Bump version: 0.26.2 → 0.27.0-beta.0

2026-03-26 18:40:42 +00:00 · 2026-02-17 00:28:07 +00:00
162 changed files with 7914 additions and 15329 deletions
--- a/.bumpversion.toml
+++ b/.bumpversion.toml
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.27.2-beta.1"
+current_version = "0.27.0-beta.0"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
--- a/.github/workflows/build_linux_wheel/action.yml
+++ b/.github/workflows/build_linux_wheel/action.yml
@@ -29,7 +29,6 @@ runs:
      if: ${{ inputs.arm-build == 'false' }}
      uses: PyO3/maturin-action@v1
      with:
-        maturin-version: "1.12.4"
        command: build
        working-directory: python
        docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/'"
@@ -45,7 +44,6 @@ runs:
      if: ${{ inputs.arm-build == 'true' }}
      uses: PyO3/maturin-action@v1
      with:
-        maturin-version: "1.12.4"
        command: build
        working-directory: python
        docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/'"
--- a/.github/workflows/build_mac_wheel/action.yml
+++ b/.github/workflows/build_mac_wheel/action.yml
@@ -20,7 +20,6 @@ runs:
      uses: PyO3/maturin-action@v1
      with:
        command: build
-        maturin-version: "1.12.4"
        # TODO: pass through interpreter
        args: ${{ inputs.args }}
        docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/'"
--- a/.github/workflows/build_windows_wheel/action.yml
+++ b/.github/workflows/build_windows_wheel/action.yml
@@ -25,7 +25,6 @@ runs:
      uses: PyO3/maturin-action@v1
      with:
        command: build
-        maturin-version: "1.12.4"
        args: ${{ inputs.args }}
        docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/'"
        working-directory: python
--- a/.github/workflows/dev.yml
+++ b/.github/workflows/dev.yml
@@ -15,7 +15,7 @@ jobs:
    name: Label PR
    runs-on: ubuntu-latest
    steps:
-      - uses: srvaroa/labeler@v1
+      - uses: srvaroa/labeler@master
        env:
          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
  commitlint:
@@ -24,7 +24,7 @@ jobs:
    name: Verify PR title / description conforms to semantic-release
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/setup-node@v4
+      - uses: actions/setup-node@v3
        with:
          node-version: "18"
      # These rules are disabled because Github will always ensure there
@@ -47,7 +47,7 @@ jobs:

            ${{ github.event.pull_request.body }}
      - if: failure()
-        uses: actions/github-script@v7
+        uses: actions/github-script@v6
        with:
          script: |
            const message = `**ACTION NEEDED**
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -53,7 +53,7 @@ jobs:
          python -m pip install --extra-index-url https://pypi.fury.io/lance-format/ --extra-index-url https://pypi.fury.io/lancedb/ -e .
          python -m pip install --extra-index-url https://pypi.fury.io/lance-format/ --extra-index-url https://pypi.fury.io/lancedb/ -r ../docs/requirements.txt
      - name: Set up node
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v3
        with:
          node-version: 20
          cache: 'npm'
@@ -68,7 +68,7 @@ jobs:
        run: |
          PYTHONPATH=. mkdocs build
      - name: Setup Pages
-        uses: actions/configure-pages@v5
+        uses: actions/configure-pages@v2
      - name: Upload artifact
        uses: actions/upload-pages-artifact@v3
        with:
--- a/.github/workflows/nodejs.yml
+++ b/.github/workflows/nodejs.yml
@@ -7,9 +7,7 @@ on:
  pull_request:
    paths:
      - Cargo.toml
-      - Cargo.lock
      - nodejs/**
-      - rust/**
      - docs/src/js/**
      - .github/workflows/nodejs.yml
      - docker-compose.yml
@@ -38,7 +36,7 @@ jobs:
      with:
        fetch-depth: 0
        lfs: true
-    - uses: actions/setup-node@v4
+    - uses: actions/setup-node@v3
      with:
        node-version: 20
        cache: 'npm'
@@ -78,12 +76,9 @@ jobs:
      with:
        fetch-depth: 0
        lfs: true
-    - uses: actions/setup-node@v4
-      name: Setup Node.js 20 for build
+    - uses: actions/setup-node@v3
      with:
-        # @napi-rs/cli v3 requires Node >= 20.12 (via @inquirer/prompts@8).
-        # Build always on Node 20; tests run on the matrix version below.
-        node-version: 20
+        node-version: ${{ matrix.node-version }}
        cache: 'npm'
        cache-dependency-path: nodejs/package-lock.json
    - uses: Swatinem/rust-cache@v2
@@ -91,16 +86,12 @@ jobs:
      run: |
        sudo apt update
        sudo apt install -y protobuf-compiler libssl-dev
+        npm install -g @napi-rs/cli
    - name: Build
      run: |
        npm ci --include=optional
        npm run build:debug -- --profile ci
-    - uses: actions/setup-node@v4
-      name: Setup Node.js ${{ matrix.node-version }} for test
-      with:
-        node-version: ${{ matrix.node-version }}
-    - name: Compile TypeScript
-      run: npm run tsc
+        npm run tsc
    - name: Setup localstack
      working-directory: .
      run: docker compose up --detach --wait
@@ -144,7 +135,7 @@ jobs:
      with:
        fetch-depth: 0
        lfs: true
-    - uses: actions/setup-node@v4
+    - uses: actions/setup-node@v3
      with:
        node-version: 20
        cache: 'npm'
@@ -153,6 +144,7 @@ jobs:
    - name: Install dependencies
      run: |
        brew install protobuf
+        npm install -g @napi-rs/cli
    - name: Build
      run: |
        npm ci --include=optional
--- a/.github/workflows/npm-publish.yml
+++ b/.github/workflows/npm-publish.yml
@@ -19,7 +19,6 @@ on:
    paths:
      - .github/workflows/npm-publish.yml
      - Cargo.toml # Change in dependency frequently breaks builds
-      - Cargo.lock

 concurrency:
  group: ${{ github.workflow }}-${{ github.ref }}
@@ -125,22 +124,20 @@ jobs:
            pre_build: |-
              set -e &&
              apt-get update &&
-              apt-get install -y protobuf-compiler pkg-config &&
-              # The base image (manylinux2014-cross) sets TARGET_CC to the old
-              # GCC 4.8 cross-compiler. aws-lc-sys checks TARGET_CC before CC,
-              # so it picks up GCC even though the napi-rs image sets CC=clang.
-              # Override to use the image's clang-18 which supports -fuse-ld=lld.
-              export TARGET_CC=clang TARGET_CXX=clang++
+              apt-get install -y protobuf-compiler pkg-config
          - target: x86_64-unknown-linux-musl
            # This one seems to need some extra memory
            host: ubuntu-2404-8x-x64
+            # https://github.com/napi-rs/napi-rs/blob/main/alpine.Dockerfile
+            docker: ghcr.io/napi-rs/napi-rs/nodejs-rust:lts-alpine
            features: fp16kernels
            pre_build: |-
              set -e &&
-              sudo apt-get update &&
-              sudo apt-get install -y protobuf-compiler pkg-config &&
-              rustup target add x86_64-unknown-linux-musl &&
-              export EXTRA_ARGS="-x"
+              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
@@ -150,20 +147,21 @@ jobs:
              set -e &&
              apt-get update &&
              apt-get install -y protobuf-compiler pkg-config &&
-              export TARGET_CC=clang TARGET_CXX=clang++ &&
-              # The manylinux2014 sysroot has glibc 2.17 headers which lack
-              # AT_HWCAP2 (added in Linux 3.17). Define it for aws-lc-sys.
-              export CFLAGS="$CFLAGS -DAT_HWCAP2=26" &&
+              # https://github.com/aws/aws-lc-rs/issues/737#issuecomment-2725918627
+              ln -s /usr/aarch64-unknown-linux-gnu/lib/gcc/aarch64-unknown-linux-gnu/4.8.5/crtbeginS.o /usr/aarch64-unknown-linux-gnu/aarch64-unknown-linux-gnu/sysroot/usr/lib/crtbeginS.o &&
+              ln -s /usr/aarch64-unknown-linux-gnu/lib/gcc /usr/aarch64-unknown-linux-gnu/aarch64-unknown-linux-gnu/sysroot/usr/lib/gcc &&
              rustup target add aarch64-unknown-linux-gnu
          - target: aarch64-unknown-linux-musl
            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 &&
-              sudo apt-get update &&
-              sudo apt-get install -y protobuf-compiler &&
+              apk add protobuf-dev &&
              rustup target add aarch64-unknown-linux-musl &&
-              export EXTRA_ARGS="-x"
+              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: ${{ matrix.settings.host }}
    defaults:
@@ -194,18 +192,12 @@ jobs:
            .cargo-cache
            target/
          key: nodejs-${{ matrix.settings.target }}-cargo-${{ matrix.settings.host }}
+      - name: Setup toolchain
+        run: ${{ matrix.settings.setup }}
+        if: ${{ matrix.settings.setup }}
+        shell: bash
      - name: Install dependencies
        run: npm ci
-      - name: Install Zig
-        uses: mlugg/setup-zig@v2
-        if: ${{ contains(matrix.settings.target, 'musl') }}
-        with:
-          version: 0.14.1
-      - name: Install cargo-zigbuild
-        uses: taiki-e/install-action@v2
-        if: ${{ contains(matrix.settings.target, 'musl') }}
-        with:
-          tool: cargo-zigbuild
      - name: Build in docker
        uses: addnab/docker-run-action@v3
        if: ${{ matrix.settings.docker }}
@@ -218,24 +210,24 @@ jobs:
          run: |
            set -e
            ${{ matrix.settings.pre_build }}
-            npx napi build --platform --release \
+            npx napi build --platform  --release --no-const-enum \
              --features ${{ matrix.settings.features }} \
              --target ${{ matrix.settings.target }} \
              --dts ../lancedb/native.d.ts \
              --js ../lancedb/native.js \
              --strip \
-              --output-dir dist/
+              dist/
      - name: Build
        run: |
          ${{ matrix.settings.pre_build }}
-          npx napi build --platform --release \
+          npx napi build --platform  --release --no-const-enum \
              --features ${{ matrix.settings.features }} \
              --target ${{ matrix.settings.target }} \
              --dts ../lancedb/native.d.ts \
              --js ../lancedb/native.js \
              --strip \
              $EXTRA_ARGS \
-              --output-dir dist/
+              dist/
        if: ${{ !matrix.settings.docker }}
        shell: bash
      - name: Upload artifact
@@ -273,7 +265,7 @@ jobs:
          - target: x86_64-unknown-linux-gnu
            host: ubuntu-latest
          - target: aarch64-unknown-linux-gnu
-            host: ubuntu-2404-8x-arm64
+            host: buildjet-16vcpu-ubuntu-2204-arm
        node:
          - '20'
    runs-on: ${{ matrix.settings.host }}
@@ -363,8 +355,7 @@ jobs:
          if [[ $DRY_RUN == "true" ]]; then
            ARGS="$ARGS --dry-run"
          fi
-          VERSION=$(node -p "require('./package.json').version")
-          if [[ $VERSION == *-* ]]; then
+          if [[ $GITHUB_REF =~ refs/tags/v(.*)-beta.* ]]; then
            ARGS="$ARGS --tag preview"
          fi
          npm publish $ARGS
--- a/.github/workflows/pypi-publish.yml
+++ b/.github/workflows/pypi-publish.yml
@@ -9,7 +9,6 @@ on:
    paths:
      - .github/workflows/pypi-publish.yml
      - Cargo.toml # Change in dependency frequently breaks builds
-      - Cargo.lock

 env:
  PIP_EXTRA_INDEX_URL: "https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/"
--- a/.github/workflows/python.yml
+++ b/.github/workflows/python.yml
@@ -7,14 +7,8 @@ on:
  pull_request:
    paths:
      - Cargo.toml
-      - Cargo.lock
      - python/**
-      - rust/**
      - .github/workflows/python.yml
-      - .github/workflows/build_linux_wheel/**
-      - .github/workflows/build_mac_wheel/**
-      - .github/workflows/build_windows_wheel/**
-      - .github/workflows/run_tests/**

 concurrency:
  group: ${{ github.workflow }}-${{ github.event.pull_request.number || github.ref }}
--- a/.github/workflows/rust.yml
+++ b/.github/workflows/rust.yml
@@ -7,7 +7,6 @@ on:
  pull_request:
    paths:
      - Cargo.toml
-      - Cargo.lock
      - rust/**
      - .github/workflows/rust.yml

@@ -101,9 +100,7 @@ jobs:
          lfs: true
      - uses: Swatinem/rust-cache@v2
      - name: Install dependencies
-        run: |
-          sudo apt update
-          sudo apt install -y protobuf-compiler libssl-dev
+        run: sudo apt install -y protobuf-compiler libssl-dev
      - uses: rui314/setup-mold@v1
      - name: Make Swap
        run: |
@@ -186,7 +183,7 @@ jobs:
    runs-on: ubuntu-24.04
    strategy:
      matrix:
-        msrv: ["1.91.0"] # This should match up with rust-version in Cargo.toml
+        msrv: ["1.88.0"] # This should match up with rust-version in Cargo.toml
    env:
      # Need up-to-date compilers for kernels
      CC: clang-18
@@ -207,14 +204,14 @@ jobs:
      - name: Downgrade  dependencies
        # These packages have newer requirements for MSRV
        run: |
-          cargo update -p aws-sdk-bedrockruntime --precise 1.77.0
-          cargo update -p aws-sdk-dynamodb --precise 1.68.0
-          cargo update -p aws-config --precise 1.6.0
-          cargo update -p aws-sdk-kms --precise 1.63.0
-          cargo update -p aws-sdk-s3 --precise 1.79.0
-          cargo update -p aws-sdk-sso --precise 1.62.0
-          cargo update -p aws-sdk-ssooidc --precise 1.63.0
-          cargo update -p aws-sdk-sts --precise 1.63.0
+          cargo update -p aws-sdk-bedrockruntime --precise 1.64.0
+          cargo update -p aws-sdk-dynamodb --precise 1.55.0
+          cargo update -p aws-config --precise 1.5.10
+          cargo update -p aws-sdk-kms --precise 1.51.0
+          cargo update -p aws-sdk-s3 --precise 1.65.0
+          cargo update -p aws-sdk-sso --precise 1.50.0
+          cargo update -p aws-sdk-ssooidc --precise 1.51.0
+          cargo update -p aws-sdk-sts --precise 1.51.0
          cargo update -p home --precise 0.5.9
      - name: cargo +${{ matrix.msrv }} check
        env:
--- a/Cargo.lock
+++ b/Cargo.lock
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -5,30 +5,30 @@ exclude = ["python"]
 resolver = "2"

 [workspace.package]
-edition = "2024"
+edition = "2021"
 authors = ["LanceDB Devs <dev@lancedb.com>"]
 license = "Apache-2.0"
 repository = "https://github.com/lancedb/lancedb"
 description = "Serverless, low-latency vector database for AI applications"
 keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]
-rust-version = "1.91.0"
+rust-version = "1.88.0"

 [workspace.dependencies]
-lance = { "version" = "=4.0.0-rc.3", default-features = false, "tag" = "v4.0.0-rc.3", "git" = "https://github.com/lance-format/lance.git" }
-lance-core = { "version" = "=4.0.0-rc.3", "tag" = "v4.0.0-rc.3", "git" = "https://github.com/lance-format/lance.git" }
-lance-datagen = { "version" = "=4.0.0-rc.3", "tag" = "v4.0.0-rc.3", "git" = "https://github.com/lance-format/lance.git" }
-lance-file = { "version" = "=4.0.0-rc.3", "tag" = "v4.0.0-rc.3", "git" = "https://github.com/lance-format/lance.git" }
-lance-io = { "version" = "=4.0.0-rc.3", default-features = false, "tag" = "v4.0.0-rc.3", "git" = "https://github.com/lance-format/lance.git" }
-lance-index = { "version" = "=4.0.0-rc.3", "tag" = "v4.0.0-rc.3", "git" = "https://github.com/lance-format/lance.git" }
-lance-linalg = { "version" = "=4.0.0-rc.3", "tag" = "v4.0.0-rc.3", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace = { "version" = "=4.0.0-rc.3", "tag" = "v4.0.0-rc.3", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace-impls = { "version" = "=4.0.0-rc.3", default-features = false, "tag" = "v4.0.0-rc.3", "git" = "https://github.com/lance-format/lance.git" }
-lance-table = { "version" = "=4.0.0-rc.3", "tag" = "v4.0.0-rc.3", "git" = "https://github.com/lance-format/lance.git" }
-lance-testing = { "version" = "=4.0.0-rc.3", "tag" = "v4.0.0-rc.3", "git" = "https://github.com/lance-format/lance.git" }
-lance-datafusion = { "version" = "=4.0.0-rc.3", "tag" = "v4.0.0-rc.3", "git" = "https://github.com/lance-format/lance.git" }
-lance-encoding = { "version" = "=4.0.0-rc.3", "tag" = "v4.0.0-rc.3", "git" = "https://github.com/lance-format/lance.git" }
-lance-arrow = { "version" = "=4.0.0-rc.3", "tag" = "v4.0.0-rc.3", "git" = "https://github.com/lance-format/lance.git" }
+lance = { "version" = "=2.0.1", default-features = false }
+lance-core = "=2.0.1"
+lance-datagen = "=2.0.1"
+lance-file = "=2.0.1"
+lance-io = { "version" = "=2.0.1", default-features = false }
+lance-index = "=2.0.1"
+lance-linalg = "=2.0.1"
+lance-namespace = "=2.0.1"
+lance-namespace-impls = { "version" = "=2.0.1", default-features = false }
+lance-table = "=2.0.1"
+lance-testing = "=2.0.1"
+lance-datafusion = "=2.0.1"
+lance-encoding = "=2.0.1"
+lance-arrow = "=2.0.1"
 ahash = "0.8"
 # Note that this one does not include pyarrow
 arrow = { version = "57.2", optional = false }
@@ -40,15 +40,13 @@ arrow-schema = "57.2"
 arrow-select = "57.2"
 arrow-cast = "57.2"
 async-trait = "0"
-datafusion = { version = "52.1", default-features = false }
-datafusion-catalog = "52.1"
-datafusion-common = { version = "52.1", default-features = false }
-datafusion-execution = "52.1"
-datafusion-expr = "52.1"
-datafusion-functions = "52.1"
-datafusion-physical-plan = "52.1"
-datafusion-physical-expr = "52.1"
-datafusion-sql = "52.1"
+datafusion = { version = "51.0", default-features = false }
+datafusion-catalog = "51.0"
+datafusion-common = { version = "51.0", default-features = false }
+datafusion-execution = "51.0"
+datafusion-expr = "51.0"
+datafusion-physical-plan = "51.0"
+datafusion-physical-expr = "51.0"
 env_logger = "0.11"
 half = { "version" = "2.7.1", default-features = false, features = [
    "num-traits",
--- a/ci/check_lance_release.py
+++ b/ci/check_lance_release.py
@@ -3,7 +3,6 @@
 from __future__ import annotations

 import argparse
-import functools
 import json
 import os
 import re
@@ -27,7 +26,6 @@ SEMVER_RE = re.compile(
 )


-@functools.total_ordering
@dataclass(frozen=True)
 class SemVer:
    major: int
@@ -158,9 +156,7 @@ def read_current_version(repo_root: Path) -> str:


 def determine_latest_tag(tags: Iterable[TagInfo]) -> TagInfo:
-    # Stable releases (no prerelease) are always preferred over pre-releases.
-    # Within each group, standard semver ordering applies.
-    return max(tags, key=lambda tag: (not tag.semver.prerelease, tag.semver))
+    return max(tags, key=lambda tag: tag.semver)


 def write_outputs(args: argparse.Namespace, payload: dict) -> None:
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -1,7 +1,7 @@
 version: "3.9"
 services:
  localstack:
-    image: localstack/localstack:4.0
+    image: localstack/localstack:3.3
    ports:
      - 4566:4566
    environment:
--- a/dockerfiles/Dockerfile
+++ b/dockerfiles/Dockerfile
@@ -1,27 +1,27 @@
-# Simple base dockerfile that supports basic dependencies required to run lance with FTS and Hybrid Search
-# Usage: docker build -t lancedb:latest -f Dockerfile .
-FROM python:3.12-slim-bookworm
+#Simple base dockerfile that supports basic dependencies required to run lance with FTS and Hybrid Search
+#Usage docker build -t lancedb:latest -f Dockerfile .
+FROM python:3.10-slim-buster

-# Install build dependencies in a single layer
-RUN apt-get update && \
-  apt-get install -y --no-install-recommends \
-    curl \
-    build-essential \
-    protobuf-compiler \
-    git \
-    ca-certificates && \
-  apt-get clean && \
-  rm -rf /var/lib/apt/lists/*
-
-# Install Rust (pinned installer, non-interactive)
-RUN curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y --default-toolchain stable --profile minimal
+# Install Rust
+RUN apt-get update && apt-get install -y curl build-essential && \
+  curl https://sh.rustup.rs -sSf | sh -s -- -y

 # Set the environment variable for Rust
 ENV PATH="/root/.cargo/bin:${PATH}"

+# Install protobuf compiler
+RUN apt-get install -y protobuf-compiler && \
+  apt-get clean && \
+  rm -rf /var/lib/apt/lists/*
+
+RUN apt-get -y update &&\
+  apt-get -y upgrade && \
+  apt-get -y install git
+
+
 # Verify installations
 RUN python --version && \
  rustc --version && \
  protoc --version

-RUN pip install --no-cache-dir tantivy lancedb
+RUN pip install tantivy lancedb
--- a/docs/mkdocs.yml
+++ b/docs/mkdocs.yml
@@ -52,21 +52,14 @@ plugins:
          options:
            docstring_style: numpy
            heading_level: 3
+            show_source: true
+            show_symbol_type_in_heading: true
            show_signature_annotations: true
            show_root_heading: true
-            show_docstring_examples: true
-            show_docstring_attributes: false
-            show_docstring_other_parameters: true
-            show_symbol_type_heading: true
-            show_labels: false
-            show_if_no_docstring: true
-            show_source: false
            members_order: source
            docstring_section_style: list
            signature_crossrefs: true
            separate_signature: true
-            filters:
-              - "!^_"
          import:
            # for cross references
            - https://arrow.apache.org/docs/objects.inv
@@ -120,7 +113,7 @@ markdown_extensions:
      emoji_index: !!python/name:material.extensions.emoji.twemoji
      emoji_generator: !!python/name:material.extensions.emoji.to_svg
  - markdown.extensions.toc:
-      toc_depth: 4
+      toc_depth: 3
      permalink: true
      permalink_title: Anchor link to this section

--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1,9 +1,9 @@
-mkdocs==1.6.1
+mkdocs==1.5.3
 mkdocs-jupyter==0.24.1
-mkdocs-material==9.6.23
-mkdocs-autorefs>=0.5,<=1.0
-mkdocstrings[python]>=0.24,<1.0
-griffe>=0.40,<1.0
-mkdocs-render-swagger-plugin>=0.1.0
-pydantic>=2.0,<3.0
-mkdocs-redirects>=1.2.0
+mkdocs-material==9.5.3
+mkdocs-autorefs<=1.0
+mkdocstrings[python]==0.25.2
+griffe
+mkdocs-render-swagger-plugin
+pydantic
+mkdocs-redirects
--- a/docs/src/java/java.md
+++ b/docs/src/java/java.md
@@ -14,7 +14,7 @@ Add the following dependency to your `pom.xml`:
 <dependency>
    <groupId>com.lancedb</groupId>
    <artifactId>lancedb-core</artifactId>
-    <version>0.27.2-beta.1</version>
+    <version>0.27.0-beta.0</version>
 </dependency>
 ```

--- a/docs/src/js/classes/Table.md
+++ b/docs/src/js/classes/Table.md
@@ -71,12 +71,11 @@ Add new columns with defined values.

 #### Parameters

-* **newColumnTransforms**: `Field`&lt;`any`&gt; \| `Field`&lt;`any`&gt;[] \| `Schema`&lt;`any`&gt; \| [`AddColumnsSql`](../interfaces/AddColumnsSql.md)[]
-    Either:
-    - An array of objects with column names and SQL expressions to calculate values
-    - A single Arrow Field defining one column with its data type (column will be initialized with null values)
-    - An array of Arrow Fields defining columns with their data types (columns will be initialized with null values)
-    - An Arrow Schema defining columns with their data types (columns will be initialized with null values)
+* **newColumnTransforms**: [`AddColumnsSql`](../interfaces/AddColumnsSql.md)[]
+    pairs of column names and
+    the SQL expression to use to calculate the value of the new column. These
+    expressions will be evaluated for each row in the table, and can
+    reference existing columns in the table.

 #### Returns

@@ -485,7 +484,19 @@ Modeled after ``VACUUM`` in PostgreSQL.
 - Prune: Removes old versions of the dataset
 - Index: Optimizes the indices, adding new data to existing indices

- The frequency an application should call optimize is based on the frequency of
+ Experimental API
+ ----------------
+
+ The optimization process is undergoing active development and may change.
+ Our goal with these changes is to improve the performance of optimization and
+ reduce the complexity.
+
+ That being said, it is essential today to run optimize if you want the best
+ performance.  It should be stable and safe to use in production, but it our
+ hope that the API may be simplified (or not even need to be called) in the
+ future.
+
+ The frequency an application shoudl call optimize is based on the frequency of
 data modifications.  If data is frequently added, deleted, or updated then
 optimize should be run frequently.  A good rule of thumb is to run optimize if
 you have added or modified 100,000 or more records or run more than 20 data
--- a/docs/src/js/interfaces/DeleteResult.md
+++ b/docs/src/js/interfaces/DeleteResult.md
@@ -8,14 +8,6 @@

 ## Properties

-### numDeletedRows
-
-```ts
-numDeletedRows: number;
-```
-
-***
-
 ### version

 ```ts
--- a/docs/src/js/interfaces/OptimizeOptions.md
+++ b/docs/src/js/interfaces/OptimizeOptions.md
@@ -37,12 +37,3 @@ tbl.optimize({cleanupOlderThan: new Date()});
 ```ts
 deleteUnverified: boolean;
 ```
-
-Because they may be part of an in-progress transaction, files newer than
-7 days old are not deleted by default. If you are sure that there are no
-in-progress transactions, then you can set this to true to delete all
-files older than `cleanupOlderThan`.
-
-**WARNING**: This should only be set to true if you can guarantee that
-no other process is currently working on this dataset. Otherwise the
-dataset could be put into a corrupted state.
--- a/java/README.md
+++ b/java/README.md
@@ -1,4 +1,4 @@
-# LanceDB Java Enterprise Client
+# LanceDB Java SDK

 ## Configuration and Initialization

--- a/java/lancedb-core/pom.xml
+++ b/java/lancedb-core/pom.xml
@@ -8,7 +8,7 @@
    <parent>
      <groupId>com.lancedb</groupId>
      <artifactId>lancedb-parent</artifactId>
-      <version>0.27.2-beta.1</version>
+      <version>0.27.0-beta.0</version>
      <relativePath>../pom.xml</relativePath>
    </parent>

@@ -56,21 +56,21 @@
        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-slf4j2-impl</artifactId>
-            <version>2.25.3</version>
+            <version>2.24.3</version>
            <scope>test</scope>
        </dependency>

        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-core</artifactId>
-            <version>2.25.3</version>
+            <version>2.24.3</version>
            <scope>test</scope>
        </dependency>

        <dependency>
            <groupId>org.apache.logging.log4j</groupId>
            <artifactId>log4j-api</artifactId>
-            <version>2.25.3</version>
+            <version>2.24.3</version>
            <scope>test</scope>
        </dependency>
    </dependencies>
--- a/java/pom.xml
+++ b/java/pom.xml
@@ -6,7 +6,7 @@

    <groupId>com.lancedb</groupId>
    <artifactId>lancedb-parent</artifactId>
-    <version>0.27.2-beta.1</version>
+    <version>0.27.0-beta.0</version>
    <packaging>pom</packaging>
    <name>${project.artifactId}</name>
    <description>LanceDB Java SDK Parent POM</description>
@@ -28,7 +28,7 @@
    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <arrow.version>15.0.0</arrow.version>
-        <lance-core.version>3.0.1</lance-core.version>
+        <lance-core.version>2.0.1</lance-core.version>
        <spotless.skip>false</spotless.skip>
        <spotless.version>2.30.0</spotless.version>
        <spotless.java.googlejavaformat.version>1.7</spotless.java.googlejavaformat.version>
@@ -111,7 +111,7 @@
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-source-plugin</artifactId>
-                <version>3.3.1</version>
+                <version>2.2.1</version>
                <executions>
                    <execution>
                        <id>attach-sources</id>
@@ -124,7 +124,7 @@
            <plugin>
                <groupId>org.apache.maven.plugins</groupId>
                <artifactId>maven-javadoc-plugin</artifactId>
-                <version>3.11.2</version>
+                <version>2.9.1</version>
                <executions>
                    <execution>
                        <id>attach-javadocs</id>
@@ -178,15 +178,15 @@
            <plugins>
                <plugin>
                    <artifactId>maven-clean-plugin</artifactId>
-                    <version>3.4.1</version>
+                    <version>3.1.0</version>
                </plugin>
                <plugin>
                    <artifactId>maven-resources-plugin</artifactId>
-                    <version>3.3.1</version>
+                    <version>3.0.2</version>
                </plugin>
                <plugin>
                    <artifactId>maven-compiler-plugin</artifactId>
-                    <version>3.14.0</version>
+                    <version>3.8.1</version>
                    <configuration>
                        <compilerArgs>
                            <arg>-h</arg>
@@ -205,11 +205,11 @@
                </plugin>
                <plugin>
                    <artifactId>maven-jar-plugin</artifactId>
-                    <version>3.4.2</version>
+                    <version>3.0.2</version>
                </plugin>
                <plugin>
                    <artifactId>maven-install-plugin</artifactId>
-                    <version>3.1.3</version>
+                    <version>2.5.2</version>
                </plugin>
                <plugin>
                    <groupId>com.diffplug.spotless</groupId>
@@ -327,7 +327,7 @@
                    <plugin>
                        <groupId>org.apache.maven.plugins</groupId>
                        <artifactId>maven-gpg-plugin</artifactId>
-                        <version>3.2.7</version>
+                        <version>1.5</version>
                        <executions>
                            <execution>
                                <id>sign-artifacts</id>
--- a/nodejs/Cargo.toml
+++ b/nodejs/Cargo.toml
@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.27.2-beta.1"
+version = "0.27.0-beta.0"
 license.workspace = true
 description.workspace = true
 repository.workspace = true
@@ -19,21 +19,21 @@ arrow-schema.workspace = true
 env_logger.workspace = true
 futures.workspace = true
 lancedb = { path = "../rust/lancedb", default-features = false }
-napi = { version = "3.8.3", default-features = false, features = [
+napi = { version = "2.16.8", default-features = false, features = [
    "napi9",
    "async"
 ] }
-napi-derive = "3.5.2"
+napi-derive = "2.16.4"
 # Prevent dynamic linking of lzma, which comes from datafusion
-lzma-sys = { version = "0.1", features = ["static"] }
+lzma-sys = { version = "*", features = ["static"] }
 log.workspace = true

-# Pin to resolve build failures; update periodically for security patches.
-aws-lc-sys = "=0.38.0"
-aws-lc-rs = "=1.16.1"
+# Workaround for build failure until we can fix it.
+aws-lc-sys = "=0.28.0"
+aws-lc-rs = "=1.13.0"

 [build-dependencies]
-napi-build = "2.3.1"
+napi-build = "2.1"

 [features]
 default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface"]
--- a/nodejs/test/arrow.test.ts
+++ b/nodejs/test/arrow.test.ts
@@ -63,7 +63,6 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
      tableFromIPC,
      DataType,
      Dictionary,
-      Uint8: ArrowUint8,
      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
    } = <any>arrow;
    type Schema = ApacheArrow["Schema"];
@@ -363,38 +362,6 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
        ).toEqual(new Float64().toString());
      });

-      it("will infer FixedSizeList<Float32> from Float32Array values", async function () {
-        const table = makeArrowTable([
-          { id: "a", vector: new Float32Array([0.1, 0.2, 0.3]) },
-          { id: "b", vector: new Float32Array([0.4, 0.5, 0.6]) },
-        ]);
-
-        expect(DataType.isFixedSizeList(table.getChild("vector")?.type)).toBe(
-          true,
-        );
-        const vectorType = table.getChild("vector")?.type;
-        expect(vectorType.listSize).toBe(3);
-        expect(vectorType.children[0].type.toString()).toEqual(
-          new Float32().toString(),
-        );
-      });
-
-      it("will infer FixedSizeList<Uint8> from Uint8Array values", async function () {
-        const table = makeArrowTable([
-          { id: "a", vector: new Uint8Array([1, 2, 3]) },
-          { id: "b", vector: new Uint8Array([4, 5, 6]) },
-        ]);
-
-        expect(DataType.isFixedSizeList(table.getChild("vector")?.type)).toBe(
-          true,
-        );
-        const vectorType = table.getChild("vector")?.type;
-        expect(vectorType.listSize).toBe(3);
-        expect(vectorType.children[0].type.toString()).toEqual(
-          new ArrowUint8().toString(),
-        );
-      });
-
      it("will use dictionary encoded strings if asked", async function () {
        const table = makeArrowTable([{ str: "hello" }]);
        expect(DataType.isUtf8(table.getChild("str")?.type)).toBe(true);
--- a/nodejs/test/table.test.ts
+++ b/nodejs/test/table.test.ts
@@ -1259,98 +1259,6 @@ describe("schema evolution", function () {
    expect(await table.schema()).toEqual(expectedSchema);
  });

-  it("can add columns with schema for explicit data types", async function () {
-    const con = await connect(tmpDir.name);
-    const table = await con.createTable("vectors", [
-      { id: 1n, vector: [0.1, 0.2] },
-    ]);
-
-    // Define schema for new columns with explicit data types
-    // Note: All columns must be nullable when using addColumns with Schema
-    // because they are initially populated with null values
-    const newColumnsSchema = new Schema([
-      new Field("price", new Float64(), true),
-      new Field("category", new Utf8(), true),
-      new Field("rating", new Int32(), true),
-    ]);
-
-    const result = await table.addColumns(newColumnsSchema);
-    expect(result).toHaveProperty("version");
-    expect(result.version).toBe(2);
-
-    const expectedSchema = new Schema([
-      new Field("id", new Int64(), true),
-      new Field(
-        "vector",
-        new FixedSizeList(2, new Field("item", new Float32(), true)),
-        true,
-      ),
-      new Field("price", new Float64(), true),
-      new Field("category", new Utf8(), true),
-      new Field("rating", new Int32(), true),
-    ]);
-    expect(await table.schema()).toEqual(expectedSchema);
-
-    // Verify that new columns are populated with null values
-    const results = await table.query().toArray();
-    expect(results).toHaveLength(1);
-    expect(results[0].price).toBeNull();
-    expect(results[0].category).toBeNull();
-    expect(results[0].rating).toBeNull();
-  });
-
-  it("can add a single column using Field", async function () {
-    const con = await connect(tmpDir.name);
-    const table = await con.createTable("vectors", [
-      { id: 1n, vector: [0.1, 0.2] },
-    ]);
-
-    // Add a single field
-    const priceField = new Field("price", new Float64(), true);
-    const result = await table.addColumns(priceField);
-    expect(result).toHaveProperty("version");
-    expect(result.version).toBe(2);
-
-    const expectedSchema = new Schema([
-      new Field("id", new Int64(), true),
-      new Field(
-        "vector",
-        new FixedSizeList(2, new Field("item", new Float32(), true)),
-        true,
-      ),
-      new Field("price", new Float64(), true),
-    ]);
-    expect(await table.schema()).toEqual(expectedSchema);
-  });
-
-  it("can add multiple columns using array of Fields", async function () {
-    const con = await connect(tmpDir.name);
-    const table = await con.createTable("vectors", [
-      { id: 1n, vector: [0.1, 0.2] },
-    ]);
-
-    // Add multiple fields as array
-    const fields = [
-      new Field("price", new Float64(), true),
-      new Field("category", new Utf8(), true),
-    ];
-    const result = await table.addColumns(fields);
-    expect(result).toHaveProperty("version");
-    expect(result.version).toBe(2);
-
-    const expectedSchema = new Schema([
-      new Field("id", new Int64(), true),
-      new Field(
-        "vector",
-        new FixedSizeList(2, new Field("item", new Float32(), true)),
-        true,
-      ),
-      new Field("price", new Float64(), true),
-      new Field("category", new Utf8(), true),
-    ]);
-    expect(await table.schema()).toEqual(expectedSchema);
-  });
-
  it("can alter the columns in the schema", async function () {
    const con = await connect(tmpDir.name);
    const schema = new Schema([
@@ -1789,65 +1697,6 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
      expect(results2[0].text).toBe(data[1].text);
    });

-    test("full text search fast search", async () => {
-      const db = await connect(tmpDir.name);
-      const data = [{ text: "hello world", vector: [0.1, 0.2, 0.3], id: 1 }];
-      const table = await db.createTable("test", data);
-      await table.createIndex("text", {
-        config: Index.fts(),
-      });
-
-      // Insert unindexed data after creating the index.
-      await table.add([{ text: "xyz", vector: [0.4, 0.5, 0.6], id: 2 }]);
-
-      const withFlatSearch = await table
-        .search("xyz", "fts")
-        .limit(10)
-        .toArray();
-      expect(withFlatSearch.length).toBeGreaterThan(0);
-
-      const fastSearchResults = await table
-        .search("xyz", "fts")
-        .fastSearch()
-        .limit(10)
-        .toArray();
-      expect(fastSearchResults.length).toBe(0);
-
-      const nearestToTextFastSearch = await table
-        .query()
-        .nearestToText("xyz")
-        .fastSearch()
-        .limit(10)
-        .toArray();
-      expect(nearestToTextFastSearch.length).toBe(0);
-
-      // fastSearch should be chainable with other methods.
-      const chainedFastSearch = await table
-        .search("xyz", "fts")
-        .fastSearch()
-        .select(["text"])
-        .limit(5)
-        .toArray();
-      expect(chainedFastSearch.length).toBe(0);
-
-      await table.optimize();
-
-      const indexedFastSearch = await table
-        .search("xyz", "fts")
-        .fastSearch()
-        .limit(10)
-        .toArray();
-      expect(indexedFastSearch.length).toBeGreaterThan(0);
-
-      const indexedNearestToTextFastSearch = await table
-        .query()
-        .nearestToText("xyz")
-        .fastSearch()
-        .limit(10)
-        .toArray();
-      expect(indexedNearestToTextFastSearch.length).toBeGreaterThan(0);
-    });
-
    test("prewarm full text search index", async () => {
      const db = await connect(tmpDir.name);
      const data = [
@@ -2296,36 +2145,3 @@ describe("when creating an empty table", () => {
    expect((actualSchema.fields[1].type as Float64).precision).toBe(2);
  });
 });
-
-// Ensure we can create float32 arrays without using Arrow
-// by utilizing native JS TypedArray support
-//
-// https://github.com/lancedb/lancedb/issues/3115
-describe("when creating a table with Float32Array vectors", () => {
-  let tmpDir: tmp.DirResult;
-  beforeEach(() => {
-    tmpDir = tmp.dirSync({ unsafeCleanup: true });
-  });
-  afterEach(() => {
-    tmpDir.removeCallback();
-  });
-
-  it("should persist Float32Array as FixedSizeList<Float32> in the LanceDB schema", async () => {
-    const db = await connect(tmpDir.name);
-    const table = await db.createTable("test", [
-      { id: "a", vector: new Float32Array([0.1, 0.2, 0.3]) },
-      { id: "b", vector: new Float32Array([0.4, 0.5, 0.6]) },
-    ]);
-
-    const schema = await table.schema();
-    const vectorField = schema.fields.find((f) => f.name === "vector");
-    expect(vectorField).toBeDefined();
-    expect(vectorField!.type).toBeInstanceOf(FixedSizeList);
-
-    const fsl = vectorField!.type as FixedSizeList;
-    expect(fsl.listSize).toBe(3);
-    expect(fsl.children[0].type.typeId).toBe(Type.Float);
-    // precision: HALF=0, SINGLE=1, DOUBLE=2
-    expect((fsl.children[0].type as Float32).precision).toBe(1);
-  });
-});
--- a/nodejs/examples/package-lock.json
+++ b/nodejs/examples/package-lock.json
@@ -30,15 +30,12 @@
        "x64",
        "arm64"
      ],
-      "dev": true,
      "license": "Apache-2.0",
-      "optional": true,
      "os": [
        "darwin",
        "linux",
        "win32"
      ],
-      "peer": true,
      "dependencies": {
        "reflect-metadata": "^0.2.2"
      },
@@ -94,15 +91,14 @@
      }
    },
    "node_modules/@babel/code-frame": {
-      "version": "7.29.0",
-      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.29.0.tgz",
-      "integrity": "sha512-9NhCeYjq9+3uxgdtp20LSiJXJvN0FeCtNGpJxuMFZ1Kv3cWUNb6DOhJwUvcVCzKGR66cw4njwM6hrJLqgOwbcw==",
+      "version": "7.26.2",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.26.2.tgz",
+      "integrity": "sha512-RJlIHRueQgwWitWgF8OdFYGZX328Ax5BCemNGlqHfplnRT9ESi8JkFlvaVYbS+UubVY6dpv87Fs2u5M29iNFVQ==",
      "dev": true,
-      "license": "MIT",
      "dependencies": {
-        "@babel/helper-validator-identifier": "^7.28.5",
+        "@babel/helper-validator-identifier": "^7.25.9",
        "js-tokens": "^4.0.0",
-        "picocolors": "^1.1.1"
+        "picocolors": "^1.0.0"
      },
      "engines": {
        "node": ">=6.9.0"
@@ -237,21 +233,19 @@
      }
    },
    "node_modules/@babel/helper-string-parser": {
-      "version": "7.27.1",
-      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.27.1.tgz",
-      "integrity": "sha512-qMlSxKbpRlAridDExk92nSobyDdpPijUq2DW6oDnUqd0iOGxmQjyqhMIihI9+zv4LPyZdRje2cavWPbCbWm3eA==",
+      "version": "7.25.9",
+      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.25.9.tgz",
+      "integrity": "sha512-4A/SCr/2KLd5jrtOMFzaKjVtAei3+2r/NChoBNoZ3EyP/+GlhoaEGoWOZUmFmoITP7zOJyHIMm+DYRd8o3PvHA==",
      "dev": true,
-      "license": "MIT",
      "engines": {
        "node": ">=6.9.0"
      }
    },
    "node_modules/@babel/helper-validator-identifier": {
-      "version": "7.28.5",
-      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.28.5.tgz",
-      "integrity": "sha512-qSs4ifwzKJSV39ucNjsvc6WVHs6b7S03sOh2OcHF9UHfVPqWWALUsNUVzhSBiItjRZoLHx7nIarVjqKVusUZ1Q==",
+      "version": "7.25.9",
+      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.25.9.tgz",
+      "integrity": "sha512-Ed61U6XJc3CVRfkERJWDz4dJwKe7iLmmJsbOGu9wSloNSFttHV0I8g6UAgb7qnK5ly5bGLPd4oXZlxCdANBOWQ==",
      "dev": true,
-      "license": "MIT",
      "engines": {
        "node": ">=6.9.0"
      }
@@ -266,27 +260,25 @@
      }
    },
    "node_modules/@babel/helpers": {
-      "version": "7.28.6",
-      "resolved": "https://registry.npmjs.org/@babel/helpers/-/helpers-7.28.6.tgz",
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    },
    "node_modules/tar": {
-      "version": "7.5.10",
-      "resolved": "https://registry.npmjs.org/tar/-/tar-7.5.10.tgz",
-      "integrity": "sha512-8mOPs1//5q/rlkNSPcCegA6hiHJYDmSLEI8aMH/CdSQJNWztHC9WHNam5zdQlfpTwB9Xp7IBEsHfV5LKMJGVAw==",
-      "license": "BlueOak-1.0.0",
+      "version": "7.4.3",
+      "resolved": "https://registry.npmjs.org/tar/-/tar-7.4.3.tgz",
+      "integrity": "sha512-5S7Va8hKfV7W5U6g3aYxXmlPoZVAwUMy9AOKyF2fVuZa2UD3qZjg578OrLRt8PcNN1PleVaL/5/yYATNL0ICUw==",
      "dependencies": {
        "@isaacs/fs-minipass": "^4.0.0",
        "chownr": "^3.0.0",
        "minipass": "^7.1.2",
-        "minizlib": "^3.1.0",
+        "minizlib": "^3.0.1",
+        "mkdirp": "^3.0.1",
        "yallist": "^5.0.0"
      },
      "engines": {
@@ -4705,7 +4782,6 @@
      "version": "2.0.2",
      "resolved": "https://registry.npmjs.org/which/-/which-2.0.2.tgz",
      "integrity": "sha512-BLI3Tl1TW3Pvl70l3yq3Y64i+awpwXqsGBYWkkqMtnbXgrMD+yj7rhW0kuEDxzJaYXGjEW5ogapKNMEKNMjibA==",
-      "dev": true,
      "dependencies": {
        "isexe": "^2.0.0"
      },
@@ -4733,6 +4809,23 @@
        "url": "https://github.com/chalk/wrap-ansi?sponsor=1"
      }
    },
+    "node_modules/wrap-ansi-cjs": {
+      "name": "wrap-ansi",
+      "version": "7.0.0",
+      "resolved": "https://registry.npmjs.org/wrap-ansi/-/wrap-ansi-7.0.0.tgz",
+      "integrity": "sha512-YVGIj2kamLSTxw6NsZjoBxfSwsn0ycdesmc4p+Q21c5zPuZ1pl+NfxVdxPtdHvmNVOQ6XSYG4AUtyt/Fi7D16Q==",
+      "dependencies": {
+        "ansi-styles": "^4.0.0",
+        "string-width": "^4.1.0",
+        "strip-ansi": "^6.0.0"
+      },
+      "engines": {
+        "node": ">=10"
+      },
+      "funding": {
+        "url": "https://github.com/chalk/wrap-ansi?sponsor=1"
+      }
+    },
    "node_modules/wrappy": {
      "version": "1.0.2",
      "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
--- a/nodejs/lancedb/arrow.ts
+++ b/nodejs/lancedb/arrow.ts
@@ -20,8 +20,6 @@ import {
  Float32,
  Float64,
  Int,
-  Int8,
-  Int16,
  Int32,
  Int64,
  LargeBinary,
@@ -37,8 +35,6 @@ import {
  Timestamp,
  Type,
  Uint8,
-  Uint16,
-  Uint32,
  Utf8,
  Vector,
  makeVector as arrowMakeVector,
@@ -533,8 +529,7 @@ function isObject(value: unknown): value is Record<string, unknown> {
    !(value instanceof Date) &&
    !(value instanceof Set) &&
    !(value instanceof Map) &&
-    !(value instanceof Buffer) &&
-    !ArrayBuffer.isView(value)
+    !(value instanceof Buffer)
  );
 }

@@ -593,13 +588,6 @@ function inferType(
    return new Bool();
  } else if (value instanceof Buffer) {
    return new Binary();
-  } else if (ArrayBuffer.isView(value) && !(value instanceof DataView)) {
-    const info = typedArrayToArrowType(value);
-    if (info !== undefined) {
-      const child = new Field("item", info.elementType, true);
-      return new FixedSizeList(info.length, child);
-    }
-    return undefined;
  } else if (Array.isArray(value)) {
    if (value.length === 0) {
      return undefined; // Without any values we can't infer the type
@@ -758,32 +746,6 @@ function makeListVector(lists: unknown[][]): Vector<unknown> {
  return listBuilder.finish().toVector();
 }

-/**
- * Map a JS TypedArray instance to the corresponding Arrow element DataType
- * and its length. Returns undefined if the value is not a recognized TypedArray.
- */
-function typedArrayToArrowType(
-  value: ArrayBufferView,
-): { elementType: DataType; length: number } | undefined {
-  if (value instanceof Float32Array)
-    return { elementType: new Float32(), length: value.length };
-  if (value instanceof Float64Array)
-    return { elementType: new Float64(), length: value.length };
-  if (value instanceof Uint8Array)
-    return { elementType: new Uint8(), length: value.length };
-  if (value instanceof Uint16Array)
-    return { elementType: new Uint16(), length: value.length };
-  if (value instanceof Uint32Array)
-    return { elementType: new Uint32(), length: value.length };
-  if (value instanceof Int8Array)
-    return { elementType: new Int8(), length: value.length };
-  if (value instanceof Int16Array)
-    return { elementType: new Int16(), length: value.length };
-  if (value instanceof Int32Array)
-    return { elementType: new Int32(), length: value.length };
-  return undefined;
-}
-
 /** Helper function to convert an Array of JS values to an Arrow Vector */
 function makeVector(
  values: unknown[],
@@ -852,16 +814,6 @@ function makeVector(
      "makeVector cannot infer the type if all values are null or undefined",
    );
  }
-  if (ArrayBuffer.isView(sampleValue) && !(sampleValue instanceof DataView)) {
-    const info = typedArrayToArrowType(sampleValue);
-    if (info !== undefined) {
-      const fslType = new FixedSizeList(
-        info.length,
-        new Field("item", info.elementType, true),
-      );
-      return vectorFromArray(values, fslType);
-    }
-  }
  if (Array.isArray(sampleValue)) {
    // Default Arrow inference doesn't handle list types
    return makeListVector(values as unknown[][]);
--- a/nodejs/lancedb/index.ts
+++ b/nodejs/lancedb/index.ts
@@ -273,9 +273,7 @@ export async function connect(
  let nativeProvider: NativeJsHeaderProvider | undefined;
  if (finalHeaderProvider) {
    if (typeof finalHeaderProvider === "function") {
-      nativeProvider = new NativeJsHeaderProvider(async () =>
-        finalHeaderProvider(),
-      );
+      nativeProvider = new NativeJsHeaderProvider(finalHeaderProvider);
    } else if (
      finalHeaderProvider &&
      typeof finalHeaderProvider.getHeaders === "function"
--- a/nodejs/lancedb/query.ts
+++ b/nodejs/lancedb/query.ts
@@ -684,17 +684,19 @@ export class VectorQuery extends StandardQueryBase<NativeVectorQuery> {

  rerank(reranker: Reranker): VectorQuery {
    super.doCall((inner) =>
-      inner.rerank(async (args) => {
-        const vecResults = await fromBufferToRecordBatch(args.vecResults);
-        const ftsResults = await fromBufferToRecordBatch(args.ftsResults);
-        const result = await reranker.rerankHybrid(
-          args.query,
-          vecResults as RecordBatch,
-          ftsResults as RecordBatch,
-        );
+      inner.rerank({
+        rerankHybrid: async (_, args) => {
+          const vecResults = await fromBufferToRecordBatch(args.vecResults);
+          const ftsResults = await fromBufferToRecordBatch(args.ftsResults);
+          const result = await reranker.rerankHybrid(
+            args.query,
+            vecResults as RecordBatch,
+            ftsResults as RecordBatch,
+          );

-        const buffer = fromRecordBatchToBuffer(result);
-        return buffer;
+          const buffer = fromRecordBatchToBuffer(result);
+          return buffer;
+        },
      }),
    );

--- a/nodejs/lancedb/table.ts
+++ b/nodejs/lancedb/table.ts
@@ -5,15 +5,12 @@ import {
  Table as ArrowTable,
  Data,
  DataType,
-  Field,
  IntoVector,
  MultiVector,
  Schema,
  dataTypeToJson,
  fromDataToBuffer,
-  fromTableToBuffer,
  isMultiVector,
-  makeEmptyTable,
  tableFromIPC,
 } from "./arrow";

@@ -87,16 +84,6 @@ export interface OptimizeOptions {
   * tbl.optimize({cleanupOlderThan: new Date()});
   */
  cleanupOlderThan: Date;
-  /**
-   * Because they may be part of an in-progress transaction, files newer than
-   * 7 days old are not deleted by default. If you are sure that there are no
-   * in-progress transactions, then you can set this to true to delete all
-   * files older than `cleanupOlderThan`.
-   *
-   * **WARNING**: This should only be set to true if you can guarantee that
-   * no other process is currently working on this dataset. Otherwise the
-   * dataset could be put into a corrupted state.
-   */
  deleteUnverified: boolean;
 }

@@ -394,16 +381,15 @@ export abstract class Table {
  abstract vectorSearch(vector: IntoVector | MultiVector): VectorQuery;
  /**
   * Add new columns with defined values.
-   * @param {AddColumnsSql[] | Field | Field[] | Schema} newColumnTransforms Either:
-   *   - An array of objects with column names and SQL expressions to calculate values
-   *   - A single Arrow Field defining one column with its data type (column will be initialized with null values)
-   *   - An array of Arrow Fields defining columns with their data types (columns will be initialized with null values)
-   *   - An Arrow Schema defining columns with their data types (columns will be initialized with null values)
+   * @param {AddColumnsSql[]} newColumnTransforms pairs of column names and
+   * the SQL expression to use to calculate the value of the new column. These
+   * expressions will be evaluated for each row in the table, and can
+   * reference existing columns in the table.
   * @returns {Promise<AddColumnsResult>} A promise that resolves to an object
   * containing the new version number of the table after adding the columns.
   */
  abstract addColumns(
-    newColumnTransforms: AddColumnsSql[] | Field | Field[] | Schema,
+    newColumnTransforms: AddColumnsSql[],
  ): Promise<AddColumnsResult>;

  /**
@@ -515,7 +501,19 @@ export abstract class Table {
   *  - Index: Optimizes the indices, adding new data to existing indices
   *
   *
-   *  The frequency an application should call optimize is based on the frequency of
+   *  Experimental API
+   *  ----------------
+   *
+   *  The optimization process is undergoing active development and may change.
+   *  Our goal with these changes is to improve the performance of optimization and
+   *  reduce the complexity.
+   *
+   *  That being said, it is essential today to run optimize if you want the best
+   *  performance.  It should be stable and safe to use in production, but it our
+   *  hope that the API may be simplified (or not even need to be called) in the
+   *  future.
+   *
+   *  The frequency an application shoudl call optimize is based on the frequency of
   *  data modifications.  If data is frequently added, deleted, or updated then
   *  optimize should be run frequently.  A good rule of thumb is to run optimize if
   *  you have added or modified 100,000 or more records or run more than 20 data
@@ -808,40 +806,9 @@ export class LocalTable extends Table {
  // TODO: Support BatchUDF

  async addColumns(
-    newColumnTransforms: AddColumnsSql[] | Field | Field[] | Schema,
+    newColumnTransforms: AddColumnsSql[],
  ): Promise<AddColumnsResult> {
-    // Handle single Field -> convert to array of Fields
-    if (newColumnTransforms instanceof Field) {
-      newColumnTransforms = [newColumnTransforms];
-    }
-
-    // Handle array of Fields -> convert to Schema
-    if (
-      Array.isArray(newColumnTransforms) &&
-      newColumnTransforms.length > 0 &&
-      newColumnTransforms[0] instanceof Field
-    ) {
-      const fields = newColumnTransforms as Field[];
-      newColumnTransforms = new Schema(fields);
-    }
-
-    // Handle Schema -> use schema-based approach
-    if (newColumnTransforms instanceof Schema) {
-      const schema = newColumnTransforms;
-      // Convert schema to buffer using Arrow IPC format
-      const emptyTable = makeEmptyTable(schema);
-      const schemaBuf = await fromTableToBuffer(emptyTable);
-      return await this.inner.addColumnsWithSchema(schemaBuf);
-    }
-
-    // Handle SQL expressions (existing functionality)
-    if (Array.isArray(newColumnTransforms)) {
-      return await this.inner.addColumns(
-        newColumnTransforms as AddColumnsSql[],
-      );
-    }
-
-    throw new Error("Invalid input type for addColumns");
+    return await this.inner.addColumns(newColumnTransforms);
  }

  async alterColumns(
--- a/nodejs/npm/darwin-arm64/package.json
+++ b/nodejs/npm/darwin-arm64/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.27.2-beta.1",
+	"version": "0.27.0-beta.0",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",
--- a/nodejs/npm/linux-arm64-gnu/package.json
+++ b/nodejs/npm/linux-arm64-gnu/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.27.2-beta.1",
+	"version": "0.27.0-beta.0",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",
--- a/nodejs/npm/linux-arm64-musl/package.json
+++ b/nodejs/npm/linux-arm64-musl/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.27.2-beta.1",
+	"version": "0.27.0-beta.0",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-musl.node",
--- a/nodejs/npm/linux-x64-gnu/package.json
+++ b/nodejs/npm/linux-x64-gnu/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.27.2-beta.1",
+	"version": "0.27.0-beta.0",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",
--- a/nodejs/npm/linux-x64-musl/package.json
+++ b/nodejs/npm/linux-x64-musl/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.27.2-beta.1",
+	"version": "0.27.0-beta.0",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-musl.node",
--- a/nodejs/npm/win32-arm64-msvc/package.json
+++ b/nodejs/npm/win32-arm64-msvc/package.json
@@ -1,6 +1,6 @@
 {
  "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.27.2-beta.1",
+  "version": "0.27.0-beta.0",
  "os": [
    "win32"
  ],
--- a/nodejs/npm/win32-x64-msvc/package.json
+++ b/nodejs/npm/win32-x64-msvc/package.json
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.27.2-beta.1",
+	"version": "0.27.0-beta.0",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",
--- a/nodejs/package-lock.json
+++ b/nodejs/package-lock.json
--- a/nodejs/package.json
+++ b/nodejs/package.json
@@ -11,7 +11,7 @@
    "ann"
  ],
  "private": false,
-  "version": "0.27.2-beta.1",
+  "version": "0.27.0-beta.0",
  "main": "dist/index.js",
  "exports": {
    ".": "./dist/index.js",
@@ -21,16 +21,19 @@
  },
  "types": "dist/index.d.ts",
  "napi": {
-    "binaryName": "lancedb",
-    "targets": [
-      "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"
-    ]
+    "name": "lancedb",
+    "triples": {
+      "defaults": false,
+      "additional": [
+        "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"
+      ]
+    }
  },
  "license": "Apache-2.0",
  "repository": {
@@ -43,7 +46,7 @@
    "@aws-sdk/client-s3": "^3.33.0",
    "@biomejs/biome": "^1.7.3",
    "@jest/globals": "^29.7.0",
-    "@napi-rs/cli": "^3.5.1",
+    "@napi-rs/cli": "^2.18.3",
    "@types/axios": "^0.14.0",
    "@types/jest": "^29.1.2",
    "@types/node": "^22.7.4",
@@ -72,9 +75,9 @@
  "os": ["darwin", "linux", "win32"],
  "scripts": {
    "artifacts": "napi artifacts",
-    "build:debug": "napi build --platform --dts ../lancedb/native.d.ts --js ../lancedb/native.js --output-dir lancedb",
+    "build:debug": "napi build --platform --no-const-enum --dts ../lancedb/native.d.ts --js ../lancedb/native.js lancedb",
    "postbuild:debug": "shx mkdir -p dist && shx cp lancedb/*.node dist/",
-    "build:release": "napi build --platform --release --dts ../lancedb/native.d.ts --js ../lancedb/native.js --output-dir dist",
+    "build:release": "napi build --platform --no-const-enum --release --dts ../lancedb/native.d.ts --js ../lancedb/native.js dist/",
    "postbuild:release": "shx mkdir -p dist && shx cp lancedb/*.node dist/",
    "build": "npm run build:debug && npm run tsc",
    "build-release": "npm run build:release && npm run tsc",
@@ -88,7 +91,7 @@
    "prepublishOnly": "napi prepublish -t npm",
    "test": "jest --verbose",
    "integration": "S3_TEST=1 npm run test",
-    "universal": "napi universalize",
+    "universal": "napi universal",
    "version": "napi version"
  },
  "dependencies": {
--- a/nodejs/src/connection.rs
+++ b/nodejs/src/connection.rs
@@ -8,10 +8,10 @@ use lancedb::database::{CreateTableMode, Database};
 use napi::bindgen_prelude::*;
 use napi_derive::*;

-use crate::ConnectionOptions;
 use crate::error::NapiErrorExt;
 use crate::header::JsHeaderProvider;
 use crate::table::Table;
+use crate::ConnectionOptions;
 use lancedb::connection::{ConnectBuilder, Connection as LanceDBConnection};

 use lancedb::ipc::{ipc_file_to_batches, ipc_file_to_schema};
--- a/nodejs/src/header.rs
+++ b/nodejs/src/header.rs
@@ -1,19 +1,20 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors

-use napi::{bindgen_prelude::*, threadsafe_function::ThreadsafeFunction};
+use napi::{
+    bindgen_prelude::*,
+    threadsafe_function::{ErrorStrategy, ThreadsafeFunction},
+};
 use napi_derive::napi;
 use std::collections::HashMap;
 use std::sync::Arc;

-type GetHeadersFn = ThreadsafeFunction<(), Promise<HashMap<String, String>>, (), Status, false>;
-
 /// JavaScript HeaderProvider implementation that wraps a JavaScript callback.
 /// This is the only native header provider - all header provider implementations
 /// should provide a JavaScript function that returns headers.
 #[napi]
 pub struct JsHeaderProvider {
-    get_headers_fn: Arc<GetHeadersFn>,
+    get_headers_fn: Arc<ThreadsafeFunction<(), ErrorStrategy::CalleeHandled>>,
 }

 impl Clone for JsHeaderProvider {
@@ -28,12 +29,9 @@ impl Clone for JsHeaderProvider {
 impl JsHeaderProvider {
    /// Create a new JsHeaderProvider from a JavaScript callback
    #[napi(constructor)]
-    pub fn new(
-        get_headers_callback: Function<(), Promise<HashMap<String, String>>>,
-    ) -> Result<Self> {
+    pub fn new(get_headers_callback: JsFunction) -> Result<Self> {
        let get_headers_fn = get_headers_callback
-            .build_threadsafe_function()
-            .build()
+            .create_threadsafe_function(0, |ctx| Ok(vec![ctx.value]))
            .map_err(|e| {
                Error::new(
                    Status::GenericFailure,
@@ -53,7 +51,7 @@ impl lancedb::remote::HeaderProvider for JsHeaderProvider {
    async fn get_headers(&self) -> lancedb::error::Result<HashMap<String, String>> {
        // Call the JavaScript function asynchronously
        let promise: Promise<HashMap<String, String>> =
-            self.get_headers_fn.call_async(()).await.map_err(|e| {
+            self.get_headers_fn.call_async(Ok(())).await.map_err(|e| {
                lancedb::error::Error::Runtime {
                    message: format!("Failed to call JavaScript get_headers: {}", e),
                }
--- a/nodejs/src/index.rs
+++ b/nodejs/src/index.rs
@@ -3,12 +3,12 @@

 use std::sync::Mutex;

-use lancedb::index::Index as LanceDbIndex;
 use lancedb::index::scalar::{BTreeIndexBuilder, FtsIndexBuilder};
 use lancedb::index::vector::{
    IvfFlatIndexBuilder, IvfHnswPqIndexBuilder, IvfHnswSqIndexBuilder, IvfPqIndexBuilder,
    IvfRqIndexBuilder,
 };
+use lancedb::index::Index as LanceDbIndex;
 use napi_derive::napi;

 use crate::util::parse_distance_type;
--- a/nodejs/src/lib.rs
+++ b/nodejs/src/lib.rs
@@ -60,7 +60,7 @@ pub struct OpenTableOptions {
    pub storage_options: Option<HashMap<String, String>>,
 }

-#[napi_derive::module_init]
+#[napi::module_init]
 fn init() {
    let env = Env::new()
        .filter_or("LANCEDB_LOG", "warn")
--- a/nodejs/src/query.rs
+++ b/nodejs/src/query.rs
@@ -17,11 +17,11 @@ use lancedb::query::VectorQuery as LanceDbVectorQuery;
 use napi::bindgen_prelude::*;
 use napi_derive::napi;

-use crate::error::NapiErrorExt;
 use crate::error::convert_error;
+use crate::error::NapiErrorExt;
 use crate::iterator::RecordBatchIterator;
-use crate::rerankers::RerankHybridCallbackArgs;
 use crate::rerankers::Reranker;
+use crate::rerankers::RerankerCallbacks;
 use crate::util::{parse_distance_type, schema_to_buffer};

 #[napi]
@@ -42,7 +42,7 @@ impl Query {
    }

    #[napi]
-    pub fn full_text_search(&mut self, query: Object) -> napi::Result<()> {
+    pub fn full_text_search(&mut self, query: napi::JsObject) -> napi::Result<()> {
        let query = parse_fts_query(query)?;
        self.inner = self.inner.clone().full_text_search(query);
        Ok(())
@@ -235,7 +235,7 @@ impl VectorQuery {
    }

    #[napi]
-    pub fn full_text_search(&mut self, query: Object) -> napi::Result<()> {
+    pub fn full_text_search(&mut self, query: napi::JsObject) -> napi::Result<()> {
        let query = parse_fts_query(query)?;
        self.inner = self.inner.clone().full_text_search(query);
        Ok(())
@@ -272,13 +272,11 @@ impl VectorQuery {
    }

    #[napi]
-    pub fn rerank(
-        &mut self,
-        rerank_hybrid: Function<RerankHybridCallbackArgs, Promise<Buffer>>,
-    ) -> napi::Result<()> {
-        let reranker = Reranker::new(rerank_hybrid)?;
-        self.inner = self.inner.clone().rerank(Arc::new(reranker));
-        Ok(())
+    pub fn rerank(&mut self, callbacks: RerankerCallbacks) {
+        self.inner = self
+            .inner
+            .clone()
+            .rerank(Arc::new(Reranker::new(callbacks)));
    }

    #[napi(catch_unwind)]
@@ -525,12 +523,12 @@ impl JsFullTextQuery {
    }
 }

-fn parse_fts_query(query: Object) -> napi::Result<FullTextSearchQuery> {
-    if let Ok(Some(query)) = query.get::<&JsFullTextQuery>("query") {
+fn parse_fts_query(query: napi::JsObject) -> napi::Result<FullTextSearchQuery> {
+    if let Ok(Some(query)) = query.get::<_, &JsFullTextQuery>("query") {
        Ok(FullTextSearchQuery::new_query(query.inner.clone()))
-    } else if let Ok(Some(query_text)) = query.get::<String>("query") {
+    } else if let Ok(Some(query_text)) = query.get::<_, String>("query") {
        let mut query_text = query_text;
-        let columns = query.get::<Option<Vec<String>>>("columns")?.flatten();
+        let columns = query.get::<_, Option<Vec<String>>>("columns")?.flatten();

        let is_phrase =
            query_text.len() >= 2 && query_text.starts_with('"') && query_text.ends_with('"');
@@ -551,12 +549,15 @@ fn parse_fts_query(query: Object) -> napi::Result<FullTextSearchQuery> {
            }
        };
        let mut query = FullTextSearchQuery::new_query(query);
-        if let Some(cols) = columns
-            && !cols.is_empty()
-        {
-            query = query.with_columns(&cols).map_err(|e| {
-                napi::Error::from_reason(format!("Failed to set full text search columns: {}", e))
-            })?;
+        if let Some(cols) = columns {
+            if !cols.is_empty() {
+                query = query.with_columns(&cols).map_err(|e| {
+                    napi::Error::from_reason(format!(
+                        "Failed to set full text search columns: {}",
+                        e
+                    ))
+                })?;
+            }
        }
        Ok(query)
    } else {
--- a/nodejs/src/rerankers.rs
+++ b/nodejs/src/rerankers.rs
@@ -3,7 +3,10 @@

 use arrow_array::RecordBatch;
 use async_trait::async_trait;
-use napi::{bindgen_prelude::*, threadsafe_function::ThreadsafeFunction};
+use napi::{
+    bindgen_prelude::*,
+    threadsafe_function::{ErrorStrategy, ThreadsafeFunction},
+};
 use napi_derive::napi;

 use lancedb::ipc::batches_to_ipc_file;
@@ -12,28 +15,27 @@ use lancedb::{error::Error, ipc::ipc_file_to_batches};

 use crate::error::NapiErrorExt;

-type RerankHybridFn = ThreadsafeFunction<
-    RerankHybridCallbackArgs,
-    Promise<Buffer>,
-    RerankHybridCallbackArgs,
-    Status,
-    false,
->;
-
 /// Reranker implementation that "wraps" a NodeJS Reranker implementation.
 /// This contains references to the callbacks that can be used to invoke the
 /// reranking methods on the NodeJS implementation and handles serializing the
 /// record batches to Arrow IPC buffers.
+#[napi]
 pub struct Reranker {
-    rerank_hybrid: RerankHybridFn,
+    /// callback to the Javascript which will call the rerankHybrid method of
+    /// some Reranker implementation
+    rerank_hybrid: ThreadsafeFunction<RerankHybridCallbackArgs, ErrorStrategy::CalleeHandled>,
 }

+#[napi]
 impl Reranker {
-    pub fn new(
-        rerank_hybrid: Function<RerankHybridCallbackArgs, Promise<Buffer>>,
-    ) -> napi::Result<Self> {
-        let rerank_hybrid = rerank_hybrid.build_threadsafe_function().build()?;
-        Ok(Self { rerank_hybrid })
+    #[napi]
+    pub fn new(callbacks: RerankerCallbacks) -> Self {
+        let rerank_hybrid = callbacks
+            .rerank_hybrid
+            .create_threadsafe_function(0, move |ctx| Ok(vec![ctx.value]))
+            .unwrap();
+
+        Self { rerank_hybrid }
    }
 }

@@ -47,16 +49,16 @@ impl lancedb::rerankers::Reranker for Reranker {
    ) -> lancedb::error::Result<RecordBatch> {
        let callback_args = RerankHybridCallbackArgs {
            query: query.to_string(),
-            vec_results: Buffer::from(batches_to_ipc_file(&[vector_results])?.as_ref()),
-            fts_results: Buffer::from(batches_to_ipc_file(&[fts_results])?.as_ref()),
+            vec_results: batches_to_ipc_file(&[vector_results])?,
+            fts_results: batches_to_ipc_file(&[fts_results])?,
        };
        let promised_buffer: Promise<Buffer> = self
            .rerank_hybrid
-            .call_async(callback_args)
+            .call_async(Ok(callback_args))
            .await
            .map_err(|e| Error::Runtime {
-            message: format!("napi error status={}, reason={}", e.status, e.reason),
-        })?;
+                message: format!("napi error status={}, reason={}", e.status, e.reason),
+            })?;
        let buffer = promised_buffer.await.map_err(|e| Error::Runtime {
            message: format!("napi error status={}, reason={}", e.status, e.reason),
        })?;
@@ -75,11 +77,16 @@ impl std::fmt::Debug for Reranker {
    }
 }

+#[napi(object)]
+pub struct RerankerCallbacks {
+    pub rerank_hybrid: JsFunction,
+}
+
 #[napi(object)]
 pub struct RerankHybridCallbackArgs {
    pub query: String,
-    pub vec_results: Buffer,
-    pub fts_results: Buffer,
+    pub vec_results: Vec<u8>,
+    pub fts_results: Vec<u8>,
 }

 fn buffer_to_record_batch(buffer: Buffer) -> Result<RecordBatch> {
--- a/nodejs/src/session.rs
+++ b/nodejs/src/session.rs
@@ -95,7 +95,8 @@ impl napi::bindgen_prelude::FromNapiValue for Session {
        napi_val: napi::sys::napi_value,
    ) -> napi::Result<Self> {
        let object: napi::bindgen_prelude::ClassInstance<Self> =
-            unsafe { napi::bindgen_prelude::ClassInstance::from_napi_value(env, napi_val)? };
-        Ok((*object).clone())
+            napi::bindgen_prelude::ClassInstance::from_napi_value(env, napi_val)?;
+        let copy = object.clone();
+        Ok(copy)
    }
 }
--- a/nodejs/src/table.rs
+++ b/nodejs/src/table.rs
@@ -3,7 +3,7 @@

 use std::collections::HashMap;

-use lancedb::ipc::{ipc_file_to_batches, ipc_file_to_schema};
+use lancedb::ipc::ipc_file_to_batches;
 use lancedb::table::{
    AddDataMode, ColumnAlteration as LanceColumnAlteration, Duration, NewColumnTransform,
    OptimizeAction, OptimizeOptions, Table as LanceDbTable,
@@ -71,17 +71,6 @@ impl Table {
    pub async fn add(&self, buf: Buffer, mode: String) -> napi::Result<AddResult> {
        let batches = ipc_file_to_batches(buf.to_vec())
            .map_err(|e| napi::Error::from_reason(format!("Failed to read IPC file: {}", e)))?;
-        let batches = batches
-            .into_iter()
-            .map(|batch| {
-                batch.map_err(|e| {
-                    napi::Error::from_reason(format!(
-                        "Failed to read record batch from IPC file: {}",
-                        e
-                    ))
-                })
-            })
-            .collect::<Result<Vec<_>>>()?;
        let mut op = self.inner_ref()?.add(batches);

        op = if mode == "append" {
@@ -279,23 +268,6 @@ impl Table {
        Ok(res.into())
    }

-    #[napi(catch_unwind)]
-    pub async fn add_columns_with_schema(
-        &self,
-        schema_buf: Buffer,
-    ) -> napi::Result<AddColumnsResult> {
-        let schema = ipc_file_to_schema(schema_buf.to_vec())
-            .map_err(|e| napi::Error::from_reason(format!("Failed to read IPC schema: {}", e)))?;
-
-        let transforms = NewColumnTransform::AllNulls(schema);
-        let res = self
-            .inner_ref()?
-            .add_columns(transforms, None)
-            .await
-            .default_error()?;
-        Ok(res.into())
-    }
-
    #[napi(catch_unwind)]
    pub async fn alter_columns(
        &self,
@@ -770,14 +742,12 @@ impl From<lancedb::table::AddResult> for AddResult {

 #[napi(object)]
 pub struct DeleteResult {
-    pub num_deleted_rows: i64,
    pub version: i64,
 }

 impl From<lancedb::table::DeleteResult> for DeleteResult {
    fn from(value: lancedb::table::DeleteResult) -> Self {
        Self {
-            num_deleted_rows: value.num_deleted_rows as i64,
            version: value.version as i64,
        }
    }
--- a/python/.bumpversion.toml
+++ b/python/.bumpversion.toml
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.30.2-beta.1"
+current_version = "0.30.0-beta.0"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
--- a/python/Cargo.toml
+++ b/python/Cargo.toml
@@ -1,13 +1,13 @@
 [package]
 name = "lancedb-python"
-version = "0.30.2-beta.1"
+version = "0.30.0-beta.0"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true
 repository.workspace = true
 keywords.workspace = true
 categories.workspace = true
-rust-version = "1.91.0"
+rust-version = "1.88.0"

 [lib]
 name = "_lancedb"
@@ -16,14 +16,11 @@ crate-type = ["cdylib"]
 [dependencies]
 arrow = { version = "57.2", features = ["pyarrow"] }
 async-trait = "0.1"
-bytes = "1"
 lancedb = { path = "../rust/lancedb", default-features = false }
 lance-core.workspace = true
 lance-namespace.workspace = true
-lance-namespace-impls.workspace = true
 lance-io.workspace = true
 env_logger.workspace = true
-log.workspace = true
 pyo3 = { version = "0.26", features = ["extension-module", "abi3-py39"] }
 pyo3-async-runtimes = { version = "0.26", features = [
    "attributes",
@@ -31,8 +28,6 @@ pyo3-async-runtimes = { version = "0.26", features = [
 ] }
 pin-project = "1.1.5"
 futures.workspace = true
-serde = "1"
-serde_json = "1"
 snafu.workspace = true
 tokio = { version = "1.40", features = ["sync"] }

--- a/python/README.md
+++ b/python/README.md
@@ -1,4 +1,4 @@
-# LanceDB Python SDK
+# LanceDB

 A Python library for [LanceDB](https://github.com/lancedb/lancedb).

--- a/python/pyproject.toml
+++ b/python/pyproject.toml
@@ -3,10 +3,10 @@ name = "lancedb"
 # version in Cargo.toml
 dynamic = ["version"]
 dependencies = [
-    "deprecation>=2.1.0",
-    "numpy>=1.24.0",
+    "deprecation",
+    "numpy",
    "overrides>=0.7; python_version<'3.12'",
-    "packaging>=23.0",
+    "packaging",
    "pyarrow>=16",
    "pydantic>=1.10",
    "tqdm>=4.27.0",
@@ -45,51 +45,51 @@ repository = "https://github.com/lancedb/lancedb"

 [project.optional-dependencies]
 pylance = [
-    "pylance>=4.0.0b7",
+    "pylance>=1.0.0b14",
 ]
 tests = [
-    "aiohttp>=3.9.0",
-    "boto3>=1.28.57",
+    "aiohttp",
+    "boto3",
    "pandas>=1.4",
-    "pytest>=7.0",
-    "pytest-mock>=3.10",
-    "pytest-asyncio>=0.21",
-    "duckdb>=0.9.0",
-    "pytz>=2023.3",
+    "pytest",
+    "pytest-mock",
+    "pytest-asyncio",
+    "duckdb",
+    "pytz",
    "polars>=0.19, <=1.3.0",
-    "tantivy>=0.20.0",
-    "pyarrow-stubs>=16.0",
-    "pylance>=4.0.0b7",
-    "requests>=2.31.0",
-    "datafusion>=52,<53",
+    "tantivy",
+    "pyarrow-stubs",
+    "pylance>=1.0.0b14",
+    "requests",
+    "datafusion",
 ]
 dev = [
-    "ruff>=0.3.0",
-    "pre-commit>=3.5.0",
-    "pyright>=1.1.350",
+    "ruff",
+    "pre-commit",
+    "pyright",
    'typing-extensions>=4.0.0; python_version < "3.11"',
 ]
 docs = ["mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings-python"]
-clip = ["torch", "pillow>=12.1.1", "open-clip-torch"]
-siglip = ["torch", "pillow>=12.1.1", "transformers>=4.41.0","sentencepiece"]
+clip = ["torch", "pillow", "open-clip-torch"]
+siglip = ["torch", "pillow", "transformers>=4.41.0","sentencepiece"]
 embeddings = [
    "requests>=2.31.0",
    "openai>=1.6.1",
-    "sentence-transformers>=2.2.0",
-    "torch>=2.0.0",
-    "pillow>=12.1.1",
-    "open-clip-torch>=2.20.0",
-    "cohere>=4.0",
+    "sentence-transformers",
+    "torch",
+    "pillow",
+    "open-clip-torch",
+    "cohere",
    "colpali-engine>=0.3.10",
-    "huggingface_hub>=0.19.0",
-    "InstructorEmbedding>=1.0.1",
-    "google.generativeai>=0.3.0",
+    "huggingface_hub",
+    "InstructorEmbedding",
+    "google.generativeai",
    "boto3>=1.28.57",
-    "awscli>=1.44.38",
+    "awscli>=1.29.57",
    "botocore>=1.31.57",
    'ibm-watsonx-ai>=1.1.2; python_version >= "3.10"',
    "ollama>=0.3.0",
-    "sentencepiece>=0.1.99"
+    "sentencepiece"
 ]
 azure = ["adlfs>=2024.2.0"]

--- a/python/python/lancedb/_lancedb.pyi
+++ b/python/python/lancedb/_lancedb.pyi
@@ -135,10 +135,7 @@ class Table:
    def close(self) -> None: ...
    async def schema(self) -> pa.Schema: ...
    async def add(
-        self,
-        data: pa.RecordBatchReader,
-        mode: Literal["append", "overwrite"],
-        progress: Optional[Any] = None,
+        self, data: pa.RecordBatchReader, mode: Literal["append", "overwrite"]
    ) -> AddResult: ...
    async def update(
        self, updates: Dict[str, str], where: Optional[str]
@@ -169,8 +166,6 @@ class Table:
    async def checkout(self, version: Union[int, str]): ...
    async def checkout_latest(self): ...
    async def restore(self, version: Optional[Union[int, str]] = None): ...
-    async def prewarm_index(self, index_name: str) -> None: ...
-    async def prewarm_data(self, columns: Optional[List[str]] = None) -> None: ...
    async def list_indices(self) -> list[IndexConfig]: ...
    async def delete(self, filter: str) -> DeleteResult: ...
    async def add_columns(self, columns: list[tuple[str, str]]) -> AddColumnsResult: ...
--- a/python/python/lancedb/arrow.py
+++ b/python/python/lancedb/arrow.py
@@ -1,10 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors

-from functools import singledispatch
 from typing import List, Optional, Tuple, Union

-from lancedb.pydantic import LanceModel, model_to_dict
 import pyarrow as pa

 from ._lancedb import RecordBatchStream
@@ -82,32 +80,3 @@ def peek_reader(
        yield from reader

    return batch, pa.RecordBatchReader.from_batches(batch.schema, all_batches())
-
-
-@singledispatch
-def to_arrow(data) -> pa.Table:
-    """Convert a single data object to a pa.Table."""
-    raise NotImplementedError(f"to_arrow not implemented for type {type(data)}")
-
-
-@to_arrow.register(pa.RecordBatch)
-def _arrow_from_batch(data: pa.RecordBatch) -> pa.Table:
-    return pa.Table.from_batches([data])
-
-
-@to_arrow.register(pa.Table)
-def _arrow_from_table(data: pa.Table) -> pa.Table:
-    return data
-
-
-@to_arrow.register(list)
-def _arrow_from_list(data: list) -> pa.Table:
-    if not data:
-        raise ValueError("Cannot create table from empty list without a schema")
-
-    if isinstance(data[0], LanceModel):
-        schema = data[0].__class__.to_arrow_schema()
-        dicts = [model_to_dict(d) for d in data]
-        return pa.Table.from_pylist(dicts, schema=schema)
-
-    return pa.Table.from_pylist(data)
--- a/python/python/lancedb/db.py
+++ b/python/python/lancedb/db.py
@@ -8,7 +8,7 @@ from abc import abstractmethod
 from datetime import timedelta
 from pathlib import Path
 import sys
-from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Literal, Optional, Union
+from typing import TYPE_CHECKING, Dict, Iterable, List, Literal, Optional, Union

 if sys.version_info >= (3, 12):
    from typing import override
@@ -1541,8 +1541,6 @@ class AsyncConnection(object):
        storage_options_provider: Optional["StorageOptionsProvider"] = None,
        index_cache_size: Optional[int] = None,
        location: Optional[str] = None,
-        namespace_client: Optional[Any] = None,
-        managed_versioning: Optional[bool] = None,
    ) -> AsyncTable:
        """Open a Lance Table in the database.

@@ -1575,9 +1573,6 @@ class AsyncConnection(object):
            The explicit location (URI) of the table. If provided, the table will be
            opened from this location instead of deriving it from the database URI
            and table name.
-        managed_versioning: bool, optional
-            Whether managed versioning is enabled for this table. If provided,
-            avoids a redundant describe_table call when namespace_client is set.

        Returns
        -------
@@ -1592,8 +1587,6 @@ class AsyncConnection(object):
            storage_options_provider=storage_options_provider,
            index_cache_size=index_cache_size,
            location=location,
-            namespace_client=namespace_client,
-            managed_versioning=managed_versioning,
        )
        return AsyncTable(table)

--- a/python/python/lancedb/embeddings/gte.py
+++ b/python/python/lancedb/embeddings/gte.py
@@ -2,7 +2,6 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors


-import warnings
 from typing import List, Union

 import numpy as np
@@ -16,8 +15,6 @@ from .utils import weak_lru
@register("gte-text")
 class GteEmbeddings(TextEmbeddingFunction):
    """
-    Deprecated: GTE embeddings should be used through sentence-transformers.
-
    An embedding function that uses GTE-LARGE MLX format(for Apple silicon devices only)
    as well as the standard cpu/gpu version from: https://huggingface.co/thenlper/gte-large.

@@ -64,13 +61,6 @@ class GteEmbeddings(TextEmbeddingFunction):

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
-        warnings.warn(
-            "GTE embeddings as a standalone embedding function are deprecated. "
-            "Use the 'sentence-transformers' embedding function with a GTE model "
-            "instead.",
-            DeprecationWarning,
-            stacklevel=3,
-        )
        self._ndims = None
        if kwargs:
            self.mlx = kwargs.get("mlx", False)
--- a/python/python/lancedb/embeddings/openai.py
+++ b/python/python/lancedb/embeddings/openai.py
@@ -110,9 +110,6 @@ class OpenAIEmbeddings(TextEmbeddingFunction):
            valid_embeddings = {
                idx: v.embedding for v, idx in zip(rs.data, valid_indices)
            }
-        except openai.AuthenticationError:
-            logging.error("Authentication failed: Invalid API key provided")
-            raise
        except openai.BadRequestError:
            logging.exception("Bad request: %s", texts)
            return [None] * len(texts)
--- a/python/python/lancedb/embeddings/siglip.py
+++ b/python/python/lancedb/embeddings/siglip.py
@@ -6,7 +6,6 @@ import io
 import os
 from typing import TYPE_CHECKING, List, Union
 import urllib.parse as urlparse
-import warnings

 import numpy as np
 import pyarrow as pa
@@ -25,7 +24,6 @@ if TYPE_CHECKING:

@register("siglip")
 class SigLipEmbeddings(EmbeddingFunction):
-    # Deprecated: prefer CLIP embeddings via `open-clip`.
    model_name: str = "google/siglip-base-patch16-224"
    device: str = "cpu"
    batch_size: int = 64
@@ -38,12 +36,6 @@ class SigLipEmbeddings(EmbeddingFunction):

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
-        warnings.warn(
-            "SigLip embeddings are deprecated. Use CLIP embeddings via the "
-            "'open-clip' embedding function instead.",
-            DeprecationWarning,
-            stacklevel=3,
-        )
        transformers = attempt_import_or_raise("transformers")
        self._torch = attempt_import_or_raise("torch")

--- a/python/python/lancedb/embeddings/utils.py
+++ b/python/python/lancedb/embeddings/utils.py
@@ -269,11 +269,6 @@ def retry_with_exponential_backoff(
            # and say that it is assumed that if this portion errors out, it's due
            # to rate limit but the user should check the error message to be sure.
            except Exception as e:  # noqa: PERF203
-                # Don't retry on authentication errors (e.g., OpenAI 401)
-                # These are permanent failures that won't be fixed by retrying
-                if _is_non_retryable_error(e):
-                    raise
-
                num_retries += 1

                if num_retries > max_retries:
@@ -294,29 +289,6 @@ def retry_with_exponential_backoff(
    return wrapper


-def _is_non_retryable_error(error: Exception) -> bool:
-    """Check if an error should not be retried.
-
-    Args:
-        error: The exception to check
-
-    Returns:
-        True if the error should not be retried, False otherwise
-    """
-    # Check for OpenAI authentication errors
-    error_type = type(error).__name__
-    if error_type == "AuthenticationError":
-        return True
-
-    # Check for other common non-retryable HTTP status codes
-    # 401 Unauthorized, 403 Forbidden
-    if hasattr(error, "status_code"):
-        if error.status_code in (401, 403):
-            return True
-
-    return False
-
-
 def url_retrieve(url: str):
    """
    Parameters
--- a/python/python/lancedb/namespace.py
+++ b/python/python/lancedb/namespace.py
@@ -12,7 +12,7 @@ from __future__ import annotations

 import asyncio
 import sys
-from typing import Any, Dict, Iterable, List, Optional, Union
+from typing import Dict, Iterable, List, Optional, Union

 if sys.version_info >= (3, 12):
    from typing import override
@@ -44,7 +44,7 @@ from lance_namespace import (
    ListNamespacesRequest,
    CreateNamespaceRequest,
    DropNamespaceRequest,
-    DeclareTableRequest,
+    CreateEmptyTableRequest,
 )
 from lancedb.table import AsyncTable, LanceTable, Table
 from lancedb.util import validate_table_name
@@ -240,7 +240,7 @@ class LanceNamespaceDBConnection(DBConnection):
        session : Optional[Session]
            A session to use for this connection
        """
-        self._namespace_client = namespace
+        self._ns = namespace
        self.read_consistency_interval = read_consistency_interval
        self.storage_options = storage_options or {}
        self.session = session
@@ -269,7 +269,7 @@ class LanceNamespaceDBConnection(DBConnection):
        if namespace is None:
            namespace = []
        request = ListTablesRequest(id=namespace, page_token=page_token, limit=limit)
-        response = self._namespace_client.list_tables(request)
+        response = self._ns.list_tables(request)
        return response.tables if response.tables else []

    @override
@@ -309,9 +309,7 @@ class LanceNamespaceDBConnection(DBConnection):
            # Try to describe the table first to see if it exists
            try:
                describe_request = DescribeTableRequest(id=table_id)
-                describe_response = self._namespace_client.describe_table(
-                    describe_request
-                )
+                describe_response = self._ns.describe_table(describe_request)
                location = describe_response.location
                namespace_storage_options = describe_response.storage_options
            except Exception:
@@ -320,20 +318,20 @@ class LanceNamespaceDBConnection(DBConnection):

        if location is None:
            # Table doesn't exist or mode is "create", reserve a new location
-            declare_request = DeclareTableRequest(
+            create_empty_request = CreateEmptyTableRequest(
                id=table_id,
                location=None,
                properties=self.storage_options if self.storage_options else None,
            )
-            declare_response = self._namespace_client.declare_table(declare_request)
+            create_empty_response = self._ns.create_empty_table(create_empty_request)

-            if not declare_response.location:
+            if not create_empty_response.location:
                raise ValueError(
-                    "Table location is missing from declare_table response"
+                    "Table location is missing from create_empty_table response"
                )

-            location = declare_response.location
-            namespace_storage_options = declare_response.storage_options
+            location = create_empty_response.location
+            namespace_storage_options = create_empty_response.storage_options

        # Merge storage options: self.storage_options < user options < namespace options
        merged_storage_options = dict(self.storage_options)
@@ -355,7 +353,7 @@ class LanceNamespaceDBConnection(DBConnection):
        # Only create if namespace returned storage_options (not None)
        if storage_options_provider is None and namespace_storage_options is not None:
            storage_options_provider = LanceNamespaceStorageOptionsProvider(
-                namespace=self._namespace_client,
+                namespace=self._ns,
                table_id=table_id,
            )

@@ -373,7 +371,6 @@ class LanceNamespaceDBConnection(DBConnection):
            storage_options=merged_storage_options,
            storage_options_provider=storage_options_provider,
            location=location,
-            namespace_client=self._namespace_client,
        )

        return tbl
@@ -392,7 +389,7 @@ class LanceNamespaceDBConnection(DBConnection):
            namespace = []
        table_id = namespace + [name]
        request = DescribeTableRequest(id=table_id)
-        response = self._namespace_client.describe_table(request)
+        response = self._ns.describe_table(request)

        # Merge storage options: self.storage_options < user options < namespace options
        merged_storage_options = dict(self.storage_options)
@@ -405,14 +402,10 @@ class LanceNamespaceDBConnection(DBConnection):
        # Only create if namespace returned storage_options (not None)
        if storage_options_provider is None and response.storage_options is not None:
            storage_options_provider = LanceNamespaceStorageOptionsProvider(
-                namespace=self._namespace_client,
+                namespace=self._ns,
                table_id=table_id,
            )

-        # Pass managed_versioning to avoid redundant describe_table call in Rust.
-        # Convert None to False since we already have the answer from describe_table.
-        managed_versioning = response.managed_versioning is True
-
        return self._lance_table_from_uri(
            name,
            response.location,
@@ -420,8 +413,6 @@ class LanceNamespaceDBConnection(DBConnection):
            storage_options=merged_storage_options,
            storage_options_provider=storage_options_provider,
            index_cache_size=index_cache_size,
-            namespace_client=self._namespace_client,
-            managed_versioning=managed_versioning,
        )

    @override
@@ -431,7 +422,7 @@ class LanceNamespaceDBConnection(DBConnection):
            namespace = []
        table_id = namespace + [name]
        request = DropTableRequest(id=table_id)
-        self._namespace_client.drop_table(request)
+        self._ns.drop_table(request)

    @override
    def rename_table(
@@ -493,7 +484,7 @@ class LanceNamespaceDBConnection(DBConnection):
        request = ListNamespacesRequest(
            id=namespace, page_token=page_token, limit=limit
        )
-        response = self._namespace_client.list_namespaces(request)
+        response = self._ns.list_namespaces(request)
        return ListNamespacesResponse(
            namespaces=response.namespaces if response.namespaces else [],
            page_token=response.page_token,
@@ -529,7 +520,7 @@ class LanceNamespaceDBConnection(DBConnection):
            mode=_normalize_create_namespace_mode(mode),
            properties=properties,
        )
-        response = self._namespace_client.create_namespace(request)
+        response = self._ns.create_namespace(request)
        return CreateNamespaceResponse(
            properties=response.properties if hasattr(response, "properties") else None
        )
@@ -564,7 +555,7 @@ class LanceNamespaceDBConnection(DBConnection):
            mode=_normalize_drop_namespace_mode(mode),
            behavior=_normalize_drop_namespace_behavior(behavior),
        )
-        response = self._namespace_client.drop_namespace(request)
+        response = self._ns.drop_namespace(request)
        return DropNamespaceResponse(
            properties=(
                response.properties if hasattr(response, "properties") else None
@@ -590,7 +581,7 @@ class LanceNamespaceDBConnection(DBConnection):
            Response containing the namespace properties.
        """
        request = DescribeNamespaceRequest(id=namespace)
-        response = self._namespace_client.describe_namespace(request)
+        response = self._ns.describe_namespace(request)
        return DescribeNamespaceResponse(
            properties=response.properties if hasattr(response, "properties") else None
        )
@@ -624,7 +615,7 @@ class LanceNamespaceDBConnection(DBConnection):
        if namespace is None:
            namespace = []
        request = ListTablesRequest(id=namespace, page_token=page_token, limit=limit)
-        response = self._namespace_client.list_tables(request)
+        response = self._ns.list_tables(request)
        return ListTablesResponse(
            tables=response.tables if response.tables else [],
            page_token=response.page_token,
@@ -639,8 +630,6 @@ class LanceNamespaceDBConnection(DBConnection):
        storage_options: Optional[Dict[str, str]] = None,
        storage_options_provider: Optional[StorageOptionsProvider] = None,
        index_cache_size: Optional[int] = None,
-        namespace_client: Optional[Any] = None,
-        managed_versioning: Optional[bool] = None,
    ) -> LanceTable:
        # Open a table directly from a URI using the location parameter
        # Note: storage_options should already be merged by the caller
@@ -654,8 +643,6 @@ class LanceNamespaceDBConnection(DBConnection):
        )

        # Open the table using the temporary connection with the location parameter
-        # Pass namespace_client to enable managed versioning support
-        # Pass managed_versioning to avoid redundant describe_table call
        return LanceTable.open(
            temp_conn,
            name,
@@ -664,8 +651,6 @@ class LanceNamespaceDBConnection(DBConnection):
            storage_options_provider=storage_options_provider,
            index_cache_size=index_cache_size,
            location=table_uri,
-            namespace_client=namespace_client,
-            managed_versioning=managed_versioning,
        )


@@ -700,7 +685,7 @@ class AsyncLanceNamespaceDBConnection:
        session : Optional[Session]
            A session to use for this connection
        """
-        self._namespace_client = namespace
+        self._ns = namespace
        self.read_consistency_interval = read_consistency_interval
        self.storage_options = storage_options or {}
        self.session = session
@@ -728,7 +713,7 @@ class AsyncLanceNamespaceDBConnection:
        if namespace is None:
            namespace = []
        request = ListTablesRequest(id=namespace, page_token=page_token, limit=limit)
-        response = self._namespace_client.list_tables(request)
+        response = self._ns.list_tables(request)
        return response.tables if response.tables else []

    async def create_table(
@@ -765,9 +750,7 @@ class AsyncLanceNamespaceDBConnection:
            # Try to describe the table first to see if it exists
            try:
                describe_request = DescribeTableRequest(id=table_id)
-                describe_response = self._namespace_client.describe_table(
-                    describe_request
-                )
+                describe_response = self._ns.describe_table(describe_request)
                location = describe_response.location
                namespace_storage_options = describe_response.storage_options
            except Exception:
@@ -776,20 +759,20 @@ class AsyncLanceNamespaceDBConnection:

        if location is None:
            # Table doesn't exist or mode is "create", reserve a new location
-            declare_request = DeclareTableRequest(
+            create_empty_request = CreateEmptyTableRequest(
                id=table_id,
                location=None,
                properties=self.storage_options if self.storage_options else None,
            )
-            declare_response = self._namespace_client.declare_table(declare_request)
+            create_empty_response = self._ns.create_empty_table(create_empty_request)

-            if not declare_response.location:
+            if not create_empty_response.location:
                raise ValueError(
-                    "Table location is missing from declare_table response"
+                    "Table location is missing from create_empty_table response"
                )

-            location = declare_response.location
-            namespace_storage_options = declare_response.storage_options
+            location = create_empty_response.location
+            namespace_storage_options = create_empty_response.storage_options

        # Merge storage options: self.storage_options < user options < namespace options
        merged_storage_options = dict(self.storage_options)
@@ -814,7 +797,7 @@ class AsyncLanceNamespaceDBConnection:
                and namespace_storage_options is not None
            ):
                provider = LanceNamespaceStorageOptionsProvider(
-                    namespace=self._namespace_client,
+                    namespace=self._ns,
                    table_id=table_id,
                )
            else:
@@ -834,7 +817,6 @@ class AsyncLanceNamespaceDBConnection:
                storage_options=merged_storage_options,
                storage_options_provider=provider,
                location=location,
-                namespace_client=self._namespace_client,
            )

        lance_table = await asyncio.to_thread(_create_table)
@@ -855,7 +837,7 @@ class AsyncLanceNamespaceDBConnection:
            namespace = []
        table_id = namespace + [name]
        request = DescribeTableRequest(id=table_id)
-        response = self._namespace_client.describe_table(request)
+        response = self._ns.describe_table(request)

        # Merge storage options: self.storage_options < user options < namespace options
        merged_storage_options = dict(self.storage_options)
@@ -867,14 +849,10 @@ class AsyncLanceNamespaceDBConnection:
        # Create a storage options provider if not provided by user
        if storage_options_provider is None and response.storage_options is not None:
            storage_options_provider = LanceNamespaceStorageOptionsProvider(
-                namespace=self._namespace_client,
+                namespace=self._ns,
                table_id=table_id,
            )

-        # Capture managed_versioning from describe response.
-        # Convert None to False since we already have the answer from describe_table.
-        managed_versioning = response.managed_versioning is True
-
        # Open table in a thread
        def _open_table():
            temp_conn = LanceDBConnection(
@@ -892,8 +870,6 @@ class AsyncLanceNamespaceDBConnection:
                storage_options_provider=storage_options_provider,
                index_cache_size=index_cache_size,
                location=response.location,
-                namespace_client=self._namespace_client,
-                managed_versioning=managed_versioning,
            )

        lance_table = await asyncio.to_thread(_open_table)
@@ -905,7 +881,7 @@ class AsyncLanceNamespaceDBConnection:
            namespace = []
        table_id = namespace + [name]
        request = DropTableRequest(id=table_id)
-        self._namespace_client.drop_table(request)
+        self._ns.drop_table(request)

    async def rename_table(
        self,
@@ -967,7 +943,7 @@ class AsyncLanceNamespaceDBConnection:
        request = ListNamespacesRequest(
            id=namespace, page_token=page_token, limit=limit
        )
-        response = self._namespace_client.list_namespaces(request)
+        response = self._ns.list_namespaces(request)
        return ListNamespacesResponse(
            namespaces=response.namespaces if response.namespaces else [],
            page_token=response.page_token,
@@ -1002,7 +978,7 @@ class AsyncLanceNamespaceDBConnection:
            mode=_normalize_create_namespace_mode(mode),
            properties=properties,
        )
-        response = self._namespace_client.create_namespace(request)
+        response = self._ns.create_namespace(request)
        return CreateNamespaceResponse(
            properties=response.properties if hasattr(response, "properties") else None
        )
@@ -1036,7 +1012,7 @@ class AsyncLanceNamespaceDBConnection:
            mode=_normalize_drop_namespace_mode(mode),
            behavior=_normalize_drop_namespace_behavior(behavior),
        )
-        response = self._namespace_client.drop_namespace(request)
+        response = self._ns.drop_namespace(request)
        return DropNamespaceResponse(
            properties=(
                response.properties if hasattr(response, "properties") else None
@@ -1063,7 +1039,7 @@ class AsyncLanceNamespaceDBConnection:
            Response containing the namespace properties.
        """
        request = DescribeNamespaceRequest(id=namespace)
-        response = self._namespace_client.describe_namespace(request)
+        response = self._ns.describe_namespace(request)
        return DescribeNamespaceResponse(
            properties=response.properties if hasattr(response, "properties") else None
        )
@@ -1096,7 +1072,7 @@ class AsyncLanceNamespaceDBConnection:
        if namespace is None:
            namespace = []
        request = ListTablesRequest(id=namespace, page_token=page_token, limit=limit)
-        response = self._namespace_client.list_tables(request)
+        response = self._ns.list_tables(request)
        return ListTablesResponse(
            tables=response.tables if response.tables else [],
            page_token=response.page_token,
--- a/python/python/lancedb/query.py
+++ b/python/python/lancedb/query.py
@@ -70,7 +70,7 @@ def ensure_vector_query(
 ) -> Union[List[float], List[List[float]], pa.Array, List[pa.Array]]:
    if isinstance(val, list):
        if len(val) == 0:
-            raise ValueError("Vector query must be a non-empty list")
+            return ValueError("Vector query must be a non-empty list")
        sample = val[0]
    else:
        if isinstance(val, float):
@@ -83,7 +83,7 @@ def ensure_vector_query(
        return val
    if isinstance(sample, list):
        if len(sample) == 0:
-            raise ValueError("Vector query must be a non-empty list")
+            return ValueError("Vector query must be a non-empty list")
        if isinstance(sample[0], float):
            # val is list of list of floats
            return val
@@ -606,7 +606,6 @@ class LanceQueryBuilder(ABC):
                query,
                ordering_field_name=ordering_field_name,
                fts_columns=fts_columns,
-                fast_search=fast_search,
            )

        if isinstance(query, list):
@@ -1457,14 +1456,12 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
        query: str | FullTextQuery,
        ordering_field_name: Optional[str] = None,
        fts_columns: Optional[Union[str, List[str]]] = None,
-        fast_search: bool = None,
    ):
        super().__init__(table)
        self._query = query
        self._phrase_query = False
        self.ordering_field_name = ordering_field_name
        self._reranker = None
-        self._fast_search = fast_search
        if isinstance(fts_columns, str):
            fts_columns = [fts_columns]
        self._fts_columns = fts_columns
@@ -1486,19 +1483,6 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
        self._phrase_query = phrase_query
        return self

-    def fast_search(self) -> LanceFtsQueryBuilder:
-        """
-        Skip a flat search of unindexed data. This will improve
-        search performance but search results will not include unindexed data.
-
-        Returns
-        -------
-        LanceFtsQueryBuilder
-            The LanceFtsQueryBuilder object.
-        """
-        self._fast_search = True
-        return self
-
    def to_query_object(self) -> Query:
        return Query(
            columns=self._columns,
@@ -1510,7 +1494,6 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
                query=self._query, columns=self._fts_columns
            ),
            offset=self._offset,
-            fast_search=self._fast_search,
        )

    def output_schema(self) -> pa.Schema:
@@ -1799,26 +1782,6 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
            vector_results = LanceHybridQueryBuilder._rank(vector_results, "_distance")
            fts_results = LanceHybridQueryBuilder._rank(fts_results, "_score")

-        # If both result sets are empty (e.g. after hard filtering),
-        # return early to avoid errors in reranking or score restoration.
-        if vector_results.num_rows == 0 and fts_results.num_rows == 0:
-            # Build a minimal empty table with the _relevance_score column
-            combined_schema = pa.unify_schemas(
-                [vector_results.schema, fts_results.schema],
-            )
-            empty = pa.table(
-                {
-                    col: pa.array([], type=combined_schema.field(col).type)
-                    for col in combined_schema.names
-                }
-            )
-            empty = empty.append_column(
-                "_relevance_score", pa.array([], type=pa.float32())
-            )
-            if not with_row_ids and "_rowid" in empty.column_names:
-                empty = empty.drop(["_rowid"])
-            return empty
-
        original_distances = None
        original_scores = None
        original_distance_row_ids = None
@@ -2205,8 +2168,8 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
            self._vector_query.select(self._columns)
            self._fts_query.select(self._columns)
        if self._where:
-            self._vector_query.where(self._where, not self._postfilter)
-            self._fts_query.where(self._where, not self._postfilter)
+            self._vector_query.where(self._where, self._postfilter)
+            self._fts_query.where(self._where, self._postfilter)
        if self._with_row_id:
            self._vector_query.with_row_id(True)
            self._fts_query.with_row_id(True)
--- a/python/python/lancedb/remote/table.py
+++ b/python/python/lancedb/remote/table.py
@@ -4,7 +4,7 @@
 from datetime import timedelta
 import logging
 from functools import cached_property
-from typing import Any, Callable, Dict, Iterable, List, Optional, Union, Literal
+from typing import Dict, Iterable, List, Optional, Union, Literal
 import warnings

 from lancedb._lancedb import (
@@ -35,7 +35,6 @@ import pyarrow as pa
 from lancedb.common import DATA, VEC, VECTOR_COLUMN_NAME
 from lancedb.merge import LanceMergeInsertBuilder
 from lancedb.embeddings import EmbeddingFunctionRegistry
-from lancedb.table import _normalize_progress

 from ..query import LanceVectorQueryBuilder, LanceQueryBuilder, LanceTakeQueryBuilder
 from ..table import AsyncTable, IndexStatistics, Query, Table, Tags
@@ -219,6 +218,8 @@ class RemoteTable(Table):
        train: bool = True,
    ):
        """Create an index on the table.
+        Currently, the only parameters that matter are
+        the metric and the vector column name.

        Parameters
        ----------
@@ -249,6 +250,11 @@ class RemoteTable(Table):
        >>> table.create_index("l2", "vector") # doctest: +SKIP
        """

+        if num_sub_vectors is not None:
+            logging.warning(
+                "num_sub_vectors is not supported on LanceDB cloud."
+                "This parameter will be tuned automatically."
+            )
        if accelerator is not None:
            logging.warning(
                "GPU accelerator is not yet supported on LanceDB cloud."
@@ -309,7 +315,6 @@ class RemoteTable(Table):
        mode: str = "append",
        on_bad_vectors: str = "error",
        fill_value: float = 0.0,
-        progress: Optional[Union[bool, Callable, Any]] = None,
    ) -> AddResult:
        """Add more data to the [Table](Table). It has the same API signature as
        the OSS version.
@@ -332,29 +337,17 @@ class RemoteTable(Table):
            One of "error", "drop", "fill".
        fill_value: float, default 0.
            The value to use when filling vectors. Only used if on_bad_vectors="fill".
-        progress: bool, callable, or tqdm-like, optional
-            A callback or tqdm-compatible progress bar. See
-            :meth:`Table.add` for details.

        Returns
        -------
        AddResult
            An object containing the new version number of the table after adding data.
        """
-        progress, owns = _normalize_progress(progress)
-        try:
-            return LOOP.run(
-                self._table.add(
-                    data,
-                    mode=mode,
-                    on_bad_vectors=on_bad_vectors,
-                    fill_value=fill_value,
-                    progress=progress,
-                )
+        return LOOP.run(
+            self._table.add(
+                data, mode=mode, on_bad_vectors=on_bad_vectors, fill_value=fill_value
            )
-        finally:
-            if owns:
-                progress.close()
+        )

    def search(
        self,
@@ -654,45 +647,6 @@ class RemoteTable(Table):
    def drop_index(self, index_name: str):
        return LOOP.run(self._table.drop_index(index_name))

-    def prewarm_index(self, name: str) -> None:
-        """Prewarm an index in the table.
-
-        This is a hint to the database that the index will be accessed in the
-        future and should be loaded into memory if possible.  This can reduce
-        cold-start latency for subsequent queries.
-
-        This call initiates prewarming and returns once the request is accepted.
-        It is idempotent and safe to call from multiple clients concurrently.
-
-        Parameters
-        ----------
-        name: str
-            The name of the index to prewarm
-        """
-        return LOOP.run(self._table.prewarm_index(name))
-
-    def prewarm_data(self, columns: Optional[List[str]] = None) -> None:
-        """Prewarm data for the table.
-
-        This is a hint to the database that the given columns will be accessed
-        in the future and the database should prefetch the data if possible.
-        Currently only supported on remote tables.
-
-        This call initiates prewarming and returns once the request is accepted.
-        It is idempotent and safe to call from multiple clients concurrently.
-
-        This operation has a large upfront cost but can speed up future queries
-        that need to fetch the given columns.  Large columns such as embeddings
-        or binary data may not be practical to prewarm.  This feature is intended
-        for workloads that issue many queries against the same columns.
-
-        Parameters
-        ----------
-        columns: list of str, optional
-            The columns to prewarm. If None, all columns are prewarmed.
-        """
-        return LOOP.run(self._table.prewarm_data(columns))
-
    def wait_for_index(
        self, index_names: Iterable[str], timeout: timedelta = timedelta(seconds=300)
    ):
--- a/python/python/lancedb/scannable.py
+++ b/python/python/lancedb/scannable.py
@@ -1,214 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-from dataclasses import dataclass
-from functools import singledispatch
-import sys
-from typing import Callable, Iterator, Optional
-from lancedb.arrow import to_arrow
-import pyarrow as pa
-import pyarrow.dataset as ds
-
-from .pydantic import LanceModel
-
-
-@dataclass
-class Scannable:
-    schema: pa.Schema
-    num_rows: Optional[int]
-    # Factory function to create a new reader each time (supports re-scanning)
-    reader: Callable[[], pa.RecordBatchReader]
-    # Whether reader can be called more than once. For example, an iterator can
-    # only be consumed once, while a DataFrame can be converted to a new reader
-    # each time.
-    rescannable: bool = True
-
-
-@singledispatch
-def to_scannable(data) -> Scannable:
-    # Fallback: try iterable protocol
-    if hasattr(data, "__iter__"):
-        return _from_iterable(iter(data))
-    raise NotImplementedError(f"to_scannable not implemented for type {type(data)}")
-
-
-@to_scannable.register(pa.RecordBatchReader)
-def _from_reader(data: pa.RecordBatchReader) -> Scannable:
-    # RecordBatchReader can only be consumed once - not rescannable
-    return Scannable(
-        schema=data.schema, num_rows=None, reader=lambda: data, rescannable=False
-    )
-
-
-@to_scannable.register(pa.RecordBatch)
-def _from_batch(data: pa.RecordBatch) -> Scannable:
-    return Scannable(
-        schema=data.schema,
-        num_rows=data.num_rows,
-        reader=lambda: pa.RecordBatchReader.from_batches(data.schema, [data]),
-    )
-
-
-@to_scannable.register(pa.Table)
-def _from_table(data: pa.Table) -> Scannable:
-    return Scannable(schema=data.schema, num_rows=data.num_rows, reader=data.to_reader)
-
-
-@to_scannable.register(ds.Dataset)
-def _from_dataset(data: ds.Dataset) -> Scannable:
-    return Scannable(
-        schema=data.schema,
-        num_rows=data.count_rows(),
-        reader=lambda: data.scanner().to_reader(),
-    )
-
-
-@to_scannable.register(ds.Scanner)
-def _from_scanner(data: ds.Scanner) -> Scannable:
-    # Scanner can only be consumed once - not rescannable
-    return Scannable(
-        schema=data.projected_schema,
-        num_rows=None,
-        reader=data.to_reader,
-        rescannable=False,
-    )
-
-
-@to_scannable.register(list)
-def _from_list(data: list) -> Scannable:
-    if not data:
-        raise ValueError("Cannot create table from empty list without a schema")
-    table = to_arrow(data)
-    return Scannable(
-        schema=table.schema, num_rows=table.num_rows, reader=table.to_reader
-    )
-
-
-@to_scannable.register(dict)
-def _from_dict(data: dict) -> Scannable:
-    raise ValueError("Cannot add a single dictionary to a table. Use a list.")
-
-
-@to_scannable.register(LanceModel)
-def _from_lance_model(data: LanceModel) -> Scannable:
-    raise ValueError("Cannot add a single LanceModel to a table. Use a list.")
-
-
-def _from_iterable(data: Iterator) -> Scannable:
-    first_item = next(data, None)
-    if first_item is None:
-        raise ValueError("Cannot create table from empty iterator")
-    first = to_arrow(first_item)
-    schema = first.schema
-
-    def iter():
-        yield from first.to_batches()
-        for item in data:
-            batch = to_arrow(item)
-            if batch.schema != schema:
-                try:
-                    batch = batch.cast(schema)
-                except pa.lib.ArrowInvalid:
-                    raise ValueError(
-                        f"Input iterator yielded a batch with schema that "
-                        f"does not match the schema of other batches.\n"
-                        f"Expected:\n{schema}\nGot:\n{batch.schema}"
-                    )
-            yield from batch.to_batches()
-
-    reader = pa.RecordBatchReader.from_batches(schema, iter())
-    return to_scannable(reader)
-
-
-_registered_modules: set[str] = set()
-
-
-def _register_optional_converters():
-    """Register converters for optional dependencies that are already imported."""
-
-    if "pandas" in sys.modules and "pandas" not in _registered_modules:
-        _registered_modules.add("pandas")
-        import pandas as pd
-
-        @to_arrow.register(pd.DataFrame)
-        def _arrow_from_pandas(data: pd.DataFrame) -> pa.Table:
-            table = pa.Table.from_pandas(data, preserve_index=False)
-            return table.replace_schema_metadata(None)
-
-        @to_scannable.register(pd.DataFrame)
-        def _from_pandas(data: pd.DataFrame) -> Scannable:
-            return to_scannable(_arrow_from_pandas(data))
-
-    if "polars" in sys.modules and "polars" not in _registered_modules:
-        _registered_modules.add("polars")
-        import polars as pl
-
-        @to_arrow.register(pl.DataFrame)
-        def _arrow_from_polars(data: pl.DataFrame) -> pa.Table:
-            return data.to_arrow()
-
-        @to_scannable.register(pl.DataFrame)
-        def _from_polars(data: pl.DataFrame) -> Scannable:
-            arrow = data.to_arrow()
-            return Scannable(
-                schema=arrow.schema, num_rows=len(data), reader=arrow.to_reader
-            )
-
-        @to_scannable.register(pl.LazyFrame)
-        def _from_polars_lazy(data: pl.LazyFrame) -> Scannable:
-            arrow = data.collect().to_arrow()
-            return Scannable(
-                schema=arrow.schema, num_rows=arrow.num_rows, reader=arrow.to_reader
-            )
-
-    if "datasets" in sys.modules and "datasets" not in _registered_modules:
-        _registered_modules.add("datasets")
-        from datasets import Dataset as HFDataset
-        from datasets import DatasetDict as HFDatasetDict
-
-        @to_scannable.register(HFDataset)
-        def _from_hf_dataset(data: HFDataset) -> Scannable:
-            table = data.data.table  # Access underlying Arrow table
-            return Scannable(
-                schema=table.schema, num_rows=len(data), reader=table.to_reader
-            )
-
-        @to_scannable.register(HFDatasetDict)
-        def _from_hf_dataset_dict(data: HFDatasetDict) -> Scannable:
-            # HuggingFace DatasetDict: combine all splits with a 'split' column
-            schema = data[list(data.keys())[0]].features.arrow_schema
-            if "split" not in schema.names:
-                schema = schema.append(pa.field("split", pa.string()))
-
-            def gen():
-                for split_name, dataset in data.items():
-                    for batch in dataset.data.to_batches():
-                        split_arr = pa.array(
-                            [split_name] * len(batch), type=pa.string()
-                        )
-                        yield pa.RecordBatch.from_arrays(
-                            list(batch.columns) + [split_arr], schema=schema
-                        )
-
-            total_rows = sum(len(dataset) for dataset in data.values())
-            return Scannable(
-                schema=schema,
-                num_rows=total_rows,
-                reader=lambda: pa.RecordBatchReader.from_batches(schema, gen()),
-            )
-
-    if "lance" in sys.modules and "lance" not in _registered_modules:
-        _registered_modules.add("lance")
-        import lance
-
-        @to_scannable.register(lance.LanceDataset)
-        def _from_lance(data: lance.LanceDataset) -> Scannable:
-            return Scannable(
-                schema=data.schema,
-                num_rows=data.count_rows(),
-                reader=lambda: data.scanner().to_reader(),
-            )
-
-
-# Register on module load
-_register_optional_converters()
--- a/python/python/lancedb/table.py
+++ b/python/python/lancedb/table.py
@@ -14,7 +14,6 @@ from functools import cached_property
 from typing import (
    TYPE_CHECKING,
    Any,
-    Callable,
    Dict,
    Iterable,
    List,
@@ -26,8 +25,6 @@ from typing import (
 )
 from urllib.parse import urlparse

-from lancedb.scannable import _register_optional_converters, to_scannable
-
 from . import __version__
 from lancedb.arrow import peek_reader
 from lancedb.background_loop import LOOP
@@ -557,21 +554,6 @@ def _table_uri(base: str, table_name: str) -> str:
    return join_uri(base, f"{table_name}.lance")


-def _normalize_progress(progress):
-    """Normalize a ``progress`` parameter for :meth:`Table.add`.
-
-    Returns ``(progress_obj, owns)`` where *owns* is True when we created a
-    tqdm bar that the caller must close.
-    """
-    if progress is True:
-        from tqdm.auto import tqdm
-
-        return tqdm(unit=" rows"), True
-    if progress is False or progress is None:
-        return None, False
-    return progress, False
-
-
 class Table(ABC):
    """
    A Table is a collection of Records in a LanceDB Database.
@@ -990,7 +972,6 @@ class Table(ABC):
        mode: AddMode = "append",
        on_bad_vectors: OnBadVectorsType = "error",
        fill_value: float = 0.0,
-        progress: Optional[Union[bool, Callable, Any]] = None,
    ) -> AddResult:
        """Add more data to the [Table](Table).

@@ -1012,29 +993,6 @@ class Table(ABC):
            One of "error", "drop", "fill".
        fill_value: float, default 0.
            The value to use when filling vectors. Only used if on_bad_vectors="fill".
-        progress: bool, callable, or tqdm-like, optional
-            Progress reporting during the add operation. Can be:
-
-            - ``True`` to automatically create and display a tqdm progress
-              bar (requires ``tqdm`` to be installed)::
-
-                table.add(data, progress=True)
-
-            - A **callable** that receives a dict with keys ``output_rows``,
-              ``output_bytes``, ``total_rows``, ``elapsed_seconds``,
-              ``active_tasks``, ``total_tasks``, and ``done``::
-
-                def on_progress(p):
-                    print(f"{p['output_rows']}/{p['total_rows']} rows, "
-                          f"{p['active_tasks']}/{p['total_tasks']} workers")
-                table.add(data, progress=on_progress)
-
-            - A **tqdm-compatible** progress bar whose ``total`` and
-              ``update()`` will be called automatically. The postfix shows
-              write throughput (MB/s) and active worker count::
-
-                with tqdm() as pbar:
-                    table.add(data, progress=pbar)

        Returns
        -------
@@ -1371,7 +1329,7 @@ class Table(ABC):
        1  2  [3.0, 4.0]
        2  3  [5.0, 6.0]
        >>> table.delete("x = 2")
-        DeleteResult(num_deleted_rows=1, version=2)
+        DeleteResult(version=2)
        >>> table.to_pandas()
           x      vector
        0  1  [1.0, 2.0]
@@ -1385,7 +1343,7 @@ class Table(ABC):
        >>> to_remove
        '1, 5'
        >>> table.delete(f"x IN ({to_remove})")
-        DeleteResult(num_deleted_rows=1, version=3)
+        DeleteResult(version=3)
        >>> table.to_pandas()
           x      vector
        0  3  [5.0, 6.0]
@@ -1546,17 +1504,22 @@ class Table(ABC):
            in-progress operation (e.g. appending new data) and these files will not
            be deleted unless they are at least 7 days old. If delete_unverified is True
            then these files will be deleted regardless of their age.
-
-            .. warning::
-
-                This should only be set to True if you can guarantee that no other
-                process is currently working on this dataset. Otherwise the dataset
-                could be put into a corrupted state.
-
        retrain: bool, default False
            This parameter is no longer used and is deprecated.

-        The frequency an application should call optimize is based on the frequency of
+        Experimental API
+        ----------------
+
+        The optimization process is undergoing active development and may change.
+        Our goal with these changes is to improve the performance of optimization and
+        reduce the complexity.
+
+        That being said, it is essential today to run optimize if you want the best
+        performance.  It should be stable and safe to use in production, but it our
+        hope that the API may be simplified (or not even need to be called) in the
+        future.
+
+        The frequency an application shoudl call optimize is based on the frequency of
        data modifications.  If data is frequently added, deleted, or updated then
        optimize should be run frequently.  A good rule of thumb is to run optimize if
        you have added or modified 100,000 or more records or run more than 20 data
@@ -1781,8 +1744,6 @@ class LanceTable(Table):
        storage_options_provider: Optional["StorageOptionsProvider"] = None,
        index_cache_size: Optional[int] = None,
        location: Optional[str] = None,
-        namespace_client: Optional[Any] = None,
-        managed_versioning: Optional[bool] = None,
        _async: AsyncTable = None,
    ):
        if namespace is None:
@@ -1790,7 +1751,6 @@ class LanceTable(Table):
        self._conn = connection
        self._namespace = namespace
        self._location = location  # Store location for use in _dataset_path
-        self._namespace_client = namespace_client
        if _async is not None:
            self._table = _async
        else:
@@ -1802,8 +1762,6 @@ class LanceTable(Table):
                    storage_options_provider=storage_options_provider,
                    index_cache_size=index_cache_size,
                    location=location,
-                    namespace_client=namespace_client,
-                    managed_versioning=managed_versioning,
                )
            )

@@ -1846,8 +1804,6 @@ class LanceTable(Table):
        storage_options_provider: Optional["StorageOptionsProvider"] = None,
        index_cache_size: Optional[int] = None,
        location: Optional[str] = None,
-        namespace_client: Optional[Any] = None,
-        managed_versioning: Optional[bool] = None,
    ):
        if namespace is None:
            namespace = []
@@ -1859,8 +1815,6 @@ class LanceTable(Table):
            storage_options_provider=storage_options_provider,
            index_cache_size=index_cache_size,
            location=location,
-            namespace_client=namespace_client,
-            managed_versioning=managed_versioning,
        )

        # check the dataset exists
@@ -1892,16 +1846,6 @@ class LanceTable(Table):
                "Please install with `pip install pylance`."
            )

-        if self._namespace_client is not None:
-            table_id = self._namespace + [self.name]
-            return lance.dataset(
-                version=self.version,
-                storage_options=self._conn.storage_options,
-                namespace=self._namespace_client,
-                table_id=table_id,
-                **kwargs,
-            )
-
        return lance.dataset(
            self._dataset_path,
            version=self.version,
@@ -2254,18 +2198,12 @@ class LanceTable(Table):

    def prewarm_index(self, name: str) -> None:
        """
-        Prewarm an index in the table.
+        Prewarms an index in the table

-        This is a hint to the database that the index will be accessed in the
-        future and should be loaded into memory if possible.  This can reduce
-        cold-start latency for subsequent queries.
+        This loads the entire index into memory

-        This call initiates prewarming and returns once the request is accepted.
-        It is idempotent and safe to call from multiple clients concurrently.
-
-        It is generally wasteful to call this if the index does not fit into the
-        available cache.  Not all index types support prewarming; unsupported
-        indices will silently ignore the request.
+        If the index does not fit into the available cache this call
+        may be wasteful

        Parameters
        ----------
@@ -2274,29 +2212,6 @@ class LanceTable(Table):
        """
        return LOOP.run(self._table.prewarm_index(name))

-    def prewarm_data(self, columns: Optional[List[str]] = None) -> None:
-        """
-        Prewarm data for the table.
-
-        This is a hint to the database that the given columns will be accessed
-        in the future and the database should prefetch the data if possible.
-        Currently only supported on remote tables.
-
-        This call initiates prewarming and returns once the request is accepted.
-        It is idempotent and safe to call from multiple clients concurrently.
-
-        This operation has a large upfront cost but can speed up future queries
-        that need to fetch the given columns.  Large columns such as embeddings
-        or binary data may not be practical to prewarm.  This feature is intended
-        for workloads that issue many queries against the same columns.
-
-        Parameters
-        ----------
-        columns: list of str, optional
-            The columns to prewarm. If None, all columns are prewarmed.
-        """
-        return LOOP.run(self._table.prewarm_data(columns))
-
    def wait_for_index(
        self, index_names: Iterable[str], timeout: timedelta = timedelta(seconds=300)
    ) -> None:
@@ -2532,7 +2447,6 @@ class LanceTable(Table):
        mode: AddMode = "append",
        on_bad_vectors: OnBadVectorsType = "error",
        fill_value: float = 0.0,
-        progress: Optional[Union[bool, Callable, Any]] = None,
    ) -> AddResult:
        """Add data to the table.
        If vector columns are missing and the table
@@ -2551,29 +2465,17 @@ class LanceTable(Table):
            One of "error", "drop", "fill", "null".
        fill_value: float, default 0.
            The value to use when filling vectors. Only used if on_bad_vectors="fill".
-        progress: bool, callable, or tqdm-like, optional
-            A callback or tqdm-compatible progress bar. See
-            :meth:`Table.add` for details.

        Returns
        -------
        int
            The number of vectors in the table.
        """
-        progress, owns = _normalize_progress(progress)
-        try:
-            return LOOP.run(
-                self._table.add(
-                    data,
-                    mode=mode,
-                    on_bad_vectors=on_bad_vectors,
-                    fill_value=fill_value,
-                    progress=progress,
-                )
+        return LOOP.run(
+            self._table.add(
+                data, mode=mode, on_bad_vectors=on_bad_vectors, fill_value=fill_value
            )
-        finally:
-            if owns:
-                progress.close()
+        )

    def merge(
        self,
@@ -2809,7 +2711,6 @@ class LanceTable(Table):
        data_storage_version: Optional[str] = None,
        enable_v2_manifest_paths: Optional[bool] = None,
        location: Optional[str] = None,
-        namespace_client: Optional[Any] = None,
    ):
        """
        Create a new table.
@@ -2870,7 +2771,6 @@ class LanceTable(Table):
        self._conn = db
        self._namespace = namespace
        self._location = location
-        self._namespace_client = namespace_client

        if data_storage_version is not None:
            warnings.warn(
@@ -3095,17 +2995,22 @@ class LanceTable(Table):
            in-progress operation (e.g. appending new data) and these files will not
            be deleted unless they are at least 7 days old. If delete_unverified is True
            then these files will be deleted regardless of their age.
-
-            .. warning::
-
-                This should only be set to True if you can guarantee that no other
-                process is currently working on this dataset. Otherwise the dataset
-                could be put into a corrupted state.
-
        retrain: bool, default False
            This parameter is no longer used and is deprecated.

-        The frequency an application should call optimize is based on the frequency of
+        Experimental API
+        ----------------
+
+        The optimization process is undergoing active development and may change.
+        Our goal with these changes is to improve the performance of optimization and
+        reduce the complexity.
+
+        That being said, it is essential today to run optimize if you want the best
+        performance.  It should be stable and safe to use in production, but it our
+        hope that the API may be simplified (or not even need to be called) in the
+        future.
+
+        The frequency an application shoudl call optimize is based on the frequency of
        data modifications.  If data is frequently added, deleted, or updated then
        optimize should be run frequently.  A good rule of thumb is to run optimize if
        you have added or modified 100,000 or more records or run more than 20 data
@@ -3706,47 +3611,19 @@ class AsyncTable:
        """
        Prewarm an index in the table.

-        This is a hint to the database that the index will be accessed in the
-        future and should be loaded into memory if possible.  This can reduce
-        cold-start latency for subsequent queries.
-
-        This call initiates prewarming and returns once the request is accepted.
-        It is idempotent and safe to call from multiple clients concurrently.
-
-        It is generally wasteful to call this if the index does not fit into the
-        available cache.  Not all index types support prewarming; unsupported
-        indices will silently ignore the request.
-
        Parameters
        ----------
        name: str
            The name of the index to prewarm
+
+        Notes
+        -----
+        This will load the index into memory.  This may reduce the cold-start time for
+        future queries.  If the index does not fit in the cache then this call may be
+        wasteful.
        """
        await self._inner.prewarm_index(name)

-    async def prewarm_data(self, columns: Optional[List[str]] = None) -> None:
-        """
-        Prewarm data for the table.
-
-        This is a hint to the database that the given columns will be accessed
-        in the future and the database should prefetch the data if possible.
-        Currently only supported on remote tables.
-
-        This call initiates prewarming and returns once the request is accepted.
-        It is idempotent and safe to call from multiple clients concurrently.
-
-        This operation has a large upfront cost but can speed up future queries
-        that need to fetch the given columns.  Large columns such as embeddings
-        or binary data may not be practical to prewarm.  This feature is intended
-        for workloads that issue many queries against the same columns.
-
-        Parameters
-        ----------
-        columns: list of str, optional
-            The columns to prewarm. If None, all columns are prewarmed.
-        """
-        await self._inner.prewarm_data(columns)
-
    async def wait_for_index(
        self, index_names: Iterable[str], timeout: timedelta = timedelta(seconds=300)
    ) -> None:
@@ -3822,7 +3699,6 @@ class AsyncTable:
        mode: Optional[Literal["append", "overwrite"]] = "append",
        on_bad_vectors: Optional[OnBadVectorsType] = None,
        fill_value: Optional[float] = None,
-        progress: Optional[Union[bool, Callable, Any]] = None,
    ) -> AddResult:
        """Add more data to the [Table](Table).

@@ -3844,9 +3720,6 @@ class AsyncTable:
            One of "error", "drop", "fill", "null".
        fill_value: float, default 0.
            The value to use when filling vectors. Only used if on_bad_vectors="fill".
-        progress: callable or tqdm-like, optional
-            A callback or tqdm-compatible progress bar. See
-            :meth:`Table.add` for details.

        """
        schema = await self.schema()
@@ -3854,35 +3727,18 @@ class AsyncTable:
            on_bad_vectors = "error"
        if fill_value is None:
            fill_value = 0.0
+        data = _sanitize_data(
+            data,
+            schema,
+            metadata=schema.metadata,
+            on_bad_vectors=on_bad_vectors,
+            fill_value=fill_value,
+            allow_subschema=True,
+        )
+        if isinstance(data, pa.Table):
+            data = data.to_reader()

-        # _santitize_data is an old code path, but we will use it until the
-        # new code path is ready.
-        if on_bad_vectors != "error" or (
-            schema.metadata is not None and b"embedding_functions" in schema.metadata
-        ):
-            data = _sanitize_data(
-                data,
-                schema,
-                metadata=schema.metadata,
-                on_bad_vectors=on_bad_vectors,
-                fill_value=fill_value,
-                allow_subschema=True,
-            )
-        _register_optional_converters()
-        data = to_scannable(data)
-        progress, owns = _normalize_progress(progress)
-        try:
-            return await self._inner.add(data, mode or "append", progress=progress)
-        except RuntimeError as e:
-            if "Cast error" in str(e):
-                raise ValueError(e)
-            elif "Vector column contains NaN" in str(e):
-                raise ValueError(e)
-            else:
-                raise
-        finally:
-            if owns:
-                progress.close()
+        return await self._inner.add(data, mode or "append")

    def merge_insert(self, on: Union[str, Iterable[str]]) -> LanceMergeInsertBuilder:
        """
@@ -4344,7 +4200,7 @@ class AsyncTable:
        1  2  [3.0, 4.0]
        2  3  [5.0, 6.0]
        >>> table.delete("x = 2")
-        DeleteResult(num_deleted_rows=1, version=2)
+        DeleteResult(version=2)
        >>> table.to_pandas()
           x      vector
        0  1  [1.0, 2.0]
@@ -4358,7 +4214,7 @@ class AsyncTable:
        >>> to_remove
        '1, 5'
        >>> table.delete(f"x IN ({to_remove})")
-        DeleteResult(num_deleted_rows=1, version=3)
+        DeleteResult(version=3)
        >>> table.to_pandas()
           x      vector
        0  3  [5.0, 6.0]
@@ -4681,17 +4537,22 @@ class AsyncTable:
            in-progress operation (e.g. appending new data) and these files will not
            be deleted unless they are at least 7 days old. If delete_unverified is True
            then these files will be deleted regardless of their age.
-
-            .. warning::
-
-                This should only be set to True if you can guarantee that no other
-                process is currently working on this dataset. Otherwise the dataset
-                could be put into a corrupted state.
-
        retrain: bool, default False
            This parameter is no longer used and is deprecated.

-        The frequency an application should call optimize is based on the frequency of
+        Experimental API
+        ----------------
+
+        The optimization process is undergoing active development and may change.
+        Our goal with these changes is to improve the performance of optimization and
+        reduce the complexity.
+
+        That being said, it is essential today to run optimize if you want the best
+        performance.  It should be stable and safe to use in production, but it our
+        hope that the API may be simplified (or not even need to be called) in the
+        future.
+
+        The frequency an application shoudl call optimize is based on the frequency of
        data modifications.  If data is frequently added, deleted, or updated then
        optimize should be run frequently.  A good rule of thumb is to run optimize if
        you have added or modified 100,000 or more records or run more than 20 data
@@ -4812,16 +4673,7 @@ class IndexStatistics:
    num_indexed_rows: int
    num_unindexed_rows: int
    index_type: Literal[
-        "IVF_FLAT",
-        "IVF_SQ",
-        "IVF_PQ",
-        "IVF_RQ",
-        "IVF_HNSW_SQ",
-        "IVF_HNSW_PQ",
-        "FTS",
-        "BTREE",
-        "BITMAP",
-        "LABEL_LIST",
+        "IVF_PQ", "IVF_HNSW_PQ", "IVF_HNSW_SQ", "FTS", "BTREE", "BITMAP", "LABEL_LIST"
    ]
    distance_type: Optional[Literal["l2", "cosine", "dot"]] = None
    num_indices: Optional[int] = None
--- a/python/python/lancedb/util.py
+++ b/python/python/lancedb/util.py
@@ -324,16 +324,6 @@ def _(value: list):
    return "[" + ", ".join(map(value_to_sql, value)) + "]"


-@value_to_sql.register(dict)
-def _(value: dict):
-    # https://datafusion.apache.org/user-guide/sql/scalar_functions.html#named-struct
-    return (
-        "named_struct("
-        + ", ".join(f"'{k}', {value_to_sql(v)}" for k, v in value.items())
-        + ")"
-    )
-
-
@value_to_sql.register(np.ndarray)
 def _(value: np.ndarray):
    return value_to_sql(value.tolist())
--- a/python/python/tests/test_embeddings.py
+++ b/python/python/tests/test_embeddings.py
@@ -515,34 +515,3 @@ def test_openai_propagates_api_key(monkeypatch):
    query = "greetings"
    actual = table.search(query).limit(1).to_pydantic(Words)[0]
    assert len(actual.text) > 0
-
-
-@patch("time.sleep")
-def test_openai_no_retry_on_401(mock_sleep):
-    """
-    Test that OpenAI embedding function does not retry on 401 authentication
-    errors.
-    """
-    from lancedb.embeddings.utils import retry_with_exponential_backoff
-
-    # Create a mock that raises an AuthenticationError
-    class MockAuthenticationError(Exception):
-        """Mock OpenAI AuthenticationError"""
-
-        pass
-
-    MockAuthenticationError.__name__ = "AuthenticationError"
-
-    mock_func = MagicMock(side_effect=MockAuthenticationError("Invalid API key"))
-
-    # Wrap the function with retry logic
-    wrapped_func = retry_with_exponential_backoff(mock_func, max_retries=3)
-
-    # Should raise without retrying
-    with pytest.raises(MockAuthenticationError):
-        wrapped_func()
-
-    # Verify that the function was only called once (no retries)
-    assert mock_func.call_count == 1
-    # Verify that sleep was never called (no retries)
-    assert mock_sleep.call_count == 0
--- a/python/python/tests/test_fts.py
+++ b/python/python/tests/test_fts.py
@@ -27,7 +27,6 @@ from lancedb.query import (
    PhraseQuery,
    BooleanQuery,
    Occur,
-    LanceFtsQueryBuilder,
 )
 import numpy as np
 import pyarrow as pa
@@ -883,109 +882,3 @@ def test_fts_query_to_json():
        '"must_not":[]}}'
    )
    assert json_str == expected
-
-
-def test_fts_fast_search(table):
-    table.create_fts_index("text", use_tantivy=False)
-
-    # Insert some unindexed data
-    table.add(
-        [
-            {
-                "text": "xyz",
-                "vector": [0 for _ in range(128)],
-                "id": 101,
-                "text2": "xyz",
-                "nested": {"text": "xyz"},
-                "count": 10,
-            }
-        ]
-    )
-
-    # Without fast_search, the query object should not have fast_search set
-    builder = table.search("xyz", query_type="fts").limit(10)
-    query = builder.to_query_object()
-    assert query.fast_search is None
-
-    # With fast_search, the query object should have fast_search=True
-    builder = table.search("xyz", query_type="fts").fast_search().limit(10)
-    query = builder.to_query_object()
-    assert query.fast_search is True
-
-    # fast_search should be chainable with other methods
-    builder = (
-        table.search("xyz", query_type="fts").fast_search().select(["text"]).limit(5)
-    )
-    query = builder.to_query_object()
-    assert query.fast_search is True
-    assert query.limit == 5
-    assert query.columns == ["text"]
-
-    # fast_search should be enabled by keyword argument too
-    query = LanceFtsQueryBuilder(table, "xyz", fast_search=True).to_query_object()
-    assert query.fast_search is True
-
-    # Verify it executes without error and skips unindexed data
-    results = table.search("xyz", query_type="fts").fast_search().limit(5).to_list()
-    assert len(results) == 0
-
-    # Update index and verify it returns results
-    table.optimize()
-    results = table.search("xyz", query_type="fts").fast_search().limit(5).to_list()
-    assert len(results) > 0
-
-
-@pytest.mark.asyncio
-async def test_fts_fast_search_async(async_table):
-    await async_table.create_index("text", config=FTS())
-
-    # Insert some unindexed data
-    await async_table.add(
-        [
-            {
-                "text": "xyz",
-                "vector": [0 for _ in range(128)],
-                "id": 101,
-                "text2": "xyz",
-                "nested": {"text": "xyz"},
-                "count": 10,
-            }
-        ]
-    )
-
-    # Without fast_search, should return results
-    results = await async_table.query().nearest_to_text("xyz").limit(5).to_list()
-    assert len(results) > 0
-
-    # With fast_search, should return no results data unindexed
-    fast_results = (
-        await async_table.query()
-        .nearest_to_text("xyz")
-        .fast_search()
-        .limit(5)
-        .to_list()
-    )
-    assert len(fast_results) == 0
-
-    # Update index and verify it returns results
-    await async_table.optimize()
-
-    fast_results = (
-        await async_table.query()
-        .nearest_to_text("xyz")
-        .fast_search()
-        .limit(5)
-        .to_list()
-    )
-    assert len(fast_results) > 0
-
-    # fast_search should be chainable with other methods
-    results = (
-        await async_table.query()
-        .nearest_to_text("xyz")
-        .fast_search()
-        .select(["text"])
-        .limit(5)
-        .to_list()
-    )
-    assert len(results) > 0
--- a/python/python/tests/test_hybrid_query.py
+++ b/python/python/tests/test_hybrid_query.py
@@ -177,60 +177,6 @@ async def test_analyze_plan(table: AsyncTable):
    assert "metrics=" in res


-@pytest.fixture
-def table_with_id(tmpdir_factory) -> Table:
-    tmp_path = str(tmpdir_factory.mktemp("data"))
-    db = lancedb.connect(tmp_path)
-    data = pa.table(
-        {
-            "id": pa.array([1, 2, 3, 4], type=pa.int64()),
-            "text": pa.array(["a", "b", "cat", "dog"]),
-            "vector": pa.array(
-                [[0.1, 0.1], [2, 2], [-0.1, -0.1], [0.5, -0.5]],
-                type=pa.list_(pa.float32(), list_size=2),
-            ),
-        }
-    )
-    table = db.create_table("test_with_id", data)
-    table.create_fts_index("text", with_position=False, use_tantivy=False)
-    return table
-
-
-def test_hybrid_prefilter_explain_plan(table_with_id: Table):
-    """
-    Verify that the prefilter logic is not inverted in LanceHybridQueryBuilder.
-    """
-    plan_prefilter = (
-        table_with_id.search(query_type="hybrid")
-        .vector([0.0, 0.0])
-        .text("dog")
-        .where("id = 1", prefilter=True)
-        .limit(2)
-        .explain_plan(verbose=True)
-    )
-
-    plan_postfilter = (
-        table_with_id.search(query_type="hybrid")
-        .vector([0.0, 0.0])
-        .text("dog")
-        .where("id = 1", prefilter=False)
-        .limit(2)
-        .explain_plan(verbose=True)
-    )
-
-    # prefilter=True: filter is pushed into the LanceRead scan.
-    # The FTS sub-plan exposes this as "full_filter=id = Int64(1)" inside LanceRead.
-    assert "full_filter=id = Int64(1)" in plan_prefilter, (
-        f"Should push the filter into the scan.\nPlan:\n{plan_prefilter}"
-    )
-
-    # prefilter=False: filter is applied as a separate FilterExec after the search.
-    # The filter must NOT be embedded in the scan.
-    assert "full_filter=id = Int64(1)" not in plan_postfilter, (
-        f"Should NOT push the filter into the scan.\nPlan:\n{plan_postfilter}"
-    )
-
-
 def test_normalize_scores():
    cases = [
        (pa.array([0.1, 0.4]), pa.array([0.0, 1.0])),
--- a/python/python/tests/test_index.py
+++ b/python/python/tests/test_index.py
@@ -3,7 +3,6 @@

 from datetime import timedelta
 import random
-from typing import get_args, get_type_hints

 import pyarrow as pa
 import pytest
@@ -23,7 +22,6 @@ from lancedb.index import (
    HnswSq,
    FTS,
 )
-from lancedb.table import IndexStatistics


@pytest_asyncio.fixture
@@ -285,23 +283,3 @@ async def test_create_index_with_binary_vectors(binary_table: AsyncTable):
    for v in range(256):
        res = await binary_table.query().nearest_to([v] * 128).to_arrow()
        assert res["id"][0].as_py() == v
-
-
-def test_index_statistics_index_type_lists_all_supported_values():
-    expected_index_types = {
-        "IVF_FLAT",
-        "IVF_SQ",
-        "IVF_PQ",
-        "IVF_RQ",
-        "IVF_HNSW_SQ",
-        "IVF_HNSW_PQ",
-        "FTS",
-        "BTREE",
-        "BITMAP",
-        "LABEL_LIST",
-    }
-
-    assert (
-        set(get_args(get_type_hints(IndexStatistics)["index_type"]))
-        == expected_index_types
-    )
--- a/python/python/tests/test_namespace_integration.py
+++ b/python/python/tests/test_namespace_integration.py
@@ -147,12 +147,7 @@ class TrackingNamespace(LanceNamespace):
        This simulates a credential rotation system where each call returns
        new credentials that expire after credential_expires_in_seconds.
        """
-        # Start from base storage options (endpoint, region, allow_http, etc.)
-        # because DirectoryNamespace returns None for storage_options from
-        # describe_table/declare_table when no credential vendor is configured.
-        modified = copy.deepcopy(self.base_storage_options)
-        if storage_options:
-            modified.update(storage_options)
+        modified = copy.deepcopy(storage_options) if storage_options else {}

        # Increment credentials to simulate rotation
        modified["aws_access_key_id"] = f"AKID_{count}"
--- a/python/python/tests/test_query.py
+++ b/python/python/tests/test_query.py
@@ -30,7 +30,6 @@ from lancedb.query import (
    PhraseQuery,
    Query,
    FullTextSearchQuery,
-    ensure_vector_query,
 )
 from lancedb.rerankers.cross_encoder import CrossEncoderReranker
 from lancedb.table import AsyncTable, LanceTable
@@ -1502,18 +1501,6 @@ def test_search_empty_table(mem_db):
    assert results == []


-def test_ensure_vector_query_empty_list():
-    """Regression: ensure_vector_query used to return instead of raise ValueError."""
-    with pytest.raises(ValueError, match="non-empty"):
-        ensure_vector_query([])
-
-
-def test_ensure_vector_query_nested_empty_list():
-    """Regression: ensure_vector_query used to return instead of raise ValueError."""
-    with pytest.raises(ValueError, match="non-empty"):
-        ensure_vector_query([[]])
-
-
 def test_fast_search(tmp_path):
    db = lancedb.connect(tmp_path)

--- a/python/python/tests/test_rerankers.py
+++ b/python/python/tests/test_rerankers.py
@@ -531,78 +531,6 @@ def test_empty_result_reranker():
        )


-def test_empty_hybrid_result_reranker():
-    """Test that hybrid search with empty results after filtering doesn't crash.
-
-    Regression test for https://github.com/lancedb/lancedb/issues/2425
-    """
-    from lancedb.query import LanceHybridQueryBuilder
-
-    # Simulate empty vector and FTS results with the expected schema
-    vector_schema = pa.schema(
-        [
-            ("text", pa.string()),
-            ("vector", pa.list_(pa.float32(), 4)),
-            ("_rowid", pa.uint64()),
-            ("_distance", pa.float32()),
-        ]
-    )
-    fts_schema = pa.schema(
-        [
-            ("text", pa.string()),
-            ("vector", pa.list_(pa.float32(), 4)),
-            ("_rowid", pa.uint64()),
-            ("_score", pa.float32()),
-        ]
-    )
-    empty_vector = pa.table(
-        {
-            "text": pa.array([], type=pa.string()),
-            "vector": pa.array([], type=pa.list_(pa.float32(), 4)),
-            "_rowid": pa.array([], type=pa.uint64()),
-            "_distance": pa.array([], type=pa.float32()),
-        },
-        schema=vector_schema,
-    )
-    empty_fts = pa.table(
-        {
-            "text": pa.array([], type=pa.string()),
-            "vector": pa.array([], type=pa.list_(pa.float32(), 4)),
-            "_rowid": pa.array([], type=pa.uint64()),
-            "_score": pa.array([], type=pa.float32()),
-        },
-        schema=fts_schema,
-    )
-
-    for reranker in [LinearCombinationReranker(), RRFReranker()]:
-        result = LanceHybridQueryBuilder._combine_hybrid_results(
-            fts_results=empty_fts,
-            vector_results=empty_vector,
-            norm="score",
-            fts_query="nonexistent query",
-            reranker=reranker,
-            limit=10,
-            with_row_ids=False,
-        )
-        assert len(result) == 0
-        assert "_relevance_score" in result.column_names
-        assert "_rowid" not in result.column_names
-
-    # Also test with with_row_ids=True
-    result = LanceHybridQueryBuilder._combine_hybrid_results(
-        fts_results=empty_fts,
-        vector_results=empty_vector,
-        norm="score",
-        fts_query="nonexistent query",
-        reranker=LinearCombinationReranker(),
-        limit=10,
-        with_row_ids=True,
-    )
-    assert len(result) == 0
-    assert "_relevance_score" in result.column_names
-    assert "_rowid" in result.column_names
-
-
@pytest.mark.parametrize("use_tantivy", [True, False])
 def test_cross_encoder_reranker_return_all(tmp_path, use_tantivy):
    pytest.importorskip("sentence_transformers")
--- a/python/python/tests/test_table.py
+++ b/python/python/tests/test_table.py
@@ -326,24 +326,6 @@ def test_add_struct(mem_db: DBConnection):
    table = mem_db.create_table("test2", schema=schema)
    table.add(data)

-    struct_type = pa.struct(
-        [
-            ("b", pa.int64()),
-            ("a", pa.int64()),
-        ]
-    )
-    expected = pa.table(
-        {
-            "s_list": [
-                [
-                    pa.scalar({"b": 1, "a": 2}, type=struct_type),
-                    pa.scalar({"b": 4, "a": None}, type=struct_type),
-                ]
-            ],
-        }
-    )
-    assert table.to_arrow() == expected
-

 def test_add_subschema(mem_db: DBConnection):
    schema = pa.schema(
@@ -527,102 +509,6 @@ async def test_add_async(mem_db_async: AsyncConnection):
    assert await table.count_rows() == 3


-def test_add_progress_callback(mem_db: DBConnection):
-    table = mem_db.create_table(
-        "test",
-        data=[{"id": 1}, {"id": 2}],
-    )
-
-    updates = []
-    table.add([{"id": 3}, {"id": 4}], progress=lambda p: updates.append(dict(p)))
-
-    assert len(table) == 4
-    # The done callback always fires, so we should always get at least one.
-    assert len(updates) >= 1, "expected at least one progress callback"
-    for p in updates:
-        assert "output_rows" in p
-        assert "output_bytes" in p
-        assert "total_rows" in p
-        assert "elapsed_seconds" in p
-        assert "active_tasks" in p
-        assert "total_tasks" in p
-        assert "done" in p
-    # The last callback should have done=True.
-    assert updates[-1]["done"] is True
-
-
-def test_add_progress_tqdm_like(mem_db: DBConnection):
-    """Test that a tqdm-like object gets total set and update() called."""
-
-    class FakeBar:
-        def __init__(self):
-            self.total = None
-            self.n = 0
-            self.postfix = None
-
-        def update(self, n):
-            self.n += n
-
-        def set_postfix_str(self, s):
-            self.postfix = s
-
-        def refresh(self):
-            pass
-
-    table = mem_db.create_table(
-        "test",
-        data=[{"id": 1}, {"id": 2}],
-    )
-
-    bar = FakeBar()
-    table.add([{"id": 3}, {"id": 4}], progress=bar)
-
-    assert len(table) == 4
-    # Postfix should contain throughput and worker count
-    if bar.postfix is not None:
-        assert "MB/s" in bar.postfix
-        assert "workers" in bar.postfix
-
-
-def test_add_progress_bool(mem_db: DBConnection):
-    """Test that progress=True creates and closes a tqdm bar automatically."""
-    table = mem_db.create_table(
-        "test",
-        data=[{"id": 1}, {"id": 2}],
-    )
-
-    table.add([{"id": 3}, {"id": 4}], progress=True)
-    assert len(table) == 4
-
-    # progress=False should be the same as None
-    table.add([{"id": 5}], progress=False)
-    assert len(table) == 5
-
-
-@pytest.mark.asyncio
-async def test_add_progress_callback_async(mem_db_async: AsyncConnection):
-    """Progress callbacks work through the async path too."""
-    table = await mem_db_async.create_table("test", data=[{"id": 1}, {"id": 2}])
-
-    updates = []
-    await table.add([{"id": 3}, {"id": 4}], progress=lambda p: updates.append(dict(p)))
-
-    assert await table.count_rows() == 4
-    assert len(updates) >= 1
-    assert updates[-1]["done"] is True
-
-
-def test_add_progress_callback_error(mem_db: DBConnection):
-    """A failing callback must not prevent the write from succeeding."""
-    table = mem_db.create_table("test", data=[{"id": 1}, {"id": 2}])
-
-    def bad_callback(p):
-        raise RuntimeError("boom")
-
-    table.add([{"id": 3}, {"id": 4}], progress=bad_callback)
-    assert len(table) == 4
-
-
 def test_polars(mem_db: DBConnection):
    data = {
        "vector": [[3.1, 4.1], [5.9, 26.5]],
@@ -924,7 +810,7 @@ def test_create_index_name_and_train_parameters(
    )


-def test_create_with_nans(mem_db: DBConnection):
+def test_add_with_nans(mem_db: DBConnection):
    # by default we raise an error on bad input vectors
    bad_data = [
        {"vector": [np.nan], "item": "bar", "price": 20.0},
@@ -968,57 +854,6 @@ def test_create_with_nans(mem_db: DBConnection):
    assert np.allclose(v, np.array([0.0, 0.0]))


-def test_add_with_nans(mem_db: DBConnection):
-    schema = pa.schema(
-        [
-            pa.field("vector", pa.list_(pa.float32(), 2), nullable=True),
-            pa.field("item", pa.string(), nullable=True),
-            pa.field("price", pa.float64(), nullable=False),
-        ],
-    )
-    table = mem_db.create_table("test", schema=schema)
-    # by default we raise an error on bad input vectors
-    bad_data = [
-        {"vector": [np.nan], "item": "bar", "price": 20.0},
-        {"vector": [5], "item": "bar", "price": 20.0},
-        {"vector": [np.nan, np.nan], "item": "bar", "price": 20.0},
-        {"vector": [np.nan, 5.0], "item": "bar", "price": 20.0},
-    ]
-    for row in bad_data:
-        with pytest.raises(ValueError):
-            table.add(
-                data=[row],
-            )
-
-    table.add(
-        [
-            {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
-            {"vector": [2.1, 4.1], "item": "foo", "price": 9.0},
-            {"vector": [np.nan], "item": "bar", "price": 20.0},
-            {"vector": [5], "item": "bar", "price": 20.0},
-            {"vector": [np.nan, np.nan], "item": "bar", "price": 20.0},
-        ],
-        on_bad_vectors="drop",
-    )
-    assert len(table) == 2
-    table.delete("true")
-
-    # We can fill bad input with some value
-    table.add(
-        data=[
-            {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
-            {"vector": [np.nan], "item": "bar", "price": 20.0},
-            {"vector": [np.nan, np.nan], "item": "bar", "price": 20.0},
-        ],
-        on_bad_vectors="fill",
-        fill_value=0.0,
-    )
-    assert len(table) == 3
-    arrow_tbl = table.search().where("item == 'bar'").to_arrow()
-    v = arrow_tbl["vector"].to_pylist()[0]
-    assert np.allclose(v, np.array([0.0, 0.0]))
-
-
 def test_restore(mem_db: DBConnection):
    table = mem_db.create_table(
        "my_table",
--- a/python/python/tests/test_util.py
+++ b/python/python/tests/test_util.py
@@ -121,32 +121,6 @@ def test_value_to_sql_string(tmp_path):
        assert table.to_pandas().query("search == @value")["replace"].item() == value


-def test_value_to_sql_dict():
-    # Simple flat struct
-    assert value_to_sql({"a": 1, "b": "hello"}) == "named_struct('a', 1, 'b', 'hello')"
-
-    # Nested struct
-    assert (
-        value_to_sql({"outer": {"inner": 1}})
-        == "named_struct('outer', named_struct('inner', 1))"
-    )
-
-    # List inside struct
-    assert value_to_sql({"a": [1, 2]}) == "named_struct('a', [1, 2])"
-
-    # Mixed types
-    assert (
-        value_to_sql({"name": "test", "count": 42, "rate": 3.14, "active": True})
-        == "named_struct('name', 'test', 'count', 42, 'rate', 3.14, 'active', TRUE)"
-    )
-
-    # Null value inside struct
-    assert value_to_sql({"a": None}) == "named_struct('a', NULL)"
-
-    # Empty dict
-    assert value_to_sql({}) == "named_struct()"
-
-
 def test_append_vector_columns():
    registry = EmbeddingFunctionRegistry.get_instance()
    registry.register("test")(MockTextEmbeddingFunction)
@@ -318,14 +292,18 @@ class TestModel(lancedb.pydantic.LanceModel):
        lambda: pa.table({"a": [1], "b": [2]}),
        lambda: pa.table({"a": [1], "b": [2]}).to_reader(),
        lambda: iter(pa.table({"a": [1], "b": [2]}).to_batches()),
-        lambda: lance.write_dataset(
-            pa.table({"a": [1], "b": [2]}),
-            "memory://test",
+        lambda: (
+            lance.write_dataset(
+                pa.table({"a": [1], "b": [2]}),
+                "memory://test",
+            )
+        ),
+        lambda: (
+            lance.write_dataset(
+                pa.table({"a": [1], "b": [2]}),
+                "memory://test",
+            ).scanner()
        ),
-        lambda: lance.write_dataset(
-            pa.table({"a": [1], "b": [2]}),
-            "memory://test",
-        ).scanner(),
        lambda: pd.DataFrame({"a": [1], "b": [2]}),
        lambda: pl.DataFrame({"a": [1], "b": [2]}),
        lambda: pl.LazyFrame({"a": [1], "b": [2]}),
--- a/python/src/arrow.rs
+++ b/python/src/arrow.rs
@@ -10,7 +10,7 @@ use arrow::{
 use futures::stream::StreamExt;
 use lancedb::arrow::SendableRecordBatchStream;
 use pyo3::{
-    Bound, Py, PyAny, PyRef, PyResult, Python, exceptions::PyStopAsyncIteration, pyclass, pymethods,
+    exceptions::PyStopAsyncIteration, pyclass, pymethods, Bound, Py, PyAny, PyRef, PyResult, Python,
 };
 use pyo3_async_runtimes::tokio::future_into_py;

--- a/python/src/connection.rs
+++ b/python/src/connection.rs
@@ -9,16 +9,15 @@ use lancedb::{
    database::{CreateTableMode, Database, ReadConsistency},
 };
 use pyo3::{
-    Bound, FromPyObject, Py, PyAny, PyRef, PyResult, Python,
    exceptions::{PyRuntimeError, PyValueError},
    pyclass, pyfunction, pymethods,
    types::{PyDict, PyDictMethods},
+    Bound, FromPyObject, Py, PyAny, PyRef, PyResult, Python,
 };
 use pyo3_async_runtimes::tokio::future_into_py;

 use crate::{
-    error::PythonErrorExt, namespace::extract_namespace_arc,
-    storage_options::py_object_to_storage_options_provider, table::Table,
+    error::PythonErrorExt, storage_options::py_object_to_storage_options_provider, table::Table,
 };

 #[pyclass]
@@ -183,8 +182,7 @@ impl Connection {
        })
    }

-    #[allow(clippy::too_many_arguments)]
-    #[pyo3(signature = (name, namespace=vec![], storage_options = None, storage_options_provider=None, index_cache_size = None, location=None, namespace_client=None, managed_versioning=None))]
+    #[pyo3(signature = (name, namespace=vec![], storage_options = None, storage_options_provider=None, index_cache_size = None, location=None))]
    pub fn open_table(
        self_: PyRef<'_, Self>,
        name: String,
@@ -193,13 +191,11 @@ impl Connection {
        storage_options_provider: Option<Py<PyAny>>,
        index_cache_size: Option<u32>,
        location: Option<String>,
-        namespace_client: Option<Py<PyAny>>,
-        managed_versioning: Option<bool>,
    ) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.get_inner()?.clone();

        let mut builder = inner.open_table(name);
-        builder = builder.namespace(namespace.clone());
+        builder = builder.namespace(namespace);
        if let Some(storage_options) = storage_options {
            builder = builder.storage_options(storage_options);
        }
@@ -213,20 +209,6 @@ impl Connection {
        if let Some(location) = location {
            builder = builder.location(location);
        }
-        // Extract namespace client from Python object if provided
-        let ns_client = if let Some(ns_obj) = namespace_client {
-            let py = self_.py();
-            Some(extract_namespace_arc(py, ns_obj)?)
-        } else {
-            None
-        };
-        if let Some(ns_client) = ns_client {
-            builder = builder.namespace_client(ns_client);
-        }
-        // Pass managed_versioning if provided to avoid redundant describe_table call
-        if let Some(enabled) = managed_versioning {
-            builder = builder.managed_versioning(enabled);
-        }

        future_into_py(self_.py(), async move {
            let table = builder.execute().await.infer_error()?;
--- a/python/src/error.rs
+++ b/python/src/error.rs
@@ -2,10 +2,10 @@
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors

 use pyo3::{
-    PyErr, PyResult, Python,
    exceptions::{PyIOError, PyNotImplementedError, PyOSError, PyRuntimeError, PyValueError},
    intern,
    types::{PyAnyMethods, PyNone},
+    PyErr, PyResult, Python,
 };

 use lancedb::error::Error as LanceError;
--- a/python/src/index.rs
+++ b/python/src/index.rs
@@ -3,17 +3,17 @@

 use lancedb::index::vector::{IvfFlatIndexBuilder, IvfRqIndexBuilder, IvfSqIndexBuilder};
 use lancedb::index::{
-    Index as LanceDbIndex,
    scalar::{BTreeIndexBuilder, FtsIndexBuilder},
    vector::{IvfHnswPqIndexBuilder, IvfHnswSqIndexBuilder, IvfPqIndexBuilder},
+    Index as LanceDbIndex,
 };
-use pyo3::IntoPyObject;
 use pyo3::types::PyStringMethods;
+use pyo3::IntoPyObject;
 use pyo3::{
-    Bound, FromPyObject, PyAny, PyResult, Python,
    exceptions::{PyKeyError, PyValueError},
    intern, pyclass, pymethods,
    types::PyAnyMethods,
+    Bound, FromPyObject, PyAny, PyResult, Python,
 };

 use crate::util::parse_distance_type;
@@ -41,12 +41,7 @@ pub fn extract_index_params(source: &Option<Bound<'_, PyAny>>) -> PyResult<Lance
                let inner_opts = FtsIndexBuilder::default()
                    .base_tokenizer(params.base_tokenizer)
                    .language(&params.language)
-                    .map_err(|_| {
-                        PyValueError::new_err(format!(
-                            "LanceDB does not support the requested language: '{}'",
-                            params.language
-                        ))
-                    })?
+                    .map_err(|_| PyValueError::new_err(format!("LanceDB does not support the requested language: '{}'", params.language)))?
                    .with_position(params.with_position)
                    .lower_case(params.lower_case)
                    .max_token_length(params.max_token_length)
@@ -57,7 +52,7 @@ pub fn extract_index_params(source: &Option<Bound<'_, PyAny>>) -> PyResult<Lance
                    .ngram_max_length(params.ngram_max_length)
                    .ngram_prefix_only(params.prefix_only);
                Ok(LanceDbIndex::FTS(inner_opts))
-            }
+            },
            "IvfFlat" => {
                let params = source.extract::<IvfFlatParams>()?;
                let distance_type = parse_distance_type(params.distance_type)?;
@@ -69,11 +64,10 @@ pub fn extract_index_params(source: &Option<Bound<'_, PyAny>>) -> PyResult<Lance
                    ivf_flat_builder = ivf_flat_builder.num_partitions(num_partitions);
                }
                if let Some(target_partition_size) = params.target_partition_size {
-                    ivf_flat_builder =
-                        ivf_flat_builder.target_partition_size(target_partition_size);
+                    ivf_flat_builder = ivf_flat_builder.target_partition_size(target_partition_size);
                }
                Ok(LanceDbIndex::IvfFlat(ivf_flat_builder))
-            }
+            },
            "IvfPq" => {
                let params = source.extract::<IvfPqParams>()?;
                let distance_type = parse_distance_type(params.distance_type)?;
@@ -92,7 +86,7 @@ pub fn extract_index_params(source: &Option<Bound<'_, PyAny>>) -> PyResult<Lance
                    ivf_pq_builder = ivf_pq_builder.num_sub_vectors(num_sub_vectors);
                }
                Ok(LanceDbIndex::IvfPq(ivf_pq_builder))
-            }
+            },
            "IvfSq" => {
                let params = source.extract::<IvfSqParams>()?;
                let distance_type = parse_distance_type(params.distance_type)?;
@@ -107,7 +101,7 @@ pub fn extract_index_params(source: &Option<Bound<'_, PyAny>>) -> PyResult<Lance
                    ivf_sq_builder = ivf_sq_builder.target_partition_size(target_partition_size);
                }
                Ok(LanceDbIndex::IvfSq(ivf_sq_builder))
-            }
+            },
            "IvfRq" => {
                let params = source.extract::<IvfRqParams>()?;
                let distance_type = parse_distance_type(params.distance_type)?;
@@ -123,7 +117,7 @@ pub fn extract_index_params(source: &Option<Bound<'_, PyAny>>) -> PyResult<Lance
                    ivf_rq_builder = ivf_rq_builder.target_partition_size(target_partition_size);
                }
                Ok(LanceDbIndex::IvfRq(ivf_rq_builder))
-            }
+            },
            "HnswPq" => {
                let params = source.extract::<IvfHnswPqParams>()?;
                let distance_type = parse_distance_type(params.distance_type)?;
@@ -144,7 +138,7 @@ pub fn extract_index_params(source: &Option<Bound<'_, PyAny>>) -> PyResult<Lance
                    hnsw_pq_builder = hnsw_pq_builder.num_sub_vectors(num_sub_vectors);
                }
                Ok(LanceDbIndex::IvfHnswPq(hnsw_pq_builder))
-            }
+            },
            "HnswSq" => {
                let params = source.extract::<IvfHnswSqParams>()?;
                let distance_type = parse_distance_type(params.distance_type)?;
@@ -161,7 +155,7 @@ pub fn extract_index_params(source: &Option<Bound<'_, PyAny>>) -> PyResult<Lance
                    hnsw_sq_builder = hnsw_sq_builder.target_partition_size(target_partition_size);
                }
                Ok(LanceDbIndex::IvfHnswSq(hnsw_sq_builder))
-            }
+            },
            not_supported => Err(PyValueError::new_err(format!(
                "Invalid index type '{}'.  Must be one of BTree, Bitmap, LabelList, FTS, IvfPq, IvfSq, IvfHnswPq, or IvfHnswSq",
                not_supported
--- a/python/src/lib.rs
+++ b/python/src/lib.rs
@@ -2,14 +2,14 @@
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors

 use arrow::RecordBatchStream;
-use connection::{Connection, connect};
+use connection::{connect, Connection};
 use env_logger::Env;
 use index::IndexConfig;
 use permutation::{PyAsyncPermutationBuilder, PyPermutationReader};
 use pyo3::{
-    Bound, PyResult, Python, pymodule,
+    pymodule,
    types::{PyModule, PyModuleMethods},
-    wrap_pyfunction,
+    wrap_pyfunction, Bound, PyResult, Python,
 };
 use query::{FTSQuery, HybridQuery, Query, VectorQuery};
 use session::Session;
@@ -23,7 +23,6 @@ pub mod connection;
 pub mod error;
 pub mod header;
 pub mod index;
-pub mod namespace;
 pub mod permutation;
 pub mod query;
 pub mod session;
--- a/python/src/namespace.rs
+++ b/python/src/namespace.rs
@@ -1,696 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-//! Namespace utilities for Python bindings
-
-use std::collections::HashMap;
-use std::sync::Arc;
-
-use async_trait::async_trait;
-use bytes::Bytes;
-use lance_namespace::LanceNamespace as LanceNamespaceTrait;
-use lance_namespace::models::*;
-use pyo3::prelude::*;
-use pyo3::types::PyDict;
-
-/// Wrapper that allows any Python object implementing LanceNamespace protocol
-/// to be used as a Rust LanceNamespace.
-///
-/// This is similar to PyLanceNamespace in lance's Python bindings - it wraps a Python
-/// object and calls back into Python when namespace methods are invoked.
-pub struct PyLanceNamespace {
-    py_namespace: Arc<Py<PyAny>>,
-    namespace_id: String,
-}
-
-impl PyLanceNamespace {
-    /// Create a new PyLanceNamespace wrapper around a Python namespace object.
-    pub fn new(_py: Python<'_>, py_namespace: &Bound<'_, PyAny>) -> PyResult<Self> {
-        let namespace_id = py_namespace
-            .call_method0("namespace_id")?
-            .extract::<String>()?;
-
-        Ok(Self {
-            py_namespace: Arc::new(py_namespace.clone().unbind()),
-            namespace_id,
-        })
-    }
-
-    /// Create an Arc<dyn LanceNamespace> from a Python namespace object.
-    pub fn create_arc(
-        py: Python<'_>,
-        py_namespace: &Bound<'_, PyAny>,
-    ) -> PyResult<Arc<dyn LanceNamespaceTrait>> {
-        let wrapper = Self::new(py, py_namespace)?;
-        Ok(Arc::new(wrapper))
-    }
-}
-
-impl std::fmt::Debug for PyLanceNamespace {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        write!(f, "PyLanceNamespace {{ id: {} }}", self.namespace_id)
-    }
-}
-
-/// Get or create the DictWithModelDump class in Python.
-/// This class acts like a dict but also has model_dump() method.
-/// This allows it to work with both:
-/// - depythonize (which expects a dict/Mapping)
-/// - Python code that calls .model_dump() (like DirectoryNamespace wrapper)
-fn get_dict_with_model_dump_class(py: Python<'_>) -> PyResult<Bound<'_, PyAny>> {
-    // Use a module-level cache via __builtins__
-    let builtins = py.import("builtins")?;
-    if builtins.hasattr("_DictWithModelDump")? {
-        return builtins.getattr("_DictWithModelDump");
-    }
-
-    // Create the class using exec
-    let locals = PyDict::new(py);
-    py.run(
-        c"class DictWithModelDump(dict):
-    def model_dump(self):
-        return dict(self)",
-        None,
-        Some(&locals),
-    )?;
-    let class = locals.get_item("DictWithModelDump")?.ok_or_else(|| {
-        pyo3::exceptions::PyRuntimeError::new_err("Failed to create DictWithModelDump class")
-    })?;
-
-    // Cache it
-    builtins.setattr("_DictWithModelDump", &class)?;
-    Ok(class)
-}
-
-/// Helper to call a Python namespace method with JSON serialization.
-/// For methods that take a request and return a response.
-/// Uses DictWithModelDump to pass a dict that also has model_dump() method,
-/// making it compatible with both depythonize and Python wrappers.
-async fn call_py_method<Req, Resp>(
-    py_namespace: Arc<Py<PyAny>>,
-    method_name: &'static str,
-    request: Req,
-) -> lance_core::Result<Resp>
-where
-    Req: serde::Serialize + Send + 'static,
-    Resp: serde::de::DeserializeOwned + Send + 'static,
-{
-    let request_json = serde_json::to_string(&request).map_err(|e| {
-        lance_core::Error::io(format!(
-            "Failed to serialize request for {}: {}",
-            method_name, e
-        ))
-    })?;
-
-    let response_json = tokio::task::spawn_blocking(move || {
-        Python::attach(|py| {
-            let json_module = py.import("json")?;
-            let request_dict = json_module.call_method1("loads", (&request_json,))?;
-
-            // Wrap dict in DictWithModelDump so it works with both depythonize and .model_dump()
-            let dict_class = get_dict_with_model_dump_class(py)?;
-            let request_arg = dict_class.call1((request_dict,))?;
-
-            // Call the Python method
-            let result = py_namespace.call_method1(py, method_name, (request_arg,))?;
-
-            // Convert response to dict, then to JSON
-            // Pydantic models have model_dump() method
-            let result_dict = if result.bind(py).hasattr("model_dump")? {
-                result.call_method0(py, "model_dump")?
-            } else {
-                result
-            };
-            let response_json: String = json_module
-                .call_method1("dumps", (result_dict,))?
-                .extract()?;
-            Ok::<_, PyErr>(response_json)
-        })
-    })
-    .await
-    .map_err(|e| lance_core::Error::io(format!("Task join error for {}: {}", method_name, e)))?
-    .map_err(|e: PyErr| lance_core::Error::io(format!("Python error in {}: {}", method_name, e)))?;
-
-    serde_json::from_str(&response_json).map_err(|e| {
-        lance_core::Error::io(format!(
-            "Failed to deserialize response from {}: {}",
-            method_name, e
-        ))
-    })
-}
-
-/// Helper for methods that return () on success
-async fn call_py_method_unit<Req>(
-    py_namespace: Arc<Py<PyAny>>,
-    method_name: &'static str,
-    request: Req,
-) -> lance_core::Result<()>
-where
-    Req: serde::Serialize + Send + 'static,
-{
-    let request_json = serde_json::to_string(&request).map_err(|e| {
-        lance_core::Error::io(format!(
-            "Failed to serialize request for {}: {}",
-            method_name, e
-        ))
-    })?;
-
-    tokio::task::spawn_blocking(move || {
-        Python::attach(|py| {
-            let json_module = py.import("json")?;
-            let request_dict = json_module.call_method1("loads", (&request_json,))?;
-
-            // Wrap dict in DictWithModelDump
-            let dict_class = get_dict_with_model_dump_class(py)?;
-            let request_arg = dict_class.call1((request_dict,))?;
-
-            // Call the Python method
-            py_namespace.call_method1(py, method_name, (request_arg,))?;
-            Ok::<_, PyErr>(())
-        })
-    })
-    .await
-    .map_err(|e| lance_core::Error::io(format!("Task join error for {}: {}", method_name, e)))?
-    .map_err(|e: PyErr| lance_core::Error::io(format!("Python error in {}: {}", method_name, e)))
-}
-
-/// Helper for methods that return a primitive type
-async fn call_py_method_primitive<Req, Resp>(
-    py_namespace: Arc<Py<PyAny>>,
-    method_name: &'static str,
-    request: Req,
-) -> lance_core::Result<Resp>
-where
-    Req: serde::Serialize + Send + 'static,
-    Resp: for<'py> pyo3::FromPyObject<'py> + Send + 'static,
-{
-    let request_json = serde_json::to_string(&request).map_err(|e| {
-        lance_core::Error::io(format!(
-            "Failed to serialize request for {}: {}",
-            method_name, e
-        ))
-    })?;
-
-    tokio::task::spawn_blocking(move || {
-        Python::attach(|py| {
-            let json_module = py.import("json")?;
-            let request_dict = json_module.call_method1("loads", (&request_json,))?;
-
-            // Wrap dict in DictWithModelDump
-            let dict_class = get_dict_with_model_dump_class(py)?;
-            let request_arg = dict_class.call1((request_dict,))?;
-
-            // Call the Python method
-            let result = py_namespace.call_method1(py, method_name, (request_arg,))?;
-            let value: Resp = result.extract(py)?;
-            Ok::<_, PyErr>(value)
-        })
-    })
-    .await
-    .map_err(|e| lance_core::Error::io(format!("Task join error for {}: {}", method_name, e)))?
-    .map_err(|e: PyErr| lance_core::Error::io(format!("Python error in {}: {}", method_name, e)))
-}
-
-/// Helper for methods that return Bytes
-async fn call_py_method_bytes<Req>(
-    py_namespace: Arc<Py<PyAny>>,
-    method_name: &'static str,
-    request: Req,
-) -> lance_core::Result<Bytes>
-where
-    Req: serde::Serialize + Send + 'static,
-{
-    let request_json = serde_json::to_string(&request).map_err(|e| {
-        lance_core::Error::io(format!(
-            "Failed to serialize request for {}: {}",
-            method_name, e
-        ))
-    })?;
-
-    tokio::task::spawn_blocking(move || {
-        Python::attach(|py| {
-            let json_module = py.import("json")?;
-            let request_dict = json_module.call_method1("loads", (&request_json,))?;
-
-            // Wrap dict in DictWithModelDump
-            let dict_class = get_dict_with_model_dump_class(py)?;
-            let request_arg = dict_class.call1((request_dict,))?;
-
-            // Call the Python method
-            let result = py_namespace.call_method1(py, method_name, (request_arg,))?;
-            let bytes_data: Vec<u8> = result.extract(py)?;
-            Ok::<_, PyErr>(Bytes::from(bytes_data))
-        })
-    })
-    .await
-    .map_err(|e| lance_core::Error::io(format!("Task join error for {}: {}", method_name, e)))?
-    .map_err(|e: PyErr| lance_core::Error::io(format!("Python error in {}: {}", method_name, e)))
-}
-
-/// Helper for methods that take request + data and return a response
-async fn call_py_method_with_data<Req, Resp>(
-    py_namespace: Arc<Py<PyAny>>,
-    method_name: &'static str,
-    request: Req,
-    data: Bytes,
-) -> lance_core::Result<Resp>
-where
-    Req: serde::Serialize + Send + 'static,
-    Resp: serde::de::DeserializeOwned + Send + 'static,
-{
-    let request_json = serde_json::to_string(&request).map_err(|e| {
-        lance_core::Error::io(format!(
-            "Failed to serialize request for {}: {}",
-            method_name, e
-        ))
-    })?;
-
-    let response_json = tokio::task::spawn_blocking(move || {
-        Python::attach(|py| {
-            let json_module = py.import("json")?;
-            let request_dict = json_module.call_method1("loads", (&request_json,))?;
-
-            // Wrap dict in DictWithModelDump
-            let dict_class = get_dict_with_model_dump_class(py)?;
-            let request_arg = dict_class.call1((request_dict,))?;
-
-            // Pass request and bytes to Python method
-            let py_bytes = pyo3::types::PyBytes::new(py, &data);
-            let result = py_namespace.call_method1(py, method_name, (request_arg, py_bytes))?;
-
-            // Convert response dict to JSON
-            let response_json: String = json_module.call_method1("dumps", (result,))?.extract()?;
-            Ok::<_, PyErr>(response_json)
-        })
-    })
-    .await
-    .map_err(|e| lance_core::Error::io(format!("Task join error for {}: {}", method_name, e)))?
-    .map_err(|e: PyErr| lance_core::Error::io(format!("Python error in {}: {}", method_name, e)))?;
-
-    serde_json::from_str(&response_json).map_err(|e| {
-        lance_core::Error::io(format!(
-            "Failed to deserialize response from {}: {}",
-            method_name, e
-        ))
-    })
-}
-
-#[async_trait]
-impl LanceNamespaceTrait for PyLanceNamespace {
-    fn namespace_id(&self) -> String {
-        self.namespace_id.clone()
-    }
-
-    async fn list_namespaces(
-        &self,
-        request: ListNamespacesRequest,
-    ) -> lance_core::Result<ListNamespacesResponse> {
-        call_py_method(self.py_namespace.clone(), "list_namespaces", request).await
-    }
-
-    async fn describe_namespace(
-        &self,
-        request: DescribeNamespaceRequest,
-    ) -> lance_core::Result<DescribeNamespaceResponse> {
-        call_py_method(self.py_namespace.clone(), "describe_namespace", request).await
-    }
-
-    async fn create_namespace(
-        &self,
-        request: CreateNamespaceRequest,
-    ) -> lance_core::Result<CreateNamespaceResponse> {
-        call_py_method(self.py_namespace.clone(), "create_namespace", request).await
-    }
-
-    async fn drop_namespace(
-        &self,
-        request: DropNamespaceRequest,
-    ) -> lance_core::Result<DropNamespaceResponse> {
-        call_py_method(self.py_namespace.clone(), "drop_namespace", request).await
-    }
-
-    async fn namespace_exists(&self, request: NamespaceExistsRequest) -> lance_core::Result<()> {
-        call_py_method_unit(self.py_namespace.clone(), "namespace_exists", request).await
-    }
-
-    async fn list_tables(
-        &self,
-        request: ListTablesRequest,
-    ) -> lance_core::Result<ListTablesResponse> {
-        call_py_method(self.py_namespace.clone(), "list_tables", request).await
-    }
-
-    async fn describe_table(
-        &self,
-        request: DescribeTableRequest,
-    ) -> lance_core::Result<DescribeTableResponse> {
-        call_py_method(self.py_namespace.clone(), "describe_table", request).await
-    }
-
-    async fn register_table(
-        &self,
-        request: RegisterTableRequest,
-    ) -> lance_core::Result<RegisterTableResponse> {
-        call_py_method(self.py_namespace.clone(), "register_table", request).await
-    }
-
-    async fn table_exists(&self, request: TableExistsRequest) -> lance_core::Result<()> {
-        call_py_method_unit(self.py_namespace.clone(), "table_exists", request).await
-    }
-
-    async fn drop_table(&self, request: DropTableRequest) -> lance_core::Result<DropTableResponse> {
-        call_py_method(self.py_namespace.clone(), "drop_table", request).await
-    }
-
-    async fn deregister_table(
-        &self,
-        request: DeregisterTableRequest,
-    ) -> lance_core::Result<DeregisterTableResponse> {
-        call_py_method(self.py_namespace.clone(), "deregister_table", request).await
-    }
-
-    async fn count_table_rows(&self, request: CountTableRowsRequest) -> lance_core::Result<i64> {
-        call_py_method_primitive(self.py_namespace.clone(), "count_table_rows", request).await
-    }
-
-    async fn create_table(
-        &self,
-        request: CreateTableRequest,
-        request_data: Bytes,
-    ) -> lance_core::Result<CreateTableResponse> {
-        call_py_method_with_data(
-            self.py_namespace.clone(),
-            "create_table",
-            request,
-            request_data,
-        )
-        .await
-    }
-
-    async fn declare_table(
-        &self,
-        request: DeclareTableRequest,
-    ) -> lance_core::Result<DeclareTableResponse> {
-        call_py_method(self.py_namespace.clone(), "declare_table", request).await
-    }
-
-    async fn insert_into_table(
-        &self,
-        request: InsertIntoTableRequest,
-        request_data: Bytes,
-    ) -> lance_core::Result<InsertIntoTableResponse> {
-        call_py_method_with_data(
-            self.py_namespace.clone(),
-            "insert_into_table",
-            request,
-            request_data,
-        )
-        .await
-    }
-
-    async fn merge_insert_into_table(
-        &self,
-        request: MergeInsertIntoTableRequest,
-        request_data: Bytes,
-    ) -> lance_core::Result<MergeInsertIntoTableResponse> {
-        call_py_method_with_data(
-            self.py_namespace.clone(),
-            "merge_insert_into_table",
-            request,
-            request_data,
-        )
-        .await
-    }
-
-    async fn update_table(
-        &self,
-        request: UpdateTableRequest,
-    ) -> lance_core::Result<UpdateTableResponse> {
-        call_py_method(self.py_namespace.clone(), "update_table", request).await
-    }
-
-    async fn delete_from_table(
-        &self,
-        request: DeleteFromTableRequest,
-    ) -> lance_core::Result<DeleteFromTableResponse> {
-        call_py_method(self.py_namespace.clone(), "delete_from_table", request).await
-    }
-
-    async fn query_table(&self, request: QueryTableRequest) -> lance_core::Result<Bytes> {
-        call_py_method_bytes(self.py_namespace.clone(), "query_table", request).await
-    }
-
-    async fn create_table_index(
-        &self,
-        request: CreateTableIndexRequest,
-    ) -> lance_core::Result<CreateTableIndexResponse> {
-        call_py_method(self.py_namespace.clone(), "create_table_index", request).await
-    }
-
-    async fn list_table_indices(
-        &self,
-        request: ListTableIndicesRequest,
-    ) -> lance_core::Result<ListTableIndicesResponse> {
-        call_py_method(self.py_namespace.clone(), "list_table_indices", request).await
-    }
-
-    async fn describe_table_index_stats(
-        &self,
-        request: DescribeTableIndexStatsRequest,
-    ) -> lance_core::Result<DescribeTableIndexStatsResponse> {
-        call_py_method(
-            self.py_namespace.clone(),
-            "describe_table_index_stats",
-            request,
-        )
-        .await
-    }
-
-    async fn describe_transaction(
-        &self,
-        request: DescribeTransactionRequest,
-    ) -> lance_core::Result<DescribeTransactionResponse> {
-        call_py_method(self.py_namespace.clone(), "describe_transaction", request).await
-    }
-
-    async fn alter_transaction(
-        &self,
-        request: AlterTransactionRequest,
-    ) -> lance_core::Result<AlterTransactionResponse> {
-        call_py_method(self.py_namespace.clone(), "alter_transaction", request).await
-    }
-
-    async fn create_table_scalar_index(
-        &self,
-        request: CreateTableIndexRequest,
-    ) -> lance_core::Result<CreateTableScalarIndexResponse> {
-        call_py_method(
-            self.py_namespace.clone(),
-            "create_table_scalar_index",
-            request,
-        )
-        .await
-    }
-
-    async fn drop_table_index(
-        &self,
-        request: DropTableIndexRequest,
-    ) -> lance_core::Result<DropTableIndexResponse> {
-        call_py_method(self.py_namespace.clone(), "drop_table_index", request).await
-    }
-
-    async fn list_all_tables(
-        &self,
-        request: ListTablesRequest,
-    ) -> lance_core::Result<ListTablesResponse> {
-        call_py_method(self.py_namespace.clone(), "list_all_tables", request).await
-    }
-
-    async fn restore_table(
-        &self,
-        request: RestoreTableRequest,
-    ) -> lance_core::Result<RestoreTableResponse> {
-        call_py_method(self.py_namespace.clone(), "restore_table", request).await
-    }
-
-    async fn rename_table(
-        &self,
-        request: RenameTableRequest,
-    ) -> lance_core::Result<RenameTableResponse> {
-        call_py_method(self.py_namespace.clone(), "rename_table", request).await
-    }
-
-    async fn list_table_versions(
-        &self,
-        request: ListTableVersionsRequest,
-    ) -> lance_core::Result<ListTableVersionsResponse> {
-        call_py_method(self.py_namespace.clone(), "list_table_versions", request).await
-    }
-
-    async fn create_table_version(
-        &self,
-        request: CreateTableVersionRequest,
-    ) -> lance_core::Result<CreateTableVersionResponse> {
-        call_py_method(self.py_namespace.clone(), "create_table_version", request).await
-    }
-
-    async fn describe_table_version(
-        &self,
-        request: DescribeTableVersionRequest,
-    ) -> lance_core::Result<DescribeTableVersionResponse> {
-        call_py_method(self.py_namespace.clone(), "describe_table_version", request).await
-    }
-
-    async fn batch_delete_table_versions(
-        &self,
-        request: BatchDeleteTableVersionsRequest,
-    ) -> lance_core::Result<BatchDeleteTableVersionsResponse> {
-        call_py_method(
-            self.py_namespace.clone(),
-            "batch_delete_table_versions",
-            request,
-        )
-        .await
-    }
-
-    async fn update_table_schema_metadata(
-        &self,
-        request: UpdateTableSchemaMetadataRequest,
-    ) -> lance_core::Result<UpdateTableSchemaMetadataResponse> {
-        call_py_method(
-            self.py_namespace.clone(),
-            "update_table_schema_metadata",
-            request,
-        )
-        .await
-    }
-
-    async fn get_table_stats(
-        &self,
-        request: GetTableStatsRequest,
-    ) -> lance_core::Result<GetTableStatsResponse> {
-        call_py_method(self.py_namespace.clone(), "get_table_stats", request).await
-    }
-
-    async fn explain_table_query_plan(
-        &self,
-        request: ExplainTableQueryPlanRequest,
-    ) -> lance_core::Result<String> {
-        call_py_method_primitive(
-            self.py_namespace.clone(),
-            "explain_table_query_plan",
-            request,
-        )
-        .await
-    }
-
-    async fn analyze_table_query_plan(
-        &self,
-        request: AnalyzeTableQueryPlanRequest,
-    ) -> lance_core::Result<String> {
-        call_py_method_primitive(
-            self.py_namespace.clone(),
-            "analyze_table_query_plan",
-            request,
-        )
-        .await
-    }
-
-    async fn alter_table_add_columns(
-        &self,
-        request: AlterTableAddColumnsRequest,
-    ) -> lance_core::Result<AlterTableAddColumnsResponse> {
-        call_py_method(
-            self.py_namespace.clone(),
-            "alter_table_add_columns",
-            request,
-        )
-        .await
-    }
-
-    async fn alter_table_alter_columns(
-        &self,
-        request: AlterTableAlterColumnsRequest,
-    ) -> lance_core::Result<AlterTableAlterColumnsResponse> {
-        call_py_method(
-            self.py_namespace.clone(),
-            "alter_table_alter_columns",
-            request,
-        )
-        .await
-    }
-
-    async fn alter_table_drop_columns(
-        &self,
-        request: AlterTableDropColumnsRequest,
-    ) -> lance_core::Result<AlterTableDropColumnsResponse> {
-        call_py_method(
-            self.py_namespace.clone(),
-            "alter_table_drop_columns",
-            request,
-        )
-        .await
-    }
-
-    async fn list_table_tags(
-        &self,
-        request: ListTableTagsRequest,
-    ) -> lance_core::Result<ListTableTagsResponse> {
-        call_py_method(self.py_namespace.clone(), "list_table_tags", request).await
-    }
-
-    async fn create_table_tag(
-        &self,
-        request: CreateTableTagRequest,
-    ) -> lance_core::Result<CreateTableTagResponse> {
-        call_py_method(self.py_namespace.clone(), "create_table_tag", request).await
-    }
-
-    async fn delete_table_tag(
-        &self,
-        request: DeleteTableTagRequest,
-    ) -> lance_core::Result<DeleteTableTagResponse> {
-        call_py_method(self.py_namespace.clone(), "delete_table_tag", request).await
-    }
-
-    async fn update_table_tag(
-        &self,
-        request: UpdateTableTagRequest,
-    ) -> lance_core::Result<UpdateTableTagResponse> {
-        call_py_method(self.py_namespace.clone(), "update_table_tag", request).await
-    }
-
-    async fn get_table_tag_version(
-        &self,
-        request: GetTableTagVersionRequest,
-    ) -> lance_core::Result<GetTableTagVersionResponse> {
-        call_py_method(self.py_namespace.clone(), "get_table_tag_version", request).await
-    }
-}
-
-/// Convert Python dict to HashMap<String, String>
-#[allow(dead_code)]
-fn dict_to_hashmap(dict: &Bound<'_, PyDict>) -> PyResult<HashMap<String, String>> {
-    let mut map = HashMap::new();
-    for (key, value) in dict.iter() {
-        let key_str: String = key.extract()?;
-        let value_str: String = value.extract()?;
-        map.insert(key_str, value_str);
-    }
-    Ok(map)
-}
-
-/// Extract an Arc<dyn LanceNamespace> from a Python namespace object.
-///
-/// This function wraps any Python namespace object with PyLanceNamespace.
-/// The PyLanceNamespace wrapper uses DictWithModelDump to pass requests,
-/// which works with both:
-/// - Native namespaces (DirectoryNamespace, RestNamespace) that use depythonize (expects dict)
-/// - Custom Python implementations that call .model_dump() on the request
-pub fn extract_namespace_arc(
-    py: Python<'_>,
-    ns: Py<PyAny>,
-) -> PyResult<Arc<dyn LanceNamespaceTrait>> {
-    let ns_ref = ns.bind(py);
-    PyLanceNamespace::create_arc(py, ns_ref)
-}
--- a/python/src/permutation.rs
+++ b/python/src/permutation.rs
@@ -16,32 +16,17 @@ use lancedb::{
    query::Select,
 };
 use pyo3::{
-    Bound, PyAny, PyRef, PyRefMut, PyResult, Python,
    exceptions::PyRuntimeError,
    pyclass, pymethods,
    types::{PyAnyMethods, PyDict, PyDictMethods, PyType},
+    Bound, PyAny, PyRef, PyRefMut, PyResult, Python,
 };
 use pyo3_async_runtimes::tokio::future_into_py;

-fn table_from_py<'a>(table: Bound<'a, PyAny>) -> PyResult<Bound<'a, Table>> {
-    if table.hasattr("_inner")? {
-        Ok(table.getattr("_inner")?.downcast_into::<Table>()?)
-    } else if table.hasattr("_table")? {
-        Ok(table
-            .getattr("_table")?
-            .getattr("_inner")?
-            .downcast_into::<Table>()?)
-    } else {
-        Err(PyRuntimeError::new_err(
-            "Provided table does not appear to be a Table or RemoteTable instance",
-        ))
-    }
-}
-
 /// Create a permutation builder for the given table
 #[pyo3::pyfunction]
 pub fn async_permutation_builder(table: Bound<'_, PyAny>) -> PyResult<PyAsyncPermutationBuilder> {
-    let table = table_from_py(table)?;
+    let table = table.getattr("_inner")?.downcast_into::<Table>()?;
    let inner_table = table.borrow().inner_ref()?.clone();
    let inner_builder = LancePermutationBuilder::new(inner_table);

@@ -265,8 +250,10 @@ impl PyPermutationReader {
        permutation_table: Option<Bound<'py, PyAny>>,
        split: u64,
    ) -> PyResult<Bound<'py, PyAny>> {
-        let base_table = table_from_py(base_table)?;
-        let permutation_table = permutation_table.map(table_from_py).transpose()?;
+        let base_table = base_table.getattr("_inner")?.downcast_into::<Table>()?;
+        let permutation_table = permutation_table
+            .map(|p| PyResult::Ok(p.getattr("_inner")?.downcast_into::<Table>()?))
+            .transpose()?;

        let base_table = base_table.borrow().inner_ref()?.base_table().clone();
        let permutation_table = permutation_table
--- a/python/src/query.rs
+++ b/python/src/query.rs
@@ -4,9 +4,9 @@
 use std::sync::Arc;
 use std::time::Duration;

+use arrow::array::make_array;
 use arrow::array::Array;
 use arrow::array::ArrayData;
-use arrow::array::make_array;
 use arrow::pyarrow::FromPyArrow;
 use arrow::pyarrow::IntoPyArrow;
 use arrow::pyarrow::ToPyArrow;
@@ -22,23 +22,23 @@ use lancedb::query::{
    VectorQuery as LanceDbVectorQuery,
 };
 use lancedb::table::AnyQuery;
+use pyo3::prelude::{PyAnyMethods, PyDictMethods};
+use pyo3::pyfunction;
+use pyo3::pymethods;
+use pyo3::types::PyList;
+use pyo3::types::{PyDict, PyString};
 use pyo3::Bound;
 use pyo3::IntoPyObject;
 use pyo3::PyAny;
 use pyo3::PyRef;
 use pyo3::PyResult;
 use pyo3::Python;
-use pyo3::prelude::{PyAnyMethods, PyDictMethods};
-use pyo3::pyfunction;
-use pyo3::pymethods;
-use pyo3::types::PyList;
-use pyo3::types::{PyDict, PyString};
-use pyo3::{FromPyObject, exceptions::PyRuntimeError};
-use pyo3::{PyErr, pyclass};
+use pyo3::{exceptions::PyRuntimeError, FromPyObject};
 use pyo3::{
    exceptions::{PyNotImplementedError, PyValueError},
    intern,
 };
+use pyo3::{pyclass, PyErr};
 use pyo3_async_runtimes::tokio::future_into_py;

 use crate::util::parse_distance_type;
@@ -316,19 +316,6 @@ impl<'py> IntoPyObject<'py> for PySelect {
            Select::All => Ok(py.None().into_bound(py).into_any()),
            Select::Columns(columns) => Ok(columns.into_pyobject(py)?.into_any()),
            Select::Dynamic(columns) => Ok(columns.into_pyobject(py)?.into_any()),
-            Select::Expr(pairs) => {
-                // Serialize DataFusion Expr -> SQL string so Python sees the same
-                // format as Select::Dynamic: a list of (name, sql_string) tuples.
-                let sql_pairs: PyResult<Vec<(String, String)>> = pairs
-                    .into_iter()
-                    .map(|(name, expr)| {
-                        lancedb::expr::expr_to_sql_string(&expr)
-                            .map(|sql| (name, sql))
-                            .map_err(|e| PyRuntimeError::new_err(e.to_string()))
-                    })
-                    .collect();
-                Ok(sql_pairs?.into_pyobject(py)?.into_any())
-            }
        }
    }
 }
--- a/python/src/session.rs
+++ b/python/src/session.rs
@@ -4,7 +4,7 @@
 use std::sync::Arc;

 use lancedb::{ObjectStoreRegistry, Session as LanceSession};
-use pyo3::{PyResult, pyclass, pymethods};
+use pyo3::{pyclass, pymethods, PyResult};

 /// A session for managing caches and object stores across LanceDB operations.
 ///
--- a/python/src/storage_options.rs
+++ b/python/src/storage_options.rs
@@ -66,10 +66,13 @@ impl StorageOptionsProvider for PyStorageOptionsProviderWrapper {
                    .inner
                    .bind(py)
                    .call_method0("fetch_storage_options")
-                    .map_err(|e| lance_core::Error::io_source(Box::new(std::io::Error::other(format!(
-                        "Failed to call fetch_storage_options: {}",
-                        e
-                    )))))?;
+                    .map_err(|e| lance_core::Error::IO {
+                        source: Box::new(std::io::Error::other(format!(
+                            "Failed to call fetch_storage_options: {}",
+                            e
+                        ))),
+                        location: snafu::location!(),
+                    })?;

                // If result is None, return None
                if result.is_none() {
@@ -78,19 +81,26 @@ impl StorageOptionsProvider for PyStorageOptionsProviderWrapper {

                // Extract the result dict - should be a flat Map<String, String>
                let result_dict = result.downcast::<PyDict>().map_err(|_| {
-                    lance_core::Error::invalid_input(
-                        "fetch_storage_options() must return a dict of string key-value pairs or None",
-                    )
+                    lance_core::Error::InvalidInput {
+                        source: "fetch_storage_options() must return None or a dict of string key-value pairs".into(),
+                        location: snafu::location!(),
+                    }
                })?;

                // Convert all entries to HashMap<String, String>
                let mut storage_options = HashMap::new();
                for (key, value) in result_dict.iter() {
                    let key_str: String = key.extract().map_err(|e| {
-                        lance_core::Error::invalid_input(format!("Storage option key must be a string: {}", e))
+                        lance_core::Error::InvalidInput {
+                            source: format!("Storage option key must be a string: {}", e).into(),
+                            location: snafu::location!(),
+                        }
                    })?;
                    let value_str: String = value.extract().map_err(|e| {
-                        lance_core::Error::invalid_input(format!("Storage option value must be a string: {}", e))
+                        lance_core::Error::InvalidInput {
+                            source: format!("Storage option value must be a string: {}", e).into(),
+                            location: snafu::location!(),
+                        }
                    })?;
                    storage_options.insert(key_str, value_str);
                }
@@ -99,10 +109,13 @@ impl StorageOptionsProvider for PyStorageOptionsProviderWrapper {
            })
        })
        .await
-        .map_err(|e| lance_core::Error::io_source(Box::new(std::io::Error::other(format!(
-            "Task join error: {}",
-            e
-        )))))?
+        .map_err(|e| lance_core::Error::IO {
+            source: Box::new(std::io::Error::other(format!(
+                "Task join error: {}",
+                e
+            ))),
+            location: snafu::location!(),
+        })?
    }

    fn provider_id(&self) -> String {
--- a/python/src/table.rs
+++ b/python/src/table.rs
@@ -5,9 +5,8 @@ use std::{collections::HashMap, sync::Arc};
 use crate::{
    connection::Connection,
    error::PythonErrorExt,
-    index::{IndexConfig, extract_index_params},
+    index::{extract_index_params, IndexConfig},
    query::{Query, TakeQuery},
-    table::scannable::PyScannable,
 };
 use arrow::{
    datatypes::{DataType, Schema},
@@ -19,15 +18,13 @@ use lancedb::table::{
    Table as LanceDbTable,
 };
 use pyo3::{
-    Bound, FromPyObject, Py, PyAny, PyRef, PyResult, Python,
    exceptions::{PyKeyError, PyRuntimeError, PyValueError},
    pyclass, pymethods,
    types::{IntoPyDict, PyAnyMethods, PyDict, PyDictMethods},
+    Bound, FromPyObject, PyAny, PyRef, PyResult, Python,
 };
 use pyo3_async_runtimes::tokio::future_into_py;

-mod scannable;
-
 /// Statistics about a compaction operation.
 #[pyclass(get_all)]
 #[derive(Clone, Debug)]
@@ -112,24 +109,19 @@ impl From<lancedb::table::AddResult> for AddResult {
 #[pyclass(get_all)]
 #[derive(Clone, Debug)]
 pub struct DeleteResult {
-    pub num_deleted_rows: u64,
    pub version: u64,
 }

 #[pymethods]
 impl DeleteResult {
    pub fn __repr__(&self) -> String {
-        format!(
-            "DeleteResult(num_deleted_rows={}, version={})",
-            self.num_deleted_rows, self.version
-        )
+        format!("DeleteResult(version={})", self.version)
    }
 }

 impl From<lancedb::table::DeleteResult> for DeleteResult {
    fn from(result: lancedb::table::DeleteResult) -> Self {
        Self {
-            num_deleted_rows: result.num_deleted_rows,
            version: result.version,
        }
    }
@@ -299,14 +291,14 @@ impl Table {
        })
    }

-    #[pyo3(signature = (data, mode, progress=None))]
    pub fn add<'a>(
        self_: PyRef<'a, Self>,
-        data: PyScannable,
+        data: Bound<'_, PyAny>,
        mode: String,
-        progress: Option<Py<PyAny>>,
    ) -> PyResult<Bound<'a, PyAny>> {
-        let mut op = self_.inner_ref()?.add(data);
+        let batches: Box<dyn arrow::array::RecordBatchReader + Send> =
+            Box::new(ArrowArrayStreamReader::from_pyarrow_bound(&data)?);
+        let mut op = self_.inner_ref()?.add(batches);
        if mode == "append" {
            op = op.mode(AddDataMode::Append);
        } else if mode == "overwrite" {
@@ -314,81 +306,6 @@ impl Table {
        } else {
            return Err(PyValueError::new_err(format!("Invalid mode: {}", mode)));
        }
-        if let Some(progress_obj) = progress {
-            let is_callable = Python::attach(|py| progress_obj.bind(py).is_callable());
-            if is_callable {
-                // Callback: call with a dict of progress info.
-                op = op.progress(move |p| {
-                    Python::attach(|py| {
-                        let dict = PyDict::new(py);
-                        if let Err(e) = dict
-                            .set_item("output_rows", p.output_rows())
-                            .and_then(|_| dict.set_item("output_bytes", p.output_bytes()))
-                            .and_then(|_| dict.set_item("total_rows", p.total_rows()))
-                            .and_then(|_| {
-                                dict.set_item("elapsed_seconds", p.elapsed().as_secs_f64())
-                            })
-                            .and_then(|_| dict.set_item("active_tasks", p.active_tasks()))
-                            .and_then(|_| dict.set_item("total_tasks", p.total_tasks()))
-                            .and_then(|_| dict.set_item("done", p.done()))
-                        {
-                            log::warn!("progress dict error: {e}");
-                            return;
-                        }
-                        if let Err(e) = progress_obj.call1(py, (dict,)) {
-                            log::warn!("progress callback error: {e}");
-                        }
-                    });
-                });
-            } else {
-                // tqdm-like: has update() method.
-                let mut last_rows: usize = 0;
-                let mut total_set = false;
-                op = op.progress(move |p| {
-                    let current = p.output_rows();
-                    let prev = last_rows;
-                    last_rows = current;
-                    Python::attach(|py| {
-                        if let Some(total) = p.total_rows()
-                            && !total_set
-                        {
-                            if let Err(e) = progress_obj.setattr(py, "total", total) {
-                                log::warn!("progress setattr error: {e}");
-                            }
-                            total_set = true;
-                        }
-                        let delta = current.saturating_sub(prev);
-                        if delta > 0 {
-                            if let Err(e) = progress_obj.call_method1(py, "update", (delta,)) {
-                                log::warn!("progress update error: {e}");
-                            }
-                            // Show throughput and active workers in tqdm postfix.
-                            let elapsed = p.elapsed().as_secs_f64();
-                            if elapsed > 0.0 {
-                                let mb_per_sec = p.output_bytes() as f64 / elapsed / 1_000_000.0;
-                                let postfix = format!(
-                                    "{:.1} MB/s | {}/{} workers",
-                                    mb_per_sec,
-                                    p.active_tasks(),
-                                    p.total_tasks()
-                                );
-                                if let Err(e) =
-                                    progress_obj.call_method1(py, "set_postfix_str", (postfix,))
-                                {
-                                    log::warn!("progress set_postfix_str error: {e}");
-                                }
-                            }
-                        }
-                        if p.done() {
-                            // Force a final refresh so the bar shows completion.
-                            if let Err(e) = progress_obj.call_method0(py, "refresh") {
-                                log::warn!("progress refresh error: {e}");
-                            }
-                        }
-                    });
-                });
-            }
-        }

        future_into_py(self_.py(), async move {
            let result = op.execute().await.infer_error()?;
@@ -503,17 +420,6 @@ impl Table {
        })
    }

-    pub fn prewarm_data(
-        self_: PyRef<'_, Self>,
-        columns: Option<Vec<String>>,
-    ) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.inner_ref()?.clone();
-        future_into_py(self_.py(), async move {
-            inner.prewarm_data(columns).await.infer_error()?;
-            Ok(())
-        })
-    }
-
    pub fn list_indices(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.inner_ref()?.clone();
        future_into_py(self_.py(), async move {
@@ -630,7 +536,7 @@ impl Table {
        let inner = self_.inner_ref()?.clone();
        future_into_py(self_.py(), async move {
            let versions = inner.list_versions().await.infer_error()?;
-            Python::attach(|py| {
+            let versions_as_dict = Python::attach(|py| {
                versions
                    .iter()
                    .map(|v| {
@@ -647,7 +553,9 @@ impl Table {
                        Ok(dict.unbind())
                    })
                    .collect::<PyResult<Vec<_>>>()
-            })
+            });
+
+            versions_as_dict
        })
    }

--- a/python/src/table/scannable.rs
+++ b/python/src/table/scannable.rs
@@ -1,145 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-use std::sync::Arc;
-
-use arrow::{
-    datatypes::{Schema, SchemaRef},
-    ffi_stream::ArrowArrayStreamReader,
-    pyarrow::{FromPyArrow, PyArrowType},
-};
-use futures::StreamExt;
-use lancedb::{
-    Error,
-    arrow::{SendableRecordBatchStream, SimpleRecordBatchStream},
-    data::scannable::Scannable,
-};
-use pyo3::{FromPyObject, Py, PyAny, Python, types::PyAnyMethods};
-
-/// Adapter that implements Scannable for a Python reader factory callable.
-///
-/// This holds a Python callable that returns a RecordBatchReader when called.
-/// For rescannable sources, the callable can be invoked multiple times to
-/// get fresh readers.
-pub struct PyScannable {
-    /// Python callable that returns a RecordBatchReader
-    reader_factory: Py<PyAny>,
-    schema: SchemaRef,
-    num_rows: Option<usize>,
-    rescannable: bool,
-}
-
-impl std::fmt::Debug for PyScannable {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        f.debug_struct("PyScannable")
-            .field("schema", &self.schema)
-            .field("num_rows", &self.num_rows)
-            .field("rescannable", &self.rescannable)
-            .finish()
-    }
-}
-
-impl Scannable for PyScannable {
-    fn schema(&self) -> SchemaRef {
-        self.schema.clone()
-    }
-
-    fn scan_as_stream(&mut self) -> SendableRecordBatchStream {
-        let reader: Result<ArrowArrayStreamReader, Error> = {
-            Python::attach(|py| {
-                let result =
-                    self.reader_factory
-                        .call0(py)
-                        .map_err(|e| lancedb::Error::Runtime {
-                            message: format!("Python reader factory failed: {}", e),
-                        })?;
-                ArrowArrayStreamReader::from_pyarrow_bound(result.bind(py)).map_err(|e| {
-                    lancedb::Error::Runtime {
-                        message: format!("Failed to create Arrow reader from Python: {}", e),
-                    }
-                })
-            })
-        };
-
-        // Reader is blocking but stream is non-blocking, so we need to spawn a task to pull.
-        let (tx, rx) = tokio::sync::mpsc::channel(1);
-
-        let join_handle = tokio::task::spawn_blocking(move || {
-            let reader = match reader {
-                Ok(reader) => reader,
-                Err(e) => {
-                    let _ = tx.blocking_send(Err(e));
-                    return;
-                }
-            };
-            for batch in reader {
-                match batch {
-                    Ok(batch) => {
-                        if tx.blocking_send(Ok(batch)).is_err() {
-                            // Receiver dropped, stop processing
-                            break;
-                        }
-                    }
-                    Err(source) => {
-                        let _ = tx.blocking_send(Err(Error::Arrow { source }));
-                        break;
-                    }
-                }
-            }
-        });
-
-        let schema = self.schema.clone();
-        let stream = futures::stream::unfold(
-            (rx, Some(join_handle)),
-            |(mut rx, join_handle)| async move {
-                match rx.recv().await {
-                    Some(Ok(batch)) => Some((Ok(batch), (rx, join_handle))),
-                    Some(Err(e)) => Some((Err(e), (rx, join_handle))),
-                    None => {
-                        // Channel closed. Check if the task panicked — a panic
-                        // drops the sender without sending an error, so without
-                        // this check we'd silently return a truncated stream.
-                        if let Some(handle) = join_handle
-                            && let Err(join_err) = handle.await
-                        {
-                            return Some((
-                                Err(Error::Runtime {
-                                    message: format!("Reader task panicked: {}", join_err),
-                                }),
-                                (rx, None),
-                            ));
-                        }
-                        None
-                    }
-                }
-            },
-        );
-        Box::pin(SimpleRecordBatchStream::new(stream.fuse(), schema))
-    }
-
-    fn num_rows(&self) -> Option<usize> {
-        self.num_rows
-    }
-
-    fn rescannable(&self) -> bool {
-        self.rescannable
-    }
-}
-
-impl<'py> FromPyObject<'py> for PyScannable {
-    fn extract_bound(ob: &pyo3::Bound<'py, PyAny>) -> pyo3::PyResult<Self> {
-        // Convert from Scannable dataclass.
-        let schema: PyArrowType<Schema> = ob.getattr("schema")?.extract()?;
-        let schema = Arc::new(schema.0);
-        let num_rows: Option<usize> = ob.getattr("num_rows")?.extract()?;
-        let rescannable: bool = ob.getattr("rescannable")?.extract()?;
-        let reader_factory: Py<PyAny> = ob.getattr("reader")?.unbind();
-
-        Ok(Self {
-            schema,
-            reader_factory,
-            num_rows,
-            rescannable,
-        })
-    }
-}
--- a/python/src/util.rs
+++ b/python/src/util.rs
@@ -5,9 +5,8 @@ use std::sync::Mutex;

 use lancedb::DistanceType;
 use pyo3::{
-    PyResult,
    exceptions::{PyRuntimeError, PyValueError},
-    pyfunction,
+    pyfunction, PyResult,
 };

 /// A wrapper around a rust builder
--- a/python/uv.lock
+++ b/python/uv.lock
@@ -2006,7 +2006,7 @@ requires-dist = [
    { name = "botocore", marker = "extra == 'embeddings'", specifier = ">=1.31.57" },
    { name = "cohere", marker = "extra == 'embeddings'" },
    { name = "colpali-engine", marker = "extra == 'embeddings'", specifier = ">=0.3.10" },
-    { name = "datafusion", marker = "extra == 'tests'", specifier = "<52" },
+    { name = "datafusion", marker = "extra == 'tests'" },
    { name = "deprecation" },
    { name = "duckdb", marker = "extra == 'tests'" },
    { name = "google-generativeai", marker = "extra == 'embeddings'" },
@@ -2035,7 +2035,7 @@ requires-dist = [
    { name = "pyarrow-stubs", marker = "extra == 'tests'" },
    { name = "pydantic", specifier = ">=1.10" },
    { name = "pylance", marker = "extra == 'pylance'", specifier = ">=1.0.0b14" },
-    { name = "pylance", marker = "extra == 'tests'", specifier = ">=1.0.0b14,<3.0.0" },
+    { name = "pylance", marker = "extra == 'tests'", specifier = ">=1.0.0b14" },
    { name = "pyright", marker = "extra == 'dev'" },
    { name = "pytest", marker = "extra == 'tests'" },
    { name = "pytest-asyncio", marker = "extra == 'tests'" },
--- a/rust-toolchain.toml
+++ b/rust-toolchain.toml
@@ -1,2 +1,2 @@
 [toolchain]
-channel = "1.91.0"
+channel = "1.90.0"
--- a/rust/lancedb/Cargo.toml
+++ b/rust/lancedb/Cargo.toml
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.27.2-beta.1"
+version = "0.27.0-beta.0"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true
@@ -25,9 +25,7 @@ datafusion-catalog.workspace = true
 datafusion-common.workspace = true
 datafusion-execution.workspace = true
 datafusion-expr.workspace = true
-datafusion-functions.workspace = true
 datafusion-physical-expr.workspace = true
-datafusion-sql.workspace = true
 datafusion-physical-plan.workspace = true
 datafusion.workspace = true
 object_store = { workspace = true }
--- a/rust/lancedb/README.md
+++ b/rust/lancedb/README.md
@@ -1,4 +1,4 @@
-# LanceDB Rust SDK
+# LanceDB Rust

 <a href="https://crates.io/crates/vectordb">![img](https://img.shields.io/crates/v/vectordb)</a>
 <a href="https://docs.rs/vectordb/latest/vectordb/">![Docs.rs](https://img.shields.io/docsrs/vectordb)</a>
--- a/rust/lancedb/examples/bedrock.rs
+++ b/rust/lancedb/examples/bedrock.rs
@@ -9,9 +9,10 @@ use aws_config::Region;
 use aws_sdk_bedrockruntime::Client;
 use futures::StreamExt;
 use lancedb::{
-    Result, connect,
-    embeddings::{EmbeddingDefinition, EmbeddingFunction, bedrock::BedrockEmbeddingFunction},
+    connect,
+    embeddings::{bedrock::BedrockEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},
    query::{ExecutableQuery, QueryBase},
+    Result,
 };

 #[tokio::main]
--- a/rust/lancedb/examples/full_text_search.rs
+++ b/rust/lancedb/examples/full_text_search.rs
@@ -10,10 +10,10 @@ use futures::TryStreamExt;
 use lance_index::scalar::FullTextSearchQuery;
 use lancedb::connection::Connection;

-use lancedb::index::Index;
 use lancedb::index::scalar::FtsIndexBuilder;
+use lancedb::index::Index;
 use lancedb::query::{ExecutableQuery, QueryBase};
-use lancedb::{Result, Table, connect};
+use lancedb::{connect, Result, Table};
 use rand::random;

 #[tokio::main]
@@ -46,21 +46,19 @@ fn create_some_records() -> Result<Box<dyn arrow_array::RecordBatchReader + Send
        .collect::<Vec<_>>();
    let n_terms = 3;
    let batches = RecordBatchIterator::new(
-        vec![
-            RecordBatch::try_new(
-                schema.clone(),
-                vec![
-                    Arc::new(Int32Array::from_iter_values(0..TOTAL as i32)),
-                    Arc::new(StringArray::from_iter_values((0..TOTAL).map(|_| {
-                        (0..n_terms)
-                            .map(|_| words[random::<u32>() as usize % words.len()])
-                            .collect::<Vec<_>>()
-                            .join(" ")
-                    }))),
-                ],
-            )
-            .unwrap(),
-        ]
+        vec![RecordBatch::try_new(
+            schema.clone(),
+            vec![
+                Arc::new(Int32Array::from_iter_values(0..TOTAL as i32)),
+                Arc::new(StringArray::from_iter_values((0..TOTAL).map(|_| {
+                    (0..n_terms)
+                        .map(|_| words[random::<u32>() as usize % words.len()])
+                        .collect::<Vec<_>>()
+                        .join(" ")
+                }))),
+            ],
+        )
+        .unwrap()]
        .into_iter()
        .map(Ok),
        schema.clone(),
--- a/rust/lancedb/examples/hybrid_search.rs
+++ b/rust/lancedb/examples/hybrid_search.rs
@@ -5,15 +5,16 @@ use arrow_array::{RecordBatch, StringArray};
 use arrow_schema::{DataType, Field, Schema};
 use futures::TryStreamExt;
 use lance_index::scalar::FullTextSearchQuery;
-use lancedb::index::Index;
 use lancedb::index::scalar::FtsIndexBuilder;
+use lancedb::index::Index;
 use lancedb::{
-    Result, Table, connect,
+    connect,
    embeddings::{
-        EmbeddingDefinition, EmbeddingFunction,
-        sentence_transformers::SentenceTransformersEmbeddings,
+        sentence_transformers::SentenceTransformersEmbeddings, EmbeddingDefinition,
+        EmbeddingFunction,
    },
    query::{QueryBase, QueryExecutionOptions},
+    Result, Table,
 };
 use std::{iter::once, sync::Arc};

--- a/rust/lancedb/examples/ivf_pq.rs
+++ b/rust/lancedb/examples/ivf_pq.rs
@@ -14,10 +14,10 @@ use arrow_schema::{DataType, Field, Schema};
 use futures::TryStreamExt;
 use lancedb::connection::Connection;

-use lancedb::index::Index;
 use lancedb::index::vector::IvfPqIndexBuilder;
+use lancedb::index::Index;
 use lancedb::query::{ExecutableQuery, QueryBase};
-use lancedb::{DistanceType, Result, Table, connect};
+use lancedb::{connect, DistanceType, Result, Table};

 #[tokio::main]
 async fn main() -> Result<()> {
@@ -51,21 +51,19 @@ fn create_some_records() -> Result<Box<dyn arrow_array::RecordBatchReader + Send

    // Create a RecordBatch stream.
    let batches = RecordBatchIterator::new(
-        vec![
-            RecordBatch::try_new(
-                schema.clone(),
-                vec![
-                    Arc::new(Int32Array::from_iter_values(0..TOTAL as i32)),
-                    Arc::new(
-                        FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
-                            (0..TOTAL).map(|_| Some(vec![Some(1.0); DIM])),
-                            DIM as i32,
-                        ),
+        vec![RecordBatch::try_new(
+            schema.clone(),
+            vec![
+                Arc::new(Int32Array::from_iter_values(0..TOTAL as i32)),
+                Arc::new(
+                    FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(
+                        (0..TOTAL).map(|_| Some(vec![Some(1.0); DIM])),
+                        DIM as i32,
                    ),
-                ],
-            )
-            .unwrap(),
-        ]
+                ),
+            ],
+        )
+        .unwrap()]
        .into_iter()
        .map(Ok),
        schema.clone(),
--- a/rust/lancedb/examples/openai.rs
+++ b/rust/lancedb/examples/openai.rs
@@ -8,9 +8,10 @@ use std::{iter::once, sync::Arc};
 use arrow_array::{RecordBatch, StringArray};
 use futures::StreamExt;
 use lancedb::{
-    Result, connect,
-    embeddings::{EmbeddingDefinition, EmbeddingFunction, openai::OpenAIEmbeddingFunction},
+    connect,
+    embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},
    query::{ExecutableQuery, QueryBase},
+    Result,
 };

 // --8<-- [end:imports]
--- a/rust/lancedb/examples/sentence_transformers.rs
+++ b/rust/lancedb/examples/sentence_transformers.rs
@@ -7,12 +7,13 @@ use arrow_array::{RecordBatch, StringArray};
 use arrow_schema::{DataType, Field, Schema};
 use futures::StreamExt;
 use lancedb::{
-    Result, connect,
+    connect,
    embeddings::{
-        EmbeddingDefinition, EmbeddingFunction,
-        sentence_transformers::SentenceTransformersEmbeddings,
+        sentence_transformers::SentenceTransformersEmbeddings, EmbeddingDefinition,
+        EmbeddingFunction,
    },
    query::{ExecutableQuery, QueryBase},
+    Result,
 };

 #[tokio::main]
--- a/rust/lancedb/examples/simple.rs
+++ b/rust/lancedb/examples/simple.rs
@@ -14,7 +14,7 @@ use futures::TryStreamExt;
 use lancedb::connection::Connection;
 use lancedb::index::Index;
 use lancedb::query::{ExecutableQuery, QueryBase};
-use lancedb::{Result, Table as LanceDbTable, connect};
+use lancedb::{connect, Result, Table as LanceDbTable};

 #[tokio::main]
 async fn main() -> Result<()> {
--- a/Show More
+++ b/Show More