_arrow, timeout=timeout
+                self._vector_query.with_row_id(True).to_arrow
            )
            fts_results = fts_future.result()
            vector_results = vector_future.result()
@@ -1761,9 +1447,7 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):

        return results

-    def to_batches(
-        self, /, batch_size: Optional[int] = None, timeout: Optional[timedelta] = None
-    ):
+    def to_batches(self):
        raise NotImplementedError("to_batches not yet supported on a hybrid query")

    @staticmethod
@@ -1969,7 +1653,7 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
        self._vector = vector
        return self

-    def text(self, text: str | FullTextQuery) -> LanceHybridQueryBuilder:
+    def text(self, text: str) -> LanceHybridQueryBuilder:
        self._text = text
        return self

@@ -2127,10 +1811,7 @@ class AsyncQueryBase(object):
        return self

    async def to_batches(
-        self,
-        *,
-        max_batch_length: Optional[int] = None,
-        timeout: Optional[timedelta] = None,
+        self, *, max_batch_length: Optional[int] = None
    ) -> AsyncRecordBatchReader:
        """
        Execute the query and return the results as an Apache Arrow RecordBatchReader.
@@ -2143,56 +1824,34 @@ class AsyncQueryBase(object):
            If not specified, a default batch length is used.
            It is possible for batches to be smaller than the provided length if the
            underlying data is stored in smaller chunks.
-        timeout: Optional[timedelta]
-            The maximum time to wait for the query to complete.
-            If not specified, no timeout is applied. If the query does not
-            complete within the specified time, an error will be raised.
        """
-        return AsyncRecordBatchReader(
-            await self._inner.execute(max_batch_length, timeout)
-        )
+        return AsyncRecordBatchReader(await self._inner.execute(max_batch_length))

-    async def to_arrow(self, timeout: Optional[timedelta] = None) -> pa.Table:
+    async def to_arrow(self) -> pa.Table:
        """
        Execute the query and collect the results into an Apache Arrow Table.

        This method will collect all results into memory before returning.  If
        you expect a large number of results, you may want to use
        [to_batches][lancedb.query.AsyncQueryBase.to_batches]
-
-        Parameters
-        ----------
-        timeout: Optional[timedelta]
-            The maximum time to wait for the query to complete.
-            If not specified, no timeout is applied. If the query does not
-            complete within the specified time, an error will be raised.
        """
-        batch_iter = await self.to_batches(timeout=timeout)
+        batch_iter = await self.to_batches()
        return pa.Table.from_batches(
            await batch_iter.read_all(), schema=batch_iter.schema
        )

-    async def to_list(self, timeout: Optional[timedelta] = None) -> List[dict]:
+    async def to_list(self) -> List[dict]:
        """
        Execute the query and return the results as a list of dictionaries.

        Each list entry is a dictionary with the selected column names as keys,
        or all table columns if `select` is not called. The vector and the "_distance"
        fields are returned whether or not they're explicitly selected.
-
-        Parameters
-        ----------
-        timeout: Optional[timedelta]
-            The maximum time to wait for the query to complete.
-            If not specified, no timeout is applied. If the query does not
-            complete within the specified time, an error will be raised.
        """
-        return (await self.to_arrow(timeout=timeout)).to_pylist()
+        return (await self.to_arrow()).to_pylist()

    async def to_pandas(
-        self,
-        flatten: Optional[Union[int, bool]] = None,
-        timeout: Optional[timedelta] = None,
+        self, flatten: Optional[Union[int, bool]] = None
    ) -> "pd.DataFrame":
        """
        Execute the query and collect the results into a pandas DataFrame.
@@ -2221,19 +1880,10 @@ class AsyncQueryBase(object):
            If flatten is an integer, flatten the nested columns up to the
            specified depth.
            If unspecified, do not flatten the nested columns.
-        timeout: Optional[timedelta]
-            The maximum time to wait for the query to complete.
-            If not specified, no timeout is applied. If the query does not
-            complete within the specified time, an error will be raised.
        """
-        return (
-            flatten_columns(await self.to_arrow(timeout=timeout), flatten)
-        ).to_pandas()
+        return (flatten_columns(await self.to_arrow(), flatten)).to_pandas()

-    async def to_polars(
-        self,
-        timeout: Optional[timedelta] = None,
-    ) -> "pl.DataFrame":
+    async def to_polars(self) -> "pl.DataFrame":
        """
        Execute the query and collect the results into a Polars DataFrame.

@@ -2242,13 +1892,6 @@ class AsyncQueryBase(object):
        [to_batches][lancedb.query.AsyncQueryBase.to_batches] and convert each batch to
        polars separately.

-        Parameters
-        ----------
-        timeout: Optional[timedelta]
-            The maximum time to wait for the query to complete.
-            If not specified, no timeout is applied. If the query does not
-            complete within the specified time, an error will be raised.
-
        Examples
        --------

@@ -2264,7 +1907,7 @@ class AsyncQueryBase(object):
        """
        import polars as pl

-        return pl.from_arrow(await self.to_arrow(timeout=timeout))
+        return pl.from_arrow(await self.to_arrow())

    async def explain_plan(self, verbose: Optional[bool] = False):
        """Return the execution plan for this query.
@@ -2298,15 +1941,6 @@ class AsyncQueryBase(object):
        """  # noqa: E501
        return await self._inner.explain_plan(verbose)

-    async def analyze_plan(self):
-        """Execute the query and display with runtime metrics.
-
-        Returns
-        -------
-        plan : str
-        """
-        return await self._inner.analyze_plan()
-

 class AsyncQuery(AsyncQueryBase):
    def __init__(self, inner: LanceQuery):
@@ -2407,7 +2041,7 @@ class AsyncQuery(AsyncQueryBase):
            )

    def nearest_to_text(
-        self, query: str | FullTextQuery, columns: Union[str, List[str], None] = None
+        self, query: str, columns: Union[str, List[str], None] = None
    ) -> AsyncFTSQuery:
        """
        Find the documents that are most relevant to the given text query.
@@ -2433,13 +2067,9 @@ class AsyncQuery(AsyncQueryBase):
            columns = [columns]
        if columns is None:
            columns = []
-
-        if isinstance(query, str):
-            return AsyncFTSQuery(
-                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, "columns": columns})
+        )


 class AsyncFTSQuery(AsyncQueryBase):
@@ -2535,12 +2165,9 @@ class AsyncFTSQuery(AsyncQueryBase):
            )

    async def to_batches(
-        self,
-        *,
-        max_batch_length: Optional[int] = None,
-        timeout: Optional[timedelta] = None,
+        self, *, max_batch_length: Optional[int] = None
    ) -> AsyncRecordBatchReader:
-        reader = await super().to_batches(timeout=timeout)
+        reader = await super().to_batches()
        results = pa.Table.from_batches(await reader.read_all(), reader.schema)
        if self._reranker:
            results = self._reranker.rerank_fts(self.get_query(), results)
@@ -2725,7 +2352,7 @@ class AsyncVectorQuery(AsyncQueryBase, AsyncVectorQueryBase):
        return self

    def nearest_to_text(
-        self, query: str | FullTextQuery, columns: Union[str, List[str], None] = None
+        self, query: str, columns: Union[str, List[str], None] = None
    ) -> AsyncHybridQuery:
        """
        Find the documents that are most relevant to the given text query,
@@ -2755,21 +2382,14 @@ class AsyncVectorQuery(AsyncQueryBase, AsyncVectorQueryBase):
            columns = [columns]
        if columns is None:
            columns = []
-
-        if isinstance(query, str):
-            return AsyncHybridQuery(
-                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, "columns": columns})
+        )

    async def to_batches(
-        self,
-        *,
-        max_batch_length: Optional[int] = None,
-        timeout: Optional[timedelta] = None,
+        self, *, max_batch_length: Optional[int] = None
    ) -> AsyncRecordBatchReader:
-        reader = await super().to_batches(timeout=timeout)
+        reader = await super().to_batches()
        results = pa.Table.from_batches(await reader.read_all(), reader.schema)
        if self._reranker:
            results = self._reranker.rerank_vector(self._query_string, results)
@@ -2825,10 +2445,7 @@ class AsyncHybridQuery(AsyncQueryBase, AsyncVectorQueryBase):
        return self

    async def to_batches(
-        self,
-        *,
-        max_batch_length: Optional[int] = None,
-        timeout: Optional[timedelta] = None,
+        self, *, max_batch_length: Optional[int] = None
    ) -> AsyncRecordBatchReader:
        fts_query = AsyncFTSQuery(self._inner.to_fts_query())
        vec_query = AsyncVectorQuery(self._inner.to_vector_query())
@@ -2840,8 +2457,8 @@ class AsyncHybridQuery(AsyncQueryBase, AsyncVectorQueryBase):
        vec_query.with_row_id()

        fts_results, vector_results = await asyncio.gather(
-            fts_query.to_arrow(timeout=timeout),
-            vec_query.to_arrow(timeout=timeout),
+            fts_query.to_arrow(),
+            vec_query.to_arrow(),
        )

        result = LanceHybridQueryBuilder._combine_hybrid_results(
@@ -2893,7 +2510,7 @@ class AsyncHybridQuery(AsyncQueryBase, AsyncVectorQueryBase):

        Returns
        -------
-        plan : str
+        plan
        """  # noqa: E501

        results = ["Vector Search Plan:"]
@@ -2902,23 +2519,3 @@ class AsyncHybridQuery(AsyncQueryBase, AsyncVectorQueryBase):
        results.append(await self._inner.to_fts_query().explain_plan(verbose))

        return "\n".join(results)
-
-    async def analyze_plan(self):
-        """
-        Execute the query and return the physical execution plan with runtime metrics.
-
-        This runs both the vector and FTS (full-text search) queries and returns
-        detailed metrics for each step of execution—such as rows processed,
-        elapsed time, I/O stats, and more. It’s useful for debugging and
-        performance analysis.
-
-        Returns
-        -------
-        plan : str
-        """
-        results = ["Vector Search Query:"]
-        results.append(await self._inner.to_vector_query().analyze_plan())
-        results.append("FTS Search Query:")
-        results.append(await self._inner.to_fts_query().analyze_plan())
-
-        return "\n".join(results)
--- a/python/python/lancedb/remote/table.py
+++ b/python/python/lancedb/remote/table.py
@@ -87,9 +87,6 @@ class RemoteTable(Table):
    def checkout_latest(self):
        return LOOP.run(self._table.checkout_latest())

-    def restore(self, version: Optional[int] = None):
-        return LOOP.run(self._table.restore(version))
-
    def list_indices(self) -> Iterable[IndexConfig]:
        """List all the indices on the table"""
        return LOOP.run(self._table.list_indices())
@@ -355,15 +352,9 @@ class RemoteTable(Table):
        )

    def _execute_query(
-        self,
-        query: Query,
-        *,
-        batch_size: Optional[int] = None,
-        timeout: Optional[timedelta] = None,
+        self, query: Query, batch_size: Optional[int] = None
    ) -> pa.RecordBatchReader:
-        async_iter = LOOP.run(
-            self._table._execute_query(query, batch_size=batch_size, timeout=timeout)
-        )
+        async_iter = LOOP.run(self._table._execute_query(query, batch_size=batch_size))

        def iter_sync():
            try:
@@ -374,12 +365,6 @@ class RemoteTable(Table):

        return pa.RecordBatchReader.from_batches(async_iter.schema, iter_sync())

-    def _explain_plan(self, query: Query, verbose: Optional[bool] = False) -> str:
-        return LOOP.run(self._table._explain_plan(query, verbose))
-
-    def _analyze_plan(self, query: Query) -> str:
-        return LOOP.run(self._table._analyze_plan(query))
-
    def merge_insert(self, on: Union[str, Iterable[str]]) -> LanceMergeInsertBuilder:
        """Returns a [`LanceMergeInsertBuilder`][lancedb.merge.LanceMergeInsertBuilder]
        that can be used to create a "merge insert" operation.
--- a/python/python/lancedb/rerankers/answerdotai.py
+++ b/python/python/lancedb/rerankers/answerdotai.py
@@ -47,9 +47,6 @@ class AnswerdotaiRerankers(Reranker):
        )

    def _rerank(self, result_set: pa.Table, query: str):
-        result_set = self._handle_empty_results(result_set)
-        if len(result_set) == 0:
-            return result_set
        docs = result_set[self.column].to_pylist()
        doc_ids = list(range(len(docs)))
        result = self.reranker.rank(query, docs, doc_ids=doc_ids)
@@ -86,6 +83,7 @@ class AnswerdotaiRerankers(Reranker):
        vector_results = self._rerank(vector_results, query)
        if self.score == "relevance":
            vector_results = vector_results.drop_columns(["_distance"])
+
        vector_results = vector_results.sort_by([("_relevance_score", "descending")])
        return vector_results

@@ -93,5 +91,7 @@ class AnswerdotaiRerankers(Reranker):
        fts_results = self._rerank(fts_results, query)
        if self.score == "relevance":
            fts_results = fts_results.drop_columns(["_score"])
+
        fts_results = fts_results.sort_by([("_relevance_score", "descending")])
+
        return fts_results
--- a/python/python/lancedb/rerankers/base.py
+++ b/python/python/lancedb/rerankers/base.py
@@ -65,16 +65,6 @@ class Reranker(ABC):
            f"{self.__class__.__name__} does not implement rerank_vector"
        )

-    def _handle_empty_results(self, results: pa.Table):
-        """
-        Helper method to handle empty FTS results consistently
-        """
-        if len(results) > 0:
-            return results
-        return results.append_column(
-            "_relevance_score", pa.array([], type=pa.float32())
-        )
-
    def rerank_fts(
        self,
        query: str,
--- a/python/python/lancedb/rerankers/cohere.py
+++ b/python/python/lancedb/rerankers/cohere.py
@@ -62,9 +62,6 @@ class CohereReranker(Reranker):
        return cohere.Client(os.environ.get("COHERE_API_KEY") or self.api_key)

    def _rerank(self, result_set: pa.Table, query: str):
-        result_set = self._handle_empty_results(result_set)
-        if len(result_set) == 0:
-            return result_set
        docs = result_set[self.column].to_pylist()
        response = self._client.rerank(
            query=query,
@@ -102,14 +99,24 @@ class CohereReranker(Reranker):
            )
        return combined_results

-    def rerank_vector(self, query: str, vector_results: pa.Table):
-        vector_results = self._rerank(vector_results, query)
+    def rerank_vector(
+        self,
+        query: str,
+        vector_results: pa.Table,
+    ):
+        result_set = self._rerank(vector_results, query)
        if self.score == "relevance":
-            vector_results = vector_results.drop_columns(["_distance"])
-        return vector_results
+            result_set = result_set.drop_columns(["_distance"])

-    def rerank_fts(self, query: str, fts_results: pa.Table):
-        fts_results = self._rerank(fts_results, query)
+        return result_set
+
+    def rerank_fts(
+        self,
+        query: str,
+        fts_results: pa.Table,
+    ):
+        result_set = self._rerank(fts_results, query)
        if self.score == "relevance":
-            fts_results = fts_results.drop_columns(["_score"])
-        return fts_results
+            result_set = result_set.drop_columns(["_score"])
+
+        return result_set
--- a/python/python/lancedb/rerankers/cross_encoder.py
+++ b/python/python/lancedb/rerankers/cross_encoder.py
@@ -63,9 +63,6 @@ class CrossEncoderReranker(Reranker):
        return cross_encoder

    def _rerank(self, result_set: pa.Table, query: str):
-        result_set = self._handle_empty_results(result_set)
-        if len(result_set) == 0:
-            return result_set
        passages = result_set[self.column].to_pylist()
        cross_inp = [[query, passage] for passage in passages]
        cross_scores = self.model.predict(cross_inp)
@@ -96,7 +93,11 @@ class CrossEncoderReranker(Reranker):

        return combined_results

-    def rerank_vector(self, query: str, vector_results: pa.Table):
+    def rerank_vector(
+        self,
+        query: str,
+        vector_results: pa.Table,
+    ):
        vector_results = self._rerank(vector_results, query)
        if self.score == "relevance":
            vector_results = vector_results.drop_columns(["_distance"])
@@ -104,7 +105,11 @@ class CrossEncoderReranker(Reranker):
        vector_results = vector_results.sort_by([("_relevance_score", "descending")])
        return vector_results

-    def rerank_fts(self, query: str, fts_results: pa.Table):
+    def rerank_fts(
+        self,
+        query: str,
+        fts_results: pa.Table,
+    ):
        fts_results = self._rerank(fts_results, query)
        if self.score == "relevance":
            fts_results = fts_results.drop_columns(["_score"])
--- a/python/python/lancedb/rerankers/jinaai.py
+++ b/python/python/lancedb/rerankers/jinaai.py
@@ -62,9 +62,6 @@ class JinaReranker(Reranker):
        return self._session

    def _rerank(self, result_set: pa.Table, query: str):
-        result_set = self._handle_empty_results(result_set)
-        if len(result_set) == 0:
-            return result_set
        docs = result_set[self.column].to_pylist()
        response = self._client.post(  # type: ignore
            API_URL,
@@ -107,14 +104,24 @@ class JinaReranker(Reranker):
            )
        return combined_results

-    def rerank_vector(self, query: str, vector_results: pa.Table):
-        vector_results = self._rerank(vector_results, query)
+    def rerank_vector(
+        self,
+        query: str,
+        vector_results: pa.Table,
+    ):
+        result_set = self._rerank(vector_results, query)
        if self.score == "relevance":
-            vector_results = vector_results.drop_columns(["_distance"])
-        return vector_results
+            result_set = result_set.drop_columns(["_distance"])

-    def rerank_fts(self, query: str, fts_results: pa.Table):
-        fts_results = self._rerank(fts_results, query)
+        return result_set
+
+    def rerank_fts(
+        self,
+        query: str,
+        fts_results: pa.Table,
+    ):
+        result_set = self._rerank(fts_results, query)
        if self.score == "relevance":
-            fts_results = fts_results.drop_columns(["_score"])
-        return fts_results
+            result_set = result_set.drop_columns(["_score"])
+
+        return result_set
--- a/python/python/lancedb/rerankers/openai.py
+++ b/python/python/lancedb/rerankers/openai.py
@@ -44,9 +44,6 @@ class OpenaiReranker(Reranker):
        self.api_key = api_key

    def _rerank(self, result_set: pa.Table, query: str):
-        result_set = self._handle_empty_results(result_set)
-        if len(result_set) == 0:
-            return result_set
        docs = result_set[self.column].to_pylist()
        response = self._client.chat.completions.create(
            model=self.model_name,
@@ -107,14 +104,18 @@ class OpenaiReranker(Reranker):
        vector_results = self._rerank(vector_results, query)
        if self.score == "relevance":
            vector_results = vector_results.drop_columns(["_distance"])
+
        vector_results = vector_results.sort_by([("_relevance_score", "descending")])
+
        return vector_results

    def rerank_fts(self, query: str, fts_results: pa.Table):
        fts_results = self._rerank(fts_results, query)
        if self.score == "relevance":
            fts_results = fts_results.drop_columns(["_score"])
+
        fts_results = fts_results.sort_by([("_relevance_score", "descending")])
+
        return fts_results

    @cached_property
--- a/python/python/lancedb/rerankers/voyageai.py
+++ b/python/python/lancedb/rerankers/voyageai.py
@@ -63,9 +63,6 @@ class VoyageAIReranker(Reranker):
        )

    def _rerank(self, result_set: pa.Table, query: str):
-        result_set = self._handle_empty_results(result_set)
-        if len(result_set) == 0:
-            return result_set
        docs = result_set[self.column].to_pylist()
        response = self._client.rerank(
            query=query,
@@ -104,14 +101,24 @@ class VoyageAIReranker(Reranker):
            )
        return combined_results

-    def rerank_vector(self, query: str, vector_results: pa.Table):
-        vector_results = self._rerank(vector_results, query)
+    def rerank_vector(
+        self,
+        query: str,
+        vector_results: pa.Table,
+    ):
+        result_set = self._rerank(vector_results, query)
        if self.score == "relevance":
-            vector_results = vector_results.drop_columns(["_distance"])
-        return vector_results
+            result_set = result_set.drop_columns(["_distance"])

-    def rerank_fts(self, query: str, fts_results: pa.Table):
-        fts_results = self._rerank(fts_results, query)
+        return result_set
+
+    def rerank_fts(
+        self,
+        query: str,
+        fts_results: pa.Table,
+    ):
+        result_set = self._rerank(fts_results, query)
        if self.score == "relevance":
-            fts_results = fts_results.drop_columns(["_score"])
-        return fts_results
+            result_set = result_set.drop_columns(["_score"])
+
+        return result_set
--- a/python/python/lancedb/table.py
+++ b/python/python/lancedb/table.py
@@ -52,7 +52,6 @@ from .query import (
    AsyncHybridQuery,
    AsyncQuery,
    AsyncVectorQuery,
-    FullTextQuery,
    LanceEmptyQueryBuilder,
    LanceFtsQueryBuilder,
    LanceHybridQueryBuilder,
@@ -920,9 +919,7 @@ class Table(ABC):
    @abstractmethod
    def search(
        self,
-        query: Optional[
-            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
-        ] = None,
+        query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
        vector_column_name: Optional[str] = None,
        query_type: QueryType = "auto",
        ordering_field_name: Optional[str] = None,
@@ -1007,19 +1004,9 @@ class Table(ABC):

    @abstractmethod
    def _execute_query(
-        self,
-        query: Query,
-        *,
-        batch_size: Optional[int] = None,
-        timeout: Optional[timedelta] = None,
+        self, query: Query, batch_size: Optional[int] = None
    ) -> pa.RecordBatchReader: ...

-    @abstractmethod
-    def _explain_plan(self, query: Query, verbose: Optional[bool] = False) -> str: ...
-
-    @abstractmethod
-    def _analyze_plan(self, query: Query) -> str: ...
-
    @abstractmethod
    def _do_merge(
        self,
@@ -1275,21 +1262,16 @@ class Table(ABC):
        """

    @abstractmethod
-    def add_columns(
-        self, transforms: Dict[str, str] | pa.Field | List[pa.Field] | pa.Schema
-    ):
+    def add_columns(self, transforms: Dict[str, str]):
        """
        Add new columns with defined values.

        Parameters
        ----------
-        transforms: Dict[str, str], pa.Field, List[pa.Field], pa.Schema
+        transforms: Dict[str, str]
            A map of column name to a SQL expression to use to calculate the
            value of the new column. These expressions will be evaluated for
            each row in the table, and can reference existing columns.
-            Alternatively, a pyarrow Field or Schema can be provided to add
-            new columns with the specified data types. The new columns will
-            be initialized with null values.
        """

    @abstractmethod
@@ -1357,21 +1339,6 @@ class Table(ABC):
        It can also be used to undo a `[Self::checkout]` operation
        """

-    @abstractmethod
-    def restore(self, version: Optional[int] = None):
-        """Restore a version of the table. This is an in-place operation.
-
-        This creates a new version where the data is equivalent to the
-        specified previous version. Data is not copied (as of python-v0.2.1).
-
-        Parameters
-        ----------
-        version : int, default None
-            The version to restore. If unspecified then restores the currently
-            checked out version. If the currently checked out version is the
-            latest version then this is a no-op.
-        """
-
    @abstractmethod
    def list_versions(self) -> List[Dict[str, Any]]:
        """List all versions of the table"""
@@ -1745,32 +1712,8 @@ class LanceTable(Table):
        )

    def drop_index(self, name: str) -> None:
-        """
-        Drops an index from the table
-
-        Parameters
-        ----------
-        name: str
-            The name of the index to drop
-        """
        return LOOP.run(self._table.drop_index(name))

-    def prewarm_index(self, name: str) -> None:
-        """
-        Prewarms an index in the table
-
-        This loads the entire index into memory
-
-        If the index does not fit into the available cache this call
-        may be wasteful
-
-        Parameters
-        ----------
-        name: str
-            The name of the index to prewarm
-        """
-        return LOOP.run(self._table.prewarm_index(name))
-
    def create_scalar_index(
        self,
        column: str,
@@ -2070,9 +2013,7 @@ class LanceTable(Table):
    @overload
    def search(
        self,
-        query: Optional[
-            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
-        ] = None,
+        query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
        vector_column_name: Optional[str] = None,
        query_type: Literal["hybrid"] = "hybrid",
        ordering_field_name: Optional[str] = None,
@@ -2091,9 +2032,7 @@ class LanceTable(Table):

    def search(
        self,
-        query: Optional[
-            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
-        ] = None,
+        query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
        vector_column_name: Optional[str] = None,
        query_type: QueryType = "auto",
        ordering_field_name: Optional[str] = None,
@@ -2165,8 +2104,6 @@ class LanceTable(Table):
            and also the "_distance" column which is the distance between the query
            vector and the returned vector.
        """
-        if isinstance(query, FullTextQuery):
-            query_type = "fts"
        vector_column_name = infer_vector_column_name(
            schema=self.schema,
            query_type=query_type,
@@ -2342,15 +2279,9 @@ class LanceTable(Table):
        LOOP.run(self._table.update(values, where=where, updates_sql=values_sql))

    def _execute_query(
-        self,
-        query: Query,
-        *,
-        batch_size: Optional[int] = None,
-        timeout: Optional[timedelta] = None,
+        self, query: Query, batch_size: Optional[int] = None
    ) -> pa.RecordBatchReader:
-        async_iter = LOOP.run(
-            self._table._execute_query(query, batch_size=batch_size, timeout=timeout)
-        )
+        async_iter = LOOP.run(self._table._execute_query(query, batch_size))

        def iter_sync():
            try:
@@ -2361,11 +2292,8 @@ class LanceTable(Table):

        return pa.RecordBatchReader.from_batches(async_iter.schema, iter_sync())

-    def _explain_plan(self, query: Query, verbose: Optional[bool] = False) -> str:
-        return LOOP.run(self._table._explain_plan(query, verbose))
-
-    def _analyze_plan(self, query: Query) -> str:
-        return LOOP.run(self._table._analyze_plan(query))
+    def _explain_plan(self, query: Query) -> str:
+        return LOOP.run(self._table._explain_plan(query))

    def _do_merge(
        self,
@@ -2514,9 +2442,7 @@ class LanceTable(Table):
        """
        return LOOP.run(self._table.index_stats(index_name))

-    def add_columns(
-        self, transforms: Dict[str, str] | pa.field | List[pa.field] | pa.Schema
-    ):
+    def add_columns(self, transforms: Dict[str, str]):
        LOOP.run(self._table.add_columns(transforms))

    def alter_columns(self, *alterations: Iterable[Dict[str, str]]):
@@ -3026,23 +2952,6 @@ class AsyncTable:
        """
        await self._inner.drop_index(name)

-    async def prewarm_index(self, name: str) -> None:
-        """
-        Prewarm an index in the table.
-
-        Parameters
-        ----------
-        name: str
-            The name of the index to prewarm
-
-        Notes
-        -----
-        This will load the index into memory.  This may reduce the cold-start time for
-        future queries.  If the index does not fit in the cache then this call may be
-        wasteful.
-        """
-        await self._inner.prewarm_index(name)
-
    async def add(
        self,
        data: DATA,
@@ -3194,9 +3103,7 @@ class AsyncTable:
    @overload
    async def search(
        self,
-        query: Optional[
-            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
-        ] = None,
+        query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
        vector_column_name: Optional[str] = None,
        query_type: Literal["vector"] = ...,
        ordering_field_name: Optional[str] = None,
@@ -3205,9 +3112,7 @@ class AsyncTable:

    async def search(
        self,
-        query: Optional[
-            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
-        ] = None,
+        query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
        vector_column_name: Optional[str] = None,
        query_type: QueryType = "auto",
        ordering_field_name: Optional[str] = None,
@@ -3266,10 +3171,8 @@ class AsyncTable:
        async def get_embedding_func(
            vector_column_name: Optional[str],
            query_type: QueryType,
-            query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]],
+            query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]],
        ) -> Tuple[str, EmbeddingFunctionConfig]:
-            if isinstance(query, FullTextQuery):
-                query_type = "fts"
            schema = await self.schema()
            vector_column_name = infer_vector_column_name(
                schema=schema,
@@ -3319,8 +3222,6 @@ class AsyncTable:
            if is_embedding(query):
                vector_query = query
                query_type = "vector"
-            elif isinstance(query, FullTextQuery):
-                query_type = "fts"
            elif isinstance(query, str):
                try:
                    (
@@ -3441,15 +3342,13 @@ class AsyncTable:
            async_query = async_query.nearest_to_text(
                query.full_text_query.query, query.full_text_query.columns
            )
+            if query.full_text_query.limit is not None:
+                async_query = async_query.limit(query.full_text_query.limit)

        return async_query

    async def _execute_query(
-        self,
-        query: Query,
-        *,
-        batch_size: Optional[int] = None,
-        timeout: Optional[timedelta] = None,
+        self, query: Query, batch_size: Optional[int] = None
    ) -> pa.RecordBatchReader:
        # The sync table calls into this method, so we need to map the
        # query to the async version of the query and run that here. This is only
@@ -3457,19 +3356,12 @@ class AsyncTable:

        async_query = self._sync_query_to_async(query)

-        return await async_query.to_batches(
-            max_batch_length=batch_size, timeout=timeout
-        )
+        return await async_query.to_batches(max_batch_length=batch_size)

-    async def _explain_plan(self, query: Query, verbose: Optional[bool]) -> str:
+    async def _explain_plan(self, query: Query) -> str:
        # This method is used by the sync table
        async_query = self._sync_query_to_async(query)
-        return await async_query.explain_plan(verbose)
-
-    async def _analyze_plan(self, query: Query) -> str:
-        # This method is used by the sync table
-        async_query = self._sync_query_to_async(query)
-        return await async_query.analyze_plan()
+        return await async_query.explain_plan()

    async def _do_merge(
        self,
@@ -3609,9 +3501,7 @@ class AsyncTable:

        return await self._inner.update(updates_sql, where)

-    async def add_columns(
-        self, transforms: dict[str, str] | pa.field | List[pa.field] | pa.Schema
-    ):
+    async def add_columns(self, transforms: dict[str, str]):
        """
        Add new columns with defined values.

@@ -3621,19 +3511,8 @@ class AsyncTable:
            A map of column name to a SQL expression to use to calculate the
            value of the new column. These expressions will be evaluated for
            each row in the table, and can reference existing columns.
-            Alternatively, you can pass a pyarrow field or schema to add
-            new columns with NULLs.
        """
-        if isinstance(transforms, pa.Field):
-            transforms = [transforms]
-        if isinstance(transforms, list) and all(
-            {isinstance(f, pa.Field) for f in transforms}
-        ):
-            transforms = pa.schema(transforms)
-        if isinstance(transforms, pa.Schema):
-            await self._inner.add_columns_with_schema(transforms)
-        else:
-            await self._inner.add_columns(list(transforms.items()))
+        await self._inner.add_columns(list(transforms.items()))

    async def alter_columns(self, *alterations: Iterable[dict[str, Any]]):
        """
@@ -3731,7 +3610,7 @@ class AsyncTable:
        """
        await self._inner.checkout_latest()

-    async def restore(self, version: Optional[int] = None):
+    async def restore(self):
        """
        Restore the table to the currently checked out version

@@ -3744,7 +3623,7 @@ class AsyncTable:
        Once the operation concludes the table will no longer be in a checked
        out state and the read_consistency_interval, if any, will apply.
        """
-        await self._inner.restore(version)
+        await self._inner.restore()

    async def optimize(
        self,
--- a/python/python/lancedb/util.py
+++ b/python/python/lancedb/util.py
@@ -253,14 +253,9 @@ def infer_vector_column_name(
    query: Optional[Any],  # inferred later in query builder
    vector_column_name: Optional[str],
 ):
-    if vector_column_name is not None:
-        return vector_column_name
-
-    if query_type == "fts":
-        # FTS queries do not require a vector column
-        return None
-
-    if query is not None or query_type == "hybrid":
+    if (vector_column_name is None and query is not None and query_type != "fts") or (
+        vector_column_name is None and query_type == "hybrid"
+    ):
        try:
            vector_column_name = inf_vector_column_query(schema)
        except Exception as e:
--- a/python/python/tests/docs/test_guide_tables.py
+++ b/python/python/tests/docs/test_guide_tables.py
@@ -562,7 +562,7 @@ async def test_table_async():
    async_db = await lancedb.connect_async(uri, read_consistency_interval=timedelta(0))
    async_tbl = await async_db.open_table("test_table_async")
    # --8<-- [end:table_async_strong_consistency]
-    # --8<-- [start:table_async_eventual_consistency]
+    # --8<-- [start:table_async_ventual_consistency]
    uri = "data/sample-lancedb"
    async_db = await lancedb.connect_async(
        uri, read_consistency_interval=timedelta(seconds=5)
--- a/python/python/tests/docs/test_search.py
+++ b/python/python/tests/docs/test_search.py
@@ -6,9 +6,7 @@ import lancedb

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

 # --8<-- [end:import-numpy]
 # --8<-- [start:import-datetime]
@@ -156,84 +154,6 @@ async def test_vector_search_async():
    # --8<-- [end:search_result_async_as_list]


-def test_fts_fuzzy_query():
-    uri = "data/fuzzy-example"
-    db = lancedb.connect(uri)
-
-    table = db.create_table(
-        "my_table_fts_fuzzy",
-        data=pa.table(
-            {
-                "text": [
-                    "fa",
-                    "fo",  # spellchecker:disable-line
-                    "fob",
-                    "focus",
-                    "foo",
-                    "food",
-                    "foul",
-                ]
-            }
-        ),
-        mode="overwrite",
-    )
-    table.create_fts_index("text", use_tantivy=False, replace=True)
-
-    results = table.search(MatchQuery("foo", "text", fuzziness=1)).to_pandas()
-    assert len(results) == 4
-    assert set(results["text"].to_list()) == {
-        "foo",
-        "fo",  # 1 deletion # spellchecker:disable-line
-        "fob",  # 1 substitution
-        "food",  # 1 insertion
-    }
-
-
-def test_fts_boost_query():
-    uri = "data/boost-example"
-    db = lancedb.connect(uri)
-
-    table = db.create_table(
-        "my_table_fts_boost",
-        data=pa.table(
-            {
-                "title": [
-                    "The Hidden Gems of Travel",
-                    "Exploring Nature's Wonders",
-                    "Cultural Treasures Unveiled",
-                    "The Nightlife Chronicles",
-                    "Scenic Escapes and Challenges",
-                ],
-                "desc": [
-                    "A vibrant city with occasional traffic jams.",
-                    "Beautiful landscapes but overpriced tourist spots.",
-                    "Rich cultural heritage but humid summers.",
-                    "Bustling nightlife but noisy streets.",
-                    "Scenic views but limited public transport options.",
-                ],
-            }
-        ),
-        mode="overwrite",
-    )
-    table.create_fts_index("desc", use_tantivy=False, replace=True)
-
-    results = table.search(
-        BoostQuery(
-            MatchQuery("beautiful, cultural, nightlife", "desc"),
-            MatchQuery("bad traffic jams, overpriced", "desc"),
-        ),
-    ).to_pandas()
-
-    # we will hit 3 results because the positive query has 3 hits
-    assert len(results) == 3
-    # the one containing "overpriced" will be negatively boosted,
-    # so it will be the last one
-    assert (
-        results["desc"].to_list()[2]
-        == "Beautiful landscapes but overpriced tourist spots."
-    )
-
-
 def test_fts_native():
    # --8<-- [start:basic_fts]
    uri = "data/sample-lancedb"
--- a/python/python/tests/test_embeddings_slow.py
+++ b/python/python/tests/test_embeddings_slow.py
@@ -12,7 +12,6 @@ import pyarrow as pa
 import pytest
 from lancedb.embeddings import get_registry
 from lancedb.pydantic import LanceModel, Vector
-import requests

 # These are integration tests for embedding functions.
 # They are slow because they require downloading models
@@ -517,61 +516,3 @@ def test_voyageai_embedding_function():

    tbl.add(df)
    assert len(tbl.to_pandas()["vector"][0]) == voyageai.ndims()
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(
-    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
-)
-def test_voyageai_multimodal_embedding_function():
-    voyageai = (
-        get_registry().get("voyageai").create(name="voyage-multimodal-3", max_retries=0)
-    )
-
-    class Images(LanceModel):
-        label: str
-        image_uri: str = voyageai.SourceField()  # image uri as the source
-        image_bytes: bytes = voyageai.SourceField()  # image bytes as the source
-        vector: Vector(voyageai.ndims()) = voyageai.VectorField()  # vector column
-        vec_from_bytes: Vector(voyageai.ndims()) = (
-            voyageai.VectorField()
-        )  # Another vector column
-
-    db = lancedb.connect("~/lancedb")
-    table = db.create_table("test", schema=Images, mode="overwrite")
-    labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
-    uris = [
-        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
-        "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
-        "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
-        "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
-        "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
-        "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
-    ]
-    # get each uri as bytes
-    image_bytes = [requests.get(uri).content for uri in uris]
-    table.add(
-        pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes})
-    )
-    assert len(table.to_pandas()["vector"][0]) == voyageai.ndims()
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(
-    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
-)
-def test_voyageai_multimodal_embedding_text_function():
-    voyageai = (
-        get_registry().get("voyageai").create(name="voyage-multimodal-3", max_retries=0)
-    )
-
-    class TextModel(LanceModel):
-        text: str = voyageai.SourceField()
-        vector: Vector(voyageai.ndims()) = voyageai.VectorField()
-
-    df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
-    db = lancedb.connect("~/lancedb")
-    tbl = db.create_table("test", schema=TextModel, mode="overwrite")
-
-    tbl.add(df)
-    assert len(tbl.to_pandas()["vector"][0]) == voyageai.ndims()
--- a/python/python/tests/test_fts.py
+++ b/python/python/tests/test_fts.py
@@ -20,9 +20,7 @@ from unittest import mock
 import lancedb as ldb
 from lancedb.db import DBConnection
 from lancedb.index import FTS
-from lancedb.query import BoostQuery, MatchQuery, MultiMatchQuery, PhraseQuery
 import numpy as np
-import pyarrow as pa
 import pandas as pd
 import pytest
 from utils import exception_output
@@ -180,47 +178,11 @@ def test_search_fts(table, use_tantivy):
    results = table.search("puppy").select(["id", "text"]).to_list()
    assert len(results) == 10

-    if not use_tantivy:
-        # Test with a query
-        results = (
-            table.search(MatchQuery("puppy", "text"))
-            .select(["id", "text"])
-            .limit(5)
-            .to_list()
-        )
-        assert len(results) == 5
-
-        # Test boost query
-        results = (
-            table.search(
-                BoostQuery(
-                    MatchQuery("puppy", "text"),
-                    MatchQuery("runs", "text"),
-                )
-            )
-            .select(["id", "text"])
-            .limit(5)
-            .to_list()
-        )
-        assert len(results) == 5
-
-        # Test multi match query
-        table.create_fts_index("text2", use_tantivy=use_tantivy)
-        results = (
-            table.search(MultiMatchQuery("puppy", ["text", "text2"]))
-            .select(["id", "text"])
-            .limit(5)
-            .to_list()
-        )
-        assert len(results) == 5
-        assert len(results[0]) == 3  # id, text, _score
-

@pytest.mark.asyncio
 async def test_fts_select_async(async_table):
    tbl = await async_table
    await tbl.create_index("text", config=FTS())
-    await tbl.create_index("text2", config=FTS())
    results = (
        await tbl.query()
        .nearest_to_text("puppy")
@@ -231,54 +193,6 @@ async def test_fts_select_async(async_table):
    assert len(results) == 5
    assert len(results[0]) == 3  # id, text, _score

-    # Test with FullTextQuery
-    results = (
-        await tbl.query()
-        .nearest_to_text(MatchQuery("puppy", "text"))
-        .select(["id", "text"])
-        .limit(5)
-        .to_list()
-    )
-    assert len(results) == 5
-    assert len(results[0]) == 3  # id, text, _score
-
-    # Test with BoostQuery
-    results = (
-        await tbl.query()
-        .nearest_to_text(
-            BoostQuery(
-                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
-
-    # Test with MultiMatchQuery
-    results = (
-        await tbl.query()
-        .nearest_to_text(MultiMatchQuery("puppy", ["text", "text2"]))
-        .select(["id", "text"])
-        .limit(5)
-        .to_list()
-    )
-    assert len(results) == 5
-    assert len(results[0]) == 3  # id, text, _score
-
-    # Test with search() API
-    results = (
-        await (await tbl.search(MatchQuery("puppy", "text")))
-        .select(["id", "text"])
-        .limit(5)
-        .to_list()
-    )
-    assert len(results) == 5
-    assert len(results[0]) == 3  # id, text, _score
-

 def test_search_fts_phrase_query(table):
    table.create_fts_index("text", use_tantivy=False, with_position=False)
@@ -293,13 +207,6 @@ def test_search_fts_phrase_query(table):
    assert len(results) > len(phrase_results)
    assert len(phrase_results) > 0

-    # Test with a query
-    phrase_results = (
-        table.search(PhraseQuery("puppy runs", "text")).limit(100).to_list()
-    )
-    assert len(results) > len(phrase_results)
-    assert len(phrase_results) > 0
-

@pytest.mark.asyncio
 async def test_search_fts_phrase_query_async(async_table):
@@ -320,16 +227,6 @@ async def test_search_fts_phrase_query_async(async_table):
    assert len(results) > len(phrase_results)
    assert len(phrase_results) > 0

-    # Test with a query
-    phrase_results = (
-        await async_table.query()
-        .nearest_to_text(PhraseQuery("puppy runs", "text"))
-        .limit(100)
-        .to_list()
-    )
-    assert len(results) > len(phrase_results)
-    assert len(phrase_results) > 0
-

 def test_search_fts_specify_column(table):
    table.create_fts_index("text", use_tantivy=False)
@@ -627,32 +524,3 @@ def test_language(mem_db: DBConnection):
    # Stop words -> no results
    results = table.search("la", query_type="fts").limit(5).to_list()
    assert len(results) == 0
-
-
-def test_fts_on_list(mem_db: DBConnection):
-    data = pa.table(
-        {
-            "text": [
-                ["lance database", "the", "search"],
-                ["lance database"],
-                ["lance", "search"],
-                ["database", "search"],
-                ["unrelated", "doc"],
-            ],
-            "vector": [
-                [1.0, 2.0, 3.0],
-                [4.0, 5.0, 6.0],
-                [7.0, 8.0, 9.0],
-                [10.0, 11.0, 12.0],
-                [13.0, 14.0, 15.0],
-            ],
-        }
-    )
-    table = mem_db.create_table("test", data=data)
-    table.create_fts_index("text", use_tantivy=False)
-
-    res = table.search("lance").limit(5).to_list()
-    assert len(res) == 3
-
-    res = table.search(PhraseQuery("lance database", "text")).limit(5).to_list()
-    assert len(res) == 2
--- a/python/python/tests/test_hybrid_query.py
+++ b/python/python/tests/test_hybrid_query.py
@@ -114,16 +114,6 @@ async def test_explain_plan(table: AsyncTable):
    assert "LanceScan" in plan


-@pytest.mark.asyncio
-async def test_analyze_plan(table: AsyncTable):
-    res = await (
-        table.query().nearest_to_text("dog").nearest_to([0.1, 0.1]).analyze_plan()
-    )
-
-    assert "AnalyzeExec" in res
-    assert "metrics=" in res
-
-
 def test_normalize_scores():
    cases = [
        (pa.array([0.1, 0.4]), pa.array([0.0, 1.0])),
--- a/python/python/tests/test_index.py
+++ b/python/python/tests/test_index.py
@@ -8,7 +8,7 @@ import pyarrow as pa
 import pytest
 import pytest_asyncio
 from lancedb import AsyncConnection, AsyncTable, connect_async
-from lancedb.index import BTree, IvfFlat, IvfPq, Bitmap, LabelList, HnswPq, HnswSq, FTS
+from lancedb.index import BTree, IvfFlat, IvfPq, Bitmap, LabelList, HnswPq, HnswSq


@pytest_asyncio.fixture
@@ -31,7 +31,6 @@ async def some_table(db_async):
        {
            "id": list(range(NROWS)),
            "vector": sample_fixed_size_list_array(NROWS, DIM),
-            "fsb": pa.array([bytes([i]) for i in range(NROWS)], pa.binary(1)),
            "tags": [
                [f"tag{random.randint(0, 8)}" for _ in range(2)] for _ in range(NROWS)
            ],
@@ -86,16 +85,6 @@ async def test_create_scalar_index(some_table: AsyncTable):
    assert len(indices) == 0


-@pytest.mark.asyncio
-async def test_create_fixed_size_binary_index(some_table: AsyncTable):
-    await some_table.create_index("fsb", config=BTree())
-    indices = await some_table.list_indices()
-    assert str(indices) == '[Index(BTree, columns=["fsb"], name="fsb_idx")]'
-    assert len(indices) == 1
-    assert indices[0].index_type == "BTree"
-    assert indices[0].columns == ["fsb"]
-
-
@pytest.mark.asyncio
 async def test_create_bitmap_index(some_table: AsyncTable):
    await some_table.create_index("id", config=Bitmap())
@@ -119,18 +108,6 @@ async def test_create_label_list_index(some_table: AsyncTable):
    assert str(indices) == '[Index(LabelList, columns=["tags"], name="tags_idx")]'


-@pytest.mark.asyncio
-async def test_full_text_search_index(some_table: AsyncTable):
-    await some_table.create_index("tags", config=FTS(with_position=False))
-    indices = await some_table.list_indices()
-    assert str(indices) == '[Index(FTS, columns=["tags"], name="tags_idx")]'
-
-    await some_table.prewarm_index("tags_idx")
-
-    res = await (await some_table.search("tag0")).to_arrow()
-    assert res.num_rows > 0
-
-
@pytest.mark.asyncio
 async def test_create_vector_index(some_table: AsyncTable):
    # Can create
--- a/python/python/tests/test_query.py
+++ b/python/python/tests/test_query.py
@@ -511,8 +511,7 @@ def test_query_builder_with_different_vector_column():
            columns=["b"],
            vector_column="foo_vector",
        ),
-        batch_size=None,
-        timeout=None,
+        None,
    )


@@ -703,20 +702,6 @@ async def test_fast_search_async(tmp_path):
    assert "LanceScan" not in plan


-def test_analyze_plan(table):
-    q = LanceVectorQueryBuilder(table, [0, 0], "vector")
-    res = q.analyze_plan()
-    assert "AnalyzeExec" in res
-    assert "metrics=" in res
-
-
-@pytest.mark.asyncio
-async def test_analyze_plan_async(table_async: AsyncTable):
-    res = await table_async.query().nearest_to(pa.array([1, 2])).analyze_plan()
-    assert "AnalyzeExec" in res
-    assert "metrics=" in res
-
-
 def test_explain_plan(table):
    q = LanceVectorQueryBuilder(table, [0, 0], "vector")
    plan = q.explain_plan(verbose=True)
@@ -1077,67 +1062,3 @@ async def test_query_serialization_async(table_async: AsyncTable):
        full_text_query=FullTextSearchQuery(columns=[], query="foo"),
        with_row_id=False,
    )
-
-
-def test_query_timeout(tmp_path):
-    # Use local directory instead of memory:// to add a bit of latency to
-    # operations so a timeout of zero will trigger exceptions.
-    db = lancedb.connect(tmp_path)
-    data = pa.table(
-        {
-            "text": ["a", "b"],
-            "vector": pa.FixedSizeListArray.from_arrays(
-                pc.random(4).cast(pa.float32()), 2
-            ),
-        }
-    )
-    table = db.create_table("test", data)
-    table.create_fts_index("text", use_tantivy=False)
-
-    with pytest.raises(Exception, match="Query timeout"):
-        table.search().where("text = 'a'").to_list(timeout=timedelta(0))
-
-    with pytest.raises(Exception, match="Query timeout"):
-        table.search([0.0, 0.0]).to_arrow(timeout=timedelta(0))
-
-    with pytest.raises(Exception, match="Query timeout"):
-        table.search("a", query_type="fts").to_pandas(timeout=timedelta(0))
-
-    with pytest.raises(Exception, match="Query timeout"):
-        table.search(query_type="hybrid").vector([0.0, 0.0]).text("a").to_arrow(
-            timeout=timedelta(0)
-        )
-
-
-@pytest.mark.asyncio
-async def test_query_timeout_async(tmp_path):
-    db = await lancedb.connect_async(tmp_path)
-    data = pa.table(
-        {
-            "text": ["a", "b"],
-            "vector": pa.FixedSizeListArray.from_arrays(
-                pc.random(4).cast(pa.float32()), 2
-            ),
-        }
-    )
-    table = await db.create_table("test", data)
-    await table.create_index("text", config=FTS())
-
-    with pytest.raises(Exception, match="Query timeout"):
-        await table.query().where("text != 'a'").to_list(timeout=timedelta(0))
-
-    with pytest.raises(Exception, match="Query timeout"):
-        await table.vector_search([0.0, 0.0]).to_arrow(timeout=timedelta(0))
-
-    with pytest.raises(Exception, match="Query timeout"):
-        await (await table.search("a", query_type="fts")).to_pandas(
-            timeout=timedelta(0)
-        )
-
-    with pytest.raises(Exception, match="Query timeout"):
-        await (
-            table.query()
-            .nearest_to_text("a")
-            .nearest_to([0.0, 0.0])
-            .to_list(timeout=timedelta(0))
-        )
--- a/python/python/tests/test_remote_db.py
+++ b/python/python/tests/test_remote_db.py
@@ -444,16 +444,6 @@ def test_query_sync_fts():
            "prefilter": True,
            "with_row_id": True,
            "version": None,
-        } or body == {
-            "full_text_query": {
-                "query": "puppy",
-                "columns": ["description", "name"],
-            },
-            "k": 42,
-            "vector": [],
-            "prefilter": True,
-            "with_row_id": True,
-            "version": None,
        }

        return pa.table({"id": [1, 2, 3]})
--- a/python/python/tests/test_rerankers.py
+++ b/python/python/tests/test_rerankers.py
@@ -457,45 +457,3 @@ def test_voyageai_reranker(tmp_path, use_tantivy):
    reranker = VoyageAIReranker(model_name="rerank-2")
    table, schema = get_test_table(tmp_path, use_tantivy)
    _run_test_reranker(reranker, table, "single player experience", None, schema)
-
-
-def test_empty_result_reranker():
-    pytest.importorskip("sentence_transformers")
-    db = lancedb.connect("memory://")
-
-    # Define schema
-    schema = pa.schema(
-        [
-            ("id", pa.int64()),
-            ("text", pa.string()),
-            ("vector", pa.list_(pa.float32(), 128)),  # 128-dimensional vector
-        ]
-    )
-
-    # Create empty table with schema
-    empty_table = db.create_table("empty_table", schema=schema, mode="overwrite")
-    empty_table.create_fts_index("text", use_tantivy=False, replace=True)
-    for reranker in [
-        CrossEncoderReranker(),
-        # ColbertReranker(),
-        # AnswerdotaiRerankers(),
-        # OpenaiReranker(),
-        # JinaReranker(),
-        # VoyageAIReranker(model_name="rerank-2"),
-    ]:
-        results = (
-            empty_table.search(list(range(128)))
-            .limit(3)
-            .rerank(reranker, "query")
-            .to_arrow()
-        )
-        # check if empty set contains _relevance_score column
-        assert "_relevance_score" in results.column_names
-        assert len(results) == 0
-
-        results = (
-            empty_table.search("query", query_type="fts")
-            .limit(3)
-            .rerank(reranker)
-            .to_arrow()
-        )
--- a/python/python/tests/test_table.py
+++ b/python/python/tests/test_table.py
@@ -1384,37 +1384,6 @@ async def test_add_columns_async(mem_db_async: AsyncConnection):
    assert data["new_col"].to_pylist() == [2, 3]


-@pytest.mark.asyncio
-async def test_add_columns_with_schema(mem_db_async: AsyncConnection):
-    data = pa.table({"id": [0, 1]})
-    table = await mem_db_async.create_table("my_table", data=data)
-    await table.add_columns(
-        [pa.field("x", pa.int64()), pa.field("vector", pa.list_(pa.float32(), 8))]
-    )
-
-    assert await table.schema() == pa.schema(
-        [
-            pa.field("id", pa.int64()),
-            pa.field("x", pa.int64()),
-            pa.field("vector", pa.list_(pa.float32(), 8)),
-        ]
-    )
-
-    table = await mem_db_async.create_table("table2", data=data)
-    await table.add_columns(
-        pa.schema(
-            [pa.field("y", pa.int64()), pa.field("emb", pa.list_(pa.float32(), 8))]
-        )
-    )
-    assert await table.schema() == pa.schema(
-        [
-            pa.field("id", pa.int64()),
-            pa.field("y", pa.int64()),
-            pa.field("emb", pa.list_(pa.float32(), 8)),
-        ]
-    )
-
-
 def test_alter_columns(mem_db: DBConnection):
    data = pa.table({"id": [0, 1]})
    table = mem_db.create_table("my_table", data=data)
--- a/python/src/query.rs
+++ b/python/src/query.rs
@@ -2,26 +2,25 @@
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors

 use std::sync::Arc;
-use std::time::Duration;

 use arrow::array::make_array;
 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::FullTextSearchQuery;
 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::PyNotImplementedError;
 use pyo3::exceptions::PyRuntimeError;
-use pyo3::exceptions::{PyNotImplementedError, PyValueError};
 use pyo3::prelude::{PyAnyMethods, PyDictMethods};
 use pyo3::pymethods;
+use pyo3::types::PyDict;
 use pyo3::types::PyList;
-use pyo3::types::{PyDict, PyString};
 use pyo3::Bound;
 use pyo3::IntoPyObject;
 use pyo3::PyAny;
@@ -32,7 +31,7 @@ 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;

 // Python representation of full text search parameters
 #[derive(Clone)]
@@ -46,9 +45,9 @@ pub struct PyFullTextSearchQuery {

 impl From<FullTextSearchQuery> for PyFullTextSearchQuery {
    fn from(query: FullTextSearchQuery) -> Self {
-        Self {
-            columns: query.columns().into_iter().collect(),
-            query: query.query.query().to_owned(),
+        PyFullTextSearchQuery {
+            columns: query.columns,
+            query: query.query,
            limit: query.limit,
            wand_factor: query.wand_factor,
        }
@@ -100,7 +99,7 @@ pub struct PyQueryRequest {
 impl From<AnyQuery> for PyQueryRequest {
    fn from(query: AnyQuery) -> Self {
        match query {
-            AnyQuery::Query(query_request) => Self {
+            AnyQuery::Query(query_request) => PyQueryRequest {
                limit: query_request.limit,
                offset: query_request.offset,
                filter: query_request.filter.map(PyQueryFilter),
@@ -122,7 +121,7 @@ impl From<AnyQuery> for PyQueryRequest {
                postfilter: None,
                norm: None,
            },
-            AnyQuery::VectorQuery(vector_query) => Self {
+            AnyQuery::VectorQuery(vector_query) => PyQueryRequest {
                limit: vector_query.base.limit,
                offset: vector_query.base.offset,
                filter: vector_query.base.filter.map(PyQueryFilter),
@@ -237,69 +236,29 @@ impl Query {
    }

    pub fn nearest_to_text(&mut self, query: Bound<'_, PyDict>) -> PyResult<FTSQuery> {
-        let fts_query = query
+        let query_text = query
            .get_item("query")?
            .ok_or(PyErr::new::<PyRuntimeError, _>(
                "Query text is required for nearest_to_text",
-            ))?;
+            ))?
+            .extract::<String>()?;
+        let columns = query
+            .get_item("columns")?
+            .map(|columns| columns.extract::<Vec<String>>())
+            .transpose()?;

-        let query = if let Ok(query_text) = fts_query.downcast::<PyString>() {
-            let mut query_text = query_text.to_string();
-            let columns = query
-                .get_item("columns")?
-                .map(|columns| columns.extract::<Vec<String>>())
-                .transpose()?;
-
-            let is_phrase =
-                query_text.len() >= 2 && query_text.starts_with('"') && query_text.ends_with('"');
-            let is_multi_match = columns.as_ref().map(|cols| cols.len() > 1).unwrap_or(false);
-
-            if is_phrase {
-                // Remove the surrounding quotes for phrase queries
-                query_text = query_text[1..query_text.len() - 1].to_string();
-            }
-
-            let query: FtsQuery = match (is_phrase, is_multi_match) {
-                (false, _) => MatchQuery::new(query_text).into(),
-                (true, false) => PhraseQuery::new(query_text).into(),
-                (true, true) => {
-                    return Err(PyValueError::new_err(
-                        "Phrase queries cannot be used with multiple columns.",
-                    ));
-                }
-            };
-            let mut query = FullTextSearchQuery::new_query(query);
-            if let Some(cols) = columns {
-                if !cols.is_empty() {
-                    query = query.with_columns(&cols).map_err(|e| {
-                        PyValueError::new_err(format!(
-                            "Failed to set full text search columns: {}",
-                            e
-                        ))
-                    })?;
-                }
-            }
-            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 fts_query = FullTextSearchQuery::new(query_text).columns(columns);

        Ok(FTSQuery {
+            fts_query,
            inner: self.inner.clone(),
-            fts_query: query,
        })
    }

-    #[pyo3(signature = (max_batch_length=None, timeout=None))]
+    #[pyo3(signature = (max_batch_length=None))]
    pub fn execute(
        self_: PyRef<'_, Self>,
        max_batch_length: Option<u32>,
-        timeout: Option<Duration>,
    ) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.inner.clone();
        future_into_py(self_.py(), async move {
@@ -307,15 +266,12 @@ impl Query {
            if let Some(max_batch_length) = max_batch_length {
                opts.max_batch_length = max_batch_length;
            }
-            if let Some(timeout) = timeout {
-                opts.timeout = Some(timeout);
-            }
            let inner_stream = inner.execute_with_options(opts).await.infer_error()?;
            Ok(RecordBatchStream::new(inner_stream))
        })
    }

-    pub fn explain_plan(self_: PyRef<'_, Self>, verbose: bool) -> PyResult<Bound<'_, PyAny>> {
+    fn explain_plan(self_: PyRef<'_, Self>, verbose: bool) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.inner.clone();
        future_into_py(self_.py(), async move {
            inner
@@ -325,16 +281,6 @@ impl Query {
        })
    }

-    pub fn analyze_plan(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.inner.clone();
-        future_into_py(self_.py(), async move {
-            inner
-                .analyze_plan()
-                .await
-                .map_err(|e| PyRuntimeError::new_err(e.to_string()))
-        })
-    }
-
    pub fn to_query_request(&self) -> PyQueryRequest {
        PyQueryRequest::from(AnyQuery::Query(self.inner.clone().into_request()))
    }
@@ -381,11 +327,10 @@ impl FTSQuery {
        self.inner = self.inner.clone().postfilter();
    }

-    #[pyo3(signature = (max_batch_length=None, timeout=None))]
+    #[pyo3(signature = (max_batch_length=None))]
    pub fn execute(
        self_: PyRef<'_, Self>,
        max_batch_length: Option<u32>,
-        timeout: Option<Duration>,
    ) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_
            .inner
@@ -397,9 +342,6 @@ impl FTSQuery {
            if let Some(max_batch_length) = max_batch_length {
                opts.max_batch_length = max_batch_length;
            }
-            if let Some(timeout) = timeout {
-                opts.timeout = Some(timeout);
-            }
            let inner_stream = inner.execute_with_options(opts).await.infer_error()?;
            Ok(RecordBatchStream::new(inner_stream))
        })
@@ -423,18 +365,8 @@ impl FTSQuery {
        })
    }

-    pub fn analyze_plan(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.inner.clone();
-        future_into_py(self_.py(), async move {
-            inner
-                .analyze_plan()
-                .await
-                .map_err(|e| PyRuntimeError::new_err(e.to_string()))
-        })
-    }
-
    pub fn get_query(&self) -> String {
-        self.fts_query.query.query().to_owned()
+        self.fts_query.query.clone()
    }

    pub fn to_query_request(&self) -> PyQueryRequest {
@@ -522,11 +454,10 @@ impl VectorQuery {
        self.inner = self.inner.clone().bypass_vector_index()
    }

-    #[pyo3(signature = (max_batch_length=None, timeout=None))]
+    #[pyo3(signature = (max_batch_length=None))]
    pub fn execute(
        self_: PyRef<'_, Self>,
        max_batch_length: Option<u32>,
-        timeout: Option<Duration>,
    ) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.inner.clone();
        future_into_py(self_.py(), async move {
@@ -534,15 +465,12 @@ impl VectorQuery {
            if let Some(max_batch_length) = max_batch_length {
                opts.max_batch_length = max_batch_length;
            }
-            if let Some(timeout) = timeout {
-                opts.timeout = Some(timeout);
-            }
            let inner_stream = inner.execute_with_options(opts).await.infer_error()?;
            Ok(RecordBatchStream::new(inner_stream))
        })
    }

-    pub fn explain_plan(self_: PyRef<'_, Self>, verbose: bool) -> PyResult<Bound<'_, PyAny>> {
+    fn explain_plan(self_: PyRef<'_, Self>, verbose: bool) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.inner.clone();
        future_into_py(self_.py(), async move {
            inner
@@ -552,16 +480,6 @@ impl VectorQuery {
        })
    }

-    pub fn analyze_plan(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.inner.clone();
-        future_into_py(self_.py(), async move {
-            inner
-                .analyze_plan()
-                .await
-                .map_err(|e| PyRuntimeError::new_err(e.to_string()))
-        })
-    }
-
    pub fn nearest_to_text(&mut self, query: Bound<'_, PyDict>) -> PyResult<HybridQuery> {
        let base_query = self.inner.clone().into_plain();
        let fts_query = Query::new(base_query).nearest_to_text(query)?;
--- a/python/src/table.rs
+++ b/python/src/table.rs
@@ -1,11 +1,9 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
-use std::{collections::HashMap, sync::Arc};
-
 use arrow::{
-    datatypes::{DataType, Schema},
+    datatypes::DataType,
    ffi_stream::ArrowArrayStreamReader,
-    pyarrow::{FromPyArrow, PyArrowType, ToPyArrow},
+    pyarrow::{FromPyArrow, ToPyArrow},
 };
 use lancedb::table::{
    AddDataMode, ColumnAlteration, Duration, NewColumnTransform, OptimizeAction, OptimizeOptions,
@@ -18,6 +16,7 @@ use pyo3::{
    Bound, FromPyObject, PyAny, PyRef, PyResult, Python,
 };
 use pyo3_async_runtimes::tokio::future_into_py;
+use std::collections::HashMap;

 use crate::{
    error::PythonErrorExt,
@@ -204,14 +203,6 @@ impl Table {
        })
    }

-    pub fn prewarm_index(self_: PyRef<'_, Self>, index_name: String) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.inner_ref()?.clone();
-        future_into_py(self_.py(), async move {
-            inner.prewarm_index(&index_name).await.infer_error()?;
-            Ok(())
-        })
-    }
-
    pub fn list_indices(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.inner_ref()?.clone();
        future_into_py(self_.py(), async move {
@@ -312,16 +303,12 @@ impl Table {
        })
    }

-    #[pyo3(signature = (version=None))]
-    pub fn restore(self_: PyRef<'_, Self>, version: Option<u64>) -> PyResult<Bound<'_, PyAny>> {
+    pub fn restore(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.inner_ref()?.clone();
-
-        future_into_py(self_.py(), async move {
-            if let Some(version) = version {
-                inner.checkout(version).await.infer_error()?;
-            }
-            inner.restore().await.infer_error()
-        })
+        future_into_py(
+            self_.py(),
+            async move { inner.restore().await.infer_error() },
+        )
    }

    pub fn query(&self) -> Query {
@@ -453,20 +440,6 @@ impl Table {
        })
    }

-    pub fn add_columns_with_schema(
-        self_: PyRef<'_, Self>,
-        schema: PyArrowType<Schema>,
-    ) -> PyResult<Bound<'_, PyAny>> {
-        let arrow_schema = &schema.0;
-        let transform = NewColumnTransform::AllNulls(Arc::new(arrow_schema.clone()));
-
-        let inner = self_.inner_ref()?.clone();
-        future_into_py(self_.py(), async move {
-            inner.add_columns(transform, None).await.infer_error()?;
-            Ok(())
-        })
-    }
-
    pub fn alter_columns<'a>(
        self_: PyRef<'a, Self>,
        alterations: Vec<Bound<PyDict>>,
--- 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
 ///
@@ -63,117 +59,3 @@ pub fn validate_table_name(table_name: &str) -> PyResult<()> {
    lancedb::utils::validate_table_name(table_name)
        .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/rust/ffi/node/Cargo.toml
+++ b/rust/ffi/node/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-node"
-version = "0.19.0-beta.7"
+version = "0.18.2-beta.1"
 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 = "0.19.0-beta.7"
+version = "0.18.2-beta.1"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true
--- a/rust/lancedb/src/connection.rs
+++ b/rust/lancedb/src/connection.rs
@@ -139,6 +139,12 @@ impl CreateTableBuilder<true> {
        }
    }

+    /// Apply the given write options when writing the initial data
+    pub fn write_options(mut self, write_options: WriteOptions) -> Self {
+        self.request.write_options = write_options;
+        self
+    }
+
    /// Execute the create table operation
    pub async fn execute(self) -> Result<Table> {
        let embedding_registry = self.embedding_registry.clone();
@@ -220,12 +226,6 @@ impl<const HAS_DATA: bool> CreateTableBuilder<HAS_DATA> {
        self
    }

-    /// Apply the given write options when writing the initial data
-    pub fn write_options(mut self, write_options: WriteOptions) -> Self {
-        self.request.write_options = write_options;
-        self
-    }
-
    /// Set an option for the storage layer.
    ///
    /// Options already set on the connection will be inherited by the table,
@@ -863,7 +863,7 @@ impl ConnectBuilder {
 /// # Arguments
 ///
 /// * `uri` - URI where the database is located, can be a local directory, supported remote cloud storage,
-///   or a LanceDB Cloud database.  See [ConnectOptions::uri] for a list of accepted formats
+///           or a LanceDB Cloud database.  See [ConnectOptions::uri] for a list of accepted formats
 pub fn connect(uri: &str) -> ConnectBuilder {
    ConnectBuilder::new(uri)
 }
--- a/rust/lancedb/src/data/inspect.rs
+++ b/rust/lancedb/src/data/inspect.rs
@@ -41,7 +41,7 @@ where
 /// ----------
 /// - reader: RecordBatchReader
 /// - strict: if set true, only `fixed_size_list<float>` is considered as vector column. If set to false,
-///   a `list<float>` column with same length is also considered as vector column.
+///           a `list<float>` column with same length is also considered as vector column.
 pub fn infer_vector_columns(
    reader: impl RecordBatchReader + Send,
    strict: bool,
--- a/rust/lancedb/src/index/scalar.rs
+++ b/rust/lancedb/src/index/scalar.rs
@@ -80,6 +80,5 @@ impl FtsIndexBuilder {
    }
 }

-pub use lance_index::scalar::inverted::query::*;
 pub use lance_index::scalar::inverted::TokenizerConfig;
 pub use lance_index::scalar::FullTextSearchQuery;
--- a/rust/lancedb/src/io/object_store.rs
+++ b/rust/lancedb/src/io/object_store.rs
@@ -14,9 +14,6 @@ use object_store::{

 use async_trait::async_trait;

-#[cfg(test)]
-pub mod io_tracking;
-
 #[derive(Debug)]
 struct MirroringObjectStore {
    primary: Arc<dyn ObjectStore>,
--- a/rust/lancedb/src/io/object_store/io_tracking.rs
+++ b/rust/lancedb/src/io/object_store/io_tracking.rs
@@ -1,237 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-use std::{
-    fmt::{Display, Formatter},
-    sync::{Arc, Mutex},
-};
-
-use bytes::Bytes;
-use futures::stream::BoxStream;
-use lance::io::WrappingObjectStore;
-use object_store::{
-    path::Path, GetOptions, GetResult, ListResult, MultipartUpload, ObjectMeta, ObjectStore,
-    PutMultipartOpts, PutOptions, PutPayload, PutResult, Result as OSResult, UploadPart,
-};
-
-#[derive(Debug, Default)]
-pub struct IoStats {
-    pub read_iops: u64,
-    pub read_bytes: u64,
-    pub write_iops: u64,
-    pub write_bytes: u64,
-}
-
-impl Display for IoStats {
-    fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
-        write!(f, "{:#?}", self)
-    }
-}
-
-#[derive(Debug, Clone)]
-pub struct IoTrackingStore {
-    target: Arc<dyn ObjectStore>,
-    stats: Arc<Mutex<IoStats>>,
-}
-
-impl Display for IoTrackingStore {
-    fn fmt(&self, f: &mut Formatter<'_>) -> std::fmt::Result {
-        write!(f, "{:#?}", self)
-    }
-}
-
-#[derive(Debug, Default, Clone)]
-pub struct IoStatsHolder(Arc<Mutex<IoStats>>);
-
-impl IoStatsHolder {
-    pub fn incremental_stats(&self) -> IoStats {
-        std::mem::take(&mut self.0.lock().expect("failed to lock IoStats"))
-    }
-}
-
-impl WrappingObjectStore for IoStatsHolder {
-    fn wrap(&self, target: Arc<dyn ObjectStore>) -> Arc<dyn ObjectStore> {
-        Arc::new(IoTrackingStore {
-            target,
-            stats: self.0.clone(),
-        })
-    }
-}
-
-impl IoTrackingStore {
-    pub fn new_wrapper() -> (Arc<dyn WrappingObjectStore>, Arc<Mutex<IoStats>>) {
-        let stats = Arc::new(Mutex::new(IoStats::default()));
-        (Arc::new(IoStatsHolder(stats.clone())), stats)
-    }
-
-    fn record_read(&self, num_bytes: u64) {
-        let mut stats = self.stats.lock().unwrap();
-        stats.read_iops += 1;
-        stats.read_bytes += num_bytes;
-    }
-
-    fn record_write(&self, num_bytes: u64) {
-        let mut stats = self.stats.lock().unwrap();
-        stats.write_iops += 1;
-        stats.write_bytes += num_bytes;
-    }
-}
-
-#[async_trait::async_trait]
-#[deny(clippy::missing_trait_methods)]
-impl ObjectStore for IoTrackingStore {
-    async fn put(&self, location: &Path, bytes: PutPayload) -> OSResult<PutResult> {
-        self.record_write(bytes.content_length() as u64);
-        self.target.put(location, bytes).await
-    }
-
-    async fn put_opts(
-        &self,
-        location: &Path,
-        bytes: PutPayload,
-        opts: PutOptions,
-    ) -> OSResult<PutResult> {
-        self.record_write(bytes.content_length() as u64);
-        self.target.put_opts(location, bytes, opts).await
-    }
-
-    async fn put_multipart(&self, location: &Path) -> OSResult<Box<dyn MultipartUpload>> {
-        let target = self.target.put_multipart(location).await?;
-        Ok(Box::new(IoTrackingMultipartUpload {
-            target,
-            stats: self.stats.clone(),
-        }))
-    }
-
-    async fn put_multipart_opts(
-        &self,
-        location: &Path,
-        opts: PutMultipartOpts,
-    ) -> OSResult<Box<dyn MultipartUpload>> {
-        let target = self.target.put_multipart_opts(location, opts).await?;
-        Ok(Box::new(IoTrackingMultipartUpload {
-            target,
-            stats: self.stats.clone(),
-        }))
-    }
-
-    async fn get(&self, location: &Path) -> OSResult<GetResult> {
-        let result = self.target.get(location).await;
-        if let Ok(result) = &result {
-            let num_bytes = result.range.end - result.range.start;
-            self.record_read(num_bytes as u64);
-        }
-        result
-    }
-
-    async fn get_opts(&self, location: &Path, options: GetOptions) -> OSResult<GetResult> {
-        let result = self.target.get_opts(location, options).await;
-        if let Ok(result) = &result {
-            let num_bytes = result.range.end - result.range.start;
-            self.record_read(num_bytes as u64);
-        }
-        result
-    }
-
-    async fn get_range(&self, location: &Path, range: std::ops::Range<usize>) -> OSResult<Bytes> {
-        let result = self.target.get_range(location, range).await;
-        if let Ok(result) = &result {
-            self.record_read(result.len() as u64);
-        }
-        result
-    }
-
-    async fn get_ranges(
-        &self,
-        location: &Path,
-        ranges: &[std::ops::Range<usize>],
-    ) -> OSResult<Vec<Bytes>> {
-        let result = self.target.get_ranges(location, ranges).await;
-        if let Ok(result) = &result {
-            self.record_read(result.iter().map(|b| b.len() as u64).sum());
-        }
-        result
-    }
-
-    async fn head(&self, location: &Path) -> OSResult<ObjectMeta> {
-        self.record_read(0);
-        self.target.head(location).await
-    }
-
-    async fn delete(&self, location: &Path) -> OSResult<()> {
-        self.record_write(0);
-        self.target.delete(location).await
-    }
-
-    fn delete_stream<'a>(
-        &'a self,
-        locations: BoxStream<'a, OSResult<Path>>,
-    ) -> BoxStream<'a, OSResult<Path>> {
-        self.target.delete_stream(locations)
-    }
-
-    fn list(&self, prefix: Option<&Path>) -> BoxStream<'_, OSResult<ObjectMeta>> {
-        self.record_read(0);
-        self.target.list(prefix)
-    }
-
-    fn list_with_offset(
-        &self,
-        prefix: Option<&Path>,
-        offset: &Path,
-    ) -> BoxStream<'_, OSResult<ObjectMeta>> {
-        self.record_read(0);
-        self.target.list_with_offset(prefix, offset)
-    }
-
-    async fn list_with_delimiter(&self, prefix: Option<&Path>) -> OSResult<ListResult> {
-        self.record_read(0);
-        self.target.list_with_delimiter(prefix).await
-    }
-
-    async fn copy(&self, from: &Path, to: &Path) -> OSResult<()> {
-        self.record_write(0);
-        self.target.copy(from, to).await
-    }
-
-    async fn rename(&self, from: &Path, to: &Path) -> OSResult<()> {
-        self.record_write(0);
-        self.target.rename(from, to).await
-    }
-
-    async fn rename_if_not_exists(&self, from: &Path, to: &Path) -> OSResult<()> {
-        self.record_write(0);
-        self.target.rename_if_not_exists(from, to).await
-    }
-
-    async fn copy_if_not_exists(&self, from: &Path, to: &Path) -> OSResult<()> {
-        self.record_write(0);
-        self.target.copy_if_not_exists(from, to).await
-    }
-}
-
-#[derive(Debug)]
-struct IoTrackingMultipartUpload {
-    target: Box<dyn MultipartUpload>,
-    stats: Arc<Mutex<IoStats>>,
-}
-
-#[async_trait::async_trait]
-impl MultipartUpload for IoTrackingMultipartUpload {
-    async fn abort(&mut self) -> OSResult<()> {
-        self.target.abort().await
-    }
-
-    async fn complete(&mut self) -> OSResult<PutResult> {
-        self.target.complete().await
-    }
-
-    fn put_part(&mut self, payload: PutPayload) -> UploadPart {
-        {
-            let mut stats = self.stats.lock().unwrap();
-            stats.write_iops += 1;
-            stats.write_bytes += payload.content_length() as u64;
-        }
-        self.target.put_part(payload)
-    }
-}
--- a/rust/lancedb/src/lib.rs
+++ b/rust/lancedb/src/lib.rs
@@ -31,7 +31,7 @@
 //! are not yet ready to be released.
 //!
 //! - `remote` - Enable remote client to connect to LanceDB cloud.  This is not yet fully implemented
-//!   and should not be enabled.
+//!              and should not be enabled.
 //!
 //! ### Quick Start
 //!
--- a/rust/lancedb/src/query.rs
+++ b/rust/lancedb/src/query.rs
@@ -1,8 +1,8 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors

+use std::future::Future;
 use std::sync::Arc;
-use std::{future::Future, time::Duration};

 use arrow::compute::concat_batches;
 use arrow_array::{make_array, Array, Float16Array, Float32Array, Float64Array};
@@ -25,7 +25,6 @@ use crate::error::{Error, Result};
 use crate::rerankers::rrf::RRFReranker;
 use crate::rerankers::{check_reranker_result, NormalizeMethod, Reranker};
 use crate::table::BaseTable;
-use crate::utils::TimeoutStream;
 use crate::DistanceType;
 use crate::{arrow::SendableRecordBatchStream, table::AnyQuery};

@@ -526,15 +525,12 @@ pub struct QueryExecutionOptions {
    ///
    /// By default, this is 1024
    pub max_batch_length: u32,
-    /// Max duration to wait for the query to execute before timing out.
-    pub timeout: Option<Duration>,
 }

 impl Default for QueryExecutionOptions {
    fn default() -> Self {
        Self {
            max_batch_length: 1024,
-            timeout: None,
        }
    }
 }
@@ -583,15 +579,6 @@ pub trait ExecutableQuery {
    ) -> impl Future<Output = Result<SendableRecordBatchStream>> + Send;

    fn explain_plan(&self, verbose: bool) -> impl Future<Output = Result<String>> + Send;
-
-    fn analyze_plan(&self) -> impl Future<Output = Result<String>> + Send {
-        self.analyze_plan_with_options(QueryExecutionOptions::default())
-    }
-
-    fn analyze_plan_with_options(
-        &self,
-        options: QueryExecutionOptions,
-    ) -> impl Future<Output = Result<String>> + Send;
 }

 /// A query filter that can be applied to a query
@@ -778,11 +765,6 @@ impl ExecutableQuery for Query {
        let query = AnyQuery::Query(self.request.clone());
        self.parent.explain_plan(&query, verbose).await
    }
-
-    async fn analyze_plan_with_options(&self, options: QueryExecutionOptions) -> Result<String> {
-        let query = AnyQuery::Query(self.request.clone());
-        self.parent.analyze_plan(&query, options).await
-    }
 }

 /// A request for a nearest-neighbors search into a table
@@ -1011,10 +993,7 @@ impl VectorQuery {
        self
    }

-    pub async fn execute_hybrid(
-        &self,
-        options: QueryExecutionOptions,
-    ) -> Result<SendableRecordBatchStream> {
+    pub async fn execute_hybrid(&self) -> Result<SendableRecordBatchStream> {
        // clone query and specify we want to include row IDs, which can be needed for reranking
        let mut fts_query = Query::new(self.parent.clone());
        fts_query.request = self.request.base.clone();
@@ -1023,10 +1002,7 @@ impl VectorQuery {
        let mut vector_query = self.clone().with_row_id();

        vector_query.request.base.full_text_search = None;
-        let (fts_results, vec_results) = try_join!(
-            fts_query.execute_with_options(options.clone()),
-            vector_query.inner_execute_with_options(options)
-        )?;
+        let (fts_results, vec_results) = try_join!(fts_query.execute(), vector_query.execute())?;

        let (fts_results, vec_results) = try_join!(
            fts_results.try_collect::<Vec<_>>(),
@@ -1066,7 +1042,7 @@ impl VectorQuery {
            })?;

        let mut results = reranker
-            .rerank_hybrid(&fts_query.query.query(), vec_results, fts_results)
+            .rerank_hybrid(&fts_query.query, vec_results, fts_results)
            .await?;

        check_reranker_result(&results)?;
@@ -1084,20 +1060,6 @@ impl VectorQuery {
            RecordBatchStreamAdapter::new(results.schema(), stream::iter([Ok(results)])),
        ))
    }
-
-    async fn inner_execute_with_options(
-        &self,
-        options: QueryExecutionOptions,
-    ) -> Result<SendableRecordBatchStream> {
-        let plan = self.create_plan(options.clone()).await?;
-        let inner = execute_plan(plan, Default::default())?;
-        let inner = if let Some(timeout) = options.timeout {
-            TimeoutStream::new_boxed(inner, timeout)
-        } else {
-            inner
-        };
-        Ok(DatasetRecordBatchStream::new(inner).into())
-    }
 }

 impl ExecutableQuery for VectorQuery {
@@ -1111,24 +1073,22 @@ impl ExecutableQuery for VectorQuery {
        options: QueryExecutionOptions,
    ) -> Result<SendableRecordBatchStream> {
        if self.request.base.full_text_search.is_some() {
-            let hybrid_result = async move { self.execute_hybrid(options).await }
-                .boxed()
-                .await?;
+            let hybrid_result = async move { self.execute_hybrid().await }.boxed().await?;
            return Ok(hybrid_result);
        }

-        self.inner_execute_with_options(options).await
+        Ok(SendableRecordBatchStream::from(
+            DatasetRecordBatchStream::new(execute_plan(
+                self.create_plan(options).await?,
+                Default::default(),
+            )?),
+        ))
    }

    async fn explain_plan(&self, verbose: bool) -> Result<String> {
        let query = AnyQuery::VectorQuery(self.request.clone());
        self.parent.explain_plan(&query, verbose).await
    }
-
-    async fn analyze_plan_with_options(&self, options: QueryExecutionOptions) -> Result<String> {
-        let query = AnyQuery::VectorQuery(self.request.clone());
-        self.parent.analyze_plan(&query, options).await
-    }
 }

 impl HasQuery for VectorQuery {
@@ -1410,31 +1370,6 @@ mod tests {
        }
    }

-    #[tokio::test]
-    async fn test_analyze_plan() {
-        let tmp_dir = tempdir().unwrap();
-        let table = make_test_table(&tmp_dir).await;
-
-        let result = table.query().analyze_plan().await.unwrap();
-        assert!(result.contains("metrics="));
-    }
-
-    #[tokio::test]
-    async fn test_analyze_plan_with_options() {
-        let tmp_dir = tempdir().unwrap();
-        let table = make_test_table(&tmp_dir).await;
-
-        let result = table
-            .query()
-            .analyze_plan_with_options(QueryExecutionOptions {
-                max_batch_length: 10,
-                ..Default::default()
-            })
-            .await
-            .unwrap();
-        assert!(result.contains("metrics="));
-    }
-
    fn assert_plan_exists(plan: &Arc<dyn ExecutionPlan>, name: &str) -> bool {
        if plan.name() == name {
            return true;
--- a/rust/lancedb/src/remote/client.rs
+++ b/rust/lancedb/src/remote/client.rs
@@ -13,7 +13,7 @@ use reqwest::{
 use crate::error::{Error, Result};
 use crate::remote::db::RemoteOptions;

-const REQUEST_ID_HEADER: HeaderName = HeaderName::from_static("x-request-id");
+const REQUEST_ID_HEADER: &str = "x-request-id";

 /// Configuration for the LanceDB Cloud HTTP client.
 #[derive(Clone, Debug)]
@@ -299,7 +299,7 @@ impl<S: HttpSend> RestfulLanceDbClient<S> {
    ) -> Result<HeaderMap> {
        let mut headers = HeaderMap::new();
        headers.insert(
-            HeaderName::from_static("x-api-key"),
+            "x-api-key",
            HeaderValue::from_str(api_key).map_err(|_| Error::InvalidInput {
                message: "non-ascii api key provided".to_string(),
            })?,
@@ -307,7 +307,7 @@ impl<S: HttpSend> RestfulLanceDbClient<S> {
        if region == "local" {
            let host = format!("{}.local.api.lancedb.com", db_name);
            headers.insert(
-                http::header::HOST,
+                "Host",
                HeaderValue::from_str(&host).map_err(|_| Error::InvalidInput {
                    message: format!("non-ascii database name '{}' provided", db_name),
                })?,
@@ -315,7 +315,7 @@ impl<S: HttpSend> RestfulLanceDbClient<S> {
        }
        if has_host_override {
            headers.insert(
-                HeaderName::from_static("x-lancedb-database"),
+                "x-lancedb-database",
                HeaderValue::from_str(db_name).map_err(|_| Error::InvalidInput {
                    message: format!("non-ascii database name '{}' provided", db_name),
                })?,
@@ -323,7 +323,7 @@ impl<S: HttpSend> RestfulLanceDbClient<S> {
        }
        if db_prefix.is_some() {
            headers.insert(
-                HeaderName::from_static("x-lancedb-database-prefix"),
+                "x-lancedb-database-prefix",
                HeaderValue::from_str(db_prefix.unwrap()).map_err(|_| Error::InvalidInput {
                    message: format!(
                        "non-ascii database prefix '{}' provided",
@@ -335,7 +335,7 @@ impl<S: HttpSend> RestfulLanceDbClient<S> {

        if let Some(v) = options.0.get("account_name") {
            headers.insert(
-                HeaderName::from_static("x-azure-storage-account-name"),
+                "x-azure-storage-account-name",
                HeaderValue::from_str(v).map_err(|_| Error::InvalidInput {
                    message: format!("non-ascii storage account name '{}' provided", db_name),
                })?,
@@ -343,7 +343,7 @@ impl<S: HttpSend> RestfulLanceDbClient<S> {
        }
        if let Some(v) = options.0.get("azure_storage_account_name") {
            headers.insert(
-                HeaderName::from_static("x-azure-storage-account-name"),
+                "x-azure-storage-account-name",
                HeaderValue::from_str(v).map_err(|_| Error::InvalidInput {
                    message: format!("non-ascii storage account name '{}' provided", db_name),
                })?,
--- a/rust/lancedb/src/remote/db.rs
+++ b/rust/lancedb/src/remote/db.rs
@@ -52,10 +52,6 @@ impl ServerVersion {
    pub fn support_multivector(&self) -> bool {
        self.0 >= semver::Version::new(0, 2, 0)
    }
-
-    pub fn support_structural_fts(&self) -> bool {
-        self.0 >= semver::Version::new(0, 3, 0)
-    }
 }

 pub const OPT_REMOTE_PREFIX: &str = "remote_database_";
--- a/rust/lancedb/src/remote/table.rs
+++ b/rust/lancedb/src/remote/table.rs
@@ -20,7 +20,7 @@ use datafusion_physical_plan::stream::RecordBatchStreamAdapter;
 use datafusion_physical_plan::{ExecutionPlan, RecordBatchStream, SendableRecordBatchStream};
 use futures::TryStreamExt;
 use http::header::CONTENT_TYPE;
-use http::{HeaderName, StatusCode};
+use http::StatusCode;
 use lance::arrow::json::{JsonDataType, JsonSchema};
 use lance::dataset::scanner::DatasetRecordBatchStream;
 use lance::dataset::{ColumnAlteration, NewColumnTransform, Version};
@@ -44,8 +44,6 @@ use super::client::{HttpSend, RestfulLanceDbClient, Sender};
 use super::db::ServerVersion;
 use super::ARROW_STREAM_CONTENT_TYPE;

-const REQUEST_TIMEOUT_HEADER: HeaderName = HeaderName::from_static("x-request-timeout-ms");
-
 #[derive(Debug)]
 pub struct RemoteTable<S: HttpSend = Sender> {
    #[allow(dead_code)]
@@ -157,11 +155,7 @@ impl<S: HttpSend> RemoteTable<S> {
        Ok(Box::pin(RecordBatchStreamAdapter::new(schema, stream)))
    }

-    fn apply_query_params(
-        &self,
-        body: &mut serde_json::Value,
-        params: &QueryRequest,
-    ) -> Result<()> {
+    fn apply_query_params(body: &mut serde_json::Value, params: &QueryRequest) -> Result<()> {
        body["prefilter"] = params.prefilter.into();
        if let Some(offset) = params.offset {
            body["offset"] = serde_json::Value::Number(serde_json::Number::from(offset));
@@ -215,17 +209,10 @@ impl<S: HttpSend> RemoteTable<S> {
                    message: "Wand factor is not yet supported in LanceDB Cloud".into(),
                });
            }
-
-            if self.server_version.support_structural_fts() {
-                body["full_text_query"] = serde_json::json!({
-                    "query": full_text_search.query.clone(),
-                });
-            } else {
-                body["full_text_query"] = serde_json::json!({
-                    "columns": full_text_search.columns().into_iter().collect::<Vec<_>>(),
-                    "query": full_text_search.query.query(),
-                })
-            }
+            body["full_text_query"] = serde_json::json!({
+                "columns": full_text_search.columns,
+                "query": full_text_search.query,
+            })
        }

        Ok(())
@@ -236,7 +223,7 @@ impl<S: HttpSend> RemoteTable<S> {
        mut body: serde_json::Value,
        query: &VectorQueryRequest,
    ) -> Result<Vec<serde_json::Value>> {
-        self.apply_query_params(&mut body, &query.base)?;
+        Self::apply_query_params(&mut body, &query.base)?;

        // Apply general parameters, before we dispatch based on number of query vectors.
        body["distance_type"] = serde_json::json!(query.distance_type.unwrap_or_default());
@@ -334,25 +321,28 @@ impl<S: HttpSend> RemoteTable<S> {
    async fn execute_query(
        &self,
        query: &AnyQuery,
-        options: &QueryExecutionOptions,
+        _options: QueryExecutionOptions,
    ) -> Result<Vec<Pin<Box<dyn RecordBatchStream + Send>>>> {
-        let mut request = self.client.post(&format!("/v1/table/{}/query/", self.name));
+        let request = self.client.post(&format!("/v1/table/{}/query/", self.name));

-        if let Some(timeout) = options.timeout {
-            // Client side timeout
-            request = request.timeout(timeout);
-            // Also send to server, so it can abort the query if it takes too long.
-            // (If it doesn't fit into u64, it's not worth sending anyways.)
-            if let Ok(timeout_ms) = u64::try_from(timeout.as_millis()) {
-                request = request.header(REQUEST_TIMEOUT_HEADER, timeout_ms);
+        let version = self.current_version().await;
+        let mut body = serde_json::json!({ "version": version });
+
+        let requests = match query {
+            AnyQuery::Query(query) => {
+                Self::apply_query_params(&mut body, query)?;
+                // Empty vector can be passed if no vector search is performed.
+                body["vector"] = serde_json::Value::Array(Vec::new());
+                vec![request.json(&body)]
            }
-        }
-
-        let query_bodies = self.prepare_query_bodies(query).await?;
-        let requests: Vec<reqwest::RequestBuilder> = query_bodies
-            .into_iter()
-            .map(|body| request.try_clone().unwrap().json(&body))
-            .collect();
+            AnyQuery::VectorQuery(query) => {
+                let bodies = self.apply_vector_query_params(body, query)?;
+                bodies
+                    .into_iter()
+                    .map(|body| request.try_clone().unwrap().json(&body))
+                    .collect()
+            }
+        };

        let futures = requests.into_iter().map(|req| async move {
            let (request_id, response) = self.client.send(req, true).await?;
@@ -361,22 +351,6 @@ impl<S: HttpSend> RemoteTable<S> {
        let streams = futures::future::try_join_all(futures).await?;
        Ok(streams)
    }
-
-    async fn prepare_query_bodies(&self, query: &AnyQuery) -> Result<Vec<serde_json::Value>> {
-        let version = self.current_version().await;
-        let base_body = serde_json::json!({ "version": version });
-
-        match query {
-            AnyQuery::Query(query) => {
-                let mut body = base_body.clone();
-                self.apply_query_params(&mut body, query)?;
-                // Empty vector can be passed if no vector search is performed.
-                body["vector"] = serde_json::Value::Array(Vec::new());
-                Ok(vec![body])
-            }
-            AnyQuery::VectorQuery(query) => self.apply_vector_query_params(base_body, query),
-        }
-    }
 }

 #[derive(Deserialize)]
@@ -448,17 +422,10 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
        Ok(())
    }
    async fn restore(&self) -> Result<()> {
-        let mut request = self
-            .client
-            .post(&format!("/v1/table/{}/restore/", self.name));
-        let version = self.current_version().await;
-        let body = serde_json::json!({ "version": version });
-        request = request.json(&body);
-
-        let (request_id, response) = self.client.send(request, true).await?;
-        self.check_table_response(&request_id, response).await?;
-        self.checkout_latest().await?;
-        Ok(())
+        self.check_mutable().await?;
+        Err(Error::NotSupported {
+            message: "restore is not supported on LanceDB cloud.".into(),
+        })
    }

    async fn list_versions(&self) -> Result<Vec<Version>> {
@@ -555,7 +522,7 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
        query: &AnyQuery,
        options: QueryExecutionOptions,
    ) -> Result<Arc<dyn ExecutionPlan>> {
-        let streams = self.execute_query(query, &options).await?;
+        let streams = self.execute_query(query, options).await?;
        if streams.len() == 1 {
            let stream = streams.into_iter().next().unwrap();
            Ok(Arc::new(OneShotExec::new(stream)))
@@ -571,9 +538,9 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
    async fn query(
        &self,
        query: &AnyQuery,
-        options: QueryExecutionOptions,
+        _options: QueryExecutionOptions,
    ) -> Result<DatasetRecordBatchStream> {
-        let streams = self.execute_query(query, &options).await?;
+        let streams = self.execute_query(query, _options).await?;

        if streams.len() == 1 {
            Ok(DatasetRecordBatchStream::new(
@@ -592,94 +559,6 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
            )?))
        }
    }
-
-    async fn explain_plan(&self, query: &AnyQuery, verbose: bool) -> Result<String> {
-        let base_request = self
-            .client
-            .post(&format!("/v1/table/{}/explain_plan/", self.name));
-
-        let query_bodies = self.prepare_query_bodies(query).await?;
-        let requests: Vec<reqwest::RequestBuilder> = query_bodies
-            .into_iter()
-            .map(|query_body| {
-                let explain_request = serde_json::json!({
-                    "verbose": verbose,
-                    "query": query_body
-                });
-
-                base_request.try_clone().unwrap().json(&explain_request)
-            })
-            .collect::<Vec<_>>();
-
-        let futures = requests.into_iter().map(|req| async move {
-            let (request_id, response) = self.client.send(req, true).await?;
-            let response = self.check_table_response(&request_id, response).await?;
-            let body = response.text().await.err_to_http(request_id.clone())?;
-
-            serde_json::from_str(&body).map_err(|e| Error::Http {
-                source: format!("Failed to parse explain plan: {}", e).into(),
-                request_id,
-                status_code: None,
-            })
-        });
-
-        let plan_texts = futures::future::try_join_all(futures).await?;
-        let final_plan = if plan_texts.len() > 1 {
-            plan_texts
-                .into_iter()
-                .enumerate()
-                .map(|(i, plan)| format!("--- Plan #{} ---\n{}", i + 1, plan))
-                .collect::<Vec<_>>()
-                .join("\n\n")
-        } else {
-            plan_texts.into_iter().next().unwrap_or_default()
-        };
-
-        Ok(final_plan)
-    }
-
-    async fn analyze_plan(
-        &self,
-        query: &AnyQuery,
-        _options: QueryExecutionOptions,
-    ) -> Result<String> {
-        let request = self
-            .client
-            .post(&format!("/v1/table/{}/analyze_plan/", self.name));
-
-        let query_bodies = self.prepare_query_bodies(query).await?;
-        let requests: Vec<reqwest::RequestBuilder> = query_bodies
-            .into_iter()
-            .map(|body| request.try_clone().unwrap().json(&body))
-            .collect();
-
-        let futures = requests.into_iter().map(|req| async move {
-            let (request_id, response) = self.client.send(req, true).await?;
-            let response = self.check_table_response(&request_id, response).await?;
-            let body = response.text().await.err_to_http(request_id.clone())?;
-
-            serde_json::from_str(&body).map_err(|e| Error::Http {
-                source: format!("Failed to execute analyze plan: {}", e).into(),
-                request_id,
-                status_code: None,
-            })
-        });
-
-        let analyze_result_texts = futures::future::try_join_all(futures).await?;
-        let final_analyze = if analyze_result_texts.len() > 1 {
-            analyze_result_texts
-                .into_iter()
-                .enumerate()
-                .map(|(i, plan)| format!("--- Query #{} ---\n{}", i + 1, plan))
-                .collect::<Vec<_>>()
-                .join("\n\n")
-        } else {
-            analyze_result_texts.into_iter().next().unwrap_or_default()
-        };
-
-        Ok(final_analyze)
-    }
-
    async fn update(&self, update: UpdateBuilder) -> Result<u64> {
        self.check_mutable().await?;
        let request = self
@@ -702,7 +581,6 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {

        Ok(0) // TODO: support returning number of modified rows once supported in SaaS.
    }
-
    async fn delete(&self, predicate: &str) -> Result<()> {
        self.check_mutable().await?;
        let body = serde_json::json!({ "predicate": predicate });
@@ -1003,12 +881,6 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
        Ok(())
    }

-    async fn prewarm_index(&self, _index_name: &str) -> Result<()> {
-        Err(Error::NotSupported {
-            message: "prewarm_index is not yet supported on LanceDB cloud.".into(),
-        })
-    }
-
    async fn table_definition(&self) -> Result<TableDefinition> {
        Err(Error::NotSupported {
            message: "table_definition is not supported on LanceDB cloud.".into(),
@@ -1066,7 +938,6 @@ mod tests {
    use arrow_schema::{DataType, Field, Schema};
    use chrono::{DateTime, Utc};
    use futures::{future::BoxFuture, StreamExt, TryFutureExt};
-    use lance_index::scalar::inverted::query::MatchQuery;
    use lance_index::scalar::FullTextSearchQuery;
    use reqwest::Body;
    use rstest::rstest;
@@ -1713,18 +1584,7 @@ mod tests {
                "prefilter": true,
                "version": null
            });
-            let expected_body_2 = serde_json::json!({
-                "full_text_query": {
-                    "columns": ["b","a"],
-                    "query": "hello world",
-                },
-                "k": 10,
-                "vector": [],
-                "with_row_id": true,
-                "prefilter": true,
-                "version": null
-            });
-            assert!(body == expected_body || body == expected_body_2);
+            assert_eq!(body, expected_body);

            let data = RecordBatch::try_new(
                Arc::new(Schema::new(vec![Field::new("a", DataType::Int32, false)])),
@@ -1743,8 +1603,7 @@ mod tests {
            .query()
            .full_text_search(
                FullTextSearchQuery::new("hello world".into())
-                    .with_columns(&["a".into(), "b".into()])
-                    .unwrap(),
+                    .columns(Some(vec!["a".into(), "b".into()])),
            )
            .with_row_id()
            .limit(10)
@@ -1753,67 +1612,6 @@ mod tests {
            .unwrap();
    }

-    #[tokio::test]
-    async fn test_query_structured_fts() {
-        let table =
-            Table::new_with_handler_version("my_table", semver::Version::new(0, 3, 0), |request| {
-                assert_eq!(request.method(), "POST");
-                assert_eq!(request.url().path(), "/v1/table/my_table/query/");
-                assert_eq!(
-                    request.headers().get("Content-Type").unwrap(),
-                    JSON_CONTENT_TYPE
-                );
-
-                let body = request.body().unwrap().as_bytes().unwrap();
-                let body: serde_json::Value = serde_json::from_slice(body).unwrap();
-                let expected_body = serde_json::json!({
-                    "full_text_query": {
-                        "query": {
-                            "match": {
-                                "terms": "hello world",
-                                "column": "a",
-                                "boost": 1.0,
-                                "fuzziness": 0,
-                                "max_expansions": 50,
-                                "operator": "Or",
-                            },
-                        }
-                    },
-                    "k": 10,
-                    "vector": [],
-                    "with_row_id": true,
-                    "prefilter": true,
-                    "version": null
-                });
-                assert_eq!(body, expected_body);
-
-                let data = RecordBatch::try_new(
-                    Arc::new(Schema::new(vec![Field::new("a", DataType::Int32, false)])),
-                    vec![Arc::new(Int32Array::from(vec![1, 2, 3]))],
-                )
-                .unwrap();
-                let response_body = write_ipc_file(&data);
-                http::Response::builder()
-                    .status(200)
-                    .header(CONTENT_TYPE, ARROW_FILE_CONTENT_TYPE)
-                    .body(response_body)
-                    .unwrap()
-            });
-
-        let _ = table
-            .query()
-            .full_text_search(FullTextSearchQuery::new_query(
-                MatchQuery::new("hello world".to_owned())
-                    .with_column(Some("a".to_owned()))
-                    .into(),
-            ))
-            .with_row_id()
-            .limit(10)
-            .execute()
-            .await
-            .unwrap();
-    }
-
    #[rstest]
    #[case(DEFAULT_SERVER_VERSION.clone())]
    #[case(semver::Version::new(0, 2, 0))]
--- a/rust/lancedb/src/rerankers.rs
+++ b/rust/lancedb/src/rerankers.rs
@@ -29,8 +29,8 @@ impl FromStr for NormalizeMethod {

    fn from_str(s: &str) -> Result<Self> {
        match s.to_lowercase().as_str() {
-            "score" => Ok(Self::Score),
-            "rank" => Ok(Self::Rank),
+            "score" => Ok(NormalizeMethod::Score),
+            "rank" => Ok(NormalizeMethod::Rank),
            _ => Err(Error::InvalidInput {
                message: format!("invalid normalize method: {}", s),
            }),
@@ -41,8 +41,8 @@ impl FromStr for NormalizeMethod {
 impl std::fmt::Display for NormalizeMethod {
    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
        match self {
-            Self::Score => write!(f, "score"),
-            Self::Rank => write!(f, "rank"),
+            NormalizeMethod::Score => write!(f, "score"),
+            NormalizeMethod::Rank => write!(f, "rank"),
        }
    }
 }
--- a/rust/lancedb/src/table.rs
+++ b/rust/lancedb/src/table.rs
@@ -33,7 +33,7 @@ use lance::dataset::{
 use lance::dataset::{MergeInsertBuilder as LanceMergeInsertBuilder, WhenNotMatchedBySource};
 use lance::index::vector::utils::infer_vector_dim;
 use lance::io::WrappingObjectStore;
-use lance_datafusion::exec::{analyze_plan as lance_analyze_plan, execute_plan};
+use lance_datafusion::exec::execute_plan;
 use lance_datafusion::utils::StreamingWriteSource;
 use lance_index::vector::hnsw::builder::HnswBuildParams;
 use lance_index::vector::ivf::IvfBuildParams;
@@ -68,7 +68,7 @@ use crate::query::{
 use crate::utils::{
    default_vector_column, supported_bitmap_data_type, supported_btree_data_type,
    supported_fts_data_type, supported_label_list_data_type, supported_vector_data_type,
-    PatchReadParam, PatchWriteParam, TimeoutStream,
+    PatchReadParam, PatchWriteParam,
 };

 use self::dataset::DatasetConsistencyWrapper;
@@ -433,12 +433,6 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {

        Ok(format!("{}", display.indent(verbose)))
    }
-    async fn analyze_plan(
-        &self,
-        query: &AnyQuery,
-        options: QueryExecutionOptions,
-    ) -> Result<String>;
-
    /// Add new records to the table.
    async fn add(
        &self,
@@ -455,8 +449,6 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
    async fn list_indices(&self) -> Result<Vec<IndexConfig>>;
    /// Drop an index from the table.
    async fn drop_index(&self, name: &str) -> Result<()>;
-    /// Prewarm an index in the table
-    async fn prewarm_index(&self, name: &str) -> Result<()>;
    /// Get statistics about the index.
    async fn index_stats(&self, index_name: &str) -> Result<Option<IndexStatistics>>;
    /// Merge insert new records into the table.
@@ -796,8 +788,8 @@ impl Table {
    /// # Arguments
    ///
    /// * `on` One or more columns to join on.  This is how records from the
-    ///   source table and target table are matched.  Typically this is some
-    ///   kind of key or id column.
+    ///    source table and target table are matched.  Typically this is some
+    ///    kind of key or id column.
    ///
    /// # Examples
    ///
@@ -1088,22 +1080,6 @@ impl Table {
        self.inner.drop_index(name).await
    }

-    /// Prewarm an index in the table
-    ///
-    /// This is a hint to fully load the index into memory.  It can be used to
-    /// avoid cold starts
-    ///
-    /// It is generally wasteful to call this if the index does not fit into the
-    /// available cache.
-    ///
-    /// Note: This function is not yet supported on all indices, in which case it
-    /// may do nothing.
-    ///
-    /// Use [`Self::list_indices()`] to find the names of the indices.
-    pub async fn prewarm_index(&self, name: &str) -> Result<()> {
-        self.inner.prewarm_index(name).await
-    }
-
    // Take many execution plans and map them into a single plan that adds
    // a query_index column and unions them.
    pub(crate) fn multi_vector_plan(
@@ -1793,14 +1769,11 @@ impl NativeTable {
        query: &AnyQuery,
        options: QueryExecutionOptions,
    ) -> Result<DatasetRecordBatchStream> {
-        let plan = self.create_plan(query, options.clone()).await?;
-        let inner = execute_plan(plan, Default::default())?;
-        let inner = if let Some(timeout) = options.timeout {
-            TimeoutStream::new_boxed(inner, timeout)
-        } else {
-            inner
-        };
-        Ok(DatasetRecordBatchStream::new(inner))
+        let plan = self.create_plan(query, options).await?;
+        Ok(DatasetRecordBatchStream::new(execute_plan(
+            plan,
+            Default::default(),
+        )?))
    }

    /// Check whether the table uses V2 manifest paths.
@@ -2024,11 +1997,6 @@ impl BaseTable for NativeTable {
        Ok(())
    }

-    async fn prewarm_index(&self, index_name: &str) -> Result<()> {
-        let dataset = self.dataset.get().await?;
-        Ok(dataset.prewarm_index(index_name).await?)
-    }
-
    async fn update(&self, update: UpdateBuilder) -> Result<u64> {
        let dataset = self.dataset.get().await?.clone();
        let mut builder = LanceUpdateBuilder::new(Arc::new(dataset));
@@ -2224,15 +2192,6 @@ impl BaseTable for NativeTable {
        self.generic_query(query, options).await
    }

-    async fn analyze_plan(
-        &self,
-        query: &AnyQuery,
-        options: QueryExecutionOptions,
-    ) -> Result<String> {
-        let plan = self.create_plan(query, options).await?;
-        Ok(lance_analyze_plan(plan, Default::default()).await?)
-    }
-
    async fn merge_insert(
        &self,
        params: MergeInsertBuilder,
@@ -3478,9 +3437,6 @@ mod tests {
        assert_eq!(stats.num_unindexed_rows, 0);
        assert_eq!(stats.index_type, crate::index::IndexType::FTS);
        assert_eq!(stats.distance_type, None);
-
-        // Make sure we can call prewarm without error
-        table.prewarm_index("text_idx").await.unwrap();
    }

    #[tokio::test]
@@ -3576,7 +3532,7 @@ mod tests {
        let native_tbl = table.as_native().unwrap();

        let manifest = native_tbl.manifest().await.unwrap();
-        let base_config_len = manifest.config.len();
+        assert_eq!(manifest.config.len(), 0);

        native_tbl
            .update_config(vec![("test_key1".to_string(), "test_val1".to_string())])
@@ -3584,7 +3540,7 @@ mod tests {
            .unwrap();

        let manifest = native_tbl.manifest().await.unwrap();
-        assert_eq!(manifest.config.len(), 1 + base_config_len);
+        assert_eq!(manifest.config.len(), 1);
        assert_eq!(
            manifest.config.get("test_key1"),
            Some(&"test_val1".to_string())
@@ -3595,7 +3551,7 @@ mod tests {
            .await
            .unwrap();
        let manifest = native_tbl.manifest().await.unwrap();
-        assert_eq!(manifest.config.len(), 2 + base_config_len);
+        assert_eq!(manifest.config.len(), 2);
        assert_eq!(
            manifest.config.get("test_key1"),
            Some(&"test_val1".to_string())
@@ -3613,7 +3569,7 @@ mod tests {
            .await
            .unwrap();
        let manifest = native_tbl.manifest().await.unwrap();
-        assert_eq!(manifest.config.len(), 2 + base_config_len);
+        assert_eq!(manifest.config.len(), 2);
        assert_eq!(
            manifest.config.get("test_key1"),
            Some(&"test_val1".to_string())
@@ -3625,7 +3581,7 @@ mod tests {

        native_tbl.delete_config_keys(&["test_key1"]).await.unwrap();
        let manifest = native_tbl.manifest().await.unwrap();
-        assert_eq!(manifest.config.len(), 1 + base_config_len);
+        assert_eq!(manifest.config.len(), 1);
        assert_eq!(
            manifest.config.get("test_key2"),
            Some(&"test_val2_update".to_string())
--- a/rust/lancedb/src/table/dataset.rs
+++ b/rust/lancedb/src/table/dataset.rs
@@ -290,48 +290,3 @@ impl DerefMut for DatasetWriteGuard<'_> {
        }
    }
 }
-
-#[cfg(test)]
-mod tests {
-    use arrow_schema::{DataType, Field, Schema};
-    use lance::{dataset::WriteParams, io::ObjectStoreParams};
-
-    use super::*;
-
-    use crate::{connect, io::object_store::io_tracking::IoStatsHolder, table::WriteOptions};
-
-    #[tokio::test]
-    async fn test_iops_open_strong_consistency() {
-        let db = connect("memory://")
-            .read_consistency_interval(Duration::ZERO)
-            .execute()
-            .await
-            .expect("Failed to connect to database");
-        let io_stats = IoStatsHolder::default();
-
-        let schema = Arc::new(Schema::new(vec![Field::new("id", DataType::Int32, false)]));
-
-        let table = db
-            .create_empty_table("test", schema)
-            .write_options(WriteOptions {
-                lance_write_params: Some(WriteParams {
-                    store_params: Some(ObjectStoreParams {
-                        object_store_wrapper: Some(Arc::new(io_stats.clone())),
-                        ..Default::default()
-                    }),
-                    ..Default::default()
-                }),
-            })
-            .execute()
-            .await
-            .unwrap();
-
-        io_stats.incremental_stats();
-
-        // We should only need 1 read IOP to check the schema: looking for the
-        // latest version.
-        table.schema().await.unwrap();
-        let stats = io_stats.incremental_stats();
-        assert_eq!(stats.read_iops, 1);
-    }
-}
--- a/rust/lancedb/src/utils.rs
+++ b/rust/lancedb/src/utils.rs
@@ -3,20 +3,14 @@

 use std::sync::Arc;

-use arrow_array::RecordBatch;
-use arrow_schema::{DataType, Schema, SchemaRef};
-use datafusion_common::{DataFusionError, Result as DataFusionResult};
-use datafusion_execution::RecordBatchStream;
-use futures::{FutureExt, Stream};
+use arrow_schema::{DataType, Schema};
 use lance::arrow::json::JsonDataType;
 use lance::dataset::{ReadParams, WriteParams};
 use lance::index::vector::utils::infer_vector_dim;
 use lance::io::{ObjectStoreParams, WrappingObjectStore};
 use lazy_static::lazy_static;
-use std::pin::Pin;

 use crate::error::{Error, Result};
-use datafusion_physical_plan::SendableRecordBatchStream;

 lazy_static! {
    static ref TABLE_NAME_REGEX: regex::Regex = regex::Regex::new(r"^[a-zA-Z0-9_\-\.]+$").unwrap();
@@ -141,7 +135,6 @@ pub fn supported_btree_data_type(dtype: &DataType) -> bool {
                | DataType::Date32
                | DataType::Date64
                | DataType::Timestamp(_, _)
-                | DataType::FixedSizeBinary(_)
        )
 }

@@ -158,17 +151,7 @@ pub fn supported_label_list_data_type(dtype: &DataType) -> bool {
 }

 pub fn supported_fts_data_type(dtype: &DataType) -> bool {
-    supported_fts_data_type_impl(dtype, false)
-}
-
-fn supported_fts_data_type_impl(dtype: &DataType, in_list: bool) -> bool {
-    match (dtype, in_list) {
-        (DataType::Utf8 | DataType::LargeUtf8, _) => true,
-        (DataType::List(field) | DataType::LargeList(field), false) => {
-            supported_fts_data_type_impl(field.data_type(), true)
-        }
-        _ => false,
-    }
+    matches!(dtype, DataType::Utf8 | DataType::LargeUtf8)
 }

 pub fn supported_vector_data_type(dtype: &DataType) -> bool {
@@ -194,98 +177,12 @@ pub fn string_to_datatype(s: &str) -> Option<DataType> {
    (&json_type).try_into().ok()
 }

-enum TimeoutState {
-    NotStarted {
-        timeout: std::time::Duration,
-    },
-    Started {
-        deadline: Pin<Box<tokio::time::Sleep>>,
-        timeout: std::time::Duration,
-    },
-    Completed,
-}
-
-/// A `Stream` wrapper that implements a timeout.
-///
-/// The timeout starts when the first `poll_next` is called. As soon as the timeout
-/// duration has passed, the stream will return an `Err` indicating a timeout error
-/// for the next poll.
-pub struct TimeoutStream {
-    inner: SendableRecordBatchStream,
-    state: TimeoutState,
-}
-
-impl TimeoutStream {
-    pub fn new(inner: SendableRecordBatchStream, timeout: std::time::Duration) -> Self {
-        Self {
-            inner,
-            state: TimeoutState::NotStarted { timeout },
-        }
-    }
-
-    pub fn new_boxed(
-        inner: SendableRecordBatchStream,
-        timeout: std::time::Duration,
-    ) -> SendableRecordBatchStream {
-        Box::pin(Self::new(inner, timeout))
-    }
-
-    fn timeout_error(timeout: &std::time::Duration) -> DataFusionError {
-        DataFusionError::Execution(format!("Query timeout after {} ms", timeout.as_millis()))
-    }
-}
-
-impl RecordBatchStream for TimeoutStream {
-    fn schema(&self) -> SchemaRef {
-        self.inner.schema()
-    }
-}
-
-impl Stream for TimeoutStream {
-    type Item = DataFusionResult<RecordBatch>;
-
-    fn poll_next(
-        mut self: std::pin::Pin<&mut Self>,
-        cx: &mut std::task::Context<'_>,
-    ) -> std::task::Poll<Option<Self::Item>> {
-        match &mut self.state {
-            TimeoutState::NotStarted { timeout } => {
-                if timeout.is_zero() {
-                    return std::task::Poll::Ready(Some(Err(Self::timeout_error(timeout))));
-                }
-                let deadline = Box::pin(tokio::time::sleep(*timeout));
-                self.state = TimeoutState::Started {
-                    deadline,
-                    timeout: *timeout,
-                };
-                self.poll_next(cx)
-            }
-            TimeoutState::Started { deadline, timeout } => match deadline.poll_unpin(cx) {
-                std::task::Poll::Ready(_) => {
-                    let err = Self::timeout_error(timeout);
-                    self.state = TimeoutState::Completed;
-                    std::task::Poll::Ready(Some(Err(err)))
-                }
-                std::task::Poll::Pending => {
-                    let inner = Pin::new(&mut self.inner);
-                    inner.poll_next(cx)
-                }
-            },
-            TimeoutState::Completed => std::task::Poll::Ready(None),
-        }
-    }
-}
-
 #[cfg(test)]
 mod tests {
-    use arrow_array::Int32Array;
-    use arrow_schema::Field;
-    use datafusion_physical_plan::stream::RecordBatchStreamAdapter;
-    use futures::{stream, StreamExt};
-    use tokio::time::sleep;
-
    use super::*;

+    use arrow_schema::{DataType, Field};
+
    #[test]
    fn test_guess_default_column() {
        let schema_no_vector = Schema::new(vec![
@@ -351,85 +248,4 @@ mod tests {
        let expected = DataType::Int32;
        assert_eq!(string_to_datatype(string), Some(expected));
    }
-
-    fn sample_batch() -> RecordBatch {
-        let schema = Arc::new(Schema::new(vec![Field::new(
-            "col1",
-            DataType::Int32,
-            false,
-        )]));
-        RecordBatch::try_new(
-            schema.clone(),
-            vec![Arc::new(Int32Array::from(vec![1, 2, 3]))],
-        )
-        .unwrap()
-    }
-
-    #[tokio::test]
-    async fn test_timeout_stream() {
-        let batch = sample_batch();
-        let schema = batch.schema();
-        let mock_stream = stream::iter(vec![Ok(batch.clone()), Ok(batch.clone())]);
-
-        let sendable_stream: SendableRecordBatchStream =
-            Box::pin(RecordBatchStreamAdapter::new(schema.clone(), mock_stream));
-        let timeout_duration = std::time::Duration::from_millis(10);
-        let mut timeout_stream = TimeoutStream::new(sendable_stream, timeout_duration);
-
-        // Poll the stream to get the first batch
-        let first_result = timeout_stream.next().await;
-        assert!(first_result.is_some());
-        assert!(first_result.unwrap().is_ok());
-
-        // Sleep for the timeout duration
-        sleep(timeout_duration).await;
-
-        // Poll the stream again and ensure it returns a timeout error
-        let second_result = timeout_stream.next().await.unwrap();
-        assert!(second_result.is_err());
-        assert!(second_result
-            .unwrap_err()
-            .to_string()
-            .contains("Query timeout"));
-    }
-
-    #[tokio::test]
-    async fn test_timeout_stream_zero_duration() {
-        let batch = sample_batch();
-        let schema = batch.schema();
-        let mock_stream = stream::iter(vec![Ok(batch.clone()), Ok(batch.clone())]);
-
-        let sendable_stream: SendableRecordBatchStream =
-            Box::pin(RecordBatchStreamAdapter::new(schema.clone(), mock_stream));
-
-        // Setup similar to test_timeout_stream
-        let timeout_duration = std::time::Duration::from_secs(0);
-        let mut timeout_stream = TimeoutStream::new(sendable_stream, timeout_duration);
-
-        // First poll should immediately return a timeout error
-        let result = timeout_stream.next().await.unwrap();
-        assert!(result.is_err());
-        assert!(result.unwrap_err().to_string().contains("Query timeout"));
-    }
-
-    #[tokio::test]
-    async fn test_timeout_stream_completes_normally() {
-        let batch = sample_batch();
-        let schema = batch.schema();
-        let mock_stream = stream::iter(vec![Ok(batch.clone()), Ok(batch.clone())]);
-
-        let sendable_stream: SendableRecordBatchStream =
-            Box::pin(RecordBatchStreamAdapter::new(schema.clone(), mock_stream));
-
-        // Setup a stream with 2 batches
-        // Use a longer timeout that won't trigger
-        let timeout_duration = std::time::Duration::from_secs(1);
-        let mut timeout_stream = TimeoutStream::new(sendable_stream, timeout_duration);
-
-        // Both polls should return data normally
-        assert!(timeout_stream.next().await.unwrap().is_ok());
-        assert!(timeout_stream.next().await.unwrap().is_ok());
-        // Stream should be empty now
-        assert!(timeout_stream.next().await.is_none());
-    }
 }
Author	SHA1	Message	Date
Lance Release	b67f13f642	Bump version: 0.21.2-beta.2 → 0.21.2	2025-03-26 16:27:05 +00:00
Lance Release	2f12d67469	Bump version: 0.21.2-beta.1 → 0.21.2-beta.2	2025-03-26 16:27:05 +00:00
Lance Release	8d7cc29abb	Bump version: 0.18.2-beta.0 → 0.18.2-beta.1	2025-03-26 16:24:17 +00:00
Lance Release	a4404e9e18	Bump version: 0.21.2-beta.0 → 0.21.2-beta.1	2025-03-26 16:23:37 +00:00
Will Jones	077e5bb586	upgrade to 0.25.0	2025-03-26 09:19:48 -07:00