Bump version: 0.22.0-beta.5 → 0.22.0-beta.6

feat: support to create FTS index on list of strings (#2317 )
## Summary by CodeRabbit - **Chores** - Updated internal library dependencies to the latest beta version for improved system stability. - **Tests** - Added automated tests to validate full-text search functionality on list-based text fields. - **Refactor** - Enhanced the search processing logic to provide robust support for list-type text data, ensuring more reliable results.  --------- Signed-off-by: BubbleCal <bubble-cal@outlook.com>
2025-12-23 05:19:58 +00:00 · 2025-04-08 06:16:14 +00:00 · 2025-04-08 14:12:35 +08:00 · 2025-04-04 22:56:57 +00:00 · 2025-04-04 21:50:28 +00:00 · 2025-04-04 21:50:12 +00:00
64 changed files with 2931 additions and 628 deletions
--- a/.bumpversion.toml
+++ b/.bumpversion.toml
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.18.3-beta.0"
+current_version = "0.19.0-beta.5"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
--- a/.github/workflows/npm-publish.yml
+++ b/.github/workflows/npm-publish.yml
@@ -18,6 +18,7 @@ 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 }}
@@ -130,29 +131,24 @@ jobs:
              set -e &&
              apt-get update &&
              apt-get install -y protobuf-compiler pkg-config
-
-          # 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: 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++
          - 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
-            # TODO: enable fp16kernels after https://github.com/lancedb/lance/pull/3559
-            features: ","
+            features: "fp16kernels"
            pre_build: |-
              set -e &&
              apt-get update &&
@@ -170,8 +166,8 @@ jobs:
              set -e &&
              apk add protobuf-dev &&
              rustup target add aarch64-unknown-linux-musl &&
-              export CC="/aarch64-linux-musl-cross/bin/aarch64-linux-musl-gcc" &&
-              export CXX="/aarch64-linux-musl-cross/bin/aarch64-linux-musl-g++"
+              export CC_aarch64_unknown_linux_musl=aarch64-linux-musl-gcc &&
+              export CXX_aarch64_unknown_linux_musl=aarch64-linux-musl-g++
    name: build - ${{ matrix.settings.target }}
    runs-on: ${{ matrix.settings.host }}
    defaults:
@@ -535,6 +531,12 @@ 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
@@ -8,6 +8,7 @@ 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
@@ -1816,27 +1816,30 @@ dependencies = [

 [[package]]
 name = "datafusion"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "eae420e7a5b0b7f1c39364cc76cbcd0f5fdc416b2514ae3847c2676bbd60702a"
+checksum = "914e6f9525599579abbd90b0f7a55afcaaaa40350b9e9ed52563f126dfe45fd3"
 dependencies = [
 "arrow",
- "arrow-array",
 "arrow-ipc",
 "arrow-schema",
 "async-trait",
 "bytes",
 "chrono",
 "datafusion-catalog",
+ "datafusion-catalog-listing",
 "datafusion-common",
 "datafusion-common-runtime",
+ "datafusion-datasource",
 "datafusion-execution",
 "datafusion-expr",
+ "datafusion-expr-common",
 "datafusion-functions",
 "datafusion-functions-aggregate",
 "datafusion-functions-nested",
 "datafusion-functions-table",
 "datafusion-functions-window",
+ "datafusion-macros",
 "datafusion-optimizer",
 "datafusion-physical-expr",
 "datafusion-physical-expr-common",
@@ -1844,14 +1847,13 @@ dependencies = [
 "datafusion-physical-plan",
 "datafusion-sql",
 "futures",
- "glob",
 "itertools 0.14.0",
 "log",
 "object_store",
 "parking_lot",
 "rand 0.8.5",
 "regex",
- "sqlparser 0.53.0",
+ "sqlparser 0.54.0",
 "tempfile",
 "tokio",
 "url",
@@ -1860,9 +1862,9 @@ dependencies = [

 [[package]]
 name = "datafusion-catalog"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6f27987bc22b810939e8dfecc55571e9d50355d6ea8ec1c47af8383a76a6d0e1"
+checksum = "998a6549e6ee4ee3980e05590b2960446a56b343ea30199ef38acd0e0b9036e2"
 dependencies = [
 "arrow",
 "async-trait",
@@ -1876,21 +1878,39 @@ dependencies = [
 "itertools 0.14.0",
 "log",
 "parking_lot",
- "sqlparser 0.53.0",
+]
+
+[[package]]
+name = "datafusion-catalog-listing"
+version = "46.0.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "a5ac10096a5b3c0d8a227176c0e543606860842e943594ccddb45cf42a526e43"
+dependencies = [
+ "arrow",
+ "async-trait",
+ "datafusion-catalog",
+ "datafusion-common",
+ "datafusion-datasource",
+ "datafusion-execution",
+ "datafusion-expr",
+ "datafusion-physical-expr",
+ "datafusion-physical-expr-common",
+ "datafusion-physical-plan",
+ "futures",
+ "log",
+ "object_store",
+ "tokio",
 ]

 [[package]]
 name = "datafusion-common"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e3f6d5b8c9408cc692f7c194b8aa0c0f9b253e065a8d960ad9cdc2a13e697602"
+checksum = "1f53d7ec508e1b3f68bd301cee3f649834fad51eff9240d898a4b2614cfd0a7a"
 dependencies = [
 "ahash",
 "arrow",
- "arrow-array",
- "arrow-buffer",
 "arrow-ipc",
- "arrow-schema",
 "base64 0.22.1",
 "half",
 "hashbrown 0.14.5",
@@ -1899,32 +1919,60 @@ dependencies = [
 "log",
 "object_store",
 "paste",
- "sqlparser 0.53.0",
+ "sqlparser 0.54.0",
 "tokio",
 "web-time",
 ]

 [[package]]
 name = "datafusion-common-runtime"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0d4603c8e8a4baf77660ab7074cc66fc15cc8a18f2ce9dfadb755fc6ee294e48"
+checksum = "e0fcf41523b22e14cc349b01526e8b9f59206653037f2949a4adbfde5f8cb668"
 dependencies = [
 "log",
 "tokio",
 ]

 [[package]]
-name = "datafusion-doc"
-version = "45.0.0"
+name = "datafusion-datasource"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e5bf4bc68623a5cf231eed601ed6eb41f46a37c4d15d11a0bff24cbc8396cd66"
+checksum = "cf7f37ad8b6e88b46c7eeab3236147d32ea64b823544f498455a8d9042839c92"
+dependencies = [
+ "arrow",
+ "async-trait",
+ "bytes",
+ "chrono",
+ "datafusion-catalog",
+ "datafusion-common",
+ "datafusion-common-runtime",
+ "datafusion-execution",
+ "datafusion-expr",
+ "datafusion-physical-expr",
+ "datafusion-physical-expr-common",
+ "datafusion-physical-plan",
+ "futures",
+ "glob",
+ "itertools 0.14.0",
+ "log",
+ "object_store",
+ "rand 0.8.5",
+ "tokio",
+ "url",
+]
+
+[[package]]
+name = "datafusion-doc"
+version = "46.0.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7db7a0239fd060f359dc56c6e7db726abaa92babaed2fb2e91c3a8b2fff8b256"

 [[package]]
 name = "datafusion-execution"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "88b491c012cdf8e051053426013429a76f74ee3c2db68496c79c323ca1084d27"
+checksum = "0938f9e5b6bc5782be4111cdfb70c02b7b5451bf34fd57e4de062a7f7c4e31f1"
 dependencies = [
 "arrow",
 "dashmap",
@@ -1941,9 +1989,9 @@ dependencies = [

 [[package]]
 name = "datafusion-expr"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e5a181408d4fc5dc22f9252781a8f39f2d0e5d1b33ec9bde242844980a2689c1"
+checksum = "b36c28b00b00019a8695ad7f1a53ee1673487b90322ecbd604e2cf32894eb14f"
 dependencies = [
 "arrow",
 "chrono",
@@ -1956,26 +2004,27 @@ dependencies = [
 "indexmap 2.8.0",
 "paste",
 "serde_json",
- "sqlparser 0.53.0",
+ "sqlparser 0.54.0",
 ]

 [[package]]
 name = "datafusion-expr-common"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d1129b48e8534d8c03c6543bcdccef0b55c8ac0c1272a15a56c67068b6eb1885"
+checksum = "18f0a851a436c5a2139189eb4617a54e6a9ccb9edc96c4b3c83b3bb7c58b950e"
 dependencies = [
 "arrow",
 "datafusion-common",
+ "indexmap 2.8.0",
 "itertools 0.14.0",
 "paste",
 ]

 [[package]]
 name = "datafusion-functions"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6125874e4856dfb09b59886784fcb74cde5cfc5930b3a80a1a728ef7a010df6b"
+checksum = "e3196e37d7b65469fb79fee4f05e5bb58a456831035f9a38aa5919aeb3298d40"
 dependencies = [
 "arrow",
 "arrow-buffer",
@@ -1989,7 +2038,6 @@ dependencies = [
 "datafusion-expr",
 "datafusion-expr-common",
 "datafusion-macros",
- "hashbrown 0.14.5",
 "hex",
 "itertools 0.14.0",
 "log",
@@ -2003,14 +2051,12 @@ dependencies = [

 [[package]]
 name = "datafusion-functions-aggregate"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "f3add7b1d3888e05e7c95f2b281af900ca69ebdcb21069ba679b33bde8b3b9d6"
+checksum = "adfc2d074d5ee4d9354fdcc9283d5b2b9037849237ddecb8942a29144b77ca05"
 dependencies = [
 "ahash",
 "arrow",
- "arrow-buffer",
- "arrow-schema",
 "datafusion-common",
 "datafusion-doc",
 "datafusion-execution",
@@ -2026,9 +2072,9 @@ dependencies = [

 [[package]]
 name = "datafusion-functions-aggregate-common"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6e18baa4cfc3d2f144f74148ed68a1f92337f5072b6dde204a0dbbdf3324989c"
+checksum = "1cbceba0f98d921309a9121b702bcd49289d383684cccabf9a92cda1602f3bbb"
 dependencies = [
 "ahash",
 "arrow",
@@ -2039,15 +2085,12 @@ dependencies = [

 [[package]]
 name = "datafusion-functions-nested"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3ec5ee8cecb0dc370291279673097ddabec03a011f73f30d7f1096457127e03e"
+checksum = "170e27ce4baa27113ddf5f77f1a7ec484b0dbeda0c7abbd4bad3fc609c8ab71a"
 dependencies = [
 "arrow",
- "arrow-array",
- "arrow-buffer",
 "arrow-ord",
- "arrow-schema",
 "datafusion-common",
 "datafusion-doc",
 "datafusion-execution",
@@ -2063,9 +2106,9 @@ dependencies = [

 [[package]]
 name = "datafusion-functions-table"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2c403ddd473bbb0952ba880008428b3c7febf0ed3ce1eec35a205db20efb2a36"
+checksum = "7d3a06a7f0817ded87b026a437e7e51de7f59d48173b0a4e803aa896a7bd6bb5"
 dependencies = [
 "arrow",
 "async-trait",
@@ -2079,9 +2122,9 @@ dependencies = [

 [[package]]
 name = "datafusion-functions-window"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "1ab18c2fb835614d06a75f24a9e09136d3a8c12a92d97c95a6af316a1787a9c5"
+checksum = "d6c608b66496a1e05e3d196131eb9bebea579eed1f59e88d962baf3dda853bc6"
 dependencies = [
 "datafusion-common",
 "datafusion-doc",
@@ -2096,9 +2139,9 @@ dependencies = [

 [[package]]
 name = "datafusion-functions-window-common"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a77b73bc15e7d1967121fdc7a55d819bfb9d6c03766a6c322247dce9094a53a4"
+checksum = "da2f9d83348957b4ad0cd87b5cb9445f2651863a36592fe5484d43b49a5f8d82"
 dependencies = [
 "datafusion-common",
 "datafusion-physical-expr-common",
@@ -2106,9 +2149,9 @@ dependencies = [

 [[package]]
 name = "datafusion-macros"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "09369b8d962291e808977cf94d495fd8b5b38647232d7ef562c27ac0f495b0af"
+checksum = "4800e1ff7ecf8f310887e9b54c9c444b8e215ccbc7b21c2f244cfae373b1ece7"
 dependencies = [
 "datafusion-expr",
 "quote",
@@ -2117,9 +2160,9 @@ dependencies = [

 [[package]]
 name = "datafusion-optimizer"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "2403a7e4a84637f3de7d8d4d7a9ccc0cc4be92d89b0161ba3ee5be82f0531c54"
+checksum = "971c51c54cd309001376fae752fb15a6b41750b6d1552345c46afbfb6458801b"
 dependencies = [
 "arrow",
 "chrono",
@@ -2135,15 +2178,12 @@ dependencies = [

 [[package]]
 name = "datafusion-physical-expr"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "86ff72ac702b62dbf2650c4e1d715ebd3e4aab14e3885e72e8549e250307347c"
+checksum = "e1447c2c6bc8674a16be4786b4abf528c302803fafa186aa6275692570e64d85"
 dependencies = [
 "ahash",
 "arrow",
- "arrow-array",
- "arrow-buffer",
- "arrow-schema",
 "datafusion-common",
 "datafusion-expr",
 "datafusion-expr-common",
@@ -2160,13 +2200,12 @@ dependencies = [

 [[package]]
 name = "datafusion-physical-expr-common"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "60982b7d684e25579ee29754b4333057ed62e2cc925383c5f0bd8cab7962f435"
+checksum = "69f8c25dcd069073a75b3d2840a79d0f81e64bdd2c05f2d3d18939afb36a7dcb"
 dependencies = [
 "ahash",
 "arrow",
- "arrow-buffer",
 "datafusion-common",
 "datafusion-expr-common",
 "hashbrown 0.14.5",
@@ -2175,12 +2214,11 @@ dependencies = [

 [[package]]
 name = "datafusion-physical-optimizer"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ac5e85c189d5238a5cf181a624e450c4cd4c66ac77ca551d6f3ff9080bac90bb"
+checksum = "68da5266b5b9847c11d1b3404ee96b1d423814e1973e1ad3789131e5ec912763"
 dependencies = [
 "arrow",
- "arrow-schema",
 "datafusion-common",
 "datafusion-execution",
 "datafusion-expr",
@@ -2188,22 +2226,18 @@ dependencies = [
 "datafusion-physical-expr",
 "datafusion-physical-expr-common",
 "datafusion-physical-plan",
- "futures",
 "itertools 0.14.0",
 "log",
- "url",
 ]

 [[package]]
 name = "datafusion-physical-plan"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c36bf163956d7e2542657c78b3383fdc78f791317ef358a359feffcdb968106f"
+checksum = "88cc160df00e413e370b3b259c8ea7bfbebc134d32de16325950e9e923846b7f"
 dependencies = [
 "ahash",
 "arrow",
- "arrow-array",
- "arrow-buffer",
 "arrow-ord",
 "arrow-schema",
 "async-trait",
@@ -2228,20 +2262,18 @@ dependencies = [

 [[package]]
 name = "datafusion-sql"
-version = "45.0.0"
+version = "46.0.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e13caa4daede211ecec53c78b13c503b592794d125f9a3cc3afe992edf9e7f43"
+checksum = "325a212b67b677c0eb91447bf9a11b630f9fc4f62d8e5d145bf859f5a6b29e64"
 dependencies = [
 "arrow",
- "arrow-array",
- "arrow-schema",
 "bigdecimal",
 "datafusion-common",
 "datafusion-expr",
 "indexmap 2.8.0",
 "log",
 "regex",
- "sqlparser 0.53.0",
+ "sqlparser 0.54.0",
 ]

 [[package]]
@@ -2687,12 +2719,21 @@ checksum = "42703706b716c37f96a77aea830392ad231f44c9e9a67872fa5548707e11b11c"

 [[package]]
 name = "fsst"
-version = "0.25.1"
-source = "git+https://github.com/lancedb/lance.git?tag=v0.25.1-beta.3#33634d3b2e8f6a54e63a97721c7fcd31206e999a"
+version = "0.25.3"
+source = "git+https://github.com/lancedb/lance?tag=v0.25.3-beta.4#236c8f986ab9e2d478d0754fab6e8d2643c31247"
 dependencies = [
 "rand 0.8.5",
 ]

+[[package]]
+name = "fst"
+version = "0.4.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "7ab85b9b05e3978cc9a9cf8fea7f01b494e1a09ed3037e16ba39edc7a29eb61a"
+dependencies = [
+ "utf8-ranges",
+]
+
 [[package]]
 name = "funty"
 version = "2.0.0"
@@ -3666,8 +3707,8 @@ dependencies = [

 [[package]]
 name = "lance"
-version = "0.25.1"
-source = "git+https://github.com/lancedb/lance.git?tag=v0.25.1-beta.3#33634d3b2e8f6a54e63a97721c7fcd31206e999a"
+version = "0.25.3"
+source = "git+https://github.com/lancedb/lance?tag=v0.25.3-beta.4#236c8f986ab9e2d478d0754fab6e8d2643c31247"
 dependencies = [
 "arrow",
 "arrow-arith",
@@ -3726,8 +3767,8 @@ dependencies = [

 [[package]]
 name = "lance-arrow"
-version = "0.25.1"
-source = "git+https://github.com/lancedb/lance.git?tag=v0.25.1-beta.3#33634d3b2e8f6a54e63a97721c7fcd31206e999a"
+version = "0.25.3"
+source = "git+https://github.com/lancedb/lance?tag=v0.25.3-beta.4#236c8f986ab9e2d478d0754fab6e8d2643c31247"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -3744,8 +3785,8 @@ dependencies = [

 [[package]]
 name = "lance-core"
-version = "0.25.1"
-source = "git+https://github.com/lancedb/lance.git?tag=v0.25.1-beta.3#33634d3b2e8f6a54e63a97721c7fcd31206e999a"
+version = "0.25.3"
+source = "git+https://github.com/lancedb/lance?tag=v0.25.3-beta.4#236c8f986ab9e2d478d0754fab6e8d2643c31247"
 dependencies = [
 "arrow-array",
 "arrow-buffer",
@@ -3781,8 +3822,8 @@ dependencies = [

 [[package]]
 name = "lance-datafusion"
-version = "0.25.1"
-source = "git+https://github.com/lancedb/lance.git?tag=v0.25.1-beta.3#33634d3b2e8f6a54e63a97721c7fcd31206e999a"
+version = "0.25.3"
+source = "git+https://github.com/lancedb/lance?tag=v0.25.3-beta.4#236c8f986ab9e2d478d0754fab6e8d2643c31247"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -3798,6 +3839,7 @@ dependencies = [
 "futures",
 "lance-arrow",
 "lance-core",
+ "lance-datagen",
 "lazy_static",
 "log",
 "prost",
@@ -3806,10 +3848,26 @@ dependencies = [
 "tracing",
 ]

+[[package]]
+name = "lance-datagen"
+version = "0.25.3"
+source = "git+https://github.com/lancedb/lance?tag=v0.25.3-beta.4#236c8f986ab9e2d478d0754fab6e8d2643c31247"
+dependencies = [
+ "arrow",
+ "arrow-array",
+ "arrow-cast",
+ "arrow-schema",
+ "chrono",
+ "futures",
+ "hex",
+ "rand 0.8.5",
+ "rand_xoshiro",
+]
+
 [[package]]
 name = "lance-encoding"
-version = "0.25.1"
-source = "git+https://github.com/lancedb/lance.git?tag=v0.25.1-beta.3#33634d3b2e8f6a54e63a97721c7fcd31206e999a"
+version = "0.25.3"
+source = "git+https://github.com/lancedb/lance?tag=v0.25.3-beta.4#236c8f986ab9e2d478d0754fab6e8d2643c31247"
 dependencies = [
 "arrayref",
 "arrow",
@@ -3832,6 +3890,7 @@ dependencies = [
 "lance-core",
 "lazy_static",
 "log",
+ "lz4",
 "num-traits",
 "paste",
 "prost",
@@ -3847,8 +3906,8 @@ dependencies = [

 [[package]]
 name = "lance-file"
-version = "0.25.1"
-source = "git+https://github.com/lancedb/lance.git?tag=v0.25.1-beta.3#33634d3b2e8f6a54e63a97721c7fcd31206e999a"
+version = "0.25.3"
+source = "git+https://github.com/lancedb/lance?tag=v0.25.3-beta.4#236c8f986ab9e2d478d0754fab6e8d2643c31247"
 dependencies = [
 "arrow-arith",
 "arrow-array",
@@ -3882,8 +3941,8 @@ dependencies = [

 [[package]]
 name = "lance-index"
-version = "0.25.1"
-source = "git+https://github.com/lancedb/lance.git?tag=v0.25.1-beta.3#33634d3b2e8f6a54e63a97721c7fcd31206e999a"
+version = "0.25.3"
+source = "git+https://github.com/lancedb/lance?tag=v0.25.3-beta.4#236c8f986ab9e2d478d0754fab6e8d2643c31247"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -3902,6 +3961,7 @@ dependencies = [
 "datafusion-sql",
 "deepsize",
 "dirs",
+ "fst",
 "futures",
 "half",
 "itertools 0.13.0",
@@ -3935,8 +3995,8 @@ dependencies = [

 [[package]]
 name = "lance-io"
-version = "0.25.1"
-source = "git+https://github.com/lancedb/lance.git?tag=v0.25.1-beta.3#33634d3b2e8f6a54e63a97721c7fcd31206e999a"
+version = "0.25.3"
+source = "git+https://github.com/lancedb/lance?tag=v0.25.3-beta.4#236c8f986ab9e2d478d0754fab6e8d2643c31247"
 dependencies = [
 "arrow",
 "arrow-arith",
@@ -3974,8 +4034,8 @@ dependencies = [

 [[package]]
 name = "lance-linalg"
-version = "0.25.1"
-source = "git+https://github.com/lancedb/lance.git?tag=v0.25.1-beta.3#33634d3b2e8f6a54e63a97721c7fcd31206e999a"
+version = "0.25.3"
+source = "git+https://github.com/lancedb/lance?tag=v0.25.3-beta.4#236c8f986ab9e2d478d0754fab6e8d2643c31247"
 dependencies = [
 "arrow-array",
 "arrow-ord",
@@ -3998,8 +4058,8 @@ dependencies = [

 [[package]]
 name = "lance-table"
-version = "0.25.1"
-source = "git+https://github.com/lancedb/lance.git?tag=v0.25.1-beta.3#33634d3b2e8f6a54e63a97721c7fcd31206e999a"
+version = "0.25.3"
+source = "git+https://github.com/lancedb/lance?tag=v0.25.3-beta.4#236c8f986ab9e2d478d0754fab6e8d2643c31247"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -4038,8 +4098,8 @@ dependencies = [

 [[package]]
 name = "lance-testing"
-version = "0.25.1"
-source = "git+https://github.com/lancedb/lance.git?tag=v0.25.1-beta.3#33634d3b2e8f6a54e63a97721c7fcd31206e999a"
+version = "0.25.3"
+source = "git+https://github.com/lancedb/lance?tag=v0.25.3-beta.4#236c8f986ab9e2d478d0754fab6e8d2643c31247"
 dependencies = [
 "arrow-array",
 "arrow-schema",
@@ -4050,7 +4110,7 @@ dependencies = [

 [[package]]
 name = "lancedb"
-version = "0.18.3-beta.0"
+version = "0.19.0-beta.5"
 dependencies = [
 "arrow",
 "arrow-array",
@@ -4137,7 +4197,7 @@ dependencies = [

 [[package]]
 name = "lancedb-node"
-version = "0.18.3-beta.0"
+version = "0.19.0-beta.5"
 dependencies = [
 "arrow-array",
 "arrow-ipc",
@@ -4162,7 +4222,7 @@ dependencies = [

 [[package]]
 name = "lancedb-nodejs"
-version = "0.18.3-beta.0"
+version = "0.19.0-beta.5"
 dependencies = [
 "arrow-array",
 "arrow-ipc",
@@ -4180,7 +4240,7 @@ dependencies = [

 [[package]]
 name = "lancedb-python"
-version = "0.21.3-beta.0"
+version = "0.22.0-beta.5"
 dependencies = [
 "arrow",
 "env_logger",
@@ -5895,6 +5955,15 @@ dependencies = [
 "rand 0.8.5",
 ]

+[[package]]
+name = "rand_xoshiro"
+version = "0.6.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "6f97cdb2a36ed4183de61b2f824cc45c9f1037f28afe0a322e9fff4c108b5aaa"
+dependencies = [
+ "rand_core 0.6.4",
+]
+
 [[package]]
 name = "random_word"
 version = "0.4.3"
@@ -6781,11 +6850,12 @@ dependencies = [

 [[package]]
 name = "sqlparser"
-version = "0.53.0"
+version = "0.54.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "05a528114c392209b3264855ad491fcce534b94a38771b0a0b97a79379275ce8"
+checksum = "c66e3b7374ad4a6af849b08b3e7a6eda0edbd82f0fd59b57e22671bf16979899"
 dependencies = [
 "log",
+ "recursive",
 "sqlparser_derive",
 ]

@@ -7636,7 +7706,9 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "458f7a779bf54acc9f347480ac654f68407d3aab21269a6e3c9f922acd9e2da9"
 dependencies = [
 "getrandom 0.3.2",
+ "js-sys",
 "serde",
+ "wasm-bindgen",
 ]

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

 [workspace.dependencies]
-lance = { "version" = "=0.25.1", "features" = [
+lance = { "version" = "=0.25.3", "features" = [
    "dynamodb",
-], tag = "v0.25.1-beta.3", git = "https://github.com/lancedb/lance.git" }
-lance-io = { version = "=0.25.1", tag = "v0.25.1-beta.3", git = "https://github.com/lancedb/lance.git" }
-lance-index = { version = "=0.25.1", tag = "v0.25.1-beta.3", git = "https://github.com/lancedb/lance.git" }
-lance-linalg = { version = "=0.25.1", tag = "v0.25.1-beta.3", git = "https://github.com/lancedb/lance.git" }
-lance-table = { version = "=0.25.1", tag = "v0.25.1-beta.3", git = "https://github.com/lancedb/lance.git" }
-lance-testing = { version = "=0.25.1", tag = "v0.25.1-beta.3", git = "https://github.com/lancedb/lance.git" }
-lance-datafusion = { version = "=0.25.1", tag = "v0.25.1-beta.3", git = "https://github.com/lancedb/lance.git" }
-lance-encoding = { version = "=0.25.1", tag = "v0.25.1-beta.3", git = "https://github.com/lancedb/lance.git" }
+], tag = "v0.25.3-beta.4", git = "https://github.com/lancedb/lance" }
+lance-io = { version = "=0.25.3", tag = "v0.25.3-beta.4", git = "https://github.com/lancedb/lance" }
+lance-index = { version = "=0.25.3", tag = "v0.25.3-beta.4", git = "https://github.com/lancedb/lance" }
+lance-linalg = { version = "=0.25.3", tag = "v0.25.3-beta.4", git = "https://github.com/lancedb/lance" }
+lance-table = { version = "=0.25.3", tag = "v0.25.3-beta.4", git = "https://github.com/lancedb/lance" }
+lance-testing = { version = "=0.25.3", tag = "v0.25.3-beta.4", git = "https://github.com/lancedb/lance" }
+lance-datafusion = { version = "=0.25.3", tag = "v0.25.3-beta.4", git = "https://github.com/lancedb/lance" }
+lance-encoding = { version = "=0.25.3", tag = "v0.25.3-beta.4", git = "https://github.com/lancedb/lance" }
 # 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 = "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"
+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"
 env_logger = "0.11"
 half = { "version" = "=2.4.1", default-features = false, features = [
    "num-traits",
--- a/docs/src/js/classes/BoostQuery.md
+++ b/docs/src/js/classes/BoostQuery.md
@@ -0,0 +1,75 @@
+[**@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,
+   negativeBoost): BoostQuery
+```
+
+Creates an instance of BoostQuery.
+
+#### 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.
+
+* **negativeBoost**: `number`
+    The factor by which the negative query reduces the score.
+
+#### Returns
+
+[`BoostQuery`](BoostQuery.md)
+
+## Methods
+
+### queryType()
+
+```ts
+queryType(): FullTextQueryType
+```
+
+#### Returns
+
+[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
+
+#### Implementation of
+
+[`FullTextQuery`](../interfaces/FullTextQuery.md).[`queryType`](../interfaces/FullTextQuery.md#querytype)
+
+***
+
+### toDict()
+
+```ts
+toDict(): Record<string, unknown>
+```
+
+#### Returns
+
+`Record`&lt;`string`, `unknown`&gt;
+
+#### Implementation of
+
+[`FullTextQuery`](../interfaces/FullTextQuery.md).[`toDict`](../interfaces/FullTextQuery.md#todict)
--- a/docs/src/js/classes/MatchQuery.md
+++ b/docs/src/js/classes/MatchQuery.md
@@ -0,0 +1,83 @@
+[**@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,
+   boost,
+   fuzziness,
+   maxExpansions): 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.
+
+* **boost**: `number` = `1.0`
+    (Optional) The boost factor to influence the relevance score of this query. Default is `1.0`.
+
+* **fuzziness**: `number` = `0`
+    (Optional) The allowed edit distance for fuzzy matching. Default is `0`.
+
+* **maxExpansions**: `number` = `50`
+    (Optional) The maximum number of terms to consider for fuzzy matching. Default is `50`.
+
+#### Returns
+
+[`MatchQuery`](MatchQuery.md)
+
+## Methods
+
+### queryType()
+
+```ts
+queryType(): FullTextQueryType
+```
+
+#### Returns
+
+[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
+
+#### Implementation of
+
+[`FullTextQuery`](../interfaces/FullTextQuery.md).[`queryType`](../interfaces/FullTextQuery.md#querytype)
+
+***
+
+### toDict()
+
+```ts
+toDict(): Record<string, unknown>
+```
+
+#### Returns
+
+`Record`&lt;`string`, `unknown`&gt;
+
+#### Implementation of
+
+[`FullTextQuery`](../interfaces/FullTextQuery.md).[`toDict`](../interfaces/FullTextQuery.md#todict)
--- a/docs/src/js/classes/MultiMatchQuery.md
+++ b/docs/src/js/classes/MultiMatchQuery.md
@@ -0,0 +1,77 @@
+[**@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,
+   boosts): 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.
+
+* **boosts**: `number`[] = `...`
+    (Optional) An array of boost factors corresponding to each column. Default is an array of 1.0 for each column.
+    The `boosts` array should have the same length as `columns`. If not provided, all columns will have a default boost of 1.0.
+    If the length of `boosts` is less than `columns`, it will be padded with 1.0s.
+
+#### Returns
+
+[`MultiMatchQuery`](MultiMatchQuery.md)
+
+## Methods
+
+### queryType()
+
+```ts
+queryType(): FullTextQueryType
+```
+
+#### Returns
+
+[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
+
+#### Implementation of
+
+[`FullTextQuery`](../interfaces/FullTextQuery.md).[`queryType`](../interfaces/FullTextQuery.md#querytype)
+
+***
+
+### toDict()
+
+```ts
+toDict(): Record<string, unknown>
+```
+
+#### Returns
+
+`Record`&lt;`string`, `unknown`&gt;
+
+#### Implementation of
+
+[`FullTextQuery`](../interfaces/FullTextQuery.md).[`toDict`](../interfaces/FullTextQuery.md#todict)
--- a/docs/src/js/classes/PhraseQuery.md
+++ b/docs/src/js/classes/PhraseQuery.md
@@ -0,0 +1,69 @@
+[**@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
+```
+
+#### Returns
+
+[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
+
+#### Implementation of
+
+[`FullTextQuery`](../interfaces/FullTextQuery.md).[`queryType`](../interfaces/FullTextQuery.md#querytype)
+
+***
+
+### toDict()
+
+```ts
+toDict(): Record<string, unknown>
+```
+
+#### Returns
+
+`Record`&lt;`string`, `unknown`&gt;
+
+#### Implementation of
+
+[`FullTextQuery`](../interfaces/FullTextQuery.md).[`toDict`](../interfaces/FullTextQuery.md#todict)
--- a/docs/src/js/classes/Query.md
+++ b/docs/src/js/classes/Query.md
@@ -206,7 +206,7 @@ fullTextSearch(query, options?): this

 #### Parameters

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

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

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

 #### Parameters

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

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

--- a/docs/src/js/classes/QueryBase.md
+++ b/docs/src/js/classes/QueryBase.md
@@ -192,7 +192,7 @@ fullTextSearch(query, options?): this

 #### Parameters

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

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

--- a/docs/src/js/classes/VectorQuery.md
+++ b/docs/src/js/classes/VectorQuery.md
@@ -347,7 +347,7 @@ fullTextSearch(query, options?): this

 #### Parameters

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

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

--- a/docs/src/js/enumerations/FullTextQueryType.md
+++ b/docs/src/js/enumerations/FullTextQueryType.md
@@ -0,0 +1,46 @@
+[**@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
@@ -9,12 +9,20 @@
 - [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)
@@ -33,6 +41,7 @@
 - [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
@@ -0,0 +1,35 @@
+[**@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
+```
+
+#### Returns
+
+[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
+
+***
+
+### toDict()
+
+```ts
+toDict(): Record<string, unknown>
+```
+
+#### Returns
+
+`Record`&lt;`string`, `unknown`&gt;
--- a/docs/src/js/interfaces/QueryExecutionOptions.md
+++ b/docs/src/js/interfaces/QueryExecutionOptions.md
@@ -20,3 +20,13 @@ 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/java/core/pom.xml
+++ b/java/core/pom.xml
@@ -8,7 +8,7 @@
    <parent>
        <groupId>com.lancedb</groupId>
        <artifactId>lancedb-parent</artifactId>
-        <version>0.18.3-beta.0</version>
+        <version>0.19.0-beta.5</version>
        <relativePath>../pom.xml</relativePath>
    </parent>

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

    <groupId>com.lancedb</groupId>
    <artifactId>lancedb-parent</artifactId>
-    <version>0.18.3-beta.0</version>
+    <version>0.19.0-beta.5</version>
    <packaging>pom</packaging>

    <name>LanceDB Parent</name>
--- a/node/package-lock.json
+++ b/node/package-lock.json
@@ -1,12 +1,12 @@
 {
  "name": "vectordb",
-  "version": "0.18.3-beta.0",
+  "version": "0.19.0-beta.5",
  "lockfileVersion": 3,
  "requires": true,
  "packages": {
    "": {
      "name": "vectordb",
-      "version": "0.18.3-beta.0",
+      "version": "0.19.0-beta.5",
      "cpu": [
        "x64",
        "arm64"
@@ -52,11 +52,11 @@
        "uuid": "^9.0.0"
      },
      "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.18.3-beta.0",
-        "@lancedb/vectordb-darwin-x64": "0.18.3-beta.0",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.18.3-beta.0",
-        "@lancedb/vectordb-linux-x64-gnu": "0.18.3-beta.0",
-        "@lancedb/vectordb-win32-x64-msvc": "0.18.3-beta.0"
+        "@lancedb/vectordb-darwin-arm64": "0.19.0-beta.5",
+        "@lancedb/vectordb-darwin-x64": "0.19.0-beta.5",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.19.0-beta.5",
+        "@lancedb/vectordb-linux-x64-gnu": "0.19.0-beta.5",
+        "@lancedb/vectordb-win32-x64-msvc": "0.19.0-beta.5"
      },
      "peerDependencies": {
        "@apache-arrow/ts": "^14.0.2",
@@ -327,9 +327,9 @@
      }
    },
    "node_modules/@lancedb/vectordb-darwin-arm64": {
-      "version": "0.18.3-beta.0",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.18.3-beta.0.tgz",
-      "integrity": "sha512-dhJ5VlXV2N/L67mIpTSePhb8krX0FyQgpuz3I+4T4vYuU5JEF3cmedQ5TF5+3cGJhZim4PHRYLkfgCyTlxcqUg==",
+      "version": "0.19.0-beta.5",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.19.0-beta.5.tgz",
+      "integrity": "sha512-NuJVGaV4b6XgH3dlkCEquvtGM1cY5sIJE5M/LgJ3HYYvAbco/seBQM5AHTV/7CULoPEY9eQeJZOj9fWP5oQLYQ==",
      "cpu": [
        "arm64"
      ],
@@ -340,9 +340,9 @@
      ]
    },
    "node_modules/@lancedb/vectordb-darwin-x64": {
-      "version": "0.18.3-beta.0",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.18.3-beta.0.tgz",
-      "integrity": "sha512-SHqPkuyfe87d5skf9GERzdeu6AKvVIbXMUwl5N+dVrE7HH6qiuP2HvOmiyHS2lJFgo0Ph8jSBVzPDxxtjF36Dg==",
+      "version": "0.19.0-beta.5",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.19.0-beta.5.tgz",
+      "integrity": "sha512-hbadwvQcUgKJfluUHhN+mx+XeFRwTuh9mD0L3Tf3t3BkDTxyHpEG5WNgOpWrh6e1RU6zW54CoCyQuSEaVqGgGw==",
      "cpu": [
        "x64"
      ],
@@ -353,9 +353,9 @@
      ]
    },
    "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.18.3-beta.0",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.18.3-beta.0.tgz",
-      "integrity": "sha512-ohnWsV1n9cxL5ik/GGL4FdQ04Ff9REELcNb1zgmJYyEfwyc6TH9m5HdySO/1ACPZJiLbML4gSvZ10J0Zyb+2SA==",
+      "version": "0.19.0-beta.5",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.19.0-beta.5.tgz",
+      "integrity": "sha512-fu/EOYLr3mx76/SP4dEgbq0vSYHfuTf68lVl5/tL6eIb1Purz42l22+jNKLJ/S3Plase2SkXdxyY90K2Y/CvSg==",
      "cpu": [
        "arm64"
      ],
@@ -366,9 +366,9 @@
      ]
    },
    "node_modules/@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.18.3-beta.0",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.18.3-beta.0.tgz",
-      "integrity": "sha512-nhbW2CKaBSUesiYCPBd9fAsDYIJLadlGsrb2gfjODlFy+2Lpnbz6T9SuV7dNqj6KBw+KHhaRhLqta7tyMZm/EA==",
+      "version": "0.19.0-beta.5",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.19.0-beta.5.tgz",
+      "integrity": "sha512-pzb8fl5M8155sc/mEFnKmuh9rCfQohHBlb+j+5qNMe84AyygQ8Me1H3b1h9fOkUPu2Y168zYfuGkjNv4Bjm9eA==",
      "cpu": [
        "x64"
      ],
@@ -379,9 +379,9 @@
      ]
    },
    "node_modules/@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.18.3-beta.0",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.18.3-beta.0.tgz",
-      "integrity": "sha512-VE4TvMdZ7DIrTC8VYylGxEcH4h2UEejSwGX4PxRzrN9QsCQ4m4pOh3L/UguSO3g+Y1QEaGE20iWQoX6wgSEUhA==",
+      "version": "0.19.0-beta.5",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.19.0-beta.5.tgz",
+      "integrity": "sha512-5z6BSfTuZYJdDL2wwRrEQlnfluahzaUH2U7vj3i4ik4zaAwvaYcrjmdYCTLRYhFscUqzxd2pVFHbfRYe+maYzA==",
      "cpu": [
        "x64"
      ],
@@ -1184,9 +1184,10 @@
      }
    },
    "node_modules/axios": {
-      "version": "1.7.7",
-      "resolved": "https://registry.npmjs.org/axios/-/axios-1.7.7.tgz",
-      "integrity": "sha512-S4kL7XrjgBmvdGut0sN3yJxqYzrDOnivkBiN0OFs6hLiUam3UPvswUo0kqGyhqUZGEOytHyumEdXsAkgCOUf3Q==",
+      "version": "1.8.4",
+      "resolved": "https://registry.npmjs.org/axios/-/axios-1.8.4.tgz",
+      "integrity": "sha512-eBSYY4Y68NNlHbHBMdeDmKNtDgXWhQsJcGqzO3iLUM0GraQFSS9cVgPX5I9b3lbdFKyYoAEGAZF1DwhTaljNAw==",
+      "license": "MIT",
      "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": "0.18.3-beta.0",
+  "version": "0.19.0-beta.5",
  "description": " Serverless, low-latency vector database for AI applications",
  "private": false,
  "main": "dist/index.js",
@@ -89,10 +89,10 @@
    }
  },
  "optionalDependencies": {
-    "@lancedb/vectordb-darwin-x64": "0.18.3-beta.0",
-    "@lancedb/vectordb-darwin-arm64": "0.18.3-beta.0",
-    "@lancedb/vectordb-linux-x64-gnu": "0.18.3-beta.0",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.18.3-beta.0",
-    "@lancedb/vectordb-win32-x64-msvc": "0.18.3-beta.0"
+    "@lancedb/vectordb-darwin-x64": "0.19.0-beta.5",
+    "@lancedb/vectordb-darwin-arm64": "0.19.0-beta.5",
+    "@lancedb/vectordb-linux-x64-gnu": "0.19.0-beta.5",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.19.0-beta.5",
+    "@lancedb/vectordb-win32-x64-msvc": "0.19.0-beta.5"
  }
 }
--- a/nodejs/Cargo.toml
+++ b/nodejs/Cargo.toml
@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.18.3-beta.0"
+version = "0.19.0-beta.5"
 license.workspace = true
 description.workspace = true
 repository.workspace = true
--- a/nodejs/test/table.test.ts
+++ b/nodejs/test/table.test.ts
@@ -867,6 +867,44 @@ 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(() => {
@@ -1266,6 +1304,27 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
      expect(results[0].text).toBe(data[0].text);
    });

+    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 () => {
      const db = await connect(tmpDir.name);
      const data = [
--- a/nodejs/lancedb/index.ts
+++ b/nodejs/lancedb/index.ts
@@ -47,6 +47,12 @@ export {
  QueryExecutionOptions,
  FullTextSearchOptions,
  RecordBatchIterator,
+  FullTextQuery,
+  MatchQuery,
+  PhraseQuery,
+  BoostQuery,
+  MultiMatchQuery,
+  FullTextQueryType,
 } from "./query";

 export {
--- a/nodejs/lancedb/query.ts
+++ b/nodejs/lancedb/query.ts
@@ -17,6 +17,7 @@ import {
  VectorQuery as NativeVectorQuery,
 } from "./native";
 import { Reranker } from "./rerankers";
+
 export class RecordBatchIterator implements AsyncIterator<RecordBatch> {
  private promisedInner?: Promise<NativeBatchIterator>;
  private inner?: NativeBatchIterator;
@@ -62,7 +63,7 @@ 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.inner.execute(this.options?.maxBatchLength, this.options?.timeoutMs),
    );
  }
 }
@@ -78,6 +79,11 @@ export interface QueryExecutionOptions {
   * in smaller chunks.
   */
  maxBatchLength?: number;
+
+  /**
+   * Timeout for query execution in milliseconds
+   */
+  timeoutMs?: number;
 }

 /**
@@ -152,7 +158,7 @@ export class QueryBase<NativeQueryType extends NativeQuery | NativeVectorQuery>
  }

  fullTextSearch(
-    query: string,
+    query: string | FullTextQuery,
    options?: Partial<FullTextSearchOptions>,
  ): this {
    let columns: string[] | null = null;
@@ -164,9 +170,18 @@ export class QueryBase<NativeQueryType extends NativeQuery | NativeVectorQuery>
      }
    }

-    this.doCall((inner: NativeQueryType) =>
-      inner.fullTextSearch(query, columns),
-    );
+    this.doCall((inner: NativeQueryType) => {
+      if (typeof query === "string") {
+        inner.fullTextSearch({
+          query: query,
+          columns: columns,
+        });
+      } else {
+        // If query is a FullTextQuery object, convert it to a dict
+        const queryObj = query.toDict();
+        inner.fullTextSearch(queryObj);
+      }
+    });
    return this;
  }

@@ -273,9 +288,11 @@ 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));
+      return this.inner.then((inner) =>
+        inner.execute(options?.maxBatchLength, options?.timeoutMs),
+      );
    } else {
-      return this.inner.execute(options?.maxBatchLength);
+      return this.inner.execute(options?.maxBatchLength, options?.timeoutMs);
    }
  }

@@ -718,8 +735,167 @@ export class Query extends QueryBase<NativeQuery> {
    }
  }

-  nearestToText(query: string, columns?: string[]): Query {
-    this.doCall((inner) => inner.fullTextSearch(query, columns));
+  nearestToText(query: string | FullTextQuery, columns?: string[]): Query {
+    this.doCall((inner) => {
+      if (typeof query === "string") {
+        inner.fullTextSearch({
+          query: query,
+          columns: columns,
+        });
+      } else {
+        const queryObj = query.toDict();
+        inner.fullTextSearch(queryObj);
+      }
+    });
    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 {
+  queryType(): FullTextQueryType;
+  toDict(): Record<string, unknown>;
+}
+
+export class MatchQuery implements FullTextQuery {
+  /**
+   * Creates an instance of MatchQuery.
+   *
+   * @param query - The text query to search for.
+   * @param column - The name of the column to search within.
+   * @param boost - (Optional) The boost factor to influence the relevance score of this query. Default is `1.0`.
+   * @param fuzziness - (Optional) The allowed edit distance for fuzzy matching. Default is `0`.
+   * @param maxExpansions - (Optional) The maximum number of terms to consider for fuzzy matching. Default is `50`.
+   */
+  constructor(
+    private query: string,
+    private column: string,
+    private boost: number = 1.0,
+    private fuzziness: number = 0,
+    private maxExpansions: number = 50,
+  ) {}
+
+  queryType(): FullTextQueryType {
+    return FullTextQueryType.Match;
+  }
+
+  toDict(): Record<string, unknown> {
+    return {
+      [this.queryType()]: {
+        [this.column]: {
+          query: this.query,
+          boost: this.boost,
+          fuzziness: this.fuzziness,
+          // biome-ignore lint/style/useNamingConvention: use underscore for consistency with the other APIs
+          max_expansions: this.maxExpansions,
+        },
+      },
+    };
+  }
+}
+
+export class PhraseQuery implements FullTextQuery {
+  /**
+   * 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(
+    private query: string,
+    private column: string,
+  ) {}
+
+  queryType(): FullTextQueryType {
+    return FullTextQueryType.MatchPhrase;
+  }
+
+  toDict(): Record<string, unknown> {
+    return {
+      [this.queryType()]: {
+        [this.column]: this.query,
+      },
+    };
+  }
+}
+
+export class BoostQuery implements FullTextQuery {
+  /**
+   * Creates an instance of BoostQuery.
+   *
+   * @param positive - The positive query that boosts the relevance score.
+   * @param negative - The negative query that reduces the relevance score.
+   * @param negativeBoost - The factor by which the negative query reduces the score.
+   */
+  constructor(
+    private positive: FullTextQuery,
+    private negative: FullTextQuery,
+    private negativeBoost: number,
+  ) {}
+
+  queryType(): FullTextQueryType {
+    return FullTextQueryType.Boost;
+  }
+
+  toDict(): Record<string, unknown> {
+    return {
+      [this.queryType()]: {
+        positive: this.positive.toDict(),
+        negative: this.negative.toDict(),
+        // biome-ignore lint/style/useNamingConvention: use underscore for consistency with the other APIs
+        negative_boost: this.negativeBoost,
+      },
+    };
+  }
+}
+
+export class MultiMatchQuery implements FullTextQuery {
+  /**
+   * 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 boosts - (Optional) An array of boost factors corresponding to each column. Default is an array of 1.0 for each column.
+   *
+   * The `boosts` array should have the same length as `columns`. If not provided, all columns will have a default boost of 1.0.
+   * If the length of `boosts` is less than `columns`, it will be padded with 1.0s.
+   */
+  constructor(
+    private query: string,
+    private columns: string[],
+    private boosts: number[] = columns.map(() => 1.0),
+  ) {}
+
+  queryType(): FullTextQueryType {
+    return FullTextQueryType.MultiMatch;
+  }
+
+  toDict(): Record<string, unknown> {
+    return {
+      [this.queryType()]: {
+        query: this.query,
+        columns: this.columns,
+        boost: this.boosts,
+      },
+    };
+  }
+}
--- a/nodejs/npm/darwin-arm64/package.json
+++ b/nodejs/npm/darwin-arm64/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.18.3-beta.0",
+	"version": "0.19.0-beta.5",
 	"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 @@
 {
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 	"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 @@
 {
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+	"version": "0.19.0-beta.5",
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 	"cpu": ["arm64"],
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+++ b/nodejs/npm/linux-arm64-musl/package.json
@@ -1,6 +1,6 @@
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+	"version": "0.19.0-beta.5",
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 	"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 @@
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+++ b/nodejs/npm/linux-x64-musl/package.json
@@ -1,6 +1,6 @@
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+	"version": "0.19.0-beta.5",
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--- a/nodejs/npm/win32-arm64-msvc/package.json
+++ b/nodejs/npm/win32-arm64-msvc/package.json
@@ -1,6 +1,6 @@
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-  "version": "0.18.3-beta.0",
+  "version": "0.19.0-beta.5",
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--- a/nodejs/npm/win32-x64-msvc/package.json
+++ b/nodejs/npm/win32-x64-msvc/package.json
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+	"version": "0.19.0-beta.5",
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+++ b/nodejs/package-lock.json
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-        "@babel/traverse": "^7.23.7",
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--- a/nodejs/package.json
+++ b/nodejs/package.json
@@ -11,7 +11,7 @@
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  "main": "dist/index.js",
  "exports": {
    ".": "./dist/index.js",
@@ -29,6 +29,7 @@
        "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
@@ -3,7 +3,7 @@

 use std::sync::Arc;

-use lancedb::index::scalar::FullTextSearchQuery;
+use lancedb::index::scalar::{FtsQuery, FullTextSearchQuery, MatchQuery, PhraseQuery};
 use lancedb::query::ExecutableQuery;
 use lancedb::query::Query as LanceDbQuery;
 use lancedb::query::QueryBase;
@@ -18,7 +18,7 @@ use crate::error::NapiErrorExt;
 use crate::iterator::RecordBatchIterator;
 use crate::rerankers::Reranker;
 use crate::rerankers::RerankerCallbacks;
-use crate::util::parse_distance_type;
+use crate::util::{parse_distance_type, parse_fts_query};

 #[napi]
 pub struct Query {
@@ -38,9 +38,53 @@ impl Query {
    }

    #[napi]
-    pub fn full_text_search(&mut self, query: String, columns: Option<Vec<String>>) {
-        let query = FullTextSearchQuery::new(query).columns(columns);
+    pub fn full_text_search(&mut self, query: napi::JsUnknown) -> napi::Result<()> {
+        let query = unsafe { query.cast::<napi::JsObject>() };
+        let query = if let Some(query_text) = query.get::<_, String>("query").transpose() {
+            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
+                        ))
+                    })?;
+                }
+            }
+            query
+        } else if let Some(query) = query.get::<_, napi::JsObject>("query")? {
+            let query = parse_fts_query(&query)?;
+            FullTextSearchQuery::new_query(query)
+        } else {
+            return Err(napi::Error::from_reason(
+                "Invalid full text search query object".to_string(),
+            ));
+        };
+
        self.inner = self.inner.clone().full_text_search(query);
+        Ok(())
    }

    #[napi]
@@ -87,11 +131,15 @@ 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)
@@ -195,9 +243,53 @@ impl VectorQuery {
    }

    #[napi]
-    pub fn full_text_search(&mut self, query: String, columns: Option<Vec<String>>) {
-        let query = FullTextSearchQuery::new(query).columns(columns);
+    pub fn full_text_search(&mut self, query: napi::JsUnknown) -> napi::Result<()> {
+        let query = unsafe { query.cast::<napi::JsObject>() };
+        let query = if let Some(query_text) = query.get::<_, String>("query").transpose() {
+            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
+                        ))
+                    })?;
+                }
+            }
+            query
+        } else if let Some(query) = query.get::<_, napi::JsObject>("query")? {
+            let query = parse_fts_query(&query)?;
+            FullTextSearchQuery::new_query(query)
+        } else {
+            return Err(napi::Error::from_reason(
+                "Invalid full text search query object".to_string(),
+            ));
+        };
+
        self.inner = self.inner.clone().full_text_search(query);
+        Ok(())
    }

    #[napi]
@@ -242,11 +334,15 @@ 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)
--- a/nodejs/src/util.rs
+++ b/nodejs/src/util.rs
@@ -1,6 +1,7 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors

+use lancedb::index::scalar::{BoostQuery, FtsQuery, MatchQuery, MultiMatchQuery, PhraseQuery};
 use lancedb::DistanceType;

 pub fn parse_distance_type(distance_type: impl AsRef<str>) -> napi::Result<DistanceType> {
@@ -15,3 +16,144 @@ pub fn parse_distance_type(distance_type: impl AsRef<str>) -> napi::Result<Dista
        ))),
    }
 }
+
+pub fn parse_fts_query(query: &napi::JsObject) -> napi::Result<FtsQuery> {
+    let query_type = query
+        .get_property_names()?
+        .get_element::<napi::JsString>(0)?;
+    let query_type = query_type.into_utf8()?.into_owned()?;
+    let query_value =
+        query
+            .get::<_, napi::JsObject>(&query_type)?
+            .ok_or(napi::Error::from_reason(format!(
+                "query value {} not found",
+                query_type
+            )))?;
+
+    match query_type.as_str() {
+        "match" => {
+            let column = query_value
+                .get_property_names()?
+                .get_element::<napi::JsString>(0)?
+                .into_utf8()?
+                .into_owned()?;
+            let params =
+                query_value
+                    .get::<_, napi::JsObject>(&column)?
+                    .ok_or(napi::Error::from_reason(format!(
+                        "column {} not found",
+                        column
+                    )))?;
+
+            let query = params
+                .get::<_, napi::JsString>("query")?
+                .ok_or(napi::Error::from_reason("query not found"))?
+                .into_utf8()?
+                .into_owned()?;
+            let boost = params
+                .get::<_, napi::JsNumber>("boost")?
+                .ok_or(napi::Error::from_reason("boost not found"))?
+                .get_double()? as f32;
+            let fuzziness = params
+                .get::<_, napi::JsNumber>("fuzziness")?
+                .map(|f| f.get_uint32())
+                .transpose()?;
+            let max_expansions = params
+                .get::<_, napi::JsNumber>("max_expansions")?
+                .ok_or(napi::Error::from_reason("max_expansions not found"))?
+                .get_uint32()? as 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
+                .get_property_names()?
+                .get_element::<napi::JsString>(0)?
+                .into_utf8()?
+                .into_owned()?;
+            let query = query_value
+                .get::<_, napi::JsString>(&column)?
+                .ok_or(napi::Error::from_reason(format!(
+                    "column {} not found",
+                    column
+                )))?
+                .into_utf8()?
+                .into_owned()?;
+
+            let query = PhraseQuery::new(query).with_column(Some(column));
+            Ok(query.into())
+        }
+
+        "boost" => {
+            let positive = query_value
+                .get::<_, napi::JsObject>("positive")?
+                .ok_or(napi::Error::from_reason("positive not found"))?;
+
+            let negative = query_value
+                .get::<_, napi::JsObject>("negative")?
+                .ok_or(napi::Error::from_reason("negative not found"))?;
+            let negative_boost = query_value
+                .get::<_, napi::JsNumber>("negative_boost")?
+                .ok_or(napi::Error::from_reason("negative_boost not found"))?
+                .get_double()? as f32;
+
+            let positive = parse_fts_query(&positive)?;
+            let negative = parse_fts_query(&negative)?;
+            let query = BoostQuery::new(positive, negative, Some(negative_boost));
+            Ok(query.into())
+        }
+
+        "multi_match" => {
+            let query = query_value
+                .get::<_, napi::JsString>("query")?
+                .ok_or(napi::Error::from_reason("query not found"))?
+                .into_utf8()?
+                .into_owned()?;
+            let columns_array = query_value
+                .get::<_, napi::JsTypedArray>("columns")?
+                .ok_or(napi::Error::from_reason("columns not found"))?;
+            let columns_num = columns_array.get_array_length()?;
+            let mut columns = Vec::with_capacity(columns_num as usize);
+            for i in 0..columns_num {
+                let column = columns_array
+                    .get_element::<napi::JsString>(i)?
+                    .into_utf8()?
+                    .into_owned()?;
+                columns.push(column);
+            }
+            let boost_array = query_value
+                .get::<_, napi::JsTypedArray>("boost")?
+                .ok_or(napi::Error::from_reason("boost not found"))?;
+            if boost_array.get_array_length()? != columns_num {
+                return Err(napi::Error::from_reason(format!(
+                    "boost array length ({}) does not match columns length ({})",
+                    boost_array.get_array_length()?,
+                    columns_num
+                )));
+            }
+            let mut boost = Vec::with_capacity(columns_num as usize);
+            for i in 0..columns_num {
+                let b = boost_array.get_element::<napi::JsNumber>(i)?.get_double()? as f32;
+                boost.push(b);
+            }
+
+            let query =
+                MultiMatchQuery::try_new_with_boosts(query, columns, boost).map_err(|e| {
+                    napi::Error::from_reason(format!("Error creating MultiMatchQuery: {}", e))
+                })?;
+
+            Ok(query.into())
+        }
+
+        _ => Err(napi::Error::from_reason(format!(
+            "Unsupported query type: {}",
+            query_type
+        ))),
+    }
+}
--- a/python/.bumpversion.toml
+++ b/python/.bumpversion.toml
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.22.0-beta.0"
+current_version = "0.22.0-beta.6"
 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.0"
+version = "0.22.0-beta.6"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true
--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -4,11 +4,12 @@ name = "lancedb"
 dynamic = ["version"]
 dependencies = [
    "deprecation",
-    "tqdm>=4.27.0",
+    "numpy",
+    "overrides>=0.7",
+    "packaging",
    "pyarrow>=14",
    "pydantic>=1.10",
-    "packaging",
-    "overrides>=0.7",
+    "tqdm>=4.27.0",
 ]
 description = "lancedb"
 authors = [{ name = "LanceDB Devs", email = "dev@lancedb.com" }]
@@ -55,6 +56,7 @@ tests = [
    "tantivy",
    "pyarrow-stubs",
    "pylance>=0.23.2",
+    "requests",
 ]
 dev = [
    "ruff",
--- a/python/python/lancedb/_lancedb.pyi
+++ b/python/python/lancedb/_lancedb.pyi
@@ -1,3 +1,4 @@
+from datetime import timedelta
 from typing import Dict, List, Optional, Tuple, Any, Union, Literal

 import pyarrow as pa
@@ -94,7 +95,9 @@ 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]) -> RecordBatchStream: ...
+    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: ...
    def to_query_request(self) -> PyQueryRequest: ...
@@ -110,7 +113,9 @@ 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]) -> RecordBatchStream: ...
+    async def execute(
+        self, max_batch_length: Optional[int], timeout: Optional[timedelta]
+    ) -> RecordBatchStream: ...
    def to_query_request(self) -> PyQueryRequest: ...

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

 import numpy as np
 import pyarrow as pa
@@ -11,12 +14,100 @@ 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
+from .utils import api_key_not_found_help, IMAGES, TEXT

 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):
    """
@@ -74,6 +165,11 @@ 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
@@ -85,55 +181,12 @@ 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]:
@@ -144,23 +197,52 @@ 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
        """
-        if isinstance(query, str):
-            return [self.generate_text_embeddings(query, input_type="query")]
+        client = VoyageAIEmbeddingFunction._get_client()
+        if self._is_multimodal_model(self.name):
+            result = client.multimodal_embed(
+                inputs=[[query]], model=self.name, input_type="query", **kwargs
+            )
        else:
-            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")
+            result = client.embed(
+                texts=[query], model=self.name, input_type="query", **kwargs
+            )
+
+        return [result.embeddings[0]]

    def compute_source_embeddings(
-        self, images: IMAGES, *args, **kwargs
+        self, inputs: Union[TEXT, IMAGES], *args, **kwargs
    ) -> List[np.array]:
-        images = self.sanitize_input(images)
-        return [
-            self.generate_image_embedding(img, input_type="document") for img in images
-        ]
+        """
+        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

    @staticmethod
    def _get_client():
--- a/python/python/lancedb/query.py
+++ b/python/python/lancedb/query.py
@@ -4,7 +4,10 @@
 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,
@@ -83,6 +86,213 @@ 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 | list[Query]
+            If a string, the query string to match against.
+
+        columns : list[str]
+            The list of columns to match against.
+
+        boosts : list[float], optional
+            The list of boost factors for each column. If not provided,
+            all columns will have the same boost factor.
+        """
+        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

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

        If None, then the table should select the column automatically.
-    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
+    query: str | FullTextQuery
+        If a string, it is treated as a MatchQuery.
+        If a FullTextQuery object, it is used directly.
    """

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


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

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

-    def to_pandas(self, flatten: Optional[Union[int, bool]] = None) -> "pd.DataFrame":
+    def to_pandas(
+        self,
+        flatten: Optional[Union[int, bool]] = None,
+        *,
+        timeout: Optional[timedelta] = 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
@@ -458,12 +670,15 @@ 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(), flatten)
+        tbl = flatten_columns(self.to_arrow(timeout=timeout), flatten)
        return tbl.to_pandas()

    @abstractmethod
-    def to_arrow(self) -> pa.Table:
+    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> 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).
@@ -471,34 +686,65 @@ 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) -> pa.RecordBatchReader:
+    def to_batches(
+        self,
+        /,
+        batch_size: Optional[int] = None,
+        *,
+        timeout: Optional[timedelta] = 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) -> List[dict]:
+    def to_list(self, *, timeout: Optional[timedelta] = None) -> 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.
-        """
-        return self.to_arrow().to_pylist()

-    def to_pydantic(self, model: Type[LanceModel]) -> List[LanceModel]:
+        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()
+
+    def to_pydantic(
+        self, model: Type[LanceModel], *, timeout: Optional[timedelta] = None
+    ) -> 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
        -------
@@ -506,19 +752,25 @@ 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().to_pylist()
+            for row in self.to_arrow(timeout=timeout).to_pylist()
        ]

-    def to_polars(self) -> "pl.DataFrame":
+    def to_polars(self, *, timeout: Optional[timedelta] = None) -> "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())
+        return pl.from_arrow(self.to_arrow(timeout=timeout))

    def limit(self, limit: Union[int, None]) -> Self:
        """Set the maximum number of results to return.
@@ -712,13 +964,14 @@ class LanceQueryBuilder(ABC):
        """
        raise NotImplementedError

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

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

        Returns
        -------
@@ -932,7 +1185,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
        self._refine_factor = refine_factor
        return self

-    def to_arrow(self) -> pa.Table:
+    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> 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).
@@ -940,8 +1193,14 @@ 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().read_all()
+        return self.to_batches(timeout=timeout).read_all()

    def to_query_object(self) -> Query:
        """
@@ -971,7 +1230,13 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
            bypass_vector_index=self._bypass_vector_index,
        )

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

@@ -979,6 +1244,9 @@ 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
        -------
@@ -988,7 +1256,9 @@ 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)
+        result_set = self._table._execute_query(
+            query, batch_size=batch_size, timeout=timeout
+        )
        if self._reranker is not None:
            rs_table = result_set.read_all()
            result_set = self._reranker.rerank_vector(self._str_query, rs_table)
@@ -1084,7 +1354,7 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
    def __init__(
        self,
        table: "Table",
-        query: str,
+        query: str | FullTextQuery,
        ordering_field_name: Optional[str] = None,
        fts_columns: Optional[Union[str, List[str]]] = None,
    ):
@@ -1127,7 +1397,7 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
            offset=self._offset,
        )

-    def to_arrow(self) -> pa.Table:
+    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
        path, fs, exist = self._table._get_fts_index_path()
        if exist:
            return self.tantivy_to_arrow()
@@ -1139,14 +1409,16 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
                "Use tantivy-based index instead for now."
            )
        query = self.to_query_object()
-        results = self._table._execute_query(query)
+        results = self._table._execute_query(query, timeout=timeout)
        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):
+    def to_batches(
+        self, /, batch_size: Optional[int] = None, timeout: Optional[timedelta] = None
+    ):
        raise NotImplementedError("to_batches on an FTS query")

    def tantivy_to_arrow(self) -> pa.Table:
@@ -1251,8 +1523,8 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):


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

    def to_query_object(self) -> Query:
        return Query(
@@ -1263,9 +1535,11 @@ class LanceEmptyQueryBuilder(LanceQueryBuilder):
            offset=self._offset,
        )

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

    def rerank(self, reranker: Reranker) -> LanceEmptyQueryBuilder:
        """Rerank the results using the specified reranker.
@@ -1298,7 +1572,7 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
    def __init__(
        self,
        table: "Table",
-        query: Optional[str] = None,
+        query: Optional[Union[str, FullTextQuery]] = None,
        vector_column: Optional[str] = None,
        fts_columns: Optional[Union[str, List[str]]] = None,
    ):
@@ -1328,8 +1602,8 @@ 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):
-            raise ValueError("Text query must be a string")
+        if not isinstance(text_query, (str, FullTextQuery)):
+            raise ValueError("Text query must be a string or FullTextQuery")

        return vector_query, text_query

@@ -1353,7 +1627,7 @@ 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) -> pa.Table:
+    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
        vector_query, fts_query = self._validate_query(
            self._query, self._vector, self._text
        )
@@ -1396,9 +1670,11 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
            self._reranker = RRFReranker()

        with ThreadPoolExecutor() as executor:
-            fts_future = executor.submit(self._fts_query.with_row_id(True).to_arrow)
+            fts_future = executor.submit(
+                self._fts_query.with_row_id(True).to_arrow, timeout=timeout
+            )
            vector_future = executor.submit(
-                self._vector_query.with_row_id(True).to_arrow
+                self._vector_query.with_row_id(True).to_arrow, timeout=timeout
            )
            fts_results = fts_future.result()
            vector_results = vector_future.result()
@@ -1485,7 +1761,9 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):

        return results

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

    @staticmethod
@@ -1691,7 +1969,7 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
        self._vector = vector
        return self

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

@@ -1849,7 +2127,10 @@ class AsyncQueryBase(object):
        return self

    async def to_batches(
-        self, *, max_batch_length: Optional[int] = None
+        self,
+        *,
+        max_batch_length: Optional[int] = None,
+        timeout: Optional[timedelta] = None,
    ) -> AsyncRecordBatchReader:
        """
        Execute the query and return the results as an Apache Arrow RecordBatchReader.
@@ -1862,34 +2143,56 @@ 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))
+        return AsyncRecordBatchReader(
+            await self._inner.execute(max_batch_length, timeout)
+        )

-    async def to_arrow(self) -> pa.Table:
+    async def to_arrow(self, timeout: Optional[timedelta] = None) -> 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()
+        batch_iter = await self.to_batches(timeout=timeout)
        return pa.Table.from_batches(
            await batch_iter.read_all(), schema=batch_iter.schema
        )

-    async def to_list(self) -> List[dict]:
+    async def to_list(self, timeout: Optional[timedelta] = None) -> 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()).to_pylist()
+        return (await self.to_arrow(timeout=timeout)).to_pylist()

    async def to_pandas(
-        self, flatten: Optional[Union[int, bool]] = None
+        self,
+        flatten: Optional[Union[int, bool]] = None,
+        timeout: Optional[timedelta] = None,
    ) -> "pd.DataFrame":
        """
        Execute the query and collect the results into a pandas DataFrame.
@@ -1918,10 +2221,19 @@ 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(), flatten)).to_pandas()
+        return (
+            flatten_columns(await self.to_arrow(timeout=timeout), flatten)
+        ).to_pandas()

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

@@ -1930,6 +2242,13 @@ 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
        --------

@@ -1945,7 +2264,7 @@ class AsyncQueryBase(object):
        """
        import polars as pl

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

    async def explain_plan(self, verbose: Optional[bool] = False):
        """Return the execution plan for this query.
@@ -2088,7 +2407,7 @@ class AsyncQuery(AsyncQueryBase):
            )

    def nearest_to_text(
-        self, query: str, columns: Union[str, List[str], None] = None
+        self, query: str | FullTextQuery, columns: Union[str, List[str], None] = None
    ) -> AsyncFTSQuery:
        """
        Find the documents that are most relevant to the given text query.
@@ -2114,9 +2433,13 @@ class AsyncQuery(AsyncQueryBase):
            columns = [columns]
        if columns is None:
            columns = []
-        return AsyncFTSQuery(
-            self._inner.nearest_to_text({"query": query, "columns": 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()}))


 class AsyncFTSQuery(AsyncQueryBase):
@@ -2212,9 +2535,12 @@ class AsyncFTSQuery(AsyncQueryBase):
            )

    async def to_batches(
-        self, *, max_batch_length: Optional[int] = None
+        self,
+        *,
+        max_batch_length: Optional[int] = None,
+        timeout: Optional[timedelta] = None,
    ) -> AsyncRecordBatchReader:
-        reader = await super().to_batches()
+        reader = await super().to_batches(timeout=timeout)
        results = pa.Table.from_batches(await reader.read_all(), reader.schema)
        if self._reranker:
            results = self._reranker.rerank_fts(self.get_query(), results)
@@ -2399,7 +2725,7 @@ class AsyncVectorQuery(AsyncQueryBase, AsyncVectorQueryBase):
        return self

    def nearest_to_text(
-        self, query: str, columns: Union[str, List[str], None] = None
+        self, query: str | FullTextQuery, columns: Union[str, List[str], None] = None
    ) -> AsyncHybridQuery:
        """
        Find the documents that are most relevant to the given text query,
@@ -2429,14 +2755,21 @@ class AsyncVectorQuery(AsyncQueryBase, AsyncVectorQueryBase):
            columns = [columns]
        if columns is None:
            columns = []
-        return AsyncHybridQuery(
-            self._inner.nearest_to_text({"query": query, "columns": 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()}))

    async def to_batches(
-        self, *, max_batch_length: Optional[int] = None
+        self,
+        *,
+        max_batch_length: Optional[int] = None,
+        timeout: Optional[timedelta] = None,
    ) -> AsyncRecordBatchReader:
-        reader = await super().to_batches()
+        reader = await super().to_batches(timeout=timeout)
        results = pa.Table.from_batches(await reader.read_all(), reader.schema)
        if self._reranker:
            results = self._reranker.rerank_vector(self._query_string, results)
@@ -2492,7 +2825,10 @@ class AsyncHybridQuery(AsyncQueryBase, AsyncVectorQueryBase):
        return self

    async def to_batches(
-        self, *, max_batch_length: Optional[int] = None
+        self,
+        *,
+        max_batch_length: Optional[int] = None,
+        timeout: Optional[timedelta] = None,
    ) -> AsyncRecordBatchReader:
        fts_query = AsyncFTSQuery(self._inner.to_fts_query())
        vec_query = AsyncVectorQuery(self._inner.to_vector_query())
@@ -2504,8 +2840,8 @@ class AsyncHybridQuery(AsyncQueryBase, AsyncVectorQueryBase):
        vec_query.with_row_id()

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

        result = LanceHybridQueryBuilder._combine_hybrid_results(
--- a/python/python/lancedb/remote/table.py
+++ b/python/python/lancedb/remote/table.py
@@ -355,9 +355,15 @@ class RemoteTable(Table):
        )

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

        def iter_sync():
            try:
--- a/python/python/lancedb/table.py
+++ b/python/python/lancedb/table.py
@@ -52,6 +52,7 @@ from .query import (
    AsyncHybridQuery,
    AsyncQuery,
    AsyncVectorQuery,
+    FullTextQuery,
    LanceEmptyQueryBuilder,
    LanceFtsQueryBuilder,
    LanceHybridQueryBuilder,
@@ -919,7 +920,9 @@ class Table(ABC):
    @abstractmethod
    def search(
        self,
-        query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
+        query: Optional[
+            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
+        ] = None,
        vector_column_name: Optional[str] = None,
        query_type: QueryType = "auto",
        ordering_field_name: Optional[str] = None,
@@ -1004,7 +1007,11 @@ class Table(ABC):

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

    @abstractmethod
@@ -2039,7 +2046,9 @@ class LanceTable(Table):
    @overload
    def search(
        self,
-        query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
+        query: Optional[
+            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
+        ] = None,
        vector_column_name: Optional[str] = None,
        query_type: Literal["hybrid"] = "hybrid",
        ordering_field_name: Optional[str] = None,
@@ -2058,7 +2067,9 @@ class LanceTable(Table):

    def search(
        self,
-        query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
+        query: Optional[
+            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
+        ] = None,
        vector_column_name: Optional[str] = None,
        query_type: QueryType = "auto",
        ordering_field_name: Optional[str] = None,
@@ -2305,9 +2316,15 @@ 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
+        self,
+        query: Query,
+        *,
+        batch_size: Optional[int] = None,
+        timeout: Optional[timedelta] = None,
    ) -> pa.RecordBatchReader:
-        async_iter = LOOP.run(self._table._execute_query(query, batch_size))
+        async_iter = LOOP.run(
+            self._table._execute_query(query, batch_size=batch_size, timeout=timeout)
+        )

        def iter_sync():
            try:
@@ -3134,7 +3151,9 @@ class AsyncTable:
    @overload
    async def search(
        self,
-        query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
+        query: Optional[
+            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
+        ] = None,
        vector_column_name: Optional[str] = None,
        query_type: Literal["vector"] = ...,
        ordering_field_name: Optional[str] = None,
@@ -3143,7 +3162,9 @@ class AsyncTable:

    async def search(
        self,
-        query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
+        query: Optional[
+            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
+        ] = None,
        vector_column_name: Optional[str] = None,
        query_type: QueryType = "auto",
        ordering_field_name: Optional[str] = None,
@@ -3253,6 +3274,8 @@ class AsyncTable:
            if is_embedding(query):
                vector_query = query
                query_type = "vector"
+            elif isinstance(query, FullTextQuery):
+                query_type = "fts"
            elif isinstance(query, str):
                try:
                    (
@@ -3373,13 +3396,15 @@ 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
+        self,
+        query: Query,
+        *,
+        batch_size: Optional[int] = None,
+        timeout: Optional[timedelta] = 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
@@ -3387,7 +3412,9 @@ class AsyncTable:

        async_query = self._sync_query_to_async(query)

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

    async def _explain_plan(self, query: Query, verbose: Optional[bool]) -> str:
        # This method is used by the sync table
--- a/python/python/tests/test_embeddings_slow.py
+++ b/python/python/tests/test_embeddings_slow.py
@@ -12,6 +12,7 @@ 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
@@ -516,3 +517,61 @@ 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
@@ -20,7 +20,9 @@ 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
@@ -178,11 +180,47 @@ 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")
@@ -193,6 +231,54 @@ 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)
@@ -207,6 +293,13 @@ 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):
@@ -227,6 +320,16 @@ 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)
@@ -524,3 +627,32 @@ 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_index.py
+++ b/python/python/tests/test_index.py
@@ -31,6 +31,7 @@ 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)
            ],
@@ -85,6 +86,16 @@ 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())
--- a/python/python/tests/test_query.py
+++ b/python/python/tests/test_query.py
@@ -511,7 +511,8 @@ def test_query_builder_with_different_vector_column():
            columns=["b"],
            vector_column="foo_vector",
        ),
-        None,
+        batch_size=None,
+        timeout=None,
    )


@@ -1076,3 +1077,67 @@ 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
@@ -444,6 +444,16 @@ 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/src/query.rs
+++ b/python/src/query.rs
@@ -2,25 +2,26 @@
 // 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::FullTextSearchQuery;
+use lancedb::index::scalar::{FtsQuery, FullTextSearchQuery, MatchQuery, PhraseQuery};
 use lancedb::query::QueryExecutionOptions;
 use lancedb::query::QueryFilter;
 use lancedb::query::{
    ExecutableQuery, Query as LanceDbQuery, QueryBase, Select, VectorQuery as LanceDbVectorQuery,
 };
 use lancedb::table::AnyQuery;
-use pyo3::exceptions::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;
@@ -31,7 +32,7 @@ use pyo3_async_runtimes::tokio::future_into_py;

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

 // Python representation of full text search parameters
 #[derive(Clone)]
@@ -46,8 +47,8 @@ pub struct PyFullTextSearchQuery {
 impl From<FullTextSearchQuery> for PyFullTextSearchQuery {
    fn from(query: FullTextSearchQuery) -> Self {
        PyFullTextSearchQuery {
-            columns: query.columns,
-            query: query.query,
+            columns: query.columns().into_iter().collect(),
+            query: query.query.query().to_owned(),
            limit: query.limit,
            wand_factor: query.wand_factor,
        }
@@ -236,29 +237,69 @@ impl Query {
    }

    pub fn nearest_to_text(&mut self, query: Bound<'_, PyDict>) -> PyResult<FTSQuery> {
-        let query_text = query
+        let fts_query = 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 fts_query = FullTextSearchQuery::new(query_text).columns(columns);
+        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",
+            ));
+        };

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

-    #[pyo3(signature = (max_batch_length=None))]
+    #[pyo3(signature = (max_batch_length=None, timeout=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 {
@@ -266,6 +307,9 @@ 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))
        })
@@ -337,10 +381,11 @@ impl FTSQuery {
        self.inner = self.inner.clone().postfilter();
    }

-    #[pyo3(signature = (max_batch_length=None))]
+    #[pyo3(signature = (max_batch_length=None, timeout=None))]
    pub fn execute(
        self_: PyRef<'_, Self>,
        max_batch_length: Option<u32>,
+        timeout: Option<Duration>,
    ) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_
            .inner
@@ -352,6 +397,9 @@ 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))
        })
@@ -386,7 +434,7 @@ impl FTSQuery {
    }

    pub fn get_query(&self) -> String {
-        self.fts_query.query.clone()
+        self.fts_query.query.query().to_owned()
    }

    pub fn to_query_request(&self) -> PyQueryRequest {
@@ -474,10 +522,11 @@ impl VectorQuery {
        self.inner = self.inner.clone().bypass_vector_index()
    }

-    #[pyo3(signature = (max_batch_length=None))]
+    #[pyo3(signature = (max_batch_length=None, timeout=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 {
@@ -485,6 +534,9 @@ 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))
        })
--- a/python/src/util.rs
+++ b/python/src/util.rs
@@ -3,11 +3,15 @@

 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
 ///
@@ -59,3 +63,116 @@ 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_with_boosts(query, columns, 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 = "0.18.3-beta.0"
+version = "0.19.0-beta.5"
 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 = "0.18.3-beta.0"
+version = "0.19.0-beta.5"
 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
@@ -142,12 +142,6 @@ 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();
@@ -229,6 +223,12 @@ 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,
--- a/rust/lancedb/src/index/scalar.rs
+++ b/rust/lancedb/src/index/scalar.rs
@@ -80,5 +80,6 @@ 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
@@ -14,6 +14,9 @@ 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
@@ -0,0 +1,237 @@
+// 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/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};
@@ -25,6 +25,7 @@ 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};

@@ -525,12 +526,15 @@ 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,
        }
    }
 }
@@ -1007,7 +1011,10 @@ impl VectorQuery {
        self
    }

-    pub async fn execute_hybrid(&self) -> Result<SendableRecordBatchStream> {
+    pub async fn execute_hybrid(
+        &self,
+        options: QueryExecutionOptions,
+    ) -> 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();
@@ -1016,7 +1023,10 @@ 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(), vector_query.execute())?;
+        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_results.try_collect::<Vec<_>>(),
@@ -1056,7 +1066,7 @@ impl VectorQuery {
            })?;

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

        check_reranker_result(&results)?;
@@ -1074,6 +1084,20 @@ 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 {
@@ -1087,16 +1111,13 @@ 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().await }.boxed().await?;
+            let hybrid_result = async move { self.execute_hybrid(options).await }
+                .boxed()
+                .await?;
            return Ok(hybrid_result);
        }

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

    async fn explain_plan(&self, verbose: bool) -> Result<String> {
--- 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: &str = "x-request-id";
+const REQUEST_ID_HEADER: HeaderName = HeaderName::from_static("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(
-            "x-api-key",
+            HeaderName::from_static("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(
-                "Host",
+                http::header::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(
-                "x-lancedb-database",
+                HeaderName::from_static("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(
-                "x-lancedb-database-prefix",
+                HeaderName::from_static("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(
-                "x-azure-storage-account-name",
+                HeaderName::from_static("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(
-                "x-azure-storage-account-name",
+                HeaderName::from_static("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
@@ -52,6 +52,10 @@ 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::StatusCode;
+use http::{HeaderName, StatusCode};
 use lance::arrow::json::{JsonDataType, JsonSchema};
 use lance::dataset::scanner::DatasetRecordBatchStream;
 use lance::dataset::{ColumnAlteration, NewColumnTransform, Version};
@@ -44,6 +44,8 @@ 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)]
@@ -155,7 +157,11 @@ impl<S: HttpSend> RemoteTable<S> {
        Ok(Box::pin(RecordBatchStreamAdapter::new(schema, stream)))
    }

-    fn apply_query_params(body: &mut serde_json::Value, params: &QueryRequest) -> Result<()> {
+    fn apply_query_params(
+        &self,
+        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));
@@ -209,10 +215,17 @@ impl<S: HttpSend> RemoteTable<S> {
                    message: "Wand factor is not yet supported in LanceDB Cloud".into(),
                });
            }
-            body["full_text_query"] = serde_json::json!({
-                "columns": full_text_search.columns,
-                "query": full_text_search.query,
-            })
+
+            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(),
+                })
+            }
        }

        Ok(())
@@ -223,7 +236,7 @@ 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());
@@ -321,9 +334,19 @@ impl<S: HttpSend> RemoteTable<S> {
    async fn execute_query(
        &self,
        query: &AnyQuery,
-        _options: QueryExecutionOptions,
+        options: &QueryExecutionOptions,
    ) -> Result<Vec<Pin<Box<dyn RecordBatchStream + Send>>>> {
-        let request = self.client.post(&format!("/v1/table/{}/query/", self.name));
+        let mut 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 query_bodies = self.prepare_query_bodies(query).await?;
        let requests: Vec<reqwest::RequestBuilder> = query_bodies
@@ -346,7 +369,7 @@ impl<S: HttpSend> RemoteTable<S> {
        match query {
            AnyQuery::Query(query) => {
                let mut body = base_body.clone();
-                Self::apply_query_params(&mut body, 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());
                Ok(vec![body])
@@ -532,7 +555,7 @@ 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)))
@@ -548,9 +571,9 @@ 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(
@@ -1037,6 +1060,7 @@ 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;
@@ -1683,7 +1707,18 @@ mod tests {
                "prefilter": true,
                "version": null
            });
-            assert_eq!(body, expected_body);
+            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);

            let data = RecordBatch::try_new(
                Arc::new(Schema::new(vec![Field::new("a", DataType::Int32, false)])),
@@ -1702,7 +1737,8 @@ mod tests {
            .query()
            .full_text_search(
                FullTextSearchQuery::new("hello world".into())
-                    .columns(Some(vec!["a".into(), "b".into()])),
+                    .with_columns(&["a".into(), "b".into()])
+                    .unwrap(),
            )
            .with_row_id()
            .limit(10)
@@ -1711,6 +1747,66 @@ 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,
+                            },
+                        }
+                    },
+                    "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/table.rs
+++ b/rust/lancedb/src/table.rs
@@ -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,
+    PatchReadParam, PatchWriteParam, TimeoutStream,
 };

 use self::dataset::DatasetConsistencyWrapper;
@@ -1775,11 +1775,14 @@ impl NativeTable {
        query: &AnyQuery,
        options: QueryExecutionOptions,
    ) -> Result<DatasetRecordBatchStream> {
-        let plan = self.create_plan(query, options).await?;
-        Ok(DatasetRecordBatchStream::new(execute_plan(
-            plan,
-            Default::default(),
-        )?))
+        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))
    }

    /// Check whether the table uses V2 manifest paths.
--- a/rust/lancedb/src/table/dataset.rs
+++ b/rust/lancedb/src/table/dataset.rs
@@ -48,7 +48,6 @@ impl DatasetRef {
                refresh_task,
                ..
            } => {
-                dataset.checkout_latest().await?;
                // Replace the refresh task
                if let Some(refresh_task) = refresh_task {
                    refresh_task.abort();
@@ -372,3 +371,48 @@ 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(Some(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
@@ -3,14 +3,20 @@

 use std::sync::Arc;

-use arrow_schema::{DataType, Schema};
+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 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();
@@ -135,6 +141,7 @@ pub fn supported_btree_data_type(dtype: &DataType) -> bool {
                | DataType::Date32
                | DataType::Date64
                | DataType::Timestamp(_, _)
+                | DataType::FixedSizeBinary(_)
        )
 }

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

 pub fn supported_fts_data_type(dtype: &DataType) -> bool {
-    matches!(dtype, DataType::Utf8 | DataType::LargeUtf8)
+    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,
+    }
 }

 pub fn supported_vector_data_type(dtype: &DataType) -> bool {
@@ -177,11 +194,97 @@ 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 super::*;
+    use arrow_array::Int32Array;
+    use arrow_schema::Field;
+    use datafusion_physical_plan::stream::RecordBatchStreamAdapter;
+    use futures::{stream, StreamExt};
+    use tokio::time::sleep;

-    use arrow_schema::{DataType, Field};
+    use super::*;

    #[test]
    fn test_guess_default_column() {
@@ -248,4 +351,85 @@ 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	27d9e5c596	Bump version: 0.22.0-beta.5 → 0.22.0-beta.6	2025-04-08 06:16:14 +00:00
BubbleCal	ec8271931f	feat: support to create FTS index on list of strings (#2317 ) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - Chores - Updated internal library dependencies to the latest beta version for improved system stability. - Tests - Added automated tests to validate full-text search functionality on list-based text fields. - Refactor - Enhanced the search processing logic to provide robust support for list-type text data, ensuring more reliable results. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: BubbleCal <bubble-cal@outlook.com>	2025-04-08 14:12:35 +08:00
Lance Release	6c6966600c	Updating package-lock.json	2025-04-04 22:56:57 +00:00
Lance Release	2e170c3c7b	Updating package-lock.json	2025-04-04 21:50:28 +00:00
Lance Release	fd92e651d1	Updating package-lock.json	2025-04-04 21:50:12 +00:00
Lance Release	c298482ee1	Bump version: 0.19.0-beta.4 → 0.19.0-beta.5	2025-04-04 21:49:53 +00:00
Lance Release	d59f64b5a3	Bump version: 0.22.0-beta.4 → 0.22.0-beta.5	2025-04-04 21:49:34 +00:00
fzowl	30ed8c4c43	fix: voyageai regression multimodal supercedes text models (#2268 ) fix #2160	2025-04-04 14:45:56 -07:00
Will Jones	4a2cdbf299	ci: provide token for deprecate call (#2309 ) This should prevent the failures we are seeing in Node release. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - Chore - Enhanced the package deprecation process with improved security measures, ensuring smoother and more reliable updates during package deprecation. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-04-04 14:44:58 -07:00
Will Jones	657843d9e9	perf: remove redundant checkout latest (#2310 ) This bug was introduced in https://github.com/lancedb/lancedb/pull/2281 Likely introduced during a rebase when fixing merge conflicts. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - Refactor - Updated the refresh process so that reloading now uses the existing dataset version instead of automatically updating to the latest version. This change may affect workflows that rely on immediate data updates during refresh. - New Features - Introduced a new module for tracking I/O statistics in object store operations, enhancing monitoring capabilities. - Added a new test module to validate the functionality of the dataset operations. - Bug Fixes - Reintroduced the `write_options` method in the `CreateTableBuilder`, ensuring consistent functionality across different builder variants. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-04-04 12:56:02 -07:00
Will Jones	1cd76b8498	feat: add timeout to query execution options (#2288 ) Closes #2287 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - New Features - Added configurable timeout support for query executions. Users can now specify maximum wait times for queries, enhancing control over long-running operations across various integrations. - Tests - Expanded test coverage to validate timeout behavior in both synchronous and asynchronous query flows, ensuring timely error responses when query execution exceeds the specified limit. - Introduced a new test suite to verify query operations when a timeout is reached, checking for appropriate error handling. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-04-04 12:34:41 -07:00
Lei Xu	a38f784081	chore: add numpy as dependency (#2308 )	2025-04-04 10:33:39 -07:00
Will Jones	647dee4e94	ci: check release builds when we change dependencies (#2299 ) The issue we fixed in https://github.com/lancedb/lancedb/pull/2296 was caused by an upgrade in dependencies. This could have been caught if we had run these CI jobs when we did the dependency change. <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - Chores - Updated our automated pipeline to trigger additional stability checks when dependency configurations change, ensuring smoother build and release processes. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-04-03 16:19:00 -07:00
Lance Release	0844c2dd64	Updating package-lock.json	2025-04-02 21:23:50 +00:00
Lance Release	fd2692295c	Updating package-lock.json	2025-04-02 21:23:34 +00:00
Lance Release	d4ea50fba1	Bump version: 0.19.0-beta.3 → 0.19.0-beta.4	2025-04-02 21:23:19 +00:00
Lance Release	0d42297cf8	Bump version: 0.22.0-beta.3 → 0.22.0-beta.4	2025-04-02 21:23:02 +00:00
Weston Pace	a6d4125cbf	feat: upgrade lance to 0.25.3b2 (#2304 ) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - Chores - Updated core dependency versions to v0.25.3-beta.2. - Enabled additional functionality with a new "dynamodb" feature. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-04-02 14:22:30 -07:00
Lance Release	5c32a99e61	Updating package-lock.json	2025-04-02 09:28:46 +00:00
Lance Release	cefaa75b24	Updating package-lock.json	2025-04-02 09:28:30 +00:00
Lance Release	bd62c2384f	Bump version: 0.19.0-beta.2 → 0.19.0-beta.3	2025-04-02 09:28:14 +00:00
Lance Release	f0bc08c0d7	Bump version: 0.22.0-beta.2 → 0.22.0-beta.3	2025-04-02 09:27:55 +00:00
BubbleCal	e52ac79c69	fix: can't do structured FTS in python (#2300 ) missed to support it in `search()` API and there were some pydantic errors <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - New Features - Enhanced full-text search capabilities by incorporating additional parameters, enabling more flexible query definitions. - Extended table search functionality to support full-text queries alongside existing search types. - Tests - Introduced new tests that validate both structured and conditional full-text search behaviors. - Expanded test coverage for various query types, including MatchQuery, BoostQuery, MultiMatchQuery, and PhraseQuery. - Bug Fixes - Fixed a logic issue in query processing to ensure correct handling of full-text search queries. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: BubbleCal <bubble-cal@outlook.com>	2025-04-02 17:27:15 +08:00
Will Jones	f091f57594	ci: fix lancedb musl builds (#2296 ) Fixes #2255 <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - Chores - Enhanced the build process to improve performance and reliability across Linux platforms. - Updated environment settings for more accurate compiler integration. - Activated previously inactive build configurations to support advanced feature support. - Added support for the x86_64 architecture on Linux systems utilizing the musl C library. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-04-01 14:44:27 -07:00
Lance Release	a997fd4108	Updating package-lock.json	2025-04-01 17:28:57 +00:00
Lance Release	1486514ccc	Updating package-lock.json	2025-04-01 17:28:40 +00:00
Lance Release	a505bc3965	Bump version: 0.19.0-beta.1 → 0.19.0-beta.2	2025-04-01 17:28:21 +00:00
Lance Release	c1738250a3	Bump version: 0.22.0-beta.1 → 0.22.0-beta.2	2025-04-01 17:27:57 +00:00
Weston Pace	1ee63984f5	feat: allow FSB to be used for btree indices (#2297 ) We recently allowed this for lance but there was a check in lancedb as well that was preventing it <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - New Features - Added support for indexing fixed-size binary data using B-tree structures for efficient data storage and retrieval. - Tests - Implemented automated tests to ensure the new binary indexing works correctly and meets the expected configuration. <!-- end of auto-generated comment: release notes by coderabbit.ai -->	2025-04-01 10:27:22 -07:00
Lance Release	2eb2c8862a	Updating package-lock.json	2025-04-01 14:27:26 +00:00
Lance Release	4ea8e178d3	Updating package-lock.json	2025-04-01 14:27:07 +00:00
Lance Release	e4485a630e	Bump version: 0.19.0-beta.0 → 0.19.0-beta.1	2025-04-01 14:26:47 +00:00
Lance Release	fb95f9b3bd	Bump version: 0.22.0-beta.0 → 0.22.0-beta.1	2025-04-01 14:26:28 +00:00
Weston Pace	625bab3f21	feat: update to lance 0.25.3b1 (#2294 ) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - Chores - Updated dependency versions for improved performance and compatibility. - New Features - Added support for structured full-text search with expanded query types (e.g., match, phrase, boost, multi-match) and flexible input formats. - Introduced a new method to check server support for structural full-text search features. - Enhanced the query system with new classes and interfaces for handling various full-text queries. - Expanded the functionality of existing methods to accept more complex query structures, including updates to method signatures. - Bug Fixes - Improved error handling and reporting for full-text search queries. - Refactor - Enhanced query processing with streamlined input handling and improved error reporting, ensuring more robust and consistent search results across platforms. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Signed-off-by: BubbleCal <bubble-cal@outlook.com> Co-authored-by: BubbleCal <bubble-cal@outlook.com>	2025-04-01 06:36:42 -07:00
Will Jones	e59f9382a0	ci: deprecate vectordb each release (#2292 ) I released each time we published, the new package was no longer deprecated. This re-deprecated the package after a new publish.	2025-03-31 12:03:04 -07:00
Lance Release	fdee7ba477	Updating package-lock.json	2025-03-30 19:09:17 +00:00
Lance Release	c44fa3abc4	Updating package-lock.json	2025-03-30 18:05:07 +00:00
Lance Release	fc43aac0ed	Updating package-lock.json	2025-03-30 18:04:51 +00:00
Lance Release	e67cd0baf9	Bump version: 0.18.3-beta.0 → 0.19.0-beta.0	2025-03-30 18:04:32 +00:00