Handle version number refs

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.25.0-beta.0"
+current_version = "0.23.1-beta.1"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

.github/workflows/build_linux_wheel/action.yml

@@ -3,7 +3,7 @@ name: build-linux-wheel
 description: "Build a manylinux wheel for lance"
 inputs:
   python-minor-version:
-    description: "10, 11, 12, 13"
+    description: "8, 9, 10, 11, 12"
     required: true
   args:
     description: "--release"

.github/workflows/build_mac_wheel/action.yml

@@ -3,7 +3,7 @@ name: build_wheel
 description: "Build a lance wheel"
 inputs:
   python-minor-version:
-    description: "10, 11, 12, 13"
+    description: "8, 9, 10, 11"
     required: true
   args:
     description: "--release"

.github/workflows/build_windows_wheel/action.yml

@@ -3,7 +3,7 @@ name: build_wheel
 description: "Build a lance wheel"
 inputs:
   python-minor-version:
-    description: "10, 11, 12, 13, 14"
+    description: "8, 9, 10, 11"
     required: true
   args:
     description: "--release"

.github/workflows/cargo-publish.yml

@@ -42,7 +42,7 @@ jobs:
     name: Report Workflow Failure
     runs-on: ubuntu-latest
     needs: [build]
-    if: always() && failure() && startsWith(github.ref, 'refs/tags/v')
+    if: always() && (github.event_name == 'release' || github.event_name == 'workflow_dispatch')
     permissions:
       contents: read
       issues: write

.github/workflows/codex-update-lance-dependency.yml

@@ -75,28 +75,20 @@ jobs:
           VERSION="${VERSION#v}"
           BRANCH_NAME="codex/update-lance-${VERSION//[^a-zA-Z0-9]/-}"
 
-          # Use "chore" for beta/rc versions, "feat" for stable releases
-          if [[ "${VERSION}" == *beta* ]] || [[ "${VERSION}" == *rc* ]]; then
-            COMMIT_TYPE="chore"
-          else
-            COMMIT_TYPE="feat"
-          fi
-
           cat <<EOF >/tmp/codex-prompt.txt
           You are running inside the lancedb repository on a GitHub Actions runner. Update the Lance dependency to version ${VERSION} and prepare a pull request for maintainers to review.
 
           Follow these steps exactly:
-          1. Use script "ci/set_lance_version.py" to update Lance Rust dependencies. The script already refreshes Cargo metadata, so allow it to finish even if it takes time.
-          2. Update the Java lance-core dependency version in "java/pom.xml": change the "<lance-core.version>...</lance-core.version>" property to "${VERSION}".
-          3. Run "cargo clippy --workspace --tests --all-features -- -D warnings". If diagnostics appear, fix them yourself and rerun clippy until it exits cleanly. Do not skip any warnings.
-          4. After clippy succeeds, run "cargo fmt --all" to format the workspace.
-          5. Ensure the repository is clean except for intentional changes. Inspect "git status --short" and "git diff" to confirm the dependency update and any required fixes.
-          6. Create and switch to a new branch named "${BRANCH_NAME}" (replace any duplicated hyphens if necessary).
-          7. Stage all relevant files with "git add -A". Commit using the message "${COMMIT_TYPE}: update lance dependency to v${VERSION}".
-          8. Push the branch to origin. If the remote branch already exists, delete it first with "gh api -X DELETE repos/lancedb/lancedb/git/refs/heads/${BRANCH_NAME}" then push with "git push origin ${BRANCH_NAME}". Do NOT use "git push --force" or "git push -f".
-          9. env "GH_TOKEN" is available, use "gh" tools for github related operations like creating pull request.
-          10. Create a pull request targeting "main" with title "${COMMIT_TYPE}: update lance dependency to v${VERSION}". First, write the PR body to /tmp/pr-body.md using a heredoc (cat <<'EOF' > /tmp/pr-body.md). The body should summarize the dependency bump, clippy/fmt verification, and link the triggering tag (${TAG}). Then run "gh pr create --body-file /tmp/pr-body.md".
-          11. After creating the PR, display the PR URL, "git status --short", and a concise summary of the commands run and their results.
+          1. Use script "ci/set_lance_version.py" to update Lance dependencies. The script already refreshes Cargo metadata, so allow it to finish even if it takes time.
+          2. Run "cargo clippy --workspace --tests --all-features -- -D warnings". If diagnostics appear, fix them yourself and rerun clippy until it exits cleanly. Do not skip any warnings.
+          3. After clippy succeeds, run "cargo fmt --all" to format the workspace.
+          4. Ensure the repository is clean except for intentional changes. Inspect "git status --short" and "git diff" to confirm the dependency update and any required fixes.
+          5. Create and switch to a new branch named "${BRANCH_NAME}" (replace any duplicated hyphens if necessary).
+          6. Stage all relevant files with "git add -A". Commit using the message "chore: update lance dependency to v${VERSION}".
+          7. Push the branch to origin. If the branch already exists, force-push your changes.
+          8. env "GH_TOKEN" is available, use "gh" tools for github related operations like creating pull request.
+          9. Create a pull request targeting "main" with title "chore: update lance dependency to v${VERSION}". In the body, summarize the dependency bump, clippy/fmt verification, and link the triggering tag (${TAG}).
+          10. After creating the PR, display the PR URL, "git status --short", and a concise summary of the commands run and their results.
 
           Constraints:
           - Use bash commands; avoid modifying GitHub workflow files other than through the scripted task above.

.github/workflows/docs.yml

@@ -41,7 +41,7 @@ jobs:
           sudo apt install -y protobuf-compiler libssl-dev
           rustup update && rustup default
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
         with:
           python-version: "3.10"
           cache: "pip"

.github/workflows/nodejs.yml

@@ -8,7 +8,6 @@ on:
     paths:
       - Cargo.toml
       - nodejs/**
-      - docs/src/js/**
       - .github/workflows/nodejs.yml
       - docker-compose.yml
 

.github/workflows/npm-publish.yml

@@ -348,6 +348,7 @@ jobs:
         run: find npm
       - name: Publish
         env:
+          NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
           DRY_RUN: ${{ !startsWith(github.ref, 'refs/tags/v') }}
         run: |
           ARGS="--access public"
@@ -362,7 +363,7 @@ jobs:
     name: Report Workflow Failure
     runs-on: ubuntu-latest
     needs: [build-lancedb, test-lancedb, publish]
-    if: always() && failure() && startsWith(github.ref, 'refs/tags/v')
+    if: always() && (github.event_name == 'release' || github.event_name == 'workflow_dispatch')
     permissions:
       contents: read
       issues: write

.github/workflows/pypi-publish.yml

@@ -44,12 +44,12 @@ jobs:
           fetch-depth: 0
           lfs: true
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v4
         with:
-          python-version: "3.10"
+          python-version: 3.8
       - uses: ./.github/workflows/build_linux_wheel
         with:
-          python-minor-version: 10
+          python-minor-version: 8
           args: "--release --strip ${{ matrix.config.extra_args }}"
           arm-build: ${{ matrix.config.platform == 'aarch64' }}
           manylinux: ${{ matrix.config.manylinux }}
@@ -74,12 +74,12 @@ jobs:
           fetch-depth: 0
           lfs: true
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v4
         with:
-          python-version: "3.13"
+          python-version: 3.12
       - uses: ./.github/workflows/build_mac_wheel
         with:
-          python-minor-version: 10
+          python-minor-version: 8
           args: "--release --strip --target ${{ matrix.config.target }} --features fp16kernels"
       - uses: ./.github/workflows/upload_wheel
         if: startsWith(github.ref, 'refs/tags/python-v')
@@ -95,12 +95,12 @@ jobs:
           fetch-depth: 0
           lfs: true
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v4
         with:
-          python-version: "3.13"
+          python-version: 3.12
       - uses: ./.github/workflows/build_windows_wheel
         with:
-          python-minor-version: 10
+          python-minor-version: 8
           args: "--release --strip"
           vcpkg_token: ${{ secrets.VCPKG_GITHUB_PACKAGES }}
       - uses: ./.github/workflows/upload_wheel
@@ -181,7 +181,7 @@ jobs:
     permissions:
       contents: read
       issues: write
-    if: always() && failure() && startsWith(github.ref, 'refs/tags/python-v')
+    if: always() && (github.event_name == 'release' || github.event_name == 'workflow_dispatch')
     steps:
       - uses: actions/checkout@v4
       - uses: ./.github/actions/create-failure-issue

.github/workflows/python.yml

@@ -36,9 +36,9 @@ jobs:
           fetch-depth: 0
           lfs: true
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
         with:
-          python-version: "3.13"
+          python-version: "3.12"
       - name: Install ruff
         run: |
           pip install ruff==0.9.9
@@ -61,9 +61,9 @@ jobs:
           fetch-depth: 0
           lfs: true
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
         with:
-          python-version: "3.13"
+          python-version: "3.12"
       - name: Install protobuf compiler
         run: |
           sudo apt update
@@ -90,9 +90,9 @@ jobs:
           fetch-depth: 0
           lfs: true
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
         with:
-          python-version: "3.13"
+          python-version: "3.12"
           cache: "pip"
       - name: Install protobuf
         run: |
@@ -110,7 +110,7 @@ jobs:
     timeout-minutes: 30
     strategy:
       matrix:
-        python-minor-version: ["10", "13"]
+        python-minor-version: ["9", "12"]
     runs-on: "ubuntu-24.04"
     defaults:
       run:
@@ -126,7 +126,7 @@ jobs:
           sudo apt update
           sudo apt install -y protobuf-compiler
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
         with:
           python-version: 3.${{ matrix.python-minor-version }}
       - uses: ./.github/workflows/build_linux_wheel
@@ -156,9 +156,9 @@ jobs:
           fetch-depth: 0
           lfs: true
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
         with:
-          python-version: "3.13"
+          python-version: "3.12"
       - uses: ./.github/workflows/build_mac_wheel
         with:
           args: --profile ci
@@ -185,9 +185,9 @@ jobs:
           fetch-depth: 0
           lfs: true
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
         with:
-          python-version: "3.13"
+          python-version: "3.12"
       - uses: ./.github/workflows/build_windows_wheel
         with:
           args: --profile ci
@@ -212,9 +212,9 @@ jobs:
           sudo apt update
           sudo apt install -y protobuf-compiler
       - name: Set up Python
-        uses: actions/setup-python@v6
+        uses: actions/setup-python@v5
         with:
-          python-version: "3.10"
+          python-version: 3.9
       - name: Install lancedb
         run: |
           pip install "pydantic<2"

.github/workflows/rust.yml

@@ -48,8 +48,6 @@ jobs:
         run: cargo fmt --all -- --check
       - name: Run clippy
         run: cargo clippy --profile ci --workspace --tests --all-features -- -D warnings
-      - name: Run clippy (without remote feature)
-        run: cargo clippy --profile ci --workspace --tests -- -D warnings
 
   build-no-lock:
     runs-on: ubuntu-24.04
@@ -169,13 +167,13 @@ jobs:
       - name: Build
         run: |
           $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
-          cargo build --profile ci --features aws,remote --tests --locked --target ${{ matrix.target }}
+          cargo build --profile ci --features remote --tests --locked --target ${{ matrix.target }}
       - name: Run tests
         # Can only run tests when target matches host
         if: ${{ matrix.target == 'x86_64-pc-windows-msvc' }}
         run: |
           $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
-          cargo test --profile ci --features aws,remote --locked
+          cargo test --profile ci --features remote --locked
 
   msrv:
     # Check the minimum supported Rust version
@@ -183,7 +181,7 @@ jobs:
     runs-on: ubuntu-24.04
     strategy:
       matrix:
-        msrv: ["1.88.0"] # This should match up with rust-version in Cargo.toml
+        msrv: ["1.78.0"] # This should match up with rust-version in Cargo.toml
     env:
       # Need up-to-date compilers for kernels
       CC: clang-18
@@ -214,6 +212,4 @@ jobs:
           cargo update -p aws-sdk-sts --precise 1.51.0
           cargo update -p home --precise 0.5.9
       - name: cargo +${{ matrix.msrv }} check
-        env:
-          RUSTUP_TOOLCHAIN: ${{ matrix.msrv }}
         run: cargo check --profile ci --workspace --tests --benches --all-features

Cargo.toml

@@ -12,43 +12,42 @@ 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 = "1.88.0"
+rust-version = "1.78.0"
 
 [workspace.dependencies]
-lance = { "version" = "=2.0.0-rc.4", default-features = false, "tag" = "v2.0.0-rc.4", "git" = "https://github.com/lance-format/lance.git" }
-lance-core = { "version" = "=2.0.0-rc.4", "tag" = "v2.0.0-rc.4", "git" = "https://github.com/lance-format/lance.git" }
-lance-datagen = { "version" = "=2.0.0-rc.4", "tag" = "v2.0.0-rc.4", "git" = "https://github.com/lance-format/lance.git" }
-lance-file = { "version" = "=2.0.0-rc.4", "tag" = "v2.0.0-rc.4", "git" = "https://github.com/lance-format/lance.git" }
-lance-io = { "version" = "=2.0.0-rc.4", default-features = false, "tag" = "v2.0.0-rc.4", "git" = "https://github.com/lance-format/lance.git" }
-lance-index = { "version" = "=2.0.0-rc.4", "tag" = "v2.0.0-rc.4", "git" = "https://github.com/lance-format/lance.git" }
-lance-linalg = { "version" = "=2.0.0-rc.4", "tag" = "v2.0.0-rc.4", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace = { "version" = "=2.0.0-rc.4", "tag" = "v2.0.0-rc.4", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace-impls = { "version" = "=2.0.0-rc.4", default-features = false, "tag" = "v2.0.0-rc.4", "git" = "https://github.com/lance-format/lance.git" }
-lance-table = { "version" = "=2.0.0-rc.4", "tag" = "v2.0.0-rc.4", "git" = "https://github.com/lance-format/lance.git" }
-lance-testing = { "version" = "=2.0.0-rc.4", "tag" = "v2.0.0-rc.4", "git" = "https://github.com/lance-format/lance.git" }
-lance-datafusion = { "version" = "=2.0.0-rc.4", "tag" = "v2.0.0-rc.4", "git" = "https://github.com/lance-format/lance.git" }
-lance-encoding = { "version" = "=2.0.0-rc.4", "tag" = "v2.0.0-rc.4", "git" = "https://github.com/lance-format/lance.git" }
-lance-arrow = { "version" = "=2.0.0-rc.4", "tag" = "v2.0.0-rc.4", "git" = "https://github.com/lance-format/lance.git" }
+lance = { "version" = "=2.0.0-beta.3", default-features = false, "tag" = "v2.0.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-core = { "version" = "=2.0.0-beta.3", "tag" = "v2.0.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-datagen = { "version" = "=2.0.0-beta.3", "tag" = "v2.0.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-file = { "version" = "=2.0.0-beta.3", "tag" = "v2.0.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-io = { "version" = "=2.0.0-beta.3", default-features = false, "tag" = "v2.0.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-index = { "version" = "=2.0.0-beta.3", "tag" = "v2.0.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-linalg = { "version" = "=2.0.0-beta.3", "tag" = "v2.0.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace = { "version" = "=2.0.0-beta.3", "tag" = "v2.0.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace-impls = { "version" = "=2.0.0-beta.3", default-features = false, "tag" = "v2.0.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-table = { "version" = "=2.0.0-beta.3", "tag" = "v2.0.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-testing = { "version" = "=2.0.0-beta.3", "tag" = "v2.0.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-datafusion = { "version" = "=2.0.0-beta.3", "tag" = "v2.0.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-encoding = { "version" = "=2.0.0-beta.3", "tag" = "v2.0.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-arrow = { "version" = "=2.0.0-beta.3", "tag" = "v2.0.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
 ahash = "0.8"
 # Note that this one does not include pyarrow
-arrow = { version = "57.2", optional = false }
-arrow-array = "57.2"
-arrow-data = "57.2"
-arrow-ipc = "57.2"
-arrow-ord = "57.2"
-arrow-schema = "57.2"
-arrow-select = "57.2"
-arrow-cast = "57.2"
+arrow = { version = "56.2", optional = false }
+arrow-array = "56.2"
+arrow-data = "56.2"
+arrow-ipc = "56.2"
+arrow-ord = "56.2"
+arrow-schema = "56.2"
+arrow-select = "56.2"
+arrow-cast = "56.2"
 async-trait = "0"
-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"
+datafusion = { version = "50.1", default-features = false }
+datafusion-catalog = "50.1"
+datafusion-common = { version = "50.1", default-features = false }
+datafusion-execution = "50.1"
+datafusion-expr = "50.1"
+datafusion-physical-plan = "50.1"
 env_logger = "0.11"
-half = { "version" = "2.7.1", default-features = false, features = [
+half = { "version" = "2.6.0", default-features = false, features = [
     "num-traits",
 ] }
 futures = "0"

README.md

@@ -66,7 +66,7 @@ Follow the [Quickstart](https://lancedb.com/docs/quickstart/) doc to set up Lanc
 | Python SDK | https://lancedb.github.io/lancedb/python/python/ |
 | Typescript SDK | https://lancedb.github.io/lancedb/js/globals/ |
 | Rust SDK | https://docs.rs/lancedb/latest/lancedb/index.html |
-| REST API | https://docs.lancedb.com/api-reference/rest |
+| REST API | https://docs.lancedb.com/api-reference/introduction |
 
 ## **Join Us and Contribute**
 

ci/run_with_test_connection.sh

@@ -16,7 +16,7 @@ check_command_exists() {
 }
 
 if [[ ! -e ./lancedb ]]; then
-    if [[ x${SOPHON_READ_TOKEN} != "x" ]]; then
+    if [[ -v SOPHON_READ_TOKEN ]]; then
         INPUT="lancedb-linux-x64"
         gh release \
             --repo lancedb/lancedb \

docs/mkdocs.yml

@@ -11,7 +11,7 @@ watch:
 theme:
   name: "material"
   logo: assets/logo.png
-  favicon: assets/favicon.ico
+  favicon: assets/logo.png
   palette:
     # Palette toggle for light mode
     - scheme: lancedb
@@ -32,6 +32,8 @@ theme:
     - content.tooltips
     - toc.follow
     - navigation.top
+    - navigation.tabs
+    - navigation.tabs.sticky
     - navigation.footer
     - navigation.tracking
     - navigation.instant
@@ -113,13 +115,12 @@ markdown_extensions:
       emoji_index: !!python/name:material.extensions.emoji.twemoji
       emoji_generator: !!python/name:material.extensions.emoji.to_svg
   - markdown.extensions.toc:
-      toc_depth: 3
-      permalink: true
-      permalink_title: Anchor link to this section
+      baselevel: 1
+      permalink: ""
 
 nav:
-  - Documentation:
-      - SDK Reference: index.md
+  - API reference:
+      - Overview: index.md
       - Python: python/python.md
       - Javascript/TypeScript: js/globals.md
       - Java: java/java.md

docs/src/embeddings/available_embedding_models/multimodal_embedding_functions/voyageai_multimodal_embedding.md

@@ -1,111 +0,0 @@
-# VoyageAI Embeddings : Multimodal
-
-VoyageAI embeddings can also be used to embed both text and image data, only some of the models support image data and you can check the list
-under [https://docs.voyageai.com/docs/multimodal-embeddings](https://docs.voyageai.com/docs/multimodal-embeddings)
-
-Supported multimodal models:
-
-- `voyage-multimodal-3` - 1024 dimensions (text + images)
-- `voyage-multimodal-3.5` - Flexible dimensions (256, 512, 1024 default, 2048). Supports text, images, and video.
-
-### Video Support (voyage-multimodal-3.5)
-
-The `voyage-multimodal-3.5` model supports video input through:
-- Video URLs (`.mp4`, `.webm`, `.mov`, `.avi`, `.mkv`, `.m4v`, `.gif`)
-- Video file paths
-
-Constraints: Max 20MB video size.
-
-Supported parameters (to be passed in `create` method) are:
-
-| Parameter | Type | Default Value           | Description                               |
-|---|---|-------------------------|-------------------------------------------|
-| `name` | `str` | `"voyage-multimodal-3"` | The model ID of the VoyageAI model to use |
-| `output_dimension` | `int` | `None` | Output dimension for voyage-multimodal-3.5. Valid: 256, 512, 1024, 2048 |
-
-Usage Example:
-
-```python
-import base64
-import os
-from io import BytesIO
-
-import requests
-import lancedb
-from lancedb.pydantic import LanceModel, Vector
-from lancedb.embeddings import get_registry
-import pandas as pd
-
-os.environ['VOYAGE_API_KEY'] = 'YOUR_VOYAGE_API_KEY'
-
-db = lancedb.connect(".lancedb")
-func = get_registry().get("voyageai").create(name="voyage-multimodal-3")
-
-
-def image_to_base64(image_bytes: bytes):
-    buffered = BytesIO(image_bytes)
-    img_str = base64.b64encode(buffered.getvalue())
-    return img_str.decode("utf-8")
-
-
-class Images(LanceModel):
-    label: str
-    image_uri: str = func.SourceField()  # image uri as the source
-    image_bytes: str = func.SourceField()  # image bytes base64 encoded as the source
-    vector: Vector(func.ndims()) = func.VectorField()  # vector column
-    vec_from_bytes: Vector(func.ndims()) = func.VectorField()  # Another vector column
-
-
-if "images" in db.table_names():
-    db.drop_table("images")
-table = db.create_table("images", schema=Images)
-labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
-uris = [
-    "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
-    "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
-    "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
-    "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
-    "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
-    "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
-]
-# get each uri as bytes
-images_bytes = [image_to_base64(requests.get(uri).content) for uri in uris]
-table.add(
-    pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": images_bytes})
-)
-```
-Now we can search using text from both the default vector column and the custom vector column
-```python
-
-# text search
-actual = table.search("man's best friend", "vec_from_bytes").limit(1).to_pydantic(Images)[0]
-print(actual.label) # prints "dog"
-
-frombytes = (
-    table.search("man's best friend", vector_column_name="vec_from_bytes")
-    .limit(1)
-    .to_pydantic(Images)[0]
-)
-print(frombytes.label)
-
-```
-
-Because we're using a multi-modal embedding function, we can also search using images
-
-```python
-# image search
-query_image_uri = "http://farm1.staticflickr.com/200/467715466_ed4a31801f_z.jpg"
-image_bytes = requests.get(query_image_uri).content
-query_image = Image.open(BytesIO(image_bytes))
-actual = table.search(query_image, "vec_from_bytes").limit(1).to_pydantic(Images)[0]
-print(actual.label == "dog")
-
-# image search using a custom vector column
-other = (
-    table.search(query_image, vector_column_name="vec_from_bytes")
-    .limit(1)
-    .to_pydantic(Images)[0]
-)
-print(actual.label)
-
-```

docs/src/embeddings/available_embedding_models/text_embedding_functions/voyageai_embedding.md

@@ -1,62 +0,0 @@
-# VoyageAI Embeddings
-
-Voyage AI provides cutting-edge embedding and rerankers.
-
-
-Using voyageai API requires voyageai package, which can be installed using `pip install voyageai`. Voyage AI embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification.
-You also need to set the `VOYAGE_API_KEY` environment variable to use the VoyageAI API.
-
-Supported models are:
-
-**Voyage-4 Series (Latest)**
-
-- voyage-4 (1024 dims, general-purpose and multilingual retrieval, 320K batch tokens)
-- voyage-4-lite (1024 dims, optimized for latency and cost, 1M batch tokens)
-- voyage-4-large (1024 dims, best retrieval quality, 120K batch tokens)
-
-**Voyage-3 Series**
-
-- voyage-3
-- voyage-3-lite
-
-**Domain-Specific Models**
-
-- voyage-finance-2
-- voyage-multilingual-2
-- voyage-law-2
-- voyage-code-2
-
-
-Supported parameters (to be passed in `create` method) are:
-
-| Parameter | Type | Default Value | Description |
-|---|---|--------|---------|
-| `name` | `str` | `None` | The model ID of the model to use. Supported base models for Text Embeddings: voyage-4, voyage-4-lite, voyage-4-large, voyage-3, voyage-3-lite, voyage-finance-2, voyage-multilingual-2, voyage-law-2, voyage-code-2 |
-| `input_type` | `str` | `None` | Type of the input text. Default to None. Other options: query, document. |
-| `truncation` | `bool` | `True` | Whether to truncate the input texts to fit within the context length. |
-
-
-Usage Example:
-    
-```python
-    import lancedb
-    from lancedb.pydantic import LanceModel, Vector
-    from lancedb.embeddings import EmbeddingFunctionRegistry
-
-    voyageai = EmbeddingFunctionRegistry
-        .get_instance()
-        .get("voyageai")
-        .create(name="voyage-3")
-
-    class TextModel(LanceModel):
-        text: str = voyageai.SourceField()
-        vector: Vector(voyageai.ndims()) =  voyageai.VectorField()
-
-    data = [ { "text": "hello world" },
-            { "text": "goodbye world" }]
-
-    db = lancedb.connect("~/.lancedb")
-    tbl = db.create_table("test", schema=TextModel, mode="overwrite")
-
-    tbl.add(data)
-```

docs/src/index.md

@@ -1,12 +1,8 @@
-# SDK Reference
+# API Reference
 
-This site contains the API reference for the client SDKs supported by [LanceDB](https://lancedb.com).
+This page contains the API reference for the SDKs supported by the LanceDB team.
 
 - [Python](python/python.md)
 - [JavaScript/TypeScript](js/globals.md)
 - [Java](java/java.md)
-- [Rust](https://docs.rs/lancedb/latest/lancedb/index.html)
-
-!!! info "LanceDB Documentation"
-
-    If you're looking for the full documentation of LanceDB, visit [docs.lancedb.com](https://docs.lancedb.com).
+- [Rust](https://docs.rs/lancedb/latest/lancedb/index.html)

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>0.25.0-beta.0</version>
+    <version>0.23.1-beta.1</version>
 </dependency>
 ```
 

docs/src/js/classes/Table.md

@@ -367,27 +367,6 @@ Use [Table.listIndices](Table.md#listindices) to find the names of the indices.
 
 ***
 
-### initialStorageOptions()
-
-```ts
-abstract initialStorageOptions(): Promise<undefined | null | Record<string, string>>
-```
-
-Get the initial storage options that were passed in when opening this table.
-
-For dynamically refreshed options (e.g., credential vending), use
-[Table.latestStorageOptions](Table.md#lateststorageoptions).
-
-Warning: This is an internal API and the return value is subject to change.
-
-#### Returns
-
-`Promise`&lt;`undefined` \| `null` \| `Record`&lt;`string`, `string`&gt;&gt;
-
-The storage options, or undefined if no storage options were configured.
-
-***
-
 ### isOpen()
 
 ```ts
@@ -402,28 +381,6 @@ Return true if the table has not been closed
 
 ***
 
-### latestStorageOptions()
-
-```ts
-abstract latestStorageOptions(): Promise<undefined | null | Record<string, string>>
-```
-
-Get the latest storage options, refreshing from provider if configured.
-
-This method is useful for credential vending scenarios where storage options
-may be refreshed dynamically. If no dynamic provider is configured, this
-returns the initial static options.
-
-Warning: This is an internal API and the return value is subject to change.
-
-#### Returns
-
-`Promise`&lt;`undefined` \| `null` \| `Record`&lt;`string`, `string`&gt;&gt;
-
-The storage options, or undefined if no storage options were configured.
-
-***
-
 ### listIndices()
 
 ```ts
@@ -748,11 +705,8 @@ Create a query that returns a subset of the rows in the table.
 
 #### Parameters
 
-* **rowIds**: readonly (`number` \| `bigint`)[]
+* **rowIds**: `number`[]
     The row ids of the rows to return.
-    Row ids returned by `withRowId()` are `bigint`, so `bigint[]` is supported.
-    For convenience / backwards compatibility, `number[]` is also accepted (for
-    small row ids that fit in a safe integer).
 
 #### Returns
 

docs/src/styles/extra.css

@@ -85,26 +85,17 @@
 
 /* Header gradient (only header area) */
 .md-header {
-  background: linear-gradient(90deg, #e4d8f8 0%, #F0B7C1 45%, #E55A2B 100%);
+  background: linear-gradient(90deg, #3B2E58 0%, #F0B7C1 45%, #E55A2B 100%);
   box-shadow: inset 0 1px 0 rgba(255,255,255,0.08), 0 1px 0 rgba(0,0,0,0.08);
 }
 
-/* Improve brand title contrast on the lavender side */
-.md-header__title,
-.md-header__topic,
-.md-header__title .md-ellipsis,
-.md-header__topic .md-ellipsis {
-  color: #2b1b3a;
-  text-shadow: 0 1px 0 rgba(255, 255, 255, 0.25);
-}
-
 /* Same colors as header for tabs (that hold the text) */
 .md-tabs {
-  background: linear-gradient(90deg, #e4d8f8 0%, #F0B7C1 45%, #E55A2B 100%);
+  background: linear-gradient(90deg, #3B2E58 0%, #F0B7C1 45%, #E55A2B 100%);
 }
 
 /* Dark scheme variant */
 [data-md-color-scheme="slate"] .md-header,
 [data-md-color-scheme="slate"] .md-tabs {
-  background: linear-gradient(90deg, #e4d8f8 0%, #F0B7C1 45%, #E55A2B 100%);
+  background: linear-gradient(90deg, #3B2E58 0%, #F0B7C1 45%, #E55A2B 100%);
 }

java/lancedb-core/pom.xml

@@ -8,7 +8,7 @@
     <parent>
       <groupId>com.lancedb</groupId>
       <artifactId>lancedb-parent</artifactId>
-      <version>0.25.0-beta.0</version>
+      <version>0.23.1-beta.1</version>
       <relativePath>../pom.xml</relativePath>
     </parent>
 

java/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-parent</artifactId>
-    <version>0.25.0-beta.0</version>
+    <version>0.23.1-beta.1</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>1.0.4</lance-core.version>
+        <lance-core.version>1.0.0-rc.2</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>

nodejs/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.25.0-beta.0"
+version = "0.23.1-beta.1"
 license.workspace = true
 description.workspace = true
 repository.workspace = true
@@ -36,6 +36,6 @@ aws-lc-rs = "=1.13.0"
 napi-build = "2.1"
 
 [features]
-default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface"]
+default = ["remote", "lancedb/default"]
 fp16kernels = ["lancedb/fp16kernels"]
 remote = ["lancedb/remote"]

nodejs/__test__/table.test.ts

@@ -312,66 +312,6 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
       expect(res.getChild("id")?.toJSON()).toEqual([2, 3]);
     });
 
-    it("should support takeRowIds with bigint array", async () => {
-      await table.add([{ id: 1 }, { id: 2 }, { id: 3 }]);
-      // Get actual row IDs using withRowId()
-      const allRows = await table.query().withRowId().toArray();
-      const rowIds = allRows.map((row) => row._rowid) as bigint[];
-
-      // Verify row IDs are bigint
-      expect(typeof rowIds[0]).toBe("bigint");
-
-      // Use takeRowIds with bigint array (the main use case from issue #2722)
-      const res = await table.takeRowIds([rowIds[0], rowIds[2]]).toArray();
-      expect(res.map((r) => r.id)).toEqual([1, 3]);
-    });
-
-    it("should support takeRowIds with number array for backwards compatibility", async () => {
-      await table.add([{ id: 1 }, { id: 2 }, { id: 3 }]);
-      // Small row IDs can be passed as numbers
-      const res = await table.takeRowIds([0, 2]).toArray();
-      expect(res.map((r) => r.id)).toEqual([1, 3]);
-    });
-
-    it("should support takeRowIds with mixed bigint and number array", async () => {
-      await table.add([{ id: 1 }, { id: 2 }, { id: 3 }]);
-      // Mixed array of bigint and number
-      const res = await table.takeRowIds([0n, 1, 2n]).toArray();
-      expect(res.map((r) => r.id)).toEqual([1, 2, 3]);
-    });
-
-    it("should throw for non-integer number in takeRowIds", () => {
-      expect(() => table.takeRowIds([1.5])).toThrow(
-        "Row id must be an integer (or bigint)",
-      );
-      expect(() => table.takeRowIds([0, 1.1, 2])).toThrow(
-        "Row id must be an integer (or bigint)",
-      );
-    });
-
-    it("should throw for negative number in takeRowIds", () => {
-      expect(() => table.takeRowIds([-1])).toThrow("Row id cannot be negative");
-      expect(() => table.takeRowIds([0, -5, 2])).toThrow(
-        "Row id cannot be negative",
-      );
-    });
-
-    it("should throw for unsafe large number in takeRowIds", () => {
-      // Number.MAX_SAFE_INTEGER + 1 is not safe
-      const unsafeNumber = Number.MAX_SAFE_INTEGER + 1;
-      expect(() => table.takeRowIds([unsafeNumber])).toThrow(
-        "Row id is too large for number; use bigint instead",
-      );
-    });
-
-    it("should reject negative bigint in takeRowIds", async () => {
-      await table.add([{ id: 1 }]);
-      // Negative bigint should be rejected by the Rust layer
-      expect(() => {
-        table.takeRowIds([-1n]);
-      }).toThrow("Row id cannot be negative");
-    });
-
     it("should return the table as an instance of an arrow table", async () => {
       const arrowTbl = await table.toArrow();
       expect(arrowTbl).toBeInstanceOf(ArrowTable);
@@ -1580,9 +1520,9 @@ describe("when optimizing a dataset", () => {
 
   it("delete unverified", async () => {
     const version = await table.version();
-    const versionFile = `${tmpDir.name}/${table.name}.lance/_versions/${String(
-      18446744073709551615n - (BigInt(version) - 1n),
-    ).padStart(20, "0")}.manifest`;
+    const versionFile = `${tmpDir.name}/${table.name}.lance/_versions/${
+      version - 1
+    }.manifest`;
     fs.rmSync(versionFile);
 
     let stats = await table.optimize({ deleteUnverified: false });

nodejs/lancedb/table.ts

@@ -347,13 +347,9 @@ export abstract class Table {
   /**
    * Create a query that returns a subset of the rows in the table.
    * @param rowIds The row ids of the rows to return.
-   *
-   * Row ids returned by `withRowId()` are `bigint`, so `bigint[]` is supported.
-   * For convenience / backwards compatibility, `number[]` is also accepted (for
-   * small row ids that fit in a safe integer).
    * @returns A builder that can be used to parameterize the query.
    */
-  abstract takeRowIds(rowIds: readonly (bigint | number)[]): TakeQuery;
+  abstract takeRowIds(rowIds: number[]): TakeQuery;
 
   /**
    * Create a search query to find the nearest neighbors
@@ -542,35 +538,6 @@ export abstract class Table {
    *
    */
   abstract stats(): Promise<TableStatistics>;
-
-  /**
-   * Get the initial storage options that were passed in when opening this table.
-   *
-   * For dynamically refreshed options (e.g., credential vending), use
-   * {@link Table.latestStorageOptions}.
-   *
-   * Warning: This is an internal API and the return value is subject to change.
-   *
-   * @returns The storage options, or undefined if no storage options were configured.
-   */
-  abstract initialStorageOptions(): Promise<
-    Record<string, string> | null | undefined
-  >;
-
-  /**
-   * Get the latest storage options, refreshing from provider if configured.
-   *
-   * This method is useful for credential vending scenarios where storage options
-   * may be refreshed dynamically. If no dynamic provider is configured, this
-   * returns the initial static options.
-   *
-   * Warning: This is an internal API and the return value is subject to change.
-   *
-   * @returns The storage options, or undefined if no storage options were configured.
-   */
-  abstract latestStorageOptions(): Promise<
-    Record<string, string> | null | undefined
-  >;
 }
 
 export class LocalTable extends Table {
@@ -719,24 +686,8 @@ export class LocalTable extends Table {
     return new TakeQuery(this.inner.takeOffsets(offsets));
   }
 
-  takeRowIds(rowIds: readonly (bigint | number)[]): TakeQuery {
-    const ids = rowIds.map((id) => {
-      if (typeof id === "bigint") {
-        return id;
-      }
-      if (!Number.isInteger(id)) {
-        throw new Error("Row id must be an integer (or bigint)");
-      }
-      if (id < 0) {
-        throw new Error("Row id cannot be negative");
-      }
-      if (!Number.isSafeInteger(id)) {
-        throw new Error("Row id is too large for number; use bigint instead");
-      }
-      return BigInt(id);
-    });
-
-    return new TakeQuery(this.inner.takeRowIds(ids));
+  takeRowIds(rowIds: number[]): TakeQuery {
+    return new TakeQuery(this.inner.takeRowIds(rowIds));
   }
 
   query(): Query {
@@ -907,18 +858,6 @@ export class LocalTable extends Table {
     return await this.inner.stats();
   }
 
-  async initialStorageOptions(): Promise<
-    Record<string, string> | null | undefined
-  > {
-    return await this.inner.initialStorageOptions();
-  }
-
-  async latestStorageOptions(): Promise<
-    Record<string, string> | null | undefined
-  > {
-    return await this.inner.latestStorageOptions();
-  }
-
   mergeInsert(on: string | string[]): MergeInsertBuilder {
     on = Array.isArray(on) ? on : [on];
     return new MergeInsertBuilder(this.inner.mergeInsert(on), this.schema());

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.25.0-beta.0",
+	"version": "0.23.1-beta.1",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",

nodejs/npm/darwin-x64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-x64",
-	"version": "0.25.0-beta.0",
+	"version": "0.23.1-beta.1",
 	"os": ["darwin"],
 	"cpu": ["x64"],
 	"main": "lancedb.darwin-x64.node",

nodejs/npm/linux-arm64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.25.0-beta.0",
+	"version": "0.23.1-beta.1",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",

nodejs/npm/linux-arm64-musl/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.25.0-beta.0",
+	"version": "0.23.1-beta.1",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-musl.node",

nodejs/npm/linux-x64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.25.0-beta.0",
+	"version": "0.23.1-beta.1",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",

nodejs/npm/linux-x64-musl/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.25.0-beta.0",
+	"version": "0.23.1-beta.1",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-musl.node",

nodejs/npm/win32-arm64-msvc/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.25.0-beta.0",
+  "version": "0.23.1-beta.1",
   "os": [
     "win32"
   ],

nodejs/npm/win32-x64-msvc/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.25.0-beta.0",
+	"version": "0.23.1-beta.1",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "@lancedb/lancedb",
-  "version": "0.25.0-beta.0",
+  "version": "0.23.1-beta.1",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@lancedb/lancedb",
-      "version": "0.25.0-beta.0",
+      "version": "0.23.1-beta.1",
       "cpu": [
         "x64",
         "arm64"

nodejs/package.json

@@ -11,7 +11,7 @@
     "ann"
   ],
   "private": false,
-  "version": "0.25.0-beta.0",
+  "version": "0.23.1-beta.1",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",

nodejs/src/table.rs

@@ -166,19 +166,6 @@ impl Table {
         Ok(stats.into())
     }
 
-    #[napi(catch_unwind)]
-    pub async fn initial_storage_options(&self) -> napi::Result<Option<HashMap<String, String>>> {
-        Ok(self.inner_ref()?.initial_storage_options().await)
-    }
-
-    #[napi(catch_unwind)]
-    pub async fn latest_storage_options(&self) -> napi::Result<Option<HashMap<String, String>>> {
-        self.inner_ref()?
-            .latest_storage_options()
-            .await
-            .default_error()
-    }
-
     #[napi(catch_unwind)]
     pub async fn update(
         &self,
@@ -221,24 +208,18 @@ impl Table {
     }
 
     #[napi(catch_unwind)]
-    pub fn take_row_ids(&self, row_ids: Vec<BigInt>) -> napi::Result<TakeQuery> {
+    pub fn take_row_ids(&self, row_ids: Vec<i64>) -> napi::Result<TakeQuery> {
         Ok(TakeQuery::new(
             self.inner_ref()?.take_row_ids(
                 row_ids
                     .into_iter()
-                    .map(|id| {
-                        let (negative, value, lossless) = id.get_u64();
-                        if negative {
-                            Err(napi::Error::from_reason(
-                                "Row id cannot be negative".to_string(),
+                    .map(|o| {
+                        u64::try_from(o).map_err(|e| {
+                            napi::Error::from_reason(format!(
+                                "Failed to convert row id to u64: {}",
+                                e
                             ))
-                        } else if !lossless {
-                            Err(napi::Error::from_reason(
-                                "Row id is too large to fit in u64".to_string(),
-                            ))
-                        } else {
-                            Ok(value)
-                        }
+                        })
                     })
                     .collect::<Result<Vec<_>>>()?,
             ),

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.28.0-beta.0"
+current_version = "0.26.1-beta.1"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

python/CONTRIBUTING.md

@@ -16,7 +16,7 @@ The Python package is a wrapper around the Rust library, `lancedb`. We use
 
 To set up your development environment, you will need to install the following:
 
-1. Python 3.10 or later
+1. Python 3.9 or later
 2. Cargo (Rust's package manager). Use [rustup](https://rustup.rs/) to install.
 3. [protoc](https://grpc.io/docs/protoc-installation/) (Protocol Buffers compiler)
 

python/Cargo.toml

@@ -1,28 +1,28 @@
 [package]
 name = "lancedb-python"
-version = "0.27.0"
+version = "0.26.1-beta.1"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true
 repository.workspace = true
 keywords.workspace = true
 categories.workspace = true
-rust-version = "1.88.0"
+rust-version = "1.75.0"
 
 [lib]
 name = "_lancedb"
 crate-type = ["cdylib"]
 
 [dependencies]
-arrow = { version = "57.2", features = ["pyarrow"] }
+arrow = { version = "56.2", features = ["pyarrow"] }
 async-trait = "0.1"
 lancedb = { path = "../rust/lancedb", default-features = false }
 lance-core.workspace = true
 lance-namespace.workspace = true
 lance-io.workspace = true
 env_logger.workspace = true
-pyo3 = { version = "0.26", features = ["extension-module", "abi3-py39"] }
-pyo3-async-runtimes = { version = "0.26", features = [
+pyo3 = { version = "0.25", features = ["extension-module", "abi3-py39"] }
+pyo3-async-runtimes = { version = "0.25", features = [
     "attributes",
     "tokio-runtime",
 ] }
@@ -32,12 +32,12 @@ snafu.workspace = true
 tokio = { version = "1.40", features = ["sync"] }
 
 [build-dependencies]
-pyo3-build-config = { version = "0.26", features = [
+pyo3-build-config = { version = "0.25", features = [
     "extension-module",
     "abi3-py39",
 ] }
 
 [features]
-default = ["remote",  "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface"]
+default = ["remote", "lancedb/default"]
 fp16kernels = ["lancedb/fp16kernels"]
 remote = ["lancedb/remote"]

python/pyproject.toml

@@ -16,7 +16,7 @@ description = "lancedb"
 authors = [{ name = "LanceDB Devs", email = "dev@lancedb.com" }]
 license = { file = "LICENSE" }
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.9"
 keywords = [
     "data-format",
     "data-science",
@@ -33,10 +33,10 @@ classifiers = [
     "Programming Language :: Python",
     "Programming Language :: Python :: 3",
     "Programming Language :: Python :: 3 :: Only",
+    "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
-    "Programming Language :: Python :: 3.13",
     "Topic :: Scientific/Engineering",
 ]
 
@@ -137,4 +137,4 @@ include = [
     "python/lancedb/_lancedb.pyi",
 ]
 exclude = ["python/tests/"]
-pythonVersion = "3.13"
+pythonVersion = "3.12"

python/python/lancedb/__init__.py

@@ -13,7 +13,6 @@ __version__ = importlib.metadata.version("lancedb")
 
 from ._lancedb import connect as lancedb_connect
 from .common import URI, sanitize_uri
-from urllib.parse import urlparse
 from .db import AsyncConnection, DBConnection, LanceDBConnection
 from .io import StorageOptionsProvider
 from .remote import ClientConfig
@@ -29,39 +28,6 @@ from .namespace import (
 )
 
 
-def _check_s3_bucket_with_dots(
-    uri: str, storage_options: Optional[Dict[str, str]]
-) -> None:
-    """
-    Check if an S3 URI has a bucket name containing dots and warn if no region
-    is specified. S3 buckets with dots cannot use virtual-hosted-style URLs,
-    which breaks automatic region detection.
-
-    See: https://github.com/lancedb/lancedb/issues/1898
-    """
-    if not isinstance(uri, str) or not uri.startswith("s3://"):
-        return
-
-    parsed = urlparse(uri)
-    bucket = parsed.netloc
-
-    if "." not in bucket:
-        return
-
-    # Check if region is provided in storage_options
-    region_keys = {"region", "aws_region"}
-    has_region = storage_options and any(k in storage_options for k in region_keys)
-
-    if not has_region:
-        raise ValueError(
-            f"S3 bucket name '{bucket}' contains dots, which prevents automatic "
-            f"region detection. Please specify the region explicitly via "
-            f"storage_options={{'region': '<your-region>'}} or "
-            f"storage_options={{'aws_region': '<your-region>'}}. "
-            f"See https://github.com/lancedb/lancedb/issues/1898 for details."
-        )
-
-
 def connect(
     uri: URI,
     *,
@@ -155,11 +121,9 @@ def connect(
             storage_options=storage_options,
             **kwargs,
         )
-    _check_s3_bucket_with_dots(str(uri), storage_options)
 
     if kwargs:
         raise ValueError(f"Unknown keyword arguments: {kwargs}")
-
     return LanceDBConnection(
         uri,
         read_consistency_interval=read_consistency_interval,
@@ -247,8 +211,6 @@ async def connect_async(
     if isinstance(client_config, dict):
         client_config = ClientConfig(**client_config)
 
-    _check_s3_bucket_with_dots(str(uri), storage_options)
-
     return AsyncConnection(
         await lancedb_connect(
             sanitize_uri(uri),

python/python/lancedb/_lancedb.pyi

@@ -179,9 +179,6 @@ class Table:
         cleanup_since_ms: Optional[int] = None,
         delete_unverified: Optional[bool] = None,
     ) -> OptimizeStats: ...
-    async def uri(self) -> str: ...
-    async def initial_storage_options(self) -> Optional[Dict[str, str]]: ...
-    async def latest_storage_options(self) -> Optional[Dict[str, str]]: ...
     @property
     def tags(self) -> Tags: ...
     def query(self) -> Query: ...

python/python/lancedb/background_loop.py

@@ -22,12 +22,7 @@ class BackgroundEventLoop:
         self.thread.start()
 
     def run(self, future):
-        concurrent_future = asyncio.run_coroutine_threadsafe(future, self.loop)
-        try:
-            return concurrent_future.result()
-        except BaseException:
-            concurrent_future.cancel()
-            raise
+        return asyncio.run_coroutine_threadsafe(future, self.loop).result()
 
 
 LOOP = BackgroundEventLoop()

python/python/lancedb/db.py

@@ -210,8 +210,10 @@ class DBConnection(EnforceOverrides):
         page_token: str, optional
             The token to use for pagination. If not present, start from the beginning.
             Typically, this token is last table name from the previous page.
+            Only supported by LanceDb Cloud.
         limit: int, default 10
             The size of the page to return.
+            Only supported by LanceDb Cloud.
 
         Returns
         -------

python/python/lancedb/embeddings/colpali.py

@@ -275,7 +275,7 @@ class ColPaliEmbeddings(EmbeddingFunction):
         """
         Convert image inputs to PIL Images.
         """
-        PIL_Image = attempt_import_or_raise("PIL.Image", "pillow")
+        PIL = attempt_import_or_raise("PIL", "pillow")
         requests = attempt_import_or_raise("requests", "requests")
         images = self.sanitize_input(images)
         pil_images = []
@@ -285,12 +285,12 @@ class ColPaliEmbeddings(EmbeddingFunction):
                     if image.startswith(("http://", "https://")):
                         response = requests.get(image, timeout=10)
                         response.raise_for_status()
-                        pil_images.append(PIL_Image.open(io.BytesIO(response.content)))
+                        pil_images.append(PIL.Image.open(io.BytesIO(response.content)))
                     else:
-                        with PIL_Image.open(image) as im:
+                        with PIL.Image.open(image) as im:
                             pil_images.append(im.copy())
                 elif isinstance(image, bytes):
-                    pil_images.append(PIL_Image.open(io.BytesIO(image)))
+                    pil_images.append(PIL.Image.open(io.BytesIO(image)))
                 else:
                     # Assume it's a PIL Image; will raise if invalid
                     pil_images.append(image)

python/python/lancedb/embeddings/jinaai.py

@@ -77,8 +77,8 @@ class JinaEmbeddings(EmbeddingFunction):
             if isinstance(inputs, list):
                 inputs = inputs
             else:
-                PIL_Image = attempt_import_or_raise("PIL.Image", "pillow")
-                if isinstance(inputs, PIL_Image.Image):
+                PIL = attempt_import_or_raise("PIL", "pillow")
+                if isinstance(inputs, PIL.Image.Image):
                     inputs = [inputs]
         return inputs
 
@@ -89,13 +89,13 @@ class JinaEmbeddings(EmbeddingFunction):
         elif isinstance(image, (str, Path)):
             parsed = urlparse.urlparse(image)
             # TODO handle drive letter on windows.
-            PIL_Image = attempt_import_or_raise("PIL.Image", "pillow")
+            PIL = attempt_import_or_raise("PIL", "pillow")
             if parsed.scheme == "file":
-                pil_image = PIL_Image.open(parsed.path)
+                pil_image = PIL.Image.open(parsed.path)
             elif parsed.scheme == "":
-                pil_image = PIL_Image.open(image if os.name == "nt" else parsed.path)
+                pil_image = PIL.Image.open(image if os.name == "nt" else parsed.path)
             elif parsed.scheme.startswith("http"):
-                pil_image = PIL_Image.open(io.BytesIO(url_retrieve(image)))
+                pil_image = PIL.Image.open(io.BytesIO(url_retrieve(image)))
             else:
                 raise NotImplementedError("Only local and http(s) urls are supported")
             buffered = io.BytesIO()
@@ -103,9 +103,9 @@ class JinaEmbeddings(EmbeddingFunction):
             image_bytes = buffered.getvalue()
             image_dict = {"image": base64.b64encode(image_bytes).decode("utf-8")}
         else:
-            PIL_Image = attempt_import_or_raise("PIL.Image", "pillow")
+            PIL = attempt_import_or_raise("PIL", "pillow")
 
-            if isinstance(image, PIL_Image.Image):
+            if isinstance(image, PIL.Image.Image):
                 buffered = io.BytesIO()
                 image.save(buffered, format="PNG")
                 image_bytes = buffered.getvalue()
@@ -136,9 +136,9 @@ class JinaEmbeddings(EmbeddingFunction):
         elif isinstance(query, (Path, bytes)):
             return [self.generate_image_embedding(query)]
         else:
-            PIL_Image = attempt_import_or_raise("PIL.Image", "pillow")
+            PIL = attempt_import_or_raise("PIL", "pillow")
 
-            if isinstance(query, PIL_Image.Image):
+            if isinstance(query, PIL.Image.Image):
                 return [self.generate_image_embedding(query)]
             else:
                 raise TypeError(

python/python/lancedb/embeddings/open_clip.py

@@ -71,8 +71,8 @@ class OpenClipEmbeddings(EmbeddingFunction):
         if isinstance(query, str):
             return [self.generate_text_embeddings(query)]
         else:
-            PIL_Image = attempt_import_or_raise("PIL.Image", "pillow")
-            if isinstance(query, PIL_Image.Image):
+            PIL = attempt_import_or_raise("PIL", "pillow")
+            if isinstance(query, PIL.Image.Image):
                 return [self.generate_image_embedding(query)]
             else:
                 raise TypeError("OpenClip supports str or PIL Image as query")
@@ -145,20 +145,20 @@ class OpenClipEmbeddings(EmbeddingFunction):
             return self._encode_and_normalize_image(image)
 
     def _to_pil(self, image: Union[str, bytes]):
-        PIL_Image = attempt_import_or_raise("PIL.Image", "pillow")
+        PIL = attempt_import_or_raise("PIL", "pillow")
         if isinstance(image, bytes):
-            return PIL_Image.open(io.BytesIO(image))
-        if isinstance(image, PIL_Image.Image):
+            return PIL.Image.open(io.BytesIO(image))
+        if isinstance(image, PIL.Image.Image):
             return image
         elif isinstance(image, str):
             parsed = urlparse.urlparse(image)
             # TODO handle drive letter on windows.
             if parsed.scheme == "file":
-                return PIL_Image.open(parsed.path)
+                return PIL.Image.open(parsed.path)
             elif parsed.scheme == "":
-                return PIL_Image.open(image if os.name == "nt" else parsed.path)
+                return PIL.Image.open(image if os.name == "nt" else parsed.path)
             elif parsed.scheme.startswith("http"):
-                return PIL_Image.open(io.BytesIO(url_retrieve(image)))
+                return PIL.Image.open(io.BytesIO(url_retrieve(image)))
             else:
                 raise NotImplementedError("Only local and http(s) urls are supported")
 

python/python/lancedb/embeddings/siglip.py

@@ -56,8 +56,8 @@ class SigLipEmbeddings(EmbeddingFunction):
         if isinstance(query, str):
             return [self.generate_text_embeddings(query)]
         else:
-            PIL_Image = attempt_import_or_raise("PIL.Image", "pillow")
-            if isinstance(query, PIL_Image.Image):
+            PIL = attempt_import_or_raise("PIL", "pillow")
+            if isinstance(query, PIL.Image.Image):
                 return [self.generate_image_embedding(query)]
             else:
                 raise TypeError("SigLIP supports str or PIL Image as query")
@@ -127,21 +127,21 @@ class SigLipEmbeddings(EmbeddingFunction):
             return image_features.cpu().detach().numpy().squeeze()
 
     def _to_pil(self, image: Union[str, bytes, "PIL.Image.Image"]):
-        PIL_Image = attempt_import_or_raise("PIL.Image", "pillow")
-        if isinstance(image, PIL_Image.Image):
+        PIL = attempt_import_or_raise("PIL", "pillow")
+        if isinstance(image, PIL.Image.Image):
             return image.convert("RGB") if image.mode != "RGB" else image
         elif isinstance(image, bytes):
-            return PIL_Image.open(io.BytesIO(image)).convert("RGB")
+            return PIL.Image.open(io.BytesIO(image)).convert("RGB")
         elif isinstance(image, str):
             parsed = urlparse.urlparse(image)
             if parsed.scheme == "file":
-                return PIL_Image.open(parsed.path).convert("RGB")
+                return PIL.Image.open(parsed.path).convert("RGB")
             elif parsed.scheme == "":
                 path = image if os.name == "nt" else parsed.path
-                return PIL_Image.open(path).convert("RGB")
+                return PIL.Image.open(path).convert("RGB")
             elif parsed.scheme.startswith("http"):
                 image_bytes = url_retrieve(image)
-                return PIL_Image.open(io.BytesIO(image_bytes)).convert("RGB")
+                return PIL.Image.open(io.BytesIO(image_bytes)).convert("RGB")
             else:
                 raise NotImplementedError("Only local and http(s) urls are supported")
         else:

python/python/lancedb/embeddings/voyageai.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 import base64
 import os
-from typing import ClassVar, TYPE_CHECKING, List, Union, Any, Generator, Optional
+from typing import ClassVar, TYPE_CHECKING, List, Union, Any, Generator
 
 from pathlib import Path
 from urllib.parse import urlparse
@@ -21,9 +21,6 @@ if TYPE_CHECKING:
 
 # Token limits for different VoyageAI models
 VOYAGE_TOTAL_TOKEN_LIMITS = {
-    "voyage-4": 320_000,
-    "voyage-4-lite": 1_000_000,
-    "voyage-4-large": 120_000,
     "voyage-context-3": 32_000,
     "voyage-3.5-lite": 1_000_000,
     "voyage-3.5": 320_000,
@@ -48,32 +45,14 @@ def is_valid_url(text):
         return False
 
 
-VIDEO_EXTENSIONS = {".mp4", ".webm", ".mov", ".avi", ".mkv", ".m4v", ".gif"}
-
-
-def is_video_url(url: str) -> bool:
-    """Check if URL points to a video file based on extension."""
-    parsed = urlparse(url)
-    path = parsed.path.lower()
-    return any(path.endswith(ext) for ext in VIDEO_EXTENSIONS)
-
-
-def is_video_path(path: Path) -> bool:
-    """Check if file path is a video file based on extension."""
-    return path.suffix.lower() in VIDEO_EXTENSIONS
-
-
 def transform_input(input_data: Union[str, bytes, Path]):
-    PIL_Image = attempt_import_or_raise("PIL.Image", "pillow")
+    PIL = attempt_import_or_raise("PIL", "pillow")
     if isinstance(input_data, str):
         if is_valid_url(input_data):
-            if is_video_url(input_data):
-                content = {"type": "video_url", "video_url": input_data}
-            else:
-                content = {"type": "image_url", "image_url": input_data}
+            content = {"type": "image_url", "image_url": input_data}
         else:
             content = {"type": "text", "text": input_data}
-    elif isinstance(input_data, PIL_Image.Image):
+    elif isinstance(input_data, PIL.Image.Image):
         buffered = BytesIO()
         input_data.save(buffered, format="JPEG")
         img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
@@ -82,7 +61,7 @@ def transform_input(input_data: Union[str, bytes, Path]):
             "image_base64": "data:image/jpeg;base64," + img_str,
         }
     elif isinstance(input_data, bytes):
-        img = PIL_Image.open(BytesIO(input_data))
+        img = PIL.Image.open(BytesIO(input_data))
         buffered = BytesIO()
         img.save(buffered, format="JPEG")
         img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
@@ -91,24 +70,14 @@ def transform_input(input_data: Union[str, bytes, Path]):
             "image_base64": "data:image/jpeg;base64," + img_str,
         }
     elif isinstance(input_data, Path):
-        if is_video_path(input_data):
-            # Read video file and encode as base64
-            with open(input_data, "rb") as f:
-                video_bytes = f.read()
-            video_str = base64.b64encode(video_bytes).decode("utf-8")
-            content = {
-                "type": "video_base64",
-                "video_base64": video_str,
-            }
-        else:
-            img = PIL_Image.open(input_data)
-            buffered = BytesIO()
-            img.save(buffered, format="JPEG")
-            img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-            content = {
-                "type": "image_base64",
-                "image_base64": "data:image/jpeg;base64," + img_str,
-            }
+        img = PIL.Image.open(input_data)
+        buffered = BytesIO()
+        img.save(buffered, format="JPEG")
+        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+        content = {
+            "type": "image_base64",
+            "image_base64": "data:image/jpeg;base64," + img_str,
+        }
     else:
         raise ValueError("Each input should be either str, bytes, Path or Image.")
 
@@ -119,11 +88,9 @@ def sanitize_multimodal_input(inputs: Union[TEXT, IMAGES]) -> List[Any]:
     """
     Sanitize the input to the embedding function.
     """
-    PIL_Image = attempt_import_or_raise("PIL.Image", "pillow")
-    if isinstance(inputs, (str, bytes, Path, PIL_Image.Image)):
+    PIL = attempt_import_or_raise("PIL", "pillow")
+    if isinstance(inputs, (str, bytes, Path, PIL.Image.Image)):
         inputs = [inputs]
-    elif isinstance(inputs, list):
-        pass  # Already a list, use as-is
     elif isinstance(inputs, pa.Array):
         inputs = inputs.to_pylist()
     elif isinstance(inputs, pa.ChunkedArray):
@@ -133,7 +100,7 @@ def sanitize_multimodal_input(inputs: Union[TEXT, IMAGES]) -> List[Any]:
             f"Input type {type(inputs)} not allowed with multimodal model."
         )
 
-    if not all(isinstance(x, (str, bytes, Path, PIL_Image.Image)) for x in inputs):
+    if not all(isinstance(x, (str, bytes, Path, PIL.Image.Image)) for x in inputs):
         raise ValueError("Each input should be either str, bytes, Path or Image.")
 
     return [transform_input(i) for i in inputs]
@@ -170,25 +137,17 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
     name: str
         The name of the model to use. List of acceptable models:
 
-            * voyage-4 (1024 dims, general-purpose and multilingual retrieval)
-            * voyage-4-lite (1024 dims, optimized for latency and cost)
-            * voyage-4-large (1024 dims, best retrieval quality)
             * voyage-context-3
             * voyage-3.5
             * voyage-3.5-lite
             * voyage-3
             * voyage-3-lite
             * voyage-multimodal-3
-            * voyage-multimodal-3.5
             * voyage-finance-2
             * voyage-multilingual-2
             * voyage-law-2
             * voyage-code-2
 
-    output_dimension: int, optional
-        The output dimension for models that support flexible dimensions.
-        Currently only voyage-multimodal-3.5 supports this feature.
-        Valid options: 256, 512, 1024 (default), 2048.
 
     Examples
     --------
@@ -216,14 +175,8 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
     """
 
     name: str
-    output_dimension: Optional[int] = None
     client: ClassVar = None
-    _FLEXIBLE_DIM_MODELS: ClassVar[list] = ["voyage-multimodal-3.5"]
-    _VALID_DIMENSIONS: ClassVar[list] = [256, 512, 1024, 2048]
     text_embedding_models: list = [
-        "voyage-4",
-        "voyage-4-lite",
-        "voyage-4-large",
         "voyage-3.5",
         "voyage-3.5-lite",
         "voyage-3",
@@ -233,7 +186,7 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
         "voyage-law-2",
         "voyage-code-2",
     ]
-    multimodal_embedding_models: list = ["voyage-multimodal-3", "voyage-multimodal-3.5"]
+    multimodal_embedding_models: list = ["voyage-multimodal-3"]
     contextual_embedding_models: list = ["voyage-context-3"]
 
     def _is_multimodal_model(self, model_name: str):
@@ -245,25 +198,11 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
         return model_name in self.contextual_embedding_models or "context" in model_name
 
     def ndims(self):
-        # Handle flexible dimension models
-        if self.name in self._FLEXIBLE_DIM_MODELS:
-            if self.output_dimension is not None:
-                if self.output_dimension not in self._VALID_DIMENSIONS:
-                    raise ValueError(
-                        f"Invalid output_dimension {self.output_dimension} "
-                        f"for {self.name}. Valid options: {self._VALID_DIMENSIONS}"
-                    )
-                return self.output_dimension
-            return 1024  # default dimension
-
         if self.name == "voyage-3-lite":
             return 512
         elif self.name == "voyage-code-2":
             return 1536
         elif self.name in [
-            "voyage-4",
-            "voyage-4-lite",
-            "voyage-4-large",
             "voyage-context-3",
             "voyage-3.5",
             "voyage-3.5-lite",
@@ -272,17 +211,12 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
             "voyage-finance-2",
             "voyage-multilingual-2",
             "voyage-law-2",
+            "voyage-multimodal-3",
         ]:
             return 1024
         else:
             raise ValueError(f"Model {self.name} not supported")
 
-    def _get_multimodal_kwargs(self, **kwargs):
-        """Get kwargs for multimodal embed call, including output_dimension if set."""
-        if self.name in self._FLEXIBLE_DIM_MODELS and self.output_dimension is not None:
-            kwargs["output_dimension"] = self.output_dimension
-        return kwargs
-
     def compute_query_embeddings(
         self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
     ) -> List[np.ndarray]:
@@ -300,7 +234,6 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
         """
         client = VoyageAIEmbeddingFunction._get_client()
         if self._is_multimodal_model(self.name):
-            kwargs = self._get_multimodal_kwargs(**kwargs)
             result = client.multimodal_embed(
                 inputs=[[query]], model=self.name, input_type="query", **kwargs
             )
@@ -342,7 +275,6 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
             )
             if has_images:
                 # Use non-batched API for images
-                kwargs = self._get_multimodal_kwargs(**kwargs)
                 result = client.multimodal_embed(
                     inputs=sanitized, model=self.name, input_type="document", **kwargs
                 )
@@ -425,7 +357,6 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
             callable: A function that takes a batch of texts and returns embeddings.
         """
         if self._is_multimodal_model(self.name):
-            multimodal_kwargs = self._get_multimodal_kwargs(**kwargs)
 
             def embed_batch(batch: List[str]) -> List[np.array]:
                 batch_inputs = sanitize_multimodal_input(batch)
@@ -433,7 +364,7 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
                     inputs=batch_inputs,
                     model=self.name,
                     input_type=input_type,
-                    **multimodal_kwargs,
+                    **kwargs,
                 )
                 return result.embeddings
 

python/python/lancedb/pydantic.py

@@ -275,7 +275,7 @@ def _py_type_to_arrow_type(py_type: Type[Any], field: FieldInfo) -> pa.DataType:
         return pa.timestamp("us", tz=tz)
     elif getattr(py_type, "__origin__", None) in (list, tuple):
         child = py_type.__args__[0]
-        return _pydantic_list_child_to_arrow(child, field)
+        return pa.list_(_py_type_to_arrow_type(child, field))
     raise TypeError(
         f"Converting Pydantic type to Arrow Type: unsupported type {py_type}."
     )
@@ -298,18 +298,12 @@ else:
 
 
 def _pydantic_type_to_arrow_type(tp: Any, field: FieldInfo) -> pa.DataType:
-    def _safe_issubclass(candidate: Any, base: type) -> bool:
-        try:
-            return issubclass(candidate, base)
-        except TypeError:
-            return False
-
     if inspect.isclass(tp):
-        if _safe_issubclass(tp, pydantic.BaseModel):
+        if issubclass(tp, pydantic.BaseModel):
             # Struct
             fields = _pydantic_model_to_fields(tp)
             return pa.struct(fields)
-        if _safe_issubclass(tp, FixedSizeListMixin):
+        if issubclass(tp, FixedSizeListMixin):
             if getattr(tp, "is_multi_vector", lambda: False)():
                 return pa.list_(pa.list_(tp.value_arrow_type(), tp.dim()))
             # For regular Vector
@@ -317,67 +311,45 @@ def _pydantic_type_to_arrow_type(tp: Any, field: FieldInfo) -> pa.DataType:
     return _py_type_to_arrow_type(tp, field)
 
 
-def _pydantic_list_child_to_arrow(child: Any, field: FieldInfo) -> pa.DataType:
-    unwrapped = _unwrap_optional_annotation(child)
-    if unwrapped is not None:
-        return pa.list_(
-            pa.field("item", _pydantic_type_to_arrow_type(unwrapped, field), True)
-        )
-    return pa.list_(_pydantic_type_to_arrow_type(child, field))
-
-
-def _unwrap_optional_annotation(annotation: Any) -> Any | None:
-    if isinstance(annotation, (_GenericAlias, GenericAlias)):
-        origin = annotation.__origin__
-        args = annotation.__args__
-        if origin == Union:
-            non_none = [arg for arg in args if arg is not type(None)]
-            if len(non_none) == 1 and len(non_none) != len(args):
-                return non_none[0]
-    elif sys.version_info >= (3, 10) and isinstance(annotation, types.UnionType):
-        args = annotation.__args__
-        non_none = [arg for arg in args if arg is not type(None)]
-        if len(non_none) == 1 and len(non_none) != len(args):
-            return non_none[0]
-    return None
-
-
 def _pydantic_to_arrow_type(field: FieldInfo) -> pa.DataType:
     """Convert a Pydantic FieldInfo to Arrow DataType"""
-    unwrapped = _unwrap_optional_annotation(field.annotation)
-    if unwrapped is not None:
-        return _pydantic_type_to_arrow_type(unwrapped, field)
     if isinstance(field.annotation, (_GenericAlias, GenericAlias)):
         origin = field.annotation.__origin__
         args = field.annotation.__args__
 
         if origin is list:
             child = args[0]
-            return _pydantic_list_child_to_arrow(child, field)
+            return pa.list_(_py_type_to_arrow_type(child, field))
+        elif origin == Union:
+            if len(args) == 2 and args[1] is type(None):
+                return _pydantic_type_to_arrow_type(args[0], field)
+    elif sys.version_info >= (3, 10) and isinstance(field.annotation, types.UnionType):
+        args = field.annotation.__args__
+        if len(args) == 2:
+            for typ in args:
+                if typ is type(None):
+                    continue
+                return _py_type_to_arrow_type(typ, field)
     return _pydantic_type_to_arrow_type(field.annotation, field)
 
 
 def is_nullable(field: FieldInfo) -> bool:
     """Check if a Pydantic FieldInfo is nullable."""
-    if _unwrap_optional_annotation(field.annotation) is not None:
-        return True
     if isinstance(field.annotation, (_GenericAlias, GenericAlias)):
         origin = field.annotation.__origin__
         args = field.annotation.__args__
         if origin == Union:
-            if any(typ is type(None) for typ in args):
+            if len(args) == 2 and args[1] is type(None):
                 return True
     elif sys.version_info >= (3, 10) and isinstance(field.annotation, types.UnionType):
         args = field.annotation.__args__
         for typ in args:
             if typ is type(None):
                 return True
-    elif inspect.isclass(field.annotation):
-        try:
-            if issubclass(field.annotation, FixedSizeListMixin):
-                return field.annotation.nullable()
-        except TypeError:
-            return False
+    elif inspect.isclass(field.annotation) and issubclass(
+        field.annotation, FixedSizeListMixin
+    ):
+        return field.annotation.nullable()
     return False
 
 

python/python/lancedb/query.py

@@ -961,27 +961,22 @@ class LanceQueryBuilder(ABC):
         >>> query = [100, 100]
         >>> plan = table.search(query).analyze_plan()
         >>> print(plan)  # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
-        AnalyzeExec verbose=true, elapsed=..., metrics=...
-          TracedExec, elapsed=..., metrics=...
-            ProjectionExec: elapsed=..., expr=[...],
-            metrics=[output_rows=..., elapsed_compute=..., output_bytes=...]
-              GlobalLimitExec: elapsed=..., skip=0, fetch=10,
-              metrics=[output_rows=..., elapsed_compute=..., output_bytes=...]
-                FilterExec: elapsed=..., _distance@2 IS NOT NULL, metrics=[...]
-                  SortExec: elapsed=..., TopK(fetch=10), expr=[...],
+        AnalyzeExec verbose=true, metrics=[], cumulative_cpu=...
+          TracedExec, metrics=[], cumulative_cpu=...
+            ProjectionExec: expr=[...], metrics=[...], cumulative_cpu=...
+              GlobalLimitExec: skip=0, fetch=10, metrics=[...], cumulative_cpu=...
+                FilterExec: _distance@2 IS NOT NULL,
+                metrics=[output_rows=..., elapsed_compute=...], cumulative_cpu=...
+                  SortExec: TopK(fetch=10), expr=[...],
                   preserve_partitioning=[...],
-                  metrics=[output_rows=..., elapsed_compute=...,
-                  output_bytes=..., row_replacements=...]
-                    KNNVectorDistance: elapsed=..., metric=l2,
-                    metrics=[output_rows=..., elapsed_compute=...,
-                    output_bytes=..., output_batches=...]
-                      LanceRead: elapsed=..., uri=..., projection=[vector],
-                      num_fragments=..., range_before=None, range_after=None,
-                      row_id=true, row_addr=false,
-                      full_filter=--, refine_filter=--,
-                      metrics=[output_rows=..., elapsed_compute=..., output_bytes=...,
-                      fragments_scanned=..., ranges_scanned=1, rows_scanned=1,
-                      bytes_read=..., iops=..., requests=..., task_wait_time=...]
+                  metrics=[output_rows=..., elapsed_compute=..., row_replacements=...],
+                  cumulative_cpu=...
+                    KNNVectorDistance: metric=l2,
+                    metrics=[output_rows=..., elapsed_compute=..., output_batches=...],
+                    cumulative_cpu=...
+                      LanceRead: uri=..., projection=[vector], ...
+                      metrics=[output_rows=..., elapsed_compute=...,
+                      bytes_read=..., iops=..., requests=...], cumulative_cpu=...
 
         Returns
         -------
@@ -1433,19 +1428,6 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         self._bypass_vector_index = True
         return self
 
-    def fast_search(self) -> LanceVectorQueryBuilder:
-        """
-        Skip a flat search of unindexed data. This will improve
-        search performance but search results will not include unindexed data.
-
-        Returns
-        -------
-        LanceVectorQueryBuilder
-            The LanceVectorQueryBuilder object.
-        """
-        self._fast_search = True
-        return self
-
 
 class LanceFtsQueryBuilder(LanceQueryBuilder):
     """A builder for full text search for LanceDB."""

python/python/lancedb/remote/db.py

@@ -384,7 +384,6 @@ class RemoteDBConnection(DBConnection):
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
         mode: Optional[str] = None,
-        exist_ok: bool = False,
         embedding_functions: Optional[List[EmbeddingFunctionConfig]] = None,
         *,
         namespace: Optional[List[str]] = None,
@@ -413,12 +412,6 @@ class RemoteDBConnection(DBConnection):
             - pyarrow.Schema
 
             - [LanceModel][lancedb.pydantic.LanceModel]
-        mode: str, default "create"
-            The mode to use when creating the table.
-            Can be either "create", "overwrite", or "exist_ok".
-        exist_ok: bool, default False
-            If exist_ok is True, and mode is None or "create", mode will be changed
-            to "exist_ok".
         on_bad_vectors: str, default "error"
             What to do if any of the vectors are not the same size or contains NaNs.
             One of "error", "drop", "fill".
@@ -490,11 +483,6 @@ class RemoteDBConnection(DBConnection):
         LanceTable(table4)
 
         """
-        if exist_ok:
-            if mode == "create":
-                mode = "exist_ok"
-            elif not mode:
-                mode = "exist_ok"
         if namespace is None:
             namespace = []
         validate_table_name(name)

python/python/lancedb/remote/table.py

@@ -18,17 +18,7 @@ from lancedb._lancedb import (
     UpdateResult,
 )
 from lancedb.embeddings.base import EmbeddingFunctionConfig
-from lancedb.index import (
-    FTS,
-    BTree,
-    Bitmap,
-    HnswSq,
-    IvfFlat,
-    IvfPq,
-    IvfRq,
-    IvfSq,
-    LabelList,
-)
+from lancedb.index import FTS, BTree, Bitmap, HnswSq, IvfFlat, IvfPq, IvfSq, LabelList
 from lancedb.remote.db import LOOP
 import pyarrow as pa
 
@@ -275,12 +265,6 @@ class RemoteTable(Table):
                 num_sub_vectors=num_sub_vectors,
                 num_bits=num_bits,
             )
-        elif index_type == "IVF_RQ":
-            config = IvfRq(
-                distance_type=metric,
-                num_partitions=num_partitions,
-                num_bits=num_bits,
-            )
         elif index_type == "IVF_SQ":
             config = IvfSq(distance_type=metric, num_partitions=num_partitions)
         elif index_type == "IVF_HNSW_PQ":
@@ -295,8 +279,7 @@ class RemoteTable(Table):
         else:
             raise ValueError(
                 f"Unknown vector index type: {index_type}. Valid options are"
-                " 'IVF_FLAT', 'IVF_PQ', 'IVF_RQ', 'IVF_SQ',"
-                " 'IVF_HNSW_PQ', 'IVF_HNSW_SQ'"
+                " 'IVF_FLAT', 'IVF_SQ', 'IVF_PQ', 'IVF_HNSW_PQ', 'IVF_HNSW_SQ'"
             )
 
         LOOP.run(
@@ -655,14 +638,6 @@ class RemoteTable(Table):
     def stats(self):
         return LOOP.run(self._table.stats())
 
-    @property
-    def uri(self) -> str:
-        """The table URI (storage location).
-
-        For remote tables, this fetches the location from the server via describe.
-        """
-        return LOOP.run(self._table.uri())
-
     def take_offsets(self, offsets: list[int]) -> LanceTakeQueryBuilder:
         return LanceTakeQueryBuilder(self._table.take_offsets(offsets))
 

python/python/lancedb/table.py

@@ -2218,41 +2218,6 @@ class LanceTable(Table):
     def stats(self) -> TableStatistics:
         return LOOP.run(self._table.stats())
 
-    @property
-    def uri(self) -> str:
-        return LOOP.run(self._table.uri())
-
-    def initial_storage_options(self) -> Optional[Dict[str, str]]:
-        """Get the initial storage options that were passed in when opening this table.
-
-        For dynamically refreshed options (e.g., credential vending), use
-        :meth:`latest_storage_options`.
-
-        Warning: This is an internal API and the return value is subject to change.
-
-        Returns
-        -------
-        Optional[Dict[str, str]]
-            The storage options, or None if no storage options were configured.
-        """
-        return LOOP.run(self._table.initial_storage_options())
-
-    def latest_storage_options(self) -> Optional[Dict[str, str]]:
-        """Get the latest storage options, refreshing from provider if configured.
-
-        This method is useful for credential vending scenarios where storage options
-        may be refreshed dynamically. If no dynamic provider is configured, this
-        returns the initial static options.
-
-        Warning: This is an internal API and the return value is subject to change.
-
-        Returns
-        -------
-        Optional[Dict[str, str]]
-            The storage options, or None if no storage options were configured.
-        """
-        return LOOP.run(self._table.latest_storage_options())
-
     def create_scalar_index(
         self,
         column: str,
@@ -3641,51 +3606,6 @@ class AsyncTable:
         """
         return await self._inner.stats()
 
-    async def uri(self) -> str:
-        """
-        Get the table URI (storage location).
-
-        For remote tables, this fetches the location from the server via describe.
-        For local tables, this returns the dataset URI.
-
-        Returns
-        -------
-        str
-            The full storage location of the table (e.g., S3/GCS path).
-        """
-        return await self._inner.uri()
-
-    async def initial_storage_options(self) -> Optional[Dict[str, str]]:
-        """Get the initial storage options that were passed in when opening this table.
-
-        For dynamically refreshed options (e.g., credential vending), use
-        :meth:`latest_storage_options`.
-
-        Warning: This is an internal API and the return value is subject to change.
-
-        Returns
-        -------
-        Optional[Dict[str, str]]
-            The storage options, or None if no storage options were configured.
-        """
-        return await self._inner.initial_storage_options()
-
-    async def latest_storage_options(self) -> Optional[Dict[str, str]]:
-        """Get the latest storage options, refreshing from provider if configured.
-
-        This method is useful for credential vending scenarios where storage options
-        may be refreshed dynamically. If no dynamic provider is configured, this
-        returns the initial static options.
-
-        Warning: This is an internal API and the return value is subject to change.
-
-        Returns
-        -------
-        Optional[Dict[str, str]]
-            The storage options, or None if no storage options were configured.
-        """
-        return await self._inner.latest_storage_options()
-
     async def add(
         self,
         data: DATA,

python/python/tests/conftest.py

@@ -2,27 +2,12 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 from datetime import timedelta
-
 from lancedb.db import AsyncConnection, DBConnection
 import lancedb
 import pytest
 import pytest_asyncio
 
 
-def pandas_string_type():
-    """Return the PyArrow string type that pandas uses for string columns.
-
-    pandas 3.0+ uses large_string for string columns, pandas 2.x uses string.
-    """
-    import pandas as pd
-    import pyarrow as pa
-
-    version = tuple(int(x) for x in pd.__version__.split(".")[:2])
-    if version >= (3, 0):
-        return pa.large_utf8()
-    return pa.utf8()
-
-
 # Use an in-memory database for most tests.
 @pytest.fixture
 def mem_db() -> DBConnection:

python/python/tests/test_db.py

@@ -268,8 +268,6 @@ async def test_create_table_from_iterator_async(mem_db_async: lancedb.AsyncConne
 
 
 def test_create_exist_ok(tmp_db: lancedb.DBConnection):
-    from conftest import pandas_string_type
-
     data = pd.DataFrame(
         {
             "vector": [[3.1, 4.1], [5.9, 26.5]],
@@ -288,11 +286,10 @@ def test_create_exist_ok(tmp_db: lancedb.DBConnection):
     assert tbl.schema == tbl2.schema
     assert len(tbl) == len(tbl2)
 
-    # pandas 3.0+ uses large_string, pandas 2.x uses string
     schema = pa.schema(
         [
             pa.field("vector", pa.list_(pa.float32(), list_size=2)),
-            pa.field("item", pandas_string_type()),
+            pa.field("item", pa.utf8()),
             pa.field("price", pa.float64()),
         ]
     )
@@ -302,7 +299,7 @@ def test_create_exist_ok(tmp_db: lancedb.DBConnection):
     bad_schema = pa.schema(
         [
             pa.field("vector", pa.list_(pa.float32(), list_size=2)),
-            pa.field("item", pandas_string_type()),
+            pa.field("item", pa.utf8()),
             pa.field("price", pa.float64()),
             pa.field("extra", pa.float32()),
         ]
@@ -368,8 +365,6 @@ async def test_create_mode_async(tmp_db_async: lancedb.AsyncConnection):
 
 @pytest.mark.asyncio
 async def test_create_exist_ok_async(tmp_db_async: lancedb.AsyncConnection):
-    from conftest import pandas_string_type
-
     data = pd.DataFrame(
         {
             "vector": [[3.1, 4.1], [5.9, 26.5]],
@@ -387,11 +382,10 @@ async def test_create_exist_ok_async(tmp_db_async: lancedb.AsyncConnection):
     assert tbl.name == tbl2.name
     assert await tbl.schema() == await tbl2.schema()
 
-    # pandas 3.0+ uses large_string, pandas 2.x uses string
     schema = pa.schema(
         [
             pa.field("vector", pa.list_(pa.float32(), list_size=2)),
-            pa.field("item", pandas_string_type()),
+            pa.field("item", pa.utf8()),
             pa.field("price", pa.float64()),
         ]
     )
@@ -601,8 +595,6 @@ def test_open_table_sync(tmp_db: lancedb.DBConnection):
 
 @pytest.mark.asyncio
 async def test_open_table(tmp_path):
-    from conftest import pandas_string_type
-
     db = await lancedb.connect_async(tmp_path)
     data = pd.DataFrame(
         {
@@ -622,11 +614,10 @@ async def test_open_table(tmp_path):
         )
         is not None
     )
-    # pandas 3.0+ uses large_string, pandas 2.x uses string
     assert await tbl.schema() == pa.schema(
         {
             "vector": pa.list_(pa.float32(), list_size=2),
-            "item": pandas_string_type(),
+            "item": pa.utf8(),
             "price": pa.float64(),
         }
     )

python/python/tests/test_embeddings_slow.py

@@ -517,36 +517,19 @@ def test_ollama_embedding(tmp_path):
 @pytest.mark.skipif(
     os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
 )
-@pytest.mark.parametrize(
-    "model_name,expected_dims",
-    [
-        ("voyage-3", 1024),
-        ("voyage-4", 1024),
-        ("voyage-4-lite", 1024),
-        ("voyage-4-large", 1024),
-    ],
-)
-def test_voyageai_embedding_function(model_name, expected_dims, tmp_path):
-    """Integration test for VoyageAI text embedding models with real API calls."""
-    voyageai = get_registry().get("voyageai").create(name=model_name, max_retries=0)
+def test_voyageai_embedding_function():
+    voyageai = get_registry().get("voyageai").create(name="voyage-3", max_retries=0)
 
     class TextModel(LanceModel):
         text: str = voyageai.SourceField()
         vector: Vector(voyageai.ndims()) = voyageai.VectorField()
 
     df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
-    db = lancedb.connect(tmp_path)
+    db = lancedb.connect("~/lancedb")
     tbl = db.create_table("test", schema=TextModel, mode="overwrite")
 
     tbl.add(df)
     assert len(tbl.to_pandas()["vector"][0]) == voyageai.ndims()
-    assert voyageai.ndims() == expected_dims, (
-        f"{model_name} should have {expected_dims} dimensions"
-    )
-
-    # Test search functionality
-    result = tbl.search("hello").limit(1).to_pandas()
-    assert result["text"][0] == "hello world"
 
 
 @pytest.mark.slow
@@ -630,133 +613,6 @@ def test_voyageai_multimodal_embedding_text_function():
     assert len(tbl.to_pandas()["vector"][0]) == voyageai.ndims()
 
 
-@pytest.mark.slow
-@pytest.mark.skipif(
-    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
-)
-def test_voyageai_multimodal_35_embedding_function():
-    """Test voyage-multimodal-3.5 model with text input."""
-    voyageai = (
-        get_registry()
-        .get("voyageai")
-        .create(name="voyage-multimodal-3.5", max_retries=0)
-    )
-
-    class TextModel(LanceModel):
-        text: str = voyageai.SourceField()
-        vector: Vector(voyageai.ndims()) = voyageai.VectorField()
-
-    df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
-    db = lancedb.connect("~/lancedb")
-    tbl = db.create_table("test_multimodal_35", schema=TextModel, mode="overwrite")
-
-    tbl.add(df)
-    assert len(tbl.to_pandas()["vector"][0]) == voyageai.ndims()
-    assert voyageai.ndims() == 1024
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(
-    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
-)
-def test_voyageai_multimodal_35_flexible_dimensions():
-    """Test voyage-multimodal-3.5 model with custom output dimension."""
-    voyageai = (
-        get_registry()
-        .get("voyageai")
-        .create(name="voyage-multimodal-3.5", output_dimension=512, max_retries=0)
-    )
-
-    class TextModel(LanceModel):
-        text: str = voyageai.SourceField()
-        vector: Vector(voyageai.ndims()) = voyageai.VectorField()
-
-    assert voyageai.ndims() == 512
-
-    df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
-    db = lancedb.connect("~/lancedb")
-    tbl = db.create_table("test_multimodal_35_dim", schema=TextModel, mode="overwrite")
-
-    tbl.add(df)
-    assert len(tbl.to_pandas()["vector"][0]) == 512
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(
-    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
-)
-def test_voyageai_multimodal_35_image_embedding():
-    """Test voyage-multimodal-3.5 model with image input."""
-    voyageai = (
-        get_registry()
-        .get("voyageai")
-        .create(name="voyage-multimodal-3.5", max_retries=0)
-    )
-
-    class Images(LanceModel):
-        label: str
-        image_uri: str = voyageai.SourceField()
-        vector: Vector(voyageai.ndims()) = voyageai.VectorField()
-
-    db = lancedb.connect("~/lancedb")
-    table = db.create_table(
-        "test_multimodal_35_images", schema=Images, mode="overwrite"
-    )
-    labels = ["cat", "dog"]
-    uris = [
-        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
-        "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
-    ]
-    table.add(pd.DataFrame({"label": labels, "image_uri": uris}))
-    assert len(table.to_pandas()["vector"][0]) == voyageai.ndims()
-    assert voyageai.ndims() == 1024
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(
-    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
-)
-@pytest.mark.parametrize("dimension", [256, 512, 1024, 2048])
-def test_voyageai_multimodal_35_all_dimensions(dimension):
-    """Test voyage-multimodal-3.5 model with all valid output dimensions."""
-    voyageai = (
-        get_registry()
-        .get("voyageai")
-        .create(name="voyage-multimodal-3.5", output_dimension=dimension, max_retries=0)
-    )
-
-    assert voyageai.ndims() == dimension
-
-    class TextModel(LanceModel):
-        text: str = voyageai.SourceField()
-        vector: Vector(voyageai.ndims()) = voyageai.VectorField()
-
-    df = pd.DataFrame({"text": ["hello world"]})
-    db = lancedb.connect("~/lancedb")
-    tbl = db.create_table(
-        f"test_multimodal_35_dim_{dimension}", schema=TextModel, mode="overwrite"
-    )
-
-    tbl.add(df)
-    assert len(tbl.to_pandas()["vector"][0]) == dimension
-
-
-@pytest.mark.slow
-@pytest.mark.skipif(
-    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
-)
-def test_voyageai_multimodal_35_invalid_dimension():
-    """Test voyage-multimodal-3.5 model raises error for invalid output dimension."""
-    with pytest.raises(ValueError, match="Invalid output_dimension"):
-        voyageai = (
-            get_registry()
-            .get("voyageai")
-            .create(name="voyage-multimodal-3.5", output_dimension=999, max_retries=0)
-        )
-        # ndims() is where the validation happens
-        voyageai.ndims()
-
-
 @pytest.mark.slow
 @pytest.mark.skipif(
     importlib.util.find_spec("colpali_engine") is None,

python/python/tests/test_namespace_integration.py

@@ -26,8 +26,6 @@ import pytest
 from lance_namespace import (
     CreateEmptyTableRequest,
     CreateEmptyTableResponse,
-    DeclareTableRequest,
-    DeclareTableResponse,
     DescribeTableRequest,
     DescribeTableResponse,
     LanceNamespace,
@@ -162,19 +160,6 @@ class TrackingNamespace(LanceNamespace):
 
         return modified
 
-    def declare_table(self, request: DeclareTableRequest) -> DeclareTableResponse:
-        """Track declare_table calls and inject rotating credentials."""
-        with self.lock:
-            self.create_call_count += 1
-            count = self.create_call_count
-
-        response = self.inner.declare_table(request)
-        response.storage_options = self._modify_storage_options(
-            response.storage_options, count
-        )
-
-        return response
-
     def create_empty_table(
         self, request: CreateEmptyTableRequest
     ) -> CreateEmptyTableResponse:

python/python/tests/test_permutation.py

@@ -438,15 +438,11 @@ def test_filter_with_splits(mem_db):
     row_count = permutation_tbl.count_rows()
     assert row_count == 67
 
-    # Verify the permutation table only contains row_id and split_id
-    assert set(permutation_tbl.schema.names) == {"row_id", "split_id"}
-
-    row_ids = permutation_tbl.search(None).to_arrow().to_pydict()["row_id"]
-    data = tbl.take_row_ids(row_ids).to_arrow().to_pydict()
+    data = permutation_tbl.search(None).to_arrow().to_pydict()
     categories = data["category"]
 
     # All categories should be A or B
-    assert all(cat in ("A", "B") for cat in categories)
+    assert all(cat in ["A", "B"] for cat in categories)
 
 
 def test_filter_with_shuffle(mem_db):

python/python/tests/test_pydantic.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 import json
+import sys
 from datetime import date, datetime
 from typing import List, Optional, Tuple
 
@@ -19,6 +20,10 @@ from pydantic import BaseModel
 from pydantic import Field
 
 
+@pytest.mark.skipif(
+    sys.version_info < (3, 9),
+    reason="using native type alias requires python3.9 or higher",
+)
 def test_pydantic_to_arrow():
     class StructModel(pydantic.BaseModel):
         a: str
@@ -78,6 +83,10 @@ def test_pydantic_to_arrow():
     assert schema == expect_schema
 
 
+@pytest.mark.skipif(
+    sys.version_info < (3, 10),
+    reason="using | type syntax requires python3.10 or higher",
+)
 def test_optional_types_py310():
     class TestModel(pydantic.BaseModel):
         a: str | None
@@ -96,233 +105,10 @@ def test_optional_types_py310():
     assert schema == expect_schema
 
 
-def test_optional_structs():
-    class SplitInfo(pydantic.BaseModel):
-        start_frame: int
-        end_frame: int
-
-    class TestModel(pydantic.BaseModel):
-        id: str
-        split: SplitInfo | None = None
-
-    schema = pydantic_to_schema(TestModel)
-
-    expect_schema = pa.schema(
-        [
-            pa.field("id", pa.utf8(), False),
-            pa.field(
-                "split",
-                pa.struct(
-                    [
-                        pa.field("start_frame", pa.int64(), False),
-                        pa.field("end_frame", pa.int64(), False),
-                    ]
-                ),
-                True,
-            ),
-        ]
-    )
-    assert schema == expect_schema
-
-
-def test_optional_struct_list_py310():
-    class SplitInfo(pydantic.BaseModel):
-        start_frame: int
-        end_frame: int
-
-    class TestModel(pydantic.BaseModel):
-        id: str
-        splits: list[SplitInfo] | None = None
-
-    schema = pydantic_to_schema(TestModel)
-
-    expect_schema = pa.schema(
-        [
-            pa.field("id", pa.utf8(), False),
-            pa.field(
-                "splits",
-                pa.list_(
-                    pa.struct(
-                        [
-                            pa.field("start_frame", pa.int64(), False),
-                            pa.field("end_frame", pa.int64(), False),
-                        ]
-                    )
-                ),
-                True,
-            ),
-        ]
-    )
-    assert schema == expect_schema
-
-
-def test_nested_struct_list():
-    class SplitInfo(pydantic.BaseModel):
-        start_frame: int
-        end_frame: int
-
-    class TestModel(pydantic.BaseModel):
-        id: str
-        splits: list[SplitInfo]
-
-    schema = pydantic_to_schema(TestModel)
-
-    expect_schema = pa.schema(
-        [
-            pa.field("id", pa.utf8(), False),
-            pa.field(
-                "splits",
-                pa.list_(
-                    pa.struct(
-                        [
-                            pa.field("start_frame", pa.int64(), False),
-                            pa.field("end_frame", pa.int64(), False),
-                        ]
-                    )
-                ),
-                False,
-            ),
-        ]
-    )
-    assert schema == expect_schema
-
-
-def test_nested_struct_list_optional():
-    class SplitInfo(pydantic.BaseModel):
-        start_frame: int
-        end_frame: int
-
-    class TestModel(pydantic.BaseModel):
-        id: str
-        splits: Optional[list[SplitInfo]] = None
-
-    schema = pydantic_to_schema(TestModel)
-
-    expect_schema = pa.schema(
-        [
-            pa.field("id", pa.utf8(), False),
-            pa.field(
-                "splits",
-                pa.list_(
-                    pa.struct(
-                        [
-                            pa.field("start_frame", pa.int64(), False),
-                            pa.field("end_frame", pa.int64(), False),
-                        ]
-                    )
-                ),
-                True,
-            ),
-        ]
-    )
-    assert schema == expect_schema
-
-
-def test_nested_struct_list_optional_items():
-    class SplitInfo(pydantic.BaseModel):
-        start_frame: int
-        end_frame: int
-
-    class TestModel(pydantic.BaseModel):
-        id: str
-        splits: list[Optional[SplitInfo]]
-
-    schema = pydantic_to_schema(TestModel)
-
-    expect_schema = pa.schema(
-        [
-            pa.field("id", pa.utf8(), False),
-            pa.field(
-                "splits",
-                pa.list_(
-                    pa.field(
-                        "item",
-                        pa.struct(
-                            [
-                                pa.field("start_frame", pa.int64(), False),
-                                pa.field("end_frame", pa.int64(), False),
-                            ]
-                        ),
-                        True,
-                    )
-                ),
-                False,
-            ),
-        ]
-    )
-    assert schema == expect_schema
-
-
-def test_nested_struct_list_optional_container_and_items():
-    class SplitInfo(pydantic.BaseModel):
-        start_frame: int
-        end_frame: int
-
-    class TestModel(pydantic.BaseModel):
-        id: str
-        splits: Optional[list[Optional[SplitInfo]]] = None
-
-    schema = pydantic_to_schema(TestModel)
-
-    expect_schema = pa.schema(
-        [
-            pa.field("id", pa.utf8(), False),
-            pa.field(
-                "splits",
-                pa.list_(
-                    pa.field(
-                        "item",
-                        pa.struct(
-                            [
-                                pa.field("start_frame", pa.int64(), False),
-                                pa.field("end_frame", pa.int64(), False),
-                            ]
-                        ),
-                        True,
-                    )
-                ),
-                True,
-            ),
-        ]
-    )
-    assert schema == expect_schema
-
-
-def test_nested_struct_list_optional_items_pep604():
-    class SplitInfo(pydantic.BaseModel):
-        start_frame: int
-        end_frame: int
-
-    class TestModel(pydantic.BaseModel):
-        id: str
-        splits: list[SplitInfo | None]
-
-    schema = pydantic_to_schema(TestModel)
-
-    expect_schema = pa.schema(
-        [
-            pa.field("id", pa.utf8(), False),
-            pa.field(
-                "splits",
-                pa.list_(
-                    pa.field(
-                        "item",
-                        pa.struct(
-                            [
-                                pa.field("start_frame", pa.int64(), False),
-                                pa.field("end_frame", pa.int64(), False),
-                            ]
-                        ),
-                        True,
-                    )
-                ),
-                False,
-            ),
-        ]
-    )
-    assert schema == expect_schema
-
-
+@pytest.mark.skipif(
+    sys.version_info > (3, 8),
+    reason="using native type alias requires python3.9 or higher",
+)
 def test_pydantic_to_arrow_py38():
     class StructModel(pydantic.BaseModel):
         a: str

python/python/tests/test_query.py

@@ -1499,30 +1499,3 @@ def test_search_empty_table(mem_db):
     # Search on empty table should return empty results, not crash
     results = table.search([1.0, 2.0]).limit(5).to_list()
     assert results == []
-
-
-def test_fast_search(tmp_path):
-    db = lancedb.connect(tmp_path)
-
-    # Generate data matching the async test style
-    vectors = pa.FixedShapeTensorArray.from_numpy_ndarray(
-        np.random.rand(256, 32)
-    ).storage
-
-    table = db.create_table("test", pa.table({"vector": vectors}))
-
-    # FIX: Pass arguments directly instead of using 'config=IvfPq(...)'
-    table.create_index(vector_column_name="vector", num_partitions=1, num_sub_vectors=1)
-
-    # Add data to ensure table has enough segments/rows
-    table.add(pa.table({"vector": vectors}))
-
-    q = [1.0] * 32
-
-    # 1. Normal Search -> Should include "LanceScan" (Brute Force / Scan)
-    plan = table.search(q).explain_plan(True)
-    assert "LanceScan" in plan
-
-    # 2. Fast Search -> Should NOT include "LanceScan" (Uses Index)
-    plan = table.search(q).fast_search().explain_plan(True)
-    assert "LanceScan" not in plan

python/python/tests/test_remote_db.py

@@ -8,7 +8,7 @@ import http.server
 import json
 import threading
 import time
-from unittest.mock import MagicMock, patch
+from unittest.mock import MagicMock
 import uuid
 from packaging.version import Version
 
@@ -168,42 +168,6 @@ def test_table_len_sync():
         assert len(table) == 1
 
 
-def test_create_table_exist_ok():
-    def handler(request):
-        if request.path == "/v1/table/test/create/?mode=exist_ok":
-            request.send_response(200)
-            request.send_header("Content-Type", "application/json")
-            request.end_headers()
-            request.wfile.write(b"{}")
-        else:
-            request.send_response(404)
-            request.end_headers()
-
-    with mock_lancedb_connection(handler) as db:
-        table = db.create_table("test", [{"id": 1}], exist_ok=True)
-        assert table is not None
-
-    with mock_lancedb_connection(handler) as db:
-        table = db.create_table("test", [{"id": 1}], mode="create", exist_ok=True)
-        assert table is not None
-
-
-def test_create_table_exist_ok_with_mode_overwrite():
-    def handler(request):
-        if request.path == "/v1/table/test/create/?mode=overwrite":
-            request.send_response(200)
-            request.send_header("Content-Type", "application/json")
-            request.end_headers()
-            request.wfile.write(b"{}")
-        else:
-            request.send_response(404)
-            request.end_headers()
-
-    with mock_lancedb_connection(handler) as db:
-        table = db.create_table("test", [{"id": 1}], mode="overwrite", exist_ok=True)
-        assert table is not None
-
-
 @pytest.mark.asyncio
 async def test_http_error():
     request_id_holder = {"request_id": None}
@@ -601,6 +565,7 @@ def test_head():
 def test_query_sync_minimal():
     def handler(body):
         assert body == {
+            "distance_type": "l2",
             "k": 10,
             "prefilter": True,
             "refine_factor": None,
@@ -684,6 +649,7 @@ def test_query_sync_maximal():
 def test_query_sync_nprobes():
     def handler(body):
         assert body == {
+            "distance_type": "l2",
             "k": 10,
             "prefilter": True,
             "fast_search": True,
@@ -713,6 +679,7 @@ def test_query_sync_nprobes():
 def test_query_sync_no_max_nprobes():
     def handler(body):
         assert body == {
+            "distance_type": "l2",
             "k": 10,
             "prefilter": True,
             "fast_search": True,
@@ -835,6 +802,7 @@ def test_query_sync_hybrid():
         else:
             # Vector query
             assert body == {
+                "distance_type": "l2",
                 "k": 42,
                 "prefilter": True,
                 "refine_factor": None,
@@ -1199,22 +1167,3 @@ async def test_header_provider_overrides_static_headers():
         extra_headers={"X-API-Key": "static-key", "X-Extra": "extra-value"},
     ) as db:
         await db.table_names()
-
-
-@pytest.mark.parametrize("exception", [KeyboardInterrupt, SystemExit, GeneratorExit])
-def test_background_loop_cancellation(exception):
-    """Test that BackgroundEventLoop.run() cancels the future on interrupt."""
-    from lancedb.background_loop import BackgroundEventLoop
-
-    mock_future = MagicMock()
-    mock_future.result.side_effect = exception()
-
-    with (
-        patch.object(BackgroundEventLoop, "__init__", return_value=None),
-        patch("asyncio.run_coroutine_threadsafe", return_value=mock_future),
-    ):
-        loop = BackgroundEventLoop()
-        loop.loop = MagicMock()
-        with pytest.raises(exception):
-            loop.run(None)
-        mock_future.cancel.assert_called_once()

python/python/tests/test_s3_bucket_dots.py

@@ -1,68 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-"""
-Tests for S3 bucket names containing dots.
-
-Related issue: https://github.com/lancedb/lancedb/issues/1898
-
-These tests validate the early error checking for S3 bucket names with dots.
-No actual S3 connection is made - validation happens before connection.
-"""
-
-import pytest
-import lancedb
-
-# Test URIs
-BUCKET_WITH_DOTS = "s3://my.bucket.name/path"
-BUCKET_WITH_DOTS_AND_REGION = ("s3://my.bucket.name", {"region": "us-east-1"})
-BUCKET_WITH_DOTS_AND_AWS_REGION = ("s3://my.bucket.name", {"aws_region": "us-east-1"})
-BUCKET_WITHOUT_DOTS = "s3://my-bucket/path"
-
-
-class TestS3BucketWithDotsSync:
-    """Tests for connect()."""
-
-    def test_bucket_with_dots_requires_region(self):
-        with pytest.raises(ValueError, match="contains dots"):
-            lancedb.connect(BUCKET_WITH_DOTS)
-
-    def test_bucket_with_dots_and_region_passes(self):
-        uri, opts = BUCKET_WITH_DOTS_AND_REGION
-        db = lancedb.connect(uri, storage_options=opts)
-        assert db is not None
-
-    def test_bucket_with_dots_and_aws_region_passes(self):
-        uri, opts = BUCKET_WITH_DOTS_AND_AWS_REGION
-        db = lancedb.connect(uri, storage_options=opts)
-        assert db is not None
-
-    def test_bucket_without_dots_passes(self):
-        db = lancedb.connect(BUCKET_WITHOUT_DOTS)
-        assert db is not None
-
-
-class TestS3BucketWithDotsAsync:
-    """Tests for connect_async()."""
-
-    @pytest.mark.asyncio
-    async def test_bucket_with_dots_requires_region(self):
-        with pytest.raises(ValueError, match="contains dots"):
-            await lancedb.connect_async(BUCKET_WITH_DOTS)
-
-    @pytest.mark.asyncio
-    async def test_bucket_with_dots_and_region_passes(self):
-        uri, opts = BUCKET_WITH_DOTS_AND_REGION
-        db = await lancedb.connect_async(uri, storage_options=opts)
-        assert db is not None
-
-    @pytest.mark.asyncio
-    async def test_bucket_with_dots_and_aws_region_passes(self):
-        uri, opts = BUCKET_WITH_DOTS_AND_AWS_REGION
-        db = await lancedb.connect_async(uri, storage_options=opts)
-        assert db is not None
-
-    @pytest.mark.asyncio
-    async def test_bucket_without_dots_passes(self):
-        db = await lancedb.connect_async(BUCKET_WITHOUT_DOTS)
-        assert db is not None

python/python/tests/test_table.py

@@ -1880,13 +1880,8 @@ async def test_optimize_delete_unverified(tmp_db_async: AsyncConnection, tmp_pat
         ],
     )
     version = await table.version()
-    assert version == 2
-
-    # By removing a manifest file, we make the data files we just inserted unverified
-    version_name = 18446744073709551615 - (version - 1)
-    path = tmp_path / "test.lance" / "_versions" / f"{version_name:020}.manifest"
+    path = tmp_path / "test.lance" / "_versions" / f"{version - 1}.manifest"
     os.remove(path)
-
     stats = await table.optimize(delete_unverified=False)
     assert stats.prune.old_versions_removed == 0
     stats = await table.optimize(
@@ -1972,9 +1967,3 @@ def test_add_table_with_empty_embeddings(tmp_path):
         on_bad_vectors="drop",
     )
     assert table.count_rows() == 1
-
-
-def test_table_uri(tmp_path):
-    db = lancedb.connect(tmp_path)
-    table = db.create_table("my_table", data=[{"x": 0}])
-    assert table.uri == str(tmp_path / "my_table.lance")

python/python/tests/test_util.py

@@ -528,19 +528,12 @@ def test_sanitize_data(
         else:
             expected_schema = schema
     else:
-        from conftest import pandas_string_type
-
-        # polars uses large_string, pandas 3.0+ uses large_string, others use string
-        if isinstance(data, pl.DataFrame):
-            text_type = pa.large_utf8()
-        elif isinstance(data, pd.DataFrame):
-            text_type = pandas_string_type()
-        else:
-            text_type = pa.string()
         expected_schema = pa.schema(
             {
                 "id": pa.int64(),
-                "text": text_type,
+                "text": pa.large_utf8()
+                if isinstance(data, pl.DataFrame)
+                else pa.string(),
                 "vector": pa.list_(pa.float32(), 10),
             }
         )

python/python/tests/test_voyageai_embeddings.py

@@ -1,108 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-"""Unit tests for VoyageAI embedding function.
-
-These tests verify model registration and configuration without requiring API calls.
-"""
-
-import pytest
-from unittest.mock import MagicMock, patch
-
-from lancedb.embeddings import get_registry
-
-
-@pytest.fixture(autouse=True)
-def reset_voyageai_client():
-    """Reset VoyageAI client before and after each test to avoid state pollution."""
-    from lancedb.embeddings.voyageai import VoyageAIEmbeddingFunction
-
-    VoyageAIEmbeddingFunction.client = None
-    yield
-    VoyageAIEmbeddingFunction.client = None
-
-
-class TestVoyageAIModelRegistration:
-    """Tests for VoyageAI model registration and configuration."""
-
-    @pytest.fixture
-    def mock_voyageai_client(self):
-        """Mock VoyageAI client to avoid API calls."""
-        with patch.dict("os.environ", {"VOYAGE_API_KEY": "test-key"}):
-            with patch("lancedb.embeddings.voyageai.attempt_import_or_raise") as mock:
-                mock_client = MagicMock()
-                mock_voyageai = MagicMock()
-                mock_voyageai.Client.return_value = mock_client
-                mock.return_value = mock_voyageai
-                yield mock_client
-
-    def test_voyageai_registered(self):
-        """Test that VoyageAI is registered in the embedding function registry."""
-        registry = get_registry()
-        assert registry.get("voyageai") is not None
-
-    @pytest.mark.parametrize(
-        "model_name,expected_dims",
-        [
-            # Voyage-4 series (all 1024 dims)
-            ("voyage-4", 1024),
-            ("voyage-4-lite", 1024),
-            ("voyage-4-large", 1024),
-            # Voyage-3 series
-            ("voyage-3", 1024),
-            ("voyage-3-lite", 512),
-            # Domain-specific models
-            ("voyage-finance-2", 1024),
-            ("voyage-multilingual-2", 1024),
-            ("voyage-law-2", 1024),
-            ("voyage-code-2", 1536),
-            # Multimodal
-            ("voyage-multimodal-3", 1024),
-        ],
-    )
-    def test_model_dimensions(self, model_name, expected_dims, mock_voyageai_client):
-        """Test that each model returns the correct dimensions."""
-        registry = get_registry()
-        func = registry.get("voyageai").create(name=model_name)
-        assert func.ndims() == expected_dims, (
-            f"Model {model_name} should have {expected_dims} dimensions"
-        )
-
-    def test_unsupported_model_raises_error(self, mock_voyageai_client):
-        """Test that unsupported models raise ValueError."""
-        registry = get_registry()
-        func = registry.get("voyageai").create(name="unsupported-model")
-        with pytest.raises(ValueError, match="not supported"):
-            func.ndims()
-
-    @pytest.mark.parametrize(
-        "model_name",
-        [
-            "voyage-4",
-            "voyage-4-lite",
-            "voyage-4-large",
-        ],
-    )
-    def test_voyage4_models_are_text_models(self, model_name, mock_voyageai_client):
-        """Test that voyage-4 models are classified as text models (not multimodal)."""
-        registry = get_registry()
-        func = registry.get("voyageai").create(name=model_name)
-        assert not func._is_multimodal_model(model_name), (
-            f"{model_name} should be a text model, not multimodal"
-        )
-
-    def test_voyage4_models_in_text_embedding_list(self, mock_voyageai_client):
-        """Test that voyage-4 models are in the text_embedding_models list."""
-        registry = get_registry()
-        func = registry.get("voyageai").create(name="voyage-4")
-        assert "voyage-4" in func.text_embedding_models
-        assert "voyage-4-lite" in func.text_embedding_models
-        assert "voyage-4-large" in func.text_embedding_models
-
-    def test_voyage4_models_not_in_multimodal_list(self, mock_voyageai_client):
-        """Test that voyage-4 models are NOT in the multimodal_embedding_models list."""
-        registry = get_registry()
-        func = registry.get("voyageai").create(name="voyage-4")
-        assert "voyage-4" not in func.multimodal_embedding_models
-        assert "voyage-4-lite" not in func.multimodal_embedding_models
-        assert "voyage-4-large" not in func.multimodal_embedding_models

python/src/arrow.rs

@@ -10,7 +10,8 @@ use arrow::{
 use futures::stream::StreamExt;
 use lancedb::arrow::SendableRecordBatchStream;
 use pyo3::{
-    exceptions::PyStopAsyncIteration, pyclass, pymethods, Bound, Py, PyAny, PyRef, PyResult, Python,
+    exceptions::PyStopAsyncIteration, pyclass, pymethods, Bound, PyAny, PyObject, PyRef, PyResult,
+    Python,
 };
 use pyo3_async_runtimes::tokio::future_into_py;
 
@@ -35,11 +36,8 @@ impl RecordBatchStream {
 #[pymethods]
 impl RecordBatchStream {
     #[getter]
-    pub fn schema(&self, py: Python) -> PyResult<Py<PyAny>> {
-        (*self.schema)
-            .clone()
-            .into_pyarrow(py)
-            .map(|obj| obj.unbind())
+    pub fn schema(&self, py: Python) -> PyResult<PyObject> {
+        (*self.schema).clone().into_pyarrow(py)
     }
 
     pub fn __aiter__(self_: PyRef<'_, Self>) -> PyRef<'_, Self> {
@@ -55,12 +53,7 @@ impl RecordBatchStream {
                 .next()
                 .await
                 .ok_or_else(|| PyStopAsyncIteration::new_err(""))?;
-            Python::attach(|py| {
-                inner_next
-                    .infer_error()?
-                    .to_pyarrow(py)
-                    .map(|obj| obj.unbind())
-            })
+            Python::with_gil(|py| inner_next.infer_error()?.to_pyarrow(py))
         })
     }
 }

python/src/connection.rs

@@ -12,7 +12,7 @@ use pyo3::{
     exceptions::{PyRuntimeError, PyValueError},
     pyclass, pyfunction, pymethods,
     types::{PyDict, PyDictMethods},
-    Bound, FromPyObject, Py, PyAny, PyRef, PyResult, Python,
+    Bound, FromPyObject, Py, PyAny, PyObject, PyRef, PyResult, Python,
 };
 use pyo3_async_runtimes::tokio::future_into_py;
 
@@ -114,7 +114,7 @@ impl Connection {
         data: Bound<'_, PyAny>,
         namespace: Vec<String>,
         storage_options: Option<HashMap<String, String>>,
-        storage_options_provider: Option<Py<PyAny>>,
+        storage_options_provider: Option<PyObject>,
         location: Option<String>,
     ) -> PyResult<Bound<'a, PyAny>> {
         let inner = self_.get_inner()?.clone();
@@ -152,7 +152,7 @@ impl Connection {
         schema: Bound<'_, PyAny>,
         namespace: Vec<String>,
         storage_options: Option<HashMap<String, String>>,
-        storage_options_provider: Option<Py<PyAny>>,
+        storage_options_provider: Option<PyObject>,
         location: Option<String>,
     ) -> PyResult<Bound<'a, PyAny>> {
         let inner = self_.get_inner()?.clone();
@@ -187,7 +187,7 @@ impl Connection {
         name: String,
         namespace: Vec<String>,
         storage_options: Option<HashMap<String, String>>,
-        storage_options_provider: Option<Py<PyAny>>,
+        storage_options_provider: Option<PyObject>,
         index_cache_size: Option<u32>,
         location: Option<String>,
     ) -> PyResult<Bound<'_, PyAny>> {
@@ -304,10 +304,9 @@ impl Connection {
                 },
                 page_token,
                 limit: limit.map(|l| l as i32),
-                ..Default::default()
             };
             let response = inner.list_namespaces(request).await.infer_error()?;
-            Python::attach(|py| -> PyResult<Py<PyDict>> {
+            Python::with_gil(|py| -> PyResult<Py<PyDict>> {
                 let dict = PyDict::new(py);
                 dict.set_item("namespaces", response.namespaces)?;
                 dict.set_item("page_token", response.page_token)?;
@@ -327,12 +326,11 @@ impl Connection {
         let py = self_.py();
         future_into_py(py, async move {
             use lance_namespace::models::CreateNamespaceRequest;
-            // Mode is now a string field
-            let mode_str = mode.and_then(|m| match m.to_lowercase().as_str() {
-                "create" => Some("Create".to_string()),
-                "exist_ok" => Some("ExistOk".to_string()),
-                "overwrite" => Some("Overwrite".to_string()),
-                _ => None,
+            let mode_value = mode.map(|m| match m.to_lowercase().as_str() {
+                "create" => "Create".to_string(),
+                "exist_ok" => "ExistOk".to_string(),
+                "overwrite" => "Overwrite".to_string(),
+                _ => m,
             });
             let request = CreateNamespaceRequest {
                 id: if namespace.is_empty() {
@@ -340,12 +338,11 @@ impl Connection {
                 } else {
                     Some(namespace)
                 },
-                mode: mode_str,
+                mode: mode_value,
                 properties,
-                ..Default::default()
             };
             let response = inner.create_namespace(request).await.infer_error()?;
-            Python::attach(|py| -> PyResult<Py<PyDict>> {
+            Python::with_gil(|py| -> PyResult<Py<PyDict>> {
                 let dict = PyDict::new(py);
                 dict.set_item("properties", response.properties)?;
                 Ok(dict.unbind())
@@ -364,16 +361,15 @@ impl Connection {
         let py = self_.py();
         future_into_py(py, async move {
             use lance_namespace::models::DropNamespaceRequest;
-            // Mode and Behavior are now string fields
-            let mode_str = mode.and_then(|m| match m.to_uppercase().as_str() {
-                "SKIP" => Some("Skip".to_string()),
-                "FAIL" => Some("Fail".to_string()),
-                _ => None,
+            let mode_value = mode.map(|m| match m.to_uppercase().as_str() {
+                "SKIP" => "Skip".to_string(),
+                "FAIL" => "Fail".to_string(),
+                _ => m,
             });
-            let behavior_str = behavior.and_then(|b| match b.to_uppercase().as_str() {
-                "RESTRICT" => Some("Restrict".to_string()),
-                "CASCADE" => Some("Cascade".to_string()),
-                _ => None,
+            let behavior_value = behavior.map(|b| match b.to_uppercase().as_str() {
+                "RESTRICT" => "Restrict".to_string(),
+                "CASCADE" => "Cascade".to_string(),
+                _ => b,
             });
             let request = DropNamespaceRequest {
                 id: if namespace.is_empty() {
@@ -381,12 +377,11 @@ impl Connection {
                 } else {
                     Some(namespace)
                 },
-                mode: mode_str,
-                behavior: behavior_str,
-                ..Default::default()
+                mode: mode_value,
+                behavior: behavior_value,
             };
             let response = inner.drop_namespace(request).await.infer_error()?;
-            Python::attach(|py| -> PyResult<Py<PyDict>> {
+            Python::with_gil(|py| -> PyResult<Py<PyDict>> {
                 let dict = PyDict::new(py);
                 dict.set_item("properties", response.properties)?;
                 dict.set_item("transaction_id", response.transaction_id)?;
@@ -410,10 +405,9 @@ impl Connection {
                 } else {
                     Some(namespace)
                 },
-                ..Default::default()
             };
             let response = inner.describe_namespace(request).await.infer_error()?;
-            Python::attach(|py| -> PyResult<Py<PyDict>> {
+            Python::with_gil(|py| -> PyResult<Py<PyDict>> {
                 let dict = PyDict::new(py);
                 dict.set_item("properties", response.properties)?;
                 Ok(dict.unbind())
@@ -440,10 +434,9 @@ impl Connection {
                 },
                 page_token,
                 limit: limit.map(|l| l as i32),
-                ..Default::default()
             };
             let response = inner.list_tables(request).await.infer_error()?;
-            Python::attach(|py| -> PyResult<Py<PyDict>> {
+            Python::with_gil(|py| -> PyResult<Py<PyDict>> {
                 let dict = PyDict::new(py);
                 dict.set_item("tables", response.tables)?;
                 dict.set_item("page_token", response.page_token)?;

python/src/error.rs

@@ -40,7 +40,7 @@ impl<T> PythonErrorExt<T> for std::result::Result<T, LanceError> {
                     request_id,
                     source,
                     status_code,
-                } => Python::attach(|py| {
+                } => Python::with_gil(|py| {
                     let message = err.to_string();
                     let http_err_cls = py
                         .import(intern!(py, "lancedb.remote.errors"))?
@@ -75,7 +75,7 @@ impl<T> PythonErrorExt<T> for std::result::Result<T, LanceError> {
                     max_read_failures,
                     source,
                     status_code,
-                } => Python::attach(|py| {
+                } => Python::with_gil(|py| {
                     let cause_err = http_from_rust_error(
                         py,
                         source.as_ref(),

python/src/header.rs

@@ -12,7 +12,7 @@ pub struct PyHeaderProvider {
 
 impl Clone for PyHeaderProvider {
     fn clone(&self) -> Self {
-        Python::attach(|py| Self {
+        Python::with_gil(|py| Self {
             provider: self.provider.clone_ref(py),
         })
     }
@@ -25,7 +25,7 @@ impl PyHeaderProvider {
 
     /// Get headers from the Python provider (internal implementation)
     fn get_headers_internal(&self) -> Result<HashMap<String, String>, String> {
-        Python::attach(|py| {
+        Python::with_gil(|py| {
             // Call the get_headers method
             let result = self.provider.call_method0(py, "get_headers");
 

python/src/permutation.rs

@@ -281,7 +281,7 @@ impl PyPermutationReader {
         let reader = slf.reader.clone();
         future_into_py(slf.py(), async move {
             let schema = reader.output_schema(selection).await.infer_error()?;
-            Python::attach(|py| schema.to_pyarrow(py).map(|obj| obj.unbind()))
+            Python::with_gil(|py| schema.to_pyarrow(py))
         })
     }
 

python/src/query.rs

@@ -453,7 +453,7 @@ impl Query {
         let inner = self_.inner.clone();
         future_into_py(self_.py(), async move {
             let schema = inner.output_schema().await.infer_error()?;
-            Python::attach(|py| schema.to_pyarrow(py).map(|obj| obj.unbind()))
+            Python::with_gil(|py| schema.to_pyarrow(py))
         })
     }
 
@@ -532,7 +532,7 @@ impl TakeQuery {
         let inner = self_.inner.clone();
         future_into_py(self_.py(), async move {
             let schema = inner.output_schema().await.infer_error()?;
-            Python::attach(|py| schema.to_pyarrow(py).map(|obj| obj.unbind()))
+            Python::with_gil(|py| schema.to_pyarrow(py))
         })
     }
 
@@ -627,7 +627,7 @@ impl FTSQuery {
         let inner = self_.inner.clone();
         future_into_py(self_.py(), async move {
             let schema = inner.output_schema().await.infer_error()?;
-            Python::attach(|py| schema.to_pyarrow(py).map(|obj| obj.unbind()))
+            Python::with_gil(|py| schema.to_pyarrow(py))
         })
     }
 
@@ -806,7 +806,7 @@ impl VectorQuery {
         let inner = self_.inner.clone();
         future_into_py(self_.py(), async move {
             let schema = inner.output_schema().await.infer_error()?;
-            Python::attach(|py| schema.to_pyarrow(py).map(|obj| obj.unbind()))
+            Python::with_gil(|py| schema.to_pyarrow(py))
         })
     }
 

python/src/storage_options.rs

@@ -17,20 +17,20 @@ use pyo3::types::PyDict;
 /// Internal wrapper around a Python object implementing StorageOptionsProvider
 pub struct PyStorageOptionsProvider {
     /// The Python object implementing fetch_storage_options()
-    inner: Py<PyAny>,
+    inner: PyObject,
 }
 
 impl Clone for PyStorageOptionsProvider {
     fn clone(&self) -> Self {
-        Python::attach(|py| Self {
+        Python::with_gil(|py| Self {
             inner: self.inner.clone_ref(py),
         })
     }
 }
 
 impl PyStorageOptionsProvider {
-    pub fn new(obj: Py<PyAny>) -> PyResult<Self> {
-        Python::attach(|py| {
+    pub fn new(obj: PyObject) -> PyResult<Self> {
+        Python::with_gil(|py| {
             // Verify the object has a fetch_storage_options method
             if !obj.bind(py).hasattr("fetch_storage_options")? {
                 return Err(pyo3::exceptions::PyTypeError::new_err(
@@ -60,7 +60,7 @@ impl StorageOptionsProvider for PyStorageOptionsProviderWrapper {
         let py_provider = self.py_provider.clone();
 
         tokio::task::spawn_blocking(move || {
-            Python::attach(|py| {
+            Python::with_gil(|py| {
                 // Call the Python fetch_storage_options method
                 let result = py_provider
                     .inner
@@ -119,7 +119,7 @@ impl StorageOptionsProvider for PyStorageOptionsProviderWrapper {
     }
 
     fn provider_id(&self) -> String {
-        Python::attach(|py| {
+        Python::with_gil(|py| {
             // Call provider_id() method on the Python object
             let obj = self.py_provider.inner.bind(py);
             obj.call_method0("provider_id")
@@ -143,7 +143,7 @@ impl std::fmt::Debug for PyStorageOptionsProviderWrapper {
 /// This is the main entry point for converting Python StorageOptionsProvider objects
 /// to Rust trait objects that can be used by the Lance ecosystem.
 pub fn py_object_to_storage_options_provider(
-    py_obj: Py<PyAny>,
+    py_obj: PyObject,
 ) -> PyResult<Arc<dyn StorageOptionsProvider>> {
     let py_provider = PyStorageOptionsProvider::new(py_obj)?;
     Ok(Arc::new(PyStorageOptionsProviderWrapper::new(py_provider)))

python/src/table.rs

@@ -287,7 +287,7 @@ impl Table {
         let inner = self_.inner_ref()?.clone();
         future_into_py(self_.py(), async move {
             let schema = inner.schema().await.infer_error()?;
-            Python::attach(|py| schema.to_pyarrow(py).map(|obj| obj.unbind()))
+            Python::with_gil(|py| schema.to_pyarrow(py))
         })
     }
 
@@ -437,7 +437,7 @@ impl Table {
         future_into_py(self_.py(), async move {
             let stats = inner.index_stats(&index_name).await.infer_error()?;
             if let Some(stats) = stats {
-                Python::attach(|py| {
+                Python::with_gil(|py| {
                     let dict = PyDict::new(py);
                     dict.set_item("num_indexed_rows", stats.num_indexed_rows)?;
                     dict.set_item("num_unindexed_rows", stats.num_unindexed_rows)?;
@@ -467,7 +467,7 @@ impl Table {
         let inner = self_.inner_ref()?.clone();
         future_into_py(self_.py(), async move {
             let stats = inner.stats().await.infer_error()?;
-            Python::attach(|py| {
+            Python::with_gil(|py| {
                 let dict = PyDict::new(py);
                 dict.set_item("total_bytes", stats.total_bytes)?;
                 dict.set_item("num_rows", stats.num_rows)?;
@@ -497,25 +497,6 @@ impl Table {
         })
     }
 
-    pub fn uri(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.inner_ref()?.clone();
-        future_into_py(self_.py(), async move { inner.uri().await.infer_error() })
-    }
-
-    pub fn initial_storage_options(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.inner_ref()?.clone();
-        future_into_py(self_.py(), async move {
-            Ok(inner.initial_storage_options().await)
-        })
-    }
-
-    pub fn latest_storage_options(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
-        let inner = self_.inner_ref()?.clone();
-        future_into_py(self_.py(), async move {
-            inner.latest_storage_options().await.infer_error()
-        })
-    }
-
     pub fn __repr__(&self) -> String {
         match &self.inner {
             None => format!("ClosedTable({})", self.name),
@@ -535,7 +516,7 @@ impl Table {
         let inner = self_.inner_ref()?.clone();
         future_into_py(self_.py(), async move {
             let versions = inner.list_versions().await.infer_error()?;
-            let versions_as_dict = Python::attach(|py| {
+            let versions_as_dict = Python::with_gil(|py| {
                 versions
                     .iter()
                     .map(|v| {
@@ -886,7 +867,7 @@ impl Tags {
             let tags = inner.tags().await.infer_error()?;
             let res = tags.list().await.infer_error()?;
 
-            Python::attach(|py| {
+            Python::with_gil(|py| {
                 let py_dict = PyDict::new(py);
                 for (key, contents) in res {
                     let value_dict = PyDict::new(py);

rust/lancedb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.25.0-beta.0"
+version = "0.23.1-beta.1"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true
@@ -25,7 +25,6 @@ datafusion-catalog.workspace = true
 datafusion-common.workspace = true
 datafusion-execution.workspace = true
 datafusion-expr.workspace = true
-datafusion-physical-expr.workspace = true
 datafusion-physical-plan.workspace = true
 datafusion.workspace = true
 object_store = { workspace = true }
@@ -105,16 +104,11 @@ test-log = "0.2"
 
 
 [features]
-default = []
+default = ["aws", "gcs", "azure", "dynamodb", "oss"]
 aws = ["lance/aws", "lance-io/aws", "lance-namespace-impls/dir-aws"]
 oss = ["lance/oss", "lance-io/oss", "lance-namespace-impls/dir-oss"]
 gcs = ["lance/gcp", "lance-io/gcp", "lance-namespace-impls/dir-gcp"]
 azure = ["lance/azure", "lance-io/azure", "lance-namespace-impls/dir-azure"]
-huggingface = [
-    "lance/huggingface",
-    "lance-io/huggingface",
-    "lance-namespace-impls/dir-huggingface",
-]
 dynamodb = ["lance/dynamodb", "aws"]
 remote = ["dep:reqwest", "dep:http", "lance-namespace-impls/rest", "lance-namespace-impls/rest-adapter"]
 fp16kernels = ["lance-linalg/fp16kernels"]
@@ -154,6 +148,3 @@ name = "ivf_pq"
 [[example]]
 name = "hybrid_search"
 required-features = ["sentence-transformers"]
-
-[package.metadata.docs.rs]
-all-features = true

rust/lancedb/src/connection.rs

@@ -36,42 +36,10 @@ use crate::remote::{
 };
 use crate::table::{TableDefinition, WriteOptions};
 use crate::Table;
-use lance::io::ObjectStoreParams;
 pub use lance_encoding::version::LanceFileVersion;
 #[cfg(feature = "remote")]
 use lance_io::object_store::StorageOptions;
-use lance_io::object_store::{StorageOptionsAccessor, StorageOptionsProvider};
-
-fn merge_storage_options(
-    store_params: &mut ObjectStoreParams,
-    pairs: impl IntoIterator<Item = (String, String)>,
-) {
-    let mut options = store_params.storage_options().cloned().unwrap_or_default();
-    for (key, value) in pairs {
-        options.insert(key, value);
-    }
-    let provider = store_params
-        .storage_options_accessor
-        .as_ref()
-        .and_then(|accessor| accessor.provider().cloned());
-    let accessor = if let Some(provider) = provider {
-        StorageOptionsAccessor::with_initial_and_provider(options, provider)
-    } else {
-        StorageOptionsAccessor::with_static_options(options)
-    };
-    store_params.storage_options_accessor = Some(Arc::new(accessor));
-}
-
-fn set_storage_options_provider(
-    store_params: &mut ObjectStoreParams,
-    provider: Arc<dyn StorageOptionsProvider>,
-) {
-    let accessor = match store_params.storage_options().cloned() {
-        Some(options) => StorageOptionsAccessor::with_initial_and_provider(options, provider),
-        None => StorageOptionsAccessor::with_provider(provider),
-    };
-    store_params.storage_options_accessor = Some(Arc::new(accessor));
-}
+use lance_io::object_store::StorageOptionsProvider;
 
 /// A builder for configuring a [`Connection::table_names`] operation
 pub struct TableNamesBuilder {
@@ -251,36 +219,8 @@ impl CreateTableBuilder<false> {
     /// Execute the create table operation
     pub async fn execute(self) -> Result<Table> {
         let parent = self.parent.clone();
-        let embedding_registry = self.embedding_registry.clone();
-        let request = self.into_request()?;
-        Ok(Table::new_with_embedding_registry(
-            parent.create_table(request).await?,
-            parent,
-            embedding_registry,
-        ))
-    }
-
-    fn into_request(self) -> Result<CreateTableRequest> {
-        if self.embeddings.is_empty() {
-            return Ok(self.request);
-        }
-
-        let CreateTableData::Empty(table_def) = self.request.data else {
-            unreachable!("CreateTableBuilder<false> should always have Empty data")
-        };
-
-        let schema = table_def.schema.clone();
-        let empty_batch = arrow_array::RecordBatch::new_empty(schema.clone());
-
-        let reader = Box::new(std::iter::once(Ok(empty_batch)).collect::<Vec<_>>());
-        let reader = arrow_array::RecordBatchIterator::new(reader.into_iter(), schema);
-        let with_embeddings = WithEmbeddings::new(reader, self.embeddings);
-        let table_definition = with_embeddings.table_definition()?;
-
-        Ok(CreateTableRequest {
-            data: CreateTableData::Empty(table_definition),
-            ..self.request
-        })
+        let table = parent.create_table(self.request).await?;
+        Ok(Table::new(table, parent))
     }
 }
 
@@ -306,14 +246,16 @@ impl<const HAS_DATA: bool> CreateTableBuilder<HAS_DATA> {
     ///
     /// See available options at <https://lancedb.com/docs/storage/>
     pub fn storage_option(mut self, key: impl Into<String>, value: impl Into<String>) -> Self {
-        let store_params = self
+        let store_options = self
             .request
             .write_options
             .lance_write_params
             .get_or_insert(Default::default())
             .store_params
+            .get_or_insert(Default::default())
+            .storage_options
             .get_or_insert(Default::default());
-        merge_storage_options(store_params, [(key.into(), value.into())]);
+        store_options.insert(key.into(), value.into());
         self
     }
 
@@ -327,17 +269,19 @@ impl<const HAS_DATA: bool> CreateTableBuilder<HAS_DATA> {
         mut self,
         pairs: impl IntoIterator<Item = (impl Into<String>, impl Into<String>)>,
     ) -> Self {
-        let store_params = self
+        let store_options = self
             .request
             .write_options
             .lance_write_params
             .get_or_insert(Default::default())
             .store_params
+            .get_or_insert(Default::default())
+            .storage_options
             .get_or_insert(Default::default());
-        let updates = pairs
-            .into_iter()
-            .map(|(key, value)| (key.into(), value.into()));
-        merge_storage_options(store_params, updates);
+
+        for (key, value) in pairs {
+            store_options.insert(key.into(), value.into());
+        }
         self
     }
 
@@ -374,21 +318,23 @@ impl<const HAS_DATA: bool> CreateTableBuilder<HAS_DATA> {
     /// This has no effect in LanceDB Cloud.
     #[deprecated(since = "0.15.1", note = "Use `database_options` instead")]
     pub fn enable_v2_manifest_paths(mut self, use_v2_manifest_paths: bool) -> Self {
-        let store_params = self
+        let storage_options = self
             .request
             .write_options
             .lance_write_params
             .get_or_insert_with(Default::default)
             .store_params
+            .get_or_insert_with(Default::default)
+            .storage_options
             .get_or_insert_with(Default::default);
-        let value = if use_v2_manifest_paths {
-            "true".to_string()
-        } else {
-            "false".to_string()
-        };
-        merge_storage_options(
-            store_params,
-            [(OPT_NEW_TABLE_V2_MANIFEST_PATHS.to_string(), value)],
+
+        storage_options.insert(
+            OPT_NEW_TABLE_V2_MANIFEST_PATHS.to_string(),
+            if use_v2_manifest_paths {
+                "true".to_string()
+            } else {
+                "false".to_string()
+            },
         );
         self
     }
@@ -398,19 +344,19 @@ impl<const HAS_DATA: bool> CreateTableBuilder<HAS_DATA> {
     /// The default is `LanceFileVersion::Stable`.
     #[deprecated(since = "0.15.1", note = "Use `database_options` instead")]
     pub fn data_storage_version(mut self, data_storage_version: LanceFileVersion) -> Self {
-        let store_params = self
+        let storage_options = self
             .request
             .write_options
             .lance_write_params
             .get_or_insert_with(Default::default)
             .store_params
+            .get_or_insert_with(Default::default)
+            .storage_options
             .get_or_insert_with(Default::default);
-        merge_storage_options(
-            store_params,
-            [(
-                OPT_NEW_TABLE_STORAGE_VERSION.to_string(),
-                data_storage_version.to_string(),
-            )],
+
+        storage_options.insert(
+            OPT_NEW_TABLE_STORAGE_VERSION.to_string(),
+            data_storage_version.to_string(),
         );
         self
     }
@@ -435,14 +381,13 @@ impl<const HAS_DATA: bool> CreateTableBuilder<HAS_DATA> {
     /// This allows tables to automatically refresh cloud storage credentials
     /// when they expire, enabling long-running operations on remote storage.
     pub fn storage_options_provider(mut self, provider: Arc<dyn StorageOptionsProvider>) -> Self {
-        let store_params = self
-            .request
+        self.request
             .write_options
             .lance_write_params
             .get_or_insert(Default::default())
             .store_params
-            .get_or_insert(Default::default());
-        set_storage_options_provider(store_params, provider);
+            .get_or_insert(Default::default())
+            .storage_options_provider = Some(provider);
         self
     }
 }
@@ -505,13 +450,15 @@ impl OpenTableBuilder {
     ///
     /// See available options at <https://lancedb.com/docs/storage/>
     pub fn storage_option(mut self, key: impl Into<String>, value: impl Into<String>) -> Self {
-        let store_params = self
+        let storage_options = self
             .request
             .lance_read_params
             .get_or_insert(Default::default())
             .store_options
+            .get_or_insert(Default::default())
+            .storage_options
             .get_or_insert(Default::default());
-        merge_storage_options(store_params, [(key.into(), value.into())]);
+        storage_options.insert(key.into(), value.into());
         self
     }
 
@@ -525,16 +472,18 @@ impl OpenTableBuilder {
         mut self,
         pairs: impl IntoIterator<Item = (impl Into<String>, impl Into<String>)>,
     ) -> Self {
-        let store_params = self
+        let storage_options = self
             .request
             .lance_read_params
             .get_or_insert(Default::default())
             .store_options
+            .get_or_insert(Default::default())
+            .storage_options
             .get_or_insert(Default::default());
-        let updates = pairs
-            .into_iter()
-            .map(|(key, value)| (key.into(), value.into()));
-        merge_storage_options(store_params, updates);
+
+        for (key, value) in pairs {
+            storage_options.insert(key.into(), value.into());
+        }
         self
     }
 
@@ -558,13 +507,12 @@ impl OpenTableBuilder {
     /// This allows tables to automatically refresh cloud storage credentials
     /// when they expire, enabling long-running operations on remote storage.
     pub fn storage_options_provider(mut self, provider: Arc<dyn StorageOptionsProvider>) -> Self {
-        let store_params = self
-            .request
+        self.request
             .lance_read_params
             .get_or_insert(Default::default())
             .store_options
-            .get_or_insert(Default::default());
-        set_storage_options_provider(store_params, provider);
+            .get_or_insert(Default::default())
+            .storage_options_provider = Some(provider);
         self
     }
 
@@ -920,10 +868,6 @@ pub struct ConnectBuilder {
     embedding_registry: Option<Arc<dyn EmbeddingRegistry>>,
 }
 
-#[cfg(feature = "remote")]
-const ENV_VARS_TO_STORAGE_OPTS: [(&str, &str); 1] =
-    [("AZURE_STORAGE_ACCOUNT_NAME", "azure_storage_account_name")];
-
 impl ConnectBuilder {
     /// Create a new [`ConnectOptions`] with the given database URI.
     pub fn new(uri: &str) -> Self {
@@ -1107,27 +1051,11 @@ impl ConnectBuilder {
         self
     }
 
-    #[cfg(feature = "remote")]
-    fn apply_env_defaults(
-        env_var_to_remote_storage_option: &[(&str, &str)],
-        options: &mut HashMap<String, String>,
-    ) {
-        for (env_key, opt_key) in env_var_to_remote_storage_option {
-            if let Ok(env_value) = std::env::var(env_key) {
-                if !options.contains_key(*opt_key) {
-                    options.insert((*opt_key).to_string(), env_value);
-                }
-            }
-        }
-    }
-
     #[cfg(feature = "remote")]
     fn execute_remote(self) -> Result<Connection> {
         use crate::remote::db::RemoteDatabaseOptions;
 
-        let mut merged_options = self.request.options.clone();
-        Self::apply_env_defaults(&ENV_VARS_TO_STORAGE_OPTS, &mut merged_options);
-        let options = RemoteDatabaseOptions::parse_from_map(&merged_options)?;
+        let options = RemoteDatabaseOptions::parse_from_map(&self.request.options)?;
 
         let region = options.region.ok_or_else(|| Error::InvalidInput {
             message: "A region is required when connecting to LanceDb Cloud".to_string(),
@@ -1349,6 +1277,8 @@ mod test_utils {
 
 #[cfg(test)]
 mod tests {
+    use std::fs::create_dir_all;
+
     use crate::database::listing::{ListingDatabaseOptions, NewTableConfig};
     use crate::query::QueryBase;
     use crate::query::{ExecutableQuery, QueryExecutionOptions};
@@ -1372,23 +1302,6 @@ mod tests {
         assert_eq!(tc.connection.uri(), tc.uri);
     }
 
-    #[cfg(feature = "remote")]
-    #[test]
-    fn test_apply_env_defaults() {
-        let env_key = "TEST_APPLY_ENV_DEFAULTS_ENVIRONMENT_VARIABLE_ENV_KEY";
-        let env_val = "TEST_APPLY_ENV_DEFAULTS_ENVIRONMENT_VARIABLE_ENV_VAL";
-        let opts_key = "test_apply_env_defaults_environment_variable_opts_key";
-        std::env::set_var(env_key, env_val);
-
-        let mut options = HashMap::new();
-        ConnectBuilder::apply_env_defaults(&[(env_key, opts_key)], &mut options);
-        assert_eq!(Some(&env_val.to_string()), options.get(opts_key));
-
-        options.insert(opts_key.to_string(), "EXPLICIT-VALUE".to_string());
-        ConnectBuilder::apply_env_defaults(&[(env_key, opts_key)], &mut options);
-        assert_eq!(Some(&"EXPLICIT-VALUE".to_string()), options.get(opts_key));
-    }
-
     #[cfg(not(windows))]
     #[tokio::test]
     async fn test_connect_relative() {
@@ -1412,27 +1325,25 @@ mod tests {
 
     #[tokio::test]
     async fn test_table_names() {
-        let tc = new_test_connection().await.unwrap();
-        let db = tc.connection;
-        let schema = Arc::new(Schema::new(vec![Field::new("x", DataType::Int32, false)]));
+        let tmp_dir = tempdir().unwrap();
         let mut names = Vec::with_capacity(100);
         for _ in 0..100 {
-            let name = uuid::Uuid::new_v4().to_string();
+            let mut name = uuid::Uuid::new_v4().to_string();
             names.push(name.clone());
-            db.create_empty_table(name, schema.clone())
-                .execute()
-                .await
-                .unwrap();
+            name.push_str(".lance");
+            create_dir_all(tmp_dir.path().join(&name)).unwrap();
         }
         names.sort();
-        let tables = db.table_names().limit(100).execute().await.unwrap();
+
+        let uri = tmp_dir.path().to_str().unwrap();
+        let db = connect(uri).execute().await.unwrap();
+        let tables = db.table_names().execute().await.unwrap();
 
         assert_eq!(tables, names);
 
         let tables = db
             .table_names()
             .start_after(&names[30])
-            .limit(100)
             .execute()
             .await
             .unwrap();
@@ -1613,27 +1524,18 @@ mod tests {
 
     #[tokio::test]
     async fn drop_table() {
-        let tc = new_test_connection().await.unwrap();
-        let db = tc.connection;
+        let tmp_dir = tempdir().unwrap();
 
-        if tc.is_remote {
-            // All the typical endpoints such as s3:///, file-object-store:///, etc. treat drop_table
-            // as idempotent.
-            assert!(db.drop_table("invalid_table", &[]).await.is_ok());
-        } else {
-            // The behavior of drop_table when using a file:/// endpoint differs from all other
-            // object providers, in that it returns an error when deleting a non-existent table.
-            assert!(matches!(
-                db.drop_table("invalid_table", &[]).await,
-                Err(crate::Error::TableNotFound { .. }),
-            ));
-        }
+        let uri = tmp_dir.path().to_str().unwrap();
+        let db = connect(uri).execute().await.unwrap();
 
-        let schema = Arc::new(Schema::new(vec![Field::new("x", DataType::Int32, false)]));
-        db.create_empty_table("table1", schema.clone())
-            .execute()
-            .await
-            .unwrap();
+        // drop non-exist table
+        assert!(matches!(
+            db.drop_table("invalid_table", &[]).await,
+            Err(crate::Error::TableNotFound { .. }),
+        ));
+
+        create_dir_all(tmp_dir.path().join("table1.lance")).unwrap();
         db.drop_table("table1", &[]).await.unwrap();
 
         let tables = db.table_names().execute().await.unwrap();
@@ -1720,128 +1622,4 @@ mod tests {
         let cloned_count = cloned_table.count_rows(None).await.unwrap();
         assert_eq!(source_count, cloned_count);
     }
-
-    #[tokio::test]
-    async fn test_create_empty_table_with_embeddings() {
-        use crate::embeddings::{EmbeddingDefinition, EmbeddingFunction};
-        use arrow_array::{
-            Array, FixedSizeListArray, Float32Array, RecordBatch, RecordBatchIterator, StringArray,
-        };
-        use std::borrow::Cow;
-
-        #[derive(Debug, Clone)]
-        struct MockEmbedding {
-            dim: usize,
-        }
-
-        impl EmbeddingFunction for MockEmbedding {
-            fn name(&self) -> &str {
-                "test_embedding"
-            }
-
-            fn source_type(&self) -> Result<Cow<'_, DataType>> {
-                Ok(Cow::Owned(DataType::Utf8))
-            }
-
-            fn dest_type(&self) -> Result<Cow<'_, DataType>> {
-                Ok(Cow::Owned(DataType::new_fixed_size_list(
-                    DataType::Float32,
-                    self.dim as i32,
-                    true,
-                )))
-            }
-
-            fn compute_source_embeddings(&self, source: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
-                let len = source.len();
-                let values = vec![1.0f32; len * self.dim];
-                let values = Arc::new(Float32Array::from(values));
-                let field = Arc::new(Field::new("item", DataType::Float32, true));
-                Ok(Arc::new(FixedSizeListArray::new(
-                    field,
-                    self.dim as i32,
-                    values,
-                    None,
-                )))
-            }
-
-            fn compute_query_embeddings(&self, _input: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
-                unimplemented!()
-            }
-        }
-
-        let tmp_dir = tempdir().unwrap();
-        let uri = tmp_dir.path().to_str().unwrap();
-        let db = connect(uri).execute().await.unwrap();
-
-        let embed_func = Arc::new(MockEmbedding { dim: 128 });
-        db.embedding_registry()
-            .register("test_embedding", embed_func.clone())
-            .unwrap();
-
-        let schema = Arc::new(Schema::new(vec![Field::new("name", DataType::Utf8, true)]));
-        let ed = EmbeddingDefinition {
-            source_column: "name".to_owned(),
-            dest_column: Some("name_embedding".to_owned()),
-            embedding_name: "test_embedding".to_owned(),
-        };
-
-        let table = db
-            .create_empty_table("test", schema)
-            .mode(CreateTableMode::Overwrite)
-            .add_embedding(ed)
-            .unwrap()
-            .execute()
-            .await
-            .unwrap();
-
-        let table_schema = table.schema().await.unwrap();
-        assert!(table_schema.column_with_name("name").is_some());
-        assert!(table_schema.column_with_name("name_embedding").is_some());
-
-        let embedding_field = table_schema.field_with_name("name_embedding").unwrap();
-        assert_eq!(
-            embedding_field.data_type(),
-            &DataType::new_fixed_size_list(DataType::Float32, 128, true)
-        );
-
-        let input_schema = Arc::new(Schema::new(vec![Field::new("name", DataType::Utf8, true)]));
-        let input_batch = RecordBatch::try_new(
-            input_schema.clone(),
-            vec![Arc::new(StringArray::from(vec![
-                Some("Alice"),
-                Some("Bob"),
-                Some("Charlie"),
-            ]))],
-        )
-        .unwrap();
-
-        let input_reader = Box::new(RecordBatchIterator::new(
-            vec![Ok(input_batch)].into_iter(),
-            input_schema,
-        ));
-
-        table.add(input_reader).execute().await.unwrap();
-
-        let results = table
-            .query()
-            .execute()
-            .await
-            .unwrap()
-            .try_collect::<Vec<_>>()
-            .await
-            .unwrap();
-
-        assert_eq!(results.len(), 1);
-        let batch = &results[0];
-        assert_eq!(batch.num_rows(), 3);
-        assert!(batch.column_by_name("name_embedding").is_some());
-
-        let embedding_col = batch
-            .column_by_name("name_embedding")
-            .unwrap()
-            .as_any()
-            .downcast_ref::<FixedSizeListArray>()
-            .unwrap();
-        assert_eq!(embedding_col.len(), 3);
-    }
 }

rust/lancedb/src/database/listing.rs

@@ -12,7 +12,7 @@ use lance::dataset::{builder::DatasetBuilder, ReadParams, WriteMode};
 use lance::io::{ObjectStore, ObjectStoreParams, WrappingObjectStore};
 use lance_datafusion::utils::StreamingWriteSource;
 use lance_encoding::version::LanceFileVersion;
-use lance_io::object_store::{StorageOptionsAccessor, StorageOptionsProvider};
+use lance_io::object_store::StorageOptionsProvider;
 use lance_table::io::commit::commit_handler_from_url;
 use object_store::local::LocalFileSystem;
 use snafu::ResultExt;
@@ -356,13 +356,7 @@ impl ListingDatabase {
                     .clone()
                     .unwrap_or_else(|| Arc::new(lance::session::Session::default()));
                 let os_params = ObjectStoreParams {
-                    storage_options_accessor: if options.storage_options.is_empty() {
-                        None
-                    } else {
-                        Some(Arc::new(StorageOptionsAccessor::with_static_options(
-                            options.storage_options.clone(),
-                        )))
-                    },
+                    storage_options: Some(options.storage_options.clone()),
                     ..Default::default()
                 };
                 let (object_store, base_path) = ObjectStore::from_uri_and_params(
@@ -469,20 +463,9 @@ impl ListingDatabase {
         validate_table_name(name)?;
 
         let mut uri = self.uri.clone();
-        // If the URI does not end with a path separator, add one
-        // Use forward slash for URIs (http://, s3://, gs://, file://, etc.)
-        // Use platform-specific separator for local paths without scheme
-        let has_scheme = uri.contains("://");
-        let ends_with_separator = uri.ends_with('/') || uri.ends_with('\\');
-
-        if !ends_with_separator {
-            if has_scheme {
-                // URIs always use forward slash
-                uri.push('/');
-            } else {
-                // Local path without scheme - use platform separator
-                uri.push(std::path::MAIN_SEPARATOR);
-            }
+        // If the URI does not end with a slash, add one
+        if !uri.ends_with('/') {
+            uri.push('/');
         }
         // Append the table name with the lance file extension
         uri.push_str(&format!("{}.{}", name, LANCE_FILE_EXTENSION));
@@ -498,13 +481,7 @@ impl ListingDatabase {
 
     async fn drop_tables(&self, names: Vec<String>) -> Result<()> {
         let object_store_params = ObjectStoreParams {
-            storage_options_accessor: if self.storage_options.is_empty() {
-                None
-            } else {
-                Some(Arc::new(StorageOptionsAccessor::with_static_options(
-                    self.storage_options.clone(),
-                )))
-            },
+            storage_options: Some(self.storage_options.clone()),
             ..Default::default()
         };
         let mut uri = self.uri.clone();
@@ -553,7 +530,7 @@ impl ListingDatabase {
             .lance_write_params
             .as_ref()
             .and_then(|p| p.store_params.as_ref())
-            .and_then(|sp| sp.storage_options());
+            .and_then(|sp| sp.storage_options.as_ref());
 
         let storage_version_override = storage_options
             .and_then(|opts| opts.get(OPT_NEW_TABLE_STORAGE_VERSION))
@@ -604,20 +581,21 @@ impl ListingDatabase {
         // will cause a new connection to be created, and that connection will
         // be dropped from the cache when python GCs the table object, which
         // confounds reuse across tables.
-        if !self.storage_options.is_empty() || self.storage_options_provider.is_some() {
-            let store_params = write_params
+        if !self.storage_options.is_empty() {
+            let storage_options = write_params
                 .store_params
+                .get_or_insert_with(Default::default)
+                .storage_options
                 .get_or_insert_with(Default::default);
-            let mut storage_options = store_params.storage_options().cloned().unwrap_or_default();
-            if !self.storage_options.is_empty() {
-                self.inherit_storage_options(&mut storage_options);
-            }
-            let accessor = if let Some(ref provider) = self.storage_options_provider {
-                StorageOptionsAccessor::with_initial_and_provider(storage_options, provider.clone())
-            } else {
-                StorageOptionsAccessor::with_static_options(storage_options)
-            };
-            store_params.storage_options_accessor = Some(Arc::new(accessor));
+            self.inherit_storage_options(storage_options);
+        }
+
+        // Set storage options provider if available
+        if self.storage_options_provider.is_some() {
+            write_params
+                .store_params
+                .get_or_insert_with(Default::default)
+                .storage_options_provider = self.storage_options_provider.clone();
         }
 
         write_params.data_storage_version = self
@@ -903,13 +881,7 @@ impl Database for ListingDatabase {
         validate_table_name(&request.target_table_name)?;
 
         let storage_params = ObjectStoreParams {
-            storage_options_accessor: if self.storage_options.is_empty() {
-                None
-            } else {
-                Some(Arc::new(StorageOptionsAccessor::with_static_options(
-                    self.storage_options.clone(),
-                )))
-            },
+            storage_options: Some(self.storage_options.clone()),
             ..Default::default()
         };
         let read_params = ReadParams {
@@ -973,28 +945,25 @@ impl Database for ListingDatabase {
         // will cause a new connection to be created, and that connection will
         // be dropped from the cache when python GCs the table object, which
         // confounds reuse across tables.
-        if !self.storage_options.is_empty() || self.storage_options_provider.is_some() {
-            let store_params = request
+        if !self.storage_options.is_empty() {
+            let storage_options = request
                 .lance_read_params
                 .get_or_insert_with(Default::default)
                 .store_options
+                .get_or_insert_with(Default::default)
+                .storage_options
                 .get_or_insert_with(Default::default);
-            let mut storage_options = store_params.storage_options().cloned().unwrap_or_default();
-            if !self.storage_options.is_empty() {
-                self.inherit_storage_options(&mut storage_options);
-            }
-            // Preserve request-level provider if no connection-level provider exists
-            let request_provider = store_params
-                .storage_options_accessor
-                .as_ref()
-                .and_then(|a| a.provider().cloned());
-            let provider = self.storage_options_provider.clone().or(request_provider);
-            let accessor = if let Some(provider) = provider {
-                StorageOptionsAccessor::with_initial_and_provider(storage_options, provider)
-            } else {
-                StorageOptionsAccessor::with_static_options(storage_options)
-            };
-            store_params.storage_options_accessor = Some(Arc::new(accessor));
+            self.inherit_storage_options(storage_options);
+        }
+
+        // Set storage options provider if available
+        if self.storage_options_provider.is_some() {
+            request
+                .lance_read_params
+                .get_or_insert_with(Default::default)
+                .store_options
+                .get_or_insert_with(Default::default)
+                .storage_options_provider = self.storage_options_provider.clone();
         }
 
         // Some ReadParams are exposed in the OpenTableBuilder, but we also
@@ -1102,7 +1071,6 @@ mod tests {
     use crate::table::{Table, TableDefinition};
     use arrow_array::{Int32Array, RecordBatch, StringArray};
     use arrow_schema::{DataType, Field, Schema};
-    use std::path::PathBuf;
     use tempfile::tempdir;
 
     async fn setup_database() -> (tempfile::TempDir, ListingDatabase) {
@@ -1901,9 +1869,7 @@ mod tests {
         let write_options = WriteOptions {
             lance_write_params: Some(lance::dataset::WriteParams {
                 store_params: Some(lance::io::ObjectStoreParams {
-                    storage_options_accessor: Some(Arc::new(
-                        StorageOptionsAccessor::with_static_options(storage_options),
-                    )),
+                    storage_options: Some(storage_options),
                     ..Default::default()
                 }),
                 ..Default::default()
@@ -1977,9 +1943,7 @@ mod tests {
         let write_options = WriteOptions {
             lance_write_params: Some(lance::dataset::WriteParams {
                 store_params: Some(lance::io::ObjectStoreParams {
-                    storage_options_accessor: Some(Arc::new(
-                        StorageOptionsAccessor::with_static_options(storage_options),
-                    )),
+                    storage_options: Some(storage_options),
                     ..Default::default()
                 }),
                 ..Default::default()
@@ -2082,19 +2046,6 @@ mod tests {
         assert_eq!(db_options.new_table_config.enable_stable_row_ids, None);
     }
 
-    #[tokio::test]
-    async fn test_table_uri() {
-        let (_tempdir, db) = setup_database().await;
-
-        let mut pb = PathBuf::new();
-        pb.push(db.uri.clone());
-        pb.push("test.lance");
-
-        let expected = pb.to_str().unwrap();
-        let uri = db.table_uri("test").ok().unwrap();
-        assert_eq!(uri, expected);
-    }
-
     #[tokio::test]
     async fn test_namespace_client() {
         let (_tempdir, db) = setup_database().await;

rust/lancedb/src/database/namespace.rs

@@ -10,14 +10,13 @@ use async_trait::async_trait;
 use lance_namespace::{
     models::{
         CreateEmptyTableRequest, CreateNamespaceRequest, CreateNamespaceResponse,
-        DeclareTableRequest, DescribeNamespaceRequest, DescribeNamespaceResponse,
-        DescribeTableRequest, DropNamespaceRequest, DropNamespaceResponse, DropTableRequest,
-        ListNamespacesRequest, ListNamespacesResponse, ListTablesRequest, ListTablesResponse,
+        DescribeNamespaceRequest, DescribeNamespaceResponse, DescribeTableRequest,
+        DropNamespaceRequest, DropNamespaceResponse, DropTableRequest, ListNamespacesRequest,
+        ListNamespacesResponse, ListTablesRequest, ListTablesResponse,
     },
     LanceNamespace,
 };
 use lance_namespace_impls::ConnectBuilder;
-use log::warn;
 
 use crate::database::ReadConsistency;
 use crate::error::{Error, Result};
@@ -138,7 +137,6 @@ impl Database for LanceNamespaceDatabase {
             id: Some(request.namespace),
             page_token: request.start_after,
             limit: request.limit.map(|l| l as i32),
-            ..Default::default()
         };
 
         let response = self.namespace.list_tables(ns_request).await?;
@@ -155,7 +153,8 @@ impl Database for LanceNamespaceDatabase {
         table_id.push(request.name.clone());
         let describe_request = DescribeTableRequest {
             id: Some(table_id.clone()),
-            ..Default::default()
+            version: None,
+            with_table_uri: None,
         };
 
         let describe_result = self.namespace.describe_table(describe_request).await;
@@ -173,7 +172,6 @@ impl Database for LanceNamespaceDatabase {
                     // Drop the existing table - must succeed
                     let drop_request = DropTableRequest {
                         id: Some(table_id.clone()),
-                        ..Default::default()
                     };
                     self.namespace
                         .drop_table(drop_request)
@@ -205,53 +203,29 @@ impl Database for LanceNamespaceDatabase {
         let mut table_id = request.namespace.clone();
         table_id.push(request.name.clone());
 
-        // Try declare_table first, falling back to create_empty_table for backwards
-        // compatibility with older namespace clients that don't support declare_table
-        let declare_request = DeclareTableRequest {
+        let create_empty_request = CreateEmptyTableRequest {
             id: Some(table_id.clone()),
-            ..Default::default()
+            location: None,
+            properties: if self.storage_options.is_empty() {
+                None
+            } else {
+                Some(self.storage_options.clone())
+            },
         };
 
-        let location = match self.namespace.declare_table(declare_request).await {
-            Ok(response) => response.location.ok_or_else(|| Error::Runtime {
-                message: "Table location is missing from declare_table response".to_string(),
-            })?,
-            Err(e) => {
-                // Check if the error is "not supported" and try create_empty_table as fallback
-                let err_str = e.to_string().to_lowercase();
-                if err_str.contains("not supported") || err_str.contains("not implemented") {
-                    warn!(
-                        "declare_table is not supported by the namespace client, \
-                        falling back to deprecated create_empty_table. \
-                        create_empty_table is deprecated and will be removed in Lance 3.0.0. \
-                        Please upgrade your namespace client to support declare_table."
-                    );
-                    #[allow(deprecated)]
-                    let create_empty_request = CreateEmptyTableRequest {
-                        id: Some(table_id.clone()),
-                        ..Default::default()
-                    };
+        let create_empty_response = self
+            .namespace
+            .create_empty_table(create_empty_request)
+            .await
+            .map_err(|e| Error::Runtime {
+                message: format!("Failed to create empty table: {}", e),
+            })?;
 
-                    #[allow(deprecated)]
-                    let create_response = self
-                        .namespace
-                        .create_empty_table(create_empty_request)
-                        .await
-                        .map_err(|e| Error::Runtime {
-                            message: format!("Failed to create empty table: {}", e),
-                        })?;
-
-                    create_response.location.ok_or_else(|| Error::Runtime {
-                        message: "Table location is missing from create_empty_table response"
-                            .to_string(),
-                    })?
-                } else {
-                    return Err(Error::Runtime {
-                        message: format!("Failed to declare table: {}", e),
-                    });
-                }
-            }
-        };
+        let location = create_empty_response
+            .location
+            .ok_or_else(|| Error::Runtime {
+                message: "Table location is missing from create_empty_table response".to_string(),
+            })?;
 
         let native_table = NativeTable::create_from_namespace(
             self.namespace.clone(),
@@ -308,10 +282,7 @@ impl Database for LanceNamespaceDatabase {
         let mut table_id = namespace.to_vec();
         table_id.push(name.to_string());
 
-        let drop_request = DropTableRequest {
-            id: Some(table_id),
-            ..Default::default()
-        };
+        let drop_request = DropTableRequest { id: Some(table_id) };
         self.namespace
             .drop_table(drop_request)
             .await
@@ -466,7 +437,8 @@ mod tests {
         // Create a child namespace first
         conn.create_namespace(CreateNamespaceRequest {
             id: Some(vec!["test_ns".into()]),
-            ..Default::default()
+            mode: None,
+            properties: None,
         })
         .await
         .expect("Failed to create namespace");
@@ -526,7 +498,8 @@ mod tests {
         // Create a child namespace first
         conn.create_namespace(CreateNamespaceRequest {
             id: Some(vec!["test_ns".into()]),
-            ..Default::default()
+            mode: None,
+            properties: None,
         })
         .await
         .expect("Failed to create namespace");
@@ -589,7 +562,8 @@ mod tests {
         // Create a child namespace first
         conn.create_namespace(CreateNamespaceRequest {
             id: Some(vec!["test_ns".into()]),
-            ..Default::default()
+            mode: None,
+            properties: None,
         })
         .await
         .expect("Failed to create namespace");
@@ -672,7 +646,8 @@ mod tests {
         // Create a child namespace first
         conn.create_namespace(CreateNamespaceRequest {
             id: Some(vec!["test_ns".into()]),
-            ..Default::default()
+            mode: None,
+            properties: None,
         })
         .await
         .expect("Failed to create namespace");
@@ -727,7 +702,8 @@ mod tests {
         // Create a child namespace first
         conn.create_namespace(CreateNamespaceRequest {
             id: Some(vec!["test_ns".into()]),
-            ..Default::default()
+            mode: None,
+            properties: None,
         })
         .await
         .expect("Failed to create namespace");
@@ -807,7 +783,8 @@ mod tests {
         // Create a child namespace first
         conn.create_namespace(CreateNamespaceRequest {
             id: Some(vec!["test_ns".into()]),
-            ..Default::default()
+            mode: None,
+            properties: None,
         })
         .await
         .expect("Failed to create namespace");
@@ -840,7 +817,8 @@ mod tests {
         // Create a child namespace first
         conn.create_namespace(CreateNamespaceRequest {
             id: Some(vec!["test_ns".into()]),
-            ..Default::default()
+            mode: None,
+            properties: None,
         })
         .await
         .expect("Failed to create namespace");

rust/lancedb/src/dataloader/permutation/builder.rs

@@ -19,7 +19,7 @@ use crate::{
         split::{SplitStrategy, Splitter, SPLIT_ID_COLUMN},
         util::{rename_column, TemporaryDirectory},
     },
-    query::{ExecutableQuery, QueryBase, Select},
+    query::{ExecutableQuery, QueryBase},
     Error, Result, Table,
 };
 
@@ -27,8 +27,6 @@ pub const SRC_ROW_ID_COL: &str = "row_id";
 
 pub const SPLIT_NAMES_CONFIG_KEY: &str = "split_names";
 
-pub const DEFAULT_MEMORY_LIMIT: usize = 100 * 1024 * 1024;
-
 /// Where to store the permutation table
 #[derive(Debug, Clone, Default)]
 enum PermutationDestination {
@@ -169,20 +167,10 @@ impl PermutationBuilder {
         &self,
         data: SendableRecordBatchStream,
     ) -> Result<SendableRecordBatchStream> {
-        let memory_limit = std::env::var("LANCEDB_PERM_BUILDER_MEMORY_LIMIT")
-            .unwrap_or_else(|_| DEFAULT_MEMORY_LIMIT.to_string())
-            .parse::<usize>()
-            .unwrap_or_else(|_| {
-                log::error!(
-                    "Failed to parse LANCEDB_PERM_BUILDER_MEMORY_LIMIT, using default: {}",
-                    DEFAULT_MEMORY_LIMIT
-                );
-                DEFAULT_MEMORY_LIMIT
-            });
         let ctx = SessionContext::new_with_config_rt(
             SessionConfig::default(),
             RuntimeEnvBuilder::new()
-                .with_memory_limit(memory_limit, 1.0)
+                .with_memory_limit(100 * 1024 * 1024, 1.0)
                 .with_disk_manager_builder(
                     DiskManagerBuilder::default()
                         .with_mode(self.config.temp_dir.to_disk_manager_mode()),
@@ -244,7 +232,7 @@ impl PermutationBuilder {
     /// Builds the permutation table and stores it in the given database.
     pub async fn build(self) -> Result<Table> {
         // First pass, apply filter and load row ids
-        let mut rows = self.base_table.query().select(Select::columns(&[ROW_ID]));
+        let mut rows = self.base_table.query().with_row_id();
 
         if let Some(filter) = &self.config.filter {
             rows = rows.only_if(filter);
@@ -333,47 +321,6 @@ mod tests {
 
     use super::*;
 
-    #[tokio::test]
-    async fn test_permutation_table_only_stores_row_id_and_split_id() {
-        let temp_dir = tempfile::tempdir().unwrap();
-
-        let db = connect(temp_dir.path().to_str().unwrap())
-            .execute()
-            .await
-            .unwrap();
-
-        let initial_data = lance_datagen::gen_batch()
-            .col("col_a", lance_datagen::array::step::<Int32Type>())
-            .col("col_b", lance_datagen::array::step::<Int32Type>())
-            .into_ldb_stream(RowCount::from(100), BatchCount::from(10));
-        let data_table = db
-            .create_table_streaming("base_tbl", initial_data)
-            .execute()
-            .await
-            .unwrap();
-
-        let permutation_table = PermutationBuilder::new(data_table.clone())
-            .with_split_strategy(
-                SplitStrategy::Sequential {
-                    sizes: SplitSizes::Percentages(vec![0.5, 0.5]),
-                },
-                None,
-            )
-            .with_filter("col_a > 57".to_string())
-            .build()
-            .await
-            .unwrap();
-
-        let schema = permutation_table.schema().await.unwrap();
-        let field_names: Vec<&str> = schema.fields().iter().map(|f| f.name().as_str()).collect();
-        assert_eq!(
-            field_names,
-            vec!["row_id", "split_id"],
-            "Permutation table should only contain row_id and split_id columns, but found: {:?}",
-            field_names,
-        );
-    }
-
     #[tokio::test]
     async fn test_permutation_builder() {
         let temp_dir = tempfile::tempdir().unwrap();
@@ -405,6 +352,8 @@ mod tests {
             .await
             .unwrap();
 
+        println!("permutation_table: {:?}", permutation_table);
+
         // Potentially brittle seed-dependent values below
         assert_eq!(permutation_table.count_rows(None).await.unwrap(), 330);
         assert_eq!(

rust/lancedb/src/dataloader/permutation/shuffle.rs

@@ -171,7 +171,7 @@ impl Shuffler {
             // This is kind of an annoying limitation but if we allow runt clumps from batches then
             // clumps will get unaligned and we will mess up the clumps when we do the in-memory
             // shuffle step.  If this is a problem we can probably figure out a better way to do this.
-            if !is_last && !(batch.num_rows() as u64).is_multiple_of(clump_size) {
+            if !is_last && batch.num_rows() as u64 % clump_size != 0 {
                 return Err(Error::Runtime {
                     message: format!(
                         "Expected batch size ({}) to be divisible by clump size ({})",

rust/lancedb/src/dataloader/permutation/split.rs

@@ -1,9 +1,12 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-use std::sync::{
-    atomic::{AtomicBool, AtomicU64, AtomicUsize, Ordering},
-    Arc,
+use std::{
+    iter,
+    sync::{
+        atomic::{AtomicBool, AtomicU64, AtomicUsize, Ordering},
+        Arc,
+    },
 };
 
 use arrow_array::{Array, BooleanArray, RecordBatch, UInt64Array};
@@ -12,8 +15,6 @@ use datafusion_common::hash_utils::create_hashes;
 use futures::{StreamExt, TryStreamExt};
 use lance_arrow::SchemaExt;
 
-use lance_core::ROW_ID;
-
 use crate::{
     arrow::{SendableRecordBatchStream, SimpleRecordBatchStream},
     dataloader::{
@@ -157,7 +158,7 @@ impl Splitter {
                 remaining_in_split
             };
 
-            split_ids.extend(std::iter::repeat_n(split_id as u64, rows_to_add as usize));
+            split_ids.extend(iter::repeat(split_id as u64).take(rows_to_add as usize));
             if done {
                 // Quit early if we've run out of splits
                 break;
@@ -362,15 +363,11 @@ impl Splitter {
 
     pub fn project(&self, query: Query) -> Query {
         match &self.strategy {
-            SplitStrategy::Calculated { calculation } => query.select(Select::Dynamic(vec![
-                (SPLIT_ID_COLUMN.to_string(), calculation.clone()),
-                (ROW_ID.to_string(), ROW_ID.to_string()),
-            ])),
-            SplitStrategy::Hash { columns, .. } => {
-                let mut cols = columns.clone();
-                cols.push(ROW_ID.to_string());
-                query.select(Select::Columns(cols))
-            }
+            SplitStrategy::Calculated { calculation } => query.select(Select::Dynamic(vec![(
+                SPLIT_ID_COLUMN.to_string(),
+                calculation.clone(),
+            )])),
+            SplitStrategy::Hash { columns, .. } => query.select(Select::Columns(columns.clone())),
             _ => query,
         }
     }
@@ -665,7 +662,7 @@ mod tests {
         assert_eq!(split_batch.num_rows(), total_split_sizes as usize);
         let mut expected = Vec::with_capacity(total_split_sizes as usize);
         for (i, size) in expected_split_sizes.iter().enumerate() {
-            expected.extend(std::iter::repeat_n(i as u64, *size as usize));
+            expected.extend(iter::repeat(i as u64).take(*size as usize));
         }
         let expected = Arc::new(UInt64Array::from(expected)) as Arc<dyn Array>;
 

rust/lancedb/src/embeddings.rs

@@ -120,13 +120,8 @@ impl MemoryRegistry {
 }
 
 /// A record batch reader that has embeddings applied to it
-///
-/// This is a wrapper around another record batch reader that applies embedding functions
-/// when reading from the record batch.
-///
-/// When multiple embedding functions are defined, they are computed in parallel using
-/// scoped threads to improve performance. For a single embedding function, computation
-/// is done inline without threading overhead.
+/// This is a wrapper around another record batch reader that applies an embedding function
+/// when reading from the record batch
 pub struct WithEmbeddings<R: RecordBatchReader> {
     inner: R,
     embeddings: Vec<(EmbeddingDefinition, Arc<dyn EmbeddingFunction>)>,
@@ -240,48 +235,6 @@ impl<R: RecordBatchReader> WithEmbeddings<R> {
             column_definitions,
         })
     }
-
-    fn compute_embeddings_parallel(&self, batch: &RecordBatch) -> Result<Vec<Arc<dyn Array>>> {
-        if self.embeddings.len() == 1 {
-            let (fld, func) = &self.embeddings[0];
-            let src_column =
-                batch
-                    .column_by_name(&fld.source_column)
-                    .ok_or_else(|| Error::InvalidInput {
-                        message: format!("Source column '{}' not found", fld.source_column),
-                    })?;
-            return Ok(vec![func.compute_source_embeddings(src_column.clone())?]);
-        }
-
-        // Parallel path: multiple embeddings
-        std::thread::scope(|s| {
-            let handles: Vec<_> = self
-                .embeddings
-                .iter()
-                .map(|(fld, func)| {
-                    let src_column = batch.column_by_name(&fld.source_column).ok_or_else(|| {
-                        Error::InvalidInput {
-                            message: format!("Source column '{}' not found", fld.source_column),
-                        }
-                    })?;
-
-                    let handle =
-                        s.spawn(move || func.compute_source_embeddings(src_column.clone()));
-
-                    Ok(handle)
-                })
-                .collect::<Result<_>>()?;
-
-            handles
-                .into_iter()
-                .map(|h| {
-                    h.join().map_err(|e| Error::Runtime {
-                        message: format!("Thread panicked during embedding computation: {:?}", e),
-                    })?
-                })
-                .collect()
-        })
-    }
 }
 
 impl<R: RecordBatchReader> Iterator for MaybeEmbedded<R> {
@@ -309,19 +262,19 @@ impl<R: RecordBatchReader> Iterator for WithEmbeddings<R> {
     fn next(&mut self) -> Option<Self::Item> {
         let batch = self.inner.next()?;
         match batch {
-            Ok(batch) => {
-                let embeddings = match self.compute_embeddings_parallel(&batch) {
-                    Ok(emb) => emb,
-                    Err(e) => {
-                        return Some(Err(arrow_schema::ArrowError::ComputeError(format!(
-                            "Error computing embedding: {}",
-                            e
-                        ))))
-                    }
-                };
-
-                let mut batch = batch;
-                for ((fld, _), embedding) in self.embeddings.iter().zip(embeddings.iter()) {
+            Ok(mut batch) => {
+                // todo: parallelize this
+                for (fld, func) in self.embeddings.iter() {
+                    let src_column = batch.column_by_name(&fld.source_column).unwrap();
+                    let embedding = match func.compute_source_embeddings(src_column.clone()) {
+                        Ok(embedding) => embedding,
+                        Err(e) => {
+                            return Some(Err(arrow_schema::ArrowError::ComputeError(format!(
+                                "Error computing embedding: {}",
+                                e
+                            ))))
+                        }
+                    };
                     let dst_field_name = fld
                         .dest_column
                         .clone()
@@ -333,7 +286,7 @@ impl<R: RecordBatchReader> Iterator for WithEmbeddings<R> {
                         embedding.nulls().is_some(),
                     );
 
-                    match batch.try_with_column(dst_field.clone(), embedding.clone()) {
+                    match batch.try_with_column(dst_field.clone(), embedding) {
                         Ok(b) => batch = b,
                         Err(e) => return Some(Err(e)),
                     };

rust/lancedb/src/index/vector.rs

@@ -297,10 +297,10 @@ impl IvfPqIndexBuilder {
 }
 
 pub(crate) fn suggested_num_sub_vectors(dim: u32) -> u32 {
-    if dim.is_multiple_of(16) {
+    if dim % 16 == 0 {
         // Should be more aggressive than this default.
         dim / 16
-    } else if dim.is_multiple_of(8) {
+    } else if dim % 8 == 0 {
         dim / 8
     } else {
         log::warn!(

rust/lancedb/src/lib.rs

@@ -25,14 +25,13 @@
 //!
 //! ## Crate Features
 //!
-//! - `aws` - Enable AWS S3 object store support.
-//! - `dynamodb` - Enable DynamoDB manifest store support.
-//! - `azure` - Enable Azure Blob Storage object store support.
-//! - `gcs` - Enable Google Cloud Storage object store support.
-//! - `oss` - Enable Alibaba Cloud OSS object store support.
-//! - `remote` - Enable remote client to connect to LanceDB cloud.
-//! - `huggingface` - Enable HuggingFace Hub integration for loading datasets from the Hub.
-//! - `fp16kernels` - Enable FP16 kernels for faster vector search on CPU.
+//! ### Experimental Features
+//!
+//! These features are not enabled by default.  They are experimental or in-development features that
+//! are not yet ready to be released.
+//!
+//! - `remote` - Enable remote client to connect to LanceDB cloud.  This is not yet fully implemented
+//!   and should not be enabled.
 //!
 //! ### Quick Start
 //!
@@ -51,15 +50,17 @@
 //! - `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud object store
 //! - `db://dbname` - Lance Cloud
 //!
-//! You can also use [`ConnectBuilder`] to configure the connection to the database.
+//! You can also use [`ConnectOptions`] to configure the connection to the database.
 //!
 //! ```rust
+//! use object_store::aws::AwsCredential;
 //! # tokio::runtime::Runtime::new().unwrap().block_on(async {
 //! let db = lancedb::connect("data/sample-lancedb")
-//!     .storage_options([
-//!         ("aws_access_key_id", "some_key"),
-//!         ("aws_secret_access_key", "some_secret"),
-//!     ])
+//!     .aws_creds(AwsCredential {
+//!         key_id: "some_key".to_string(),
+//!         secret_key: "some_secret".to_string(),
+//!         token: None,
+//!     })
 //!     .execute()
 //!     .await
 //!     .unwrap();

rust/lancedb/src/remote/db.rs

@@ -1718,7 +1718,8 @@ mod tests {
             let namespace = vec!["test_ns".to_string()];
             conn.create_namespace(CreateNamespaceRequest {
                 id: Some(namespace.clone()),
-                ..Default::default()
+                mode: None,
+                properties: None,
             })
             .await
             .expect("Failed to create namespace");
@@ -1743,7 +1744,8 @@ mod tests {
             let list_response = conn
                 .list_tables(ListTablesRequest {
                     id: Some(namespace.clone()),
-                    ..Default::default()
+                    page_token: None,
+                    limit: None,
                 })
                 .await
                 .expect("Failed to list tables");
@@ -1754,7 +1756,8 @@ mod tests {
             let list_response = namespace_client
                 .list_tables(ListTablesRequest {
                     id: Some(namespace.clone()),
-                    ..Default::default()
+                    page_token: None,
+                    limit: None,
                 })
                 .await
                 .unwrap();
@@ -1794,7 +1797,8 @@ mod tests {
             let namespace = vec!["multi_table_ns".to_string()];
             conn.create_namespace(CreateNamespaceRequest {
                 id: Some(namespace.clone()),
-                ..Default::default()
+                mode: None,
+                properties: None,
             })
             .await
             .expect("Failed to create namespace");
@@ -1819,7 +1823,8 @@ mod tests {
             let list_response = conn
                 .list_tables(ListTablesRequest {
                     id: Some(namespace.clone()),
-                    ..Default::default()
+                    page_token: None,
+                    limit: None,
                 })
                 .await
                 .unwrap();

rust/lancedb/src/remote/table.rs

@@ -1,8 +1,6 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-pub mod insert;
-
 use crate::index::Index;
 use crate::index::IndexStatistics;
 use crate::query::{QueryFilter, QueryRequest, Select, VectorQueryRequest};
@@ -206,7 +204,6 @@ pub struct RemoteTable<S: HttpSend = Sender> {
     server_version: ServerVersion,
 
     version: RwLock<Option<u64>>,
-    location: RwLock<Option<String>>,
 }
 
 impl<S: HttpSend> RemoteTable<S> {
@@ -224,7 +221,6 @@ impl<S: HttpSend> RemoteTable<S> {
             identifier,
             server_version,
             version: RwLock::new(None),
-            location: RwLock::new(None),
         }
     }
 
@@ -470,9 +466,7 @@ impl<S: HttpSend> RemoteTable<S> {
         self.apply_query_params(&mut body, &query.base)?;
 
         // Apply general parameters, before we dispatch based on number of query vectors.
-        if let Some(distance_type) = query.distance_type {
-            body["distance_type"] = serde_json::json!(distance_type);
-        }
+        body["distance_type"] = serde_json::json!(query.distance_type.unwrap_or_default());
         // In 0.23.1 we migrated from `nprobes` to `minimum_nprobes` and `maximum_nprobes`.
         // Old client / new server: since minimum_nprobes is missing, fallback to nprobes
         // New client / old server: old server will only see nprobes, make sure to set both
@@ -645,7 +639,6 @@ impl<S: HttpSend> RemoteTable<S> {
 struct TableDescription {
     version: u64,
     schema: JsonSchema,
-    location: Option<String>,
 }
 
 impl<S: HttpSend> std::fmt::Display for RemoteTable<S> {
@@ -674,7 +667,6 @@ mod test_utils {
                 identifier: name,
                 server_version: version.map(ServerVersion).unwrap_or_default(),
                 version: RwLock::new(None),
-                location: RwLock::new(None),
             }
         }
     }
@@ -1096,17 +1088,6 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
                     body["num_partitions"] = serde_json::Value::Number(num_partitions.into());
                 }
             }
-            Index::IvfRq(index) => {
-                body[INDEX_TYPE_KEY] = serde_json::Value::String("IVF_RQ".to_string());
-                body[METRIC_TYPE_KEY] =
-                    serde_json::Value::String(index.distance_type.to_string().to_lowercase());
-                if let Some(num_partitions) = index.num_partitions {
-                    body["num_partitions"] = serde_json::Value::Number(num_partitions.into());
-                }
-                if let Some(num_bits) = index.num_bits {
-                    body["num_bits"] = serde_json::Value::Number(num_bits.into());
-                }
-            }
             Index::BTree(_) => {
                 body[INDEX_TYPE_KEY] = serde_json::Value::String("BTREE".to_string());
             }
@@ -1469,42 +1450,14 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
             message: "table_definition is not supported on LanceDB cloud.".into(),
         })
     }
-    async fn uri(&self) -> Result<String> {
-        // Check if we already have the location cached
-        {
-            let location = self.location.read().await;
-            if let Some(ref loc) = *location {
-                return Ok(loc.clone());
-            }
-        }
-
-        // Fetch from server via describe
-        let description = self.describe().await?;
-        let location = description.location.ok_or_else(|| Error::NotSupported {
-            message: "Table URI not supported by the server".into(),
-        })?;
-
-        // Cache the location for future use
-        {
-            let mut cached_location = self.location.write().await;
-            *cached_location = Some(location.clone());
-        }
-
-        Ok(location)
+    fn dataset_uri(&self) -> &str {
+        "NOT_SUPPORTED"
     }
 
     async fn storage_options(&self) -> Option<HashMap<String, String>> {
         None
     }
 
-    async fn initial_storage_options(&self) -> Option<HashMap<String, String>> {
-        None
-    }
-
-    async fn latest_storage_options(&self) -> Result<Option<HashMap<String, String>>> {
-        Ok(None)
-    }
-
     async fn stats(&self) -> Result<TableStatistics> {
         let request = self
             .client
@@ -1520,21 +1473,6 @@ impl<S: HttpSend> BaseTable for RemoteTable<S> {
         })?;
         Ok(stats)
     }
-
-    async fn create_insert_exec(
-        &self,
-        input: Arc<dyn ExecutionPlan>,
-        write_params: lance::dataset::WriteParams,
-    ) -> Result<Arc<dyn ExecutionPlan>> {
-        let overwrite = matches!(write_params.mode, lance::dataset::WriteMode::Overwrite);
-        Ok(Arc::new(insert::RemoteInsertExec::new(
-            self.name.clone(),
-            self.identifier.clone(),
-            self.client.clone(),
-            input,
-            overwrite,
-        )))
-    }
 }
 
 #[derive(Serialize)]
@@ -2257,6 +2195,7 @@ mod tests {
             let body: serde_json::Value = serde_json::from_slice(body).unwrap();
             let mut expected_body = serde_json::json!({
                 "prefilter": true,
+                "distance_type": "l2",
                 "nprobes": 20,
                 "minimum_nprobes": 20,
                 "maximum_nprobes": 20,
@@ -3382,69 +3321,4 @@ mod tests {
         let result = table.drop_columns(&["old_col1", "old_col2"]).await.unwrap();
         assert_eq!(result.version, 5);
     }
-
-    #[tokio::test]
-    async fn test_uri() {
-        let table = Table::new_with_handler("my_table", |request| {
-            assert_eq!(request.method(), "POST");
-            assert_eq!(request.url().path(), "/v1/table/my_table/describe/");
-
-            http::Response::builder()
-                .status(200)
-                .body(r#"{"version": 1, "schema": {"fields": []}, "location": "s3://bucket/path/to/table"}"#)
-                .unwrap()
-        });
-
-        let uri = table.uri().await.unwrap();
-        assert_eq!(uri, "s3://bucket/path/to/table");
-    }
-
-    #[tokio::test]
-    async fn test_uri_missing_location() {
-        let table = Table::new_with_handler("my_table", |request| {
-            assert_eq!(request.method(), "POST");
-            assert_eq!(request.url().path(), "/v1/table/my_table/describe/");
-
-            // Server returns response without location field
-            http::Response::builder()
-                .status(200)
-                .body(r#"{"version": 1, "schema": {"fields": []}}"#)
-                .unwrap()
-        });
-
-        let result = table.uri().await;
-        assert!(result.is_err());
-        assert!(matches!(&result, Err(Error::NotSupported { .. })));
-    }
-
-    #[tokio::test]
-    async fn test_uri_caching() {
-        use std::sync::atomic::{AtomicUsize, Ordering};
-        use std::sync::Arc;
-
-        let call_count = Arc::new(AtomicUsize::new(0));
-        let call_count_clone = call_count.clone();
-
-        let table = Table::new_with_handler("my_table", move |request| {
-            assert_eq!(request.url().path(), "/v1/table/my_table/describe/");
-            call_count_clone.fetch_add(1, Ordering::SeqCst);
-
-            http::Response::builder()
-                .status(200)
-                .body(
-                    r#"{"version": 1, "schema": {"fields": []}, "location": "gs://bucket/table"}"#,
-                )
-                .unwrap()
-        });
-
-        // First call should fetch from server
-        let uri1 = table.uri().await.unwrap();
-        assert_eq!(uri1, "gs://bucket/table");
-        assert_eq!(call_count.load(Ordering::SeqCst), 1);
-
-        // Second call should use cached value
-        let uri2 = table.uri().await.unwrap();
-        assert_eq!(uri2, "gs://bucket/table");
-        assert_eq!(call_count.load(Ordering::SeqCst), 1); // Still 1, no new call
-    }
 }

rust/lancedb/src/remote/table/insert.rs

@@ -1,438 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-//! DataFusion ExecutionPlan for inserting data into remote LanceDB tables.
-
-use std::any::Any;
-use std::sync::{Arc, Mutex};
-
-use arrow_array::{ArrayRef, RecordBatch, UInt64Array};
-use arrow_ipc::CompressionType;
-use arrow_schema::ArrowError;
-use datafusion_common::{DataFusionError, Result as DataFusionResult};
-use datafusion_execution::{SendableRecordBatchStream, TaskContext};
-use datafusion_physical_expr::EquivalenceProperties;
-use datafusion_physical_plan::stream::RecordBatchStreamAdapter;
-use datafusion_physical_plan::{DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties};
-use futures::StreamExt;
-use http::header::CONTENT_TYPE;
-
-use crate::remote::client::{HttpSend, RestfulLanceDbClient, Sender};
-use crate::remote::table::RemoteTable;
-use crate::remote::ARROW_STREAM_CONTENT_TYPE;
-use crate::table::datafusion::insert::COUNT_SCHEMA;
-use crate::table::AddResult;
-use crate::Error;
-
-/// ExecutionPlan for inserting data into a remote LanceDB table.
-///
-/// This plan:
-/// 1. Requires single partition (no parallel remote inserts yet)
-/// 2. Streams data as Arrow IPC to `/v1/table/{id}/insert/` endpoint
-/// 3. Stores AddResult for retrieval after execution
-#[derive(Debug)]
-pub struct RemoteInsertExec<S: HttpSend = Sender> {
-    table_name: String,
-    identifier: String,
-    client: RestfulLanceDbClient<S>,
-    input: Arc<dyn ExecutionPlan>,
-    overwrite: bool,
-    properties: PlanProperties,
-    add_result: Arc<Mutex<Option<AddResult>>>,
-}
-
-impl<S: HttpSend + 'static> RemoteInsertExec<S> {
-    /// Create a new RemoteInsertExec.
-    pub fn new(
-        table_name: String,
-        identifier: String,
-        client: RestfulLanceDbClient<S>,
-        input: Arc<dyn ExecutionPlan>,
-        overwrite: bool,
-    ) -> Self {
-        let schema = COUNT_SCHEMA.clone();
-        let properties = PlanProperties::new(
-            EquivalenceProperties::new(schema),
-            datafusion_physical_plan::Partitioning::UnknownPartitioning(1),
-            datafusion_physical_plan::execution_plan::EmissionType::Final,
-            datafusion_physical_plan::execution_plan::Boundedness::Bounded,
-        );
-
-        Self {
-            table_name,
-            identifier,
-            client,
-            input,
-            overwrite,
-            properties,
-            add_result: Arc::new(Mutex::new(None)),
-        }
-    }
-
-    /// Get the add result after execution.
-    // TODO: this will be used when we wire this up to Table::add().
-    #[allow(dead_code)]
-    pub fn add_result(&self) -> Option<AddResult> {
-        self.add_result.lock().unwrap().clone()
-    }
-
-    fn stream_as_body(data: SendableRecordBatchStream) -> DataFusionResult<reqwest::Body> {
-        let options = arrow_ipc::writer::IpcWriteOptions::default()
-            .try_with_compression(Some(CompressionType::LZ4_FRAME))?;
-        let writer = arrow_ipc::writer::StreamWriter::try_new_with_options(
-            Vec::new(),
-            &data.schema(),
-            options,
-        )?;
-
-        let stream = futures::stream::try_unfold((data, writer), move |(mut data, mut writer)| {
-            async move {
-                match data.next().await {
-                    Some(Ok(batch)) => {
-                        writer.write(&batch)?;
-                        let buffer = std::mem::take(writer.get_mut());
-                        Ok(Some((buffer, (data, writer))))
-                    }
-                    Some(Err(e)) => Err(e),
-                    None => {
-                        if let Err(ArrowError::IpcError(_msg)) = writer.finish() {
-                            // Will error if already closed.
-                            return Ok(None);
-                        };
-                        let buffer = std::mem::take(writer.get_mut());
-                        Ok(Some((buffer, (data, writer))))
-                    }
-                }
-            }
-        });
-
-        Ok(reqwest::Body::wrap_stream(stream))
-    }
-}
-
-impl<S: HttpSend + 'static> DisplayAs for RemoteInsertExec<S> {
-    fn fmt_as(&self, t: DisplayFormatType, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        match t {
-            DisplayFormatType::Default | DisplayFormatType::Verbose => {
-                write!(
-                    f,
-                    "RemoteInsertExec: table={}, overwrite={}",
-                    self.table_name, self.overwrite
-                )
-            }
-            DisplayFormatType::TreeRender => {
-                write!(f, "RemoteInsertExec")
-            }
-        }
-    }
-}
-
-impl<S: HttpSend + 'static> ExecutionPlan for RemoteInsertExec<S> {
-    fn name(&self) -> &str {
-        Self::static_name()
-    }
-
-    fn as_any(&self) -> &dyn Any {
-        self
-    }
-
-    fn properties(&self) -> &PlanProperties {
-        &self.properties
-    }
-
-    fn children(&self) -> Vec<&Arc<dyn ExecutionPlan>> {
-        vec![&self.input]
-    }
-
-    fn maintains_input_order(&self) -> Vec<bool> {
-        vec![false]
-    }
-
-    fn required_input_distribution(&self) -> Vec<datafusion_physical_plan::Distribution> {
-        // Until we have a separate commit endpoint, we need to do all inserts in a single partition
-        vec![datafusion_physical_plan::Distribution::SinglePartition]
-    }
-
-    fn benefits_from_input_partitioning(&self) -> Vec<bool> {
-        vec![false]
-    }
-
-    fn with_new_children(
-        self: Arc<Self>,
-        children: Vec<Arc<dyn ExecutionPlan>>,
-    ) -> DataFusionResult<Arc<dyn ExecutionPlan>> {
-        if children.len() != 1 {
-            return Err(DataFusionError::Internal(
-                "RemoteInsertExec requires exactly one child".to_string(),
-            ));
-        }
-        Ok(Arc::new(Self::new(
-            self.table_name.clone(),
-            self.identifier.clone(),
-            self.client.clone(),
-            children[0].clone(),
-            self.overwrite,
-        )))
-    }
-
-    fn execute(
-        &self,
-        partition: usize,
-        context: Arc<TaskContext>,
-    ) -> DataFusionResult<SendableRecordBatchStream> {
-        if partition != 0 {
-            return Err(DataFusionError::Internal(
-                "RemoteInsertExec only supports single partition execution".to_string(),
-            ));
-        }
-
-        let input_stream = self.input.execute(0, context)?;
-        let client = self.client.clone();
-        let identifier = self.identifier.clone();
-        let overwrite = self.overwrite;
-        let add_result = self.add_result.clone();
-        let table_name = self.table_name.clone();
-
-        let stream = futures::stream::once(async move {
-            let mut request = client
-                .post(&format!("/v1/table/{}/insert/", identifier))
-                .header(CONTENT_TYPE, ARROW_STREAM_CONTENT_TYPE);
-
-            if overwrite {
-                request = request.query(&[("mode", "overwrite")]);
-            }
-
-            let body = Self::stream_as_body(input_stream)?;
-            let request = request.body(body);
-
-            let (request_id, response) = client
-                .send(request)
-                .await
-                .map_err(|e| DataFusionError::External(Box::new(e)))?;
-
-            let response =
-                RemoteTable::<Sender>::handle_table_not_found(&table_name, response, &request_id)
-                    .await
-                    .map_err(|e| DataFusionError::External(Box::new(e)))?;
-
-            let response = client
-                .check_response(&request_id, response)
-                .await
-                .map_err(|e| DataFusionError::External(Box::new(e)))?;
-
-            let body_text = response.text().await.map_err(|e| {
-                DataFusionError::External(Box::new(Error::Http {
-                    source: Box::new(e),
-                    request_id: request_id.clone(),
-                    status_code: None,
-                }))
-            })?;
-
-            let parsed_result = if body_text.trim().is_empty() {
-                // Backward compatible with old servers
-                AddResult { version: 0 }
-            } else {
-                serde_json::from_str(&body_text).map_err(|e| {
-                    DataFusionError::External(Box::new(Error::Http {
-                        source: format!("Failed to parse add response: {}", e).into(),
-                        request_id: request_id.clone(),
-                        status_code: None,
-                    }))
-                })?
-            };
-
-            {
-                let mut res_lock = add_result.lock().map_err(|_| {
-                    DataFusionError::Execution("Failed to acquire lock for add_result".to_string())
-                })?;
-                *res_lock = Some(parsed_result);
-            }
-
-            // Return a single batch with count 0 (actual count is tracked in add_result)
-            let count_array: ArrayRef = Arc::new(UInt64Array::from(vec![0u64]));
-            let batch = RecordBatch::try_new(COUNT_SCHEMA.clone(), vec![count_array])?;
-            Ok::<_, DataFusionError>(batch)
-        });
-
-        Ok(Box::pin(RecordBatchStreamAdapter::new(
-            COUNT_SCHEMA.clone(),
-            stream,
-        )))
-    }
-}
-
-#[cfg(test)]
-mod tests {
-    use arrow_array::record_batch;
-    use arrow_schema::{DataType, Field, Schema as ArrowSchema};
-    use datafusion::prelude::SessionContext;
-    use datafusion_catalog::MemTable;
-    use std::sync::atomic::{AtomicUsize, Ordering};
-    use std::sync::Arc;
-
-    use crate::remote::ARROW_STREAM_CONTENT_TYPE;
-    use crate::table::datafusion::BaseTableAdapter;
-    use crate::Table;
-
-    fn schema_json() -> &'static str {
-        r#"{"fields": [{"name": "id", "type": {"type": "int32"}, "nullable": true}]}"#
-    }
-
-    #[tokio::test]
-    async fn test_remote_insert_exec_execute_empty() {
-        let request_count = Arc::new(AtomicUsize::new(0));
-        let request_count_clone = request_count.clone();
-
-        let table = Table::new_with_handler("my_table", move |request| {
-            let path = request.url().path();
-
-            if path == "/v1/table/my_table/describe/" {
-                // Return schema for BaseTableAdapter::try_new
-                return http::Response::builder()
-                    .status(200)
-                    .body(format!(r#"{{"version": 1, "schema": {}}}"#, schema_json()))
-                    .unwrap();
-            }
-
-            if path == "/v1/table/my_table/insert/" {
-                assert_eq!(request.method(), "POST");
-                assert_eq!(
-                    request.headers().get("Content-Type").unwrap(),
-                    ARROW_STREAM_CONTENT_TYPE
-                );
-                request_count_clone.fetch_add(1, Ordering::SeqCst);
-
-                return http::Response::builder()
-                    .status(200)
-                    .body(r#"{"version": 2}"#.to_string())
-                    .unwrap();
-            }
-
-            panic!("Unexpected request path: {}", path);
-        });
-
-        let schema = Arc::new(ArrowSchema::new(vec![Field::new(
-            "id",
-            DataType::Int32,
-            true,
-        )]));
-
-        // Create empty MemTable (no batches)
-        let source_table = MemTable::try_new(schema, vec![vec![]]).unwrap();
-
-        let ctx = SessionContext::new();
-
-        // Register the remote table as insert target
-        let provider = BaseTableAdapter::try_new(table.base_table().clone())
-            .await
-            .unwrap();
-        ctx.register_table("my_table", Arc::new(provider)).unwrap();
-
-        // Register empty source
-        ctx.register_table("empty_source", Arc::new(source_table))
-            .unwrap();
-
-        // Execute the INSERT
-        ctx.sql("INSERT INTO my_table SELECT * FROM empty_source")
-            .await
-            .unwrap()
-            .collect()
-            .await
-            .unwrap();
-
-        // Verify: should have made exactly one HTTP request even with empty input
-        assert_eq!(request_count.load(Ordering::SeqCst), 1);
-    }
-
-    #[tokio::test]
-    async fn test_remote_insert_exec_multi_partition() {
-        let request_count = Arc::new(AtomicUsize::new(0));
-        let request_count_clone = request_count.clone();
-
-        let table = Table::new_with_handler("my_table", move |request| {
-            let path = request.url().path();
-
-            if path == "/v1/table/my_table/describe/" {
-                // Return schema for BaseTableAdapter::try_new
-                return http::Response::builder()
-                    .status(200)
-                    .body(format!(r#"{{"version": 1, "schema": {}}}"#, schema_json()))
-                    .unwrap();
-            }
-
-            if path == "/v1/table/my_table/insert/" {
-                assert_eq!(request.method(), "POST");
-                assert_eq!(
-                    request.headers().get("Content-Type").unwrap(),
-                    ARROW_STREAM_CONTENT_TYPE
-                );
-                request_count_clone.fetch_add(1, Ordering::SeqCst);
-
-                return http::Response::builder()
-                    .status(200)
-                    .body(r#"{"version": 2}"#.to_string())
-                    .unwrap();
-            }
-
-            panic!("Unexpected request path: {}", path);
-        });
-
-        let schema = Arc::new(ArrowSchema::new(vec![Field::new(
-            "id",
-            DataType::Int32,
-            true,
-        )]));
-
-        // Create MemTable with multiple partitions and multiple batches
-        let source_table = MemTable::try_new(
-            schema,
-            vec![
-                // Partition 0
-                vec![
-                    record_batch!(("id", Int32, [1, 2])).unwrap(),
-                    record_batch!(("id", Int32, [3, 4])).unwrap(),
-                ],
-                // Partition 1
-                vec![record_batch!(("id", Int32, [5, 6, 7])).unwrap()],
-                // Partition 2
-                vec![record_batch!(("id", Int32, [8])).unwrap()],
-            ],
-        )
-        .unwrap();
-
-        let ctx = SessionContext::new();
-
-        // Register the remote table as insert target
-        let provider = BaseTableAdapter::try_new(table.base_table().clone())
-            .await
-            .unwrap();
-        ctx.register_table("my_table", Arc::new(provider)).unwrap();
-
-        // Register multi-partition source
-        ctx.register_table("multi_partition_source", Arc::new(source_table))
-            .unwrap();
-
-        // Get the physical plan and verify it includes a repartition to 1
-        let df = ctx
-            .sql("INSERT INTO my_table SELECT * FROM multi_partition_source")
-            .await
-            .unwrap();
-        let plan = df.clone().create_physical_plan().await.unwrap();
-        let plan_str = datafusion::physical_plan::displayable(plan.as_ref())
-            .indent(true)
-            .to_string();
-
-        // The plan should include a CoalescePartitionsExec to merge partitions
-        assert!(
-            plan_str.contains("CoalescePartitionsExec"),
-            "Expected CoalescePartitionsExec in plan:\n{}",
-            plan_str
-        );
-
-        // Execute the INSERT
-        df.collect().await.unwrap();
-
-        // Verify: should have made exactly one HTTP request despite multiple input partitions
-        assert_eq!(request_count.load(Ordering::SeqCst), 1);
-    }
-}

rust/lancedb/src/table.rs

@@ -23,7 +23,9 @@ pub use lance::dataset::ColumnAlteration;
 pub use lance::dataset::NewColumnTransform;
 pub use lance::dataset::ReadParams;
 pub use lance::dataset::Version;
-use lance::dataset::{InsertBuilder, WhenMatched, WriteMode, WriteParams};
+use lance::dataset::{
+    InsertBuilder, UpdateBuilder as LanceUpdateBuilder, WhenMatched, WriteMode, WriteParams,
+};
 use lance::dataset::{MergeInsertBuilder as LanceMergeInsertBuilder, WhenNotMatchedBySource};
 use lance::index::vector::utils::infer_vector_dim;
 use lance::index::vector::VectorIndexParams;
@@ -38,7 +40,7 @@ use lance_index::vector::pq::PQBuildParams;
 use lance_index::vector::sq::builder::SQBuildParams;
 use lance_index::DatasetIndexExt;
 use lance_index::IndexType;
-use lance_io::object_store::{LanceNamespaceStorageOptionsProvider, StorageOptionsAccessor};
+use lance_io::object_store::LanceNamespaceStorageOptionsProvider;
 use lance_namespace::models::{
     QueryTableRequest as NsQueryTableRequest, QueryTableRequestColumns,
     QueryTableRequestFullTextQuery, QueryTableRequestVector, StringFtsQuery,
@@ -77,13 +79,10 @@ use self::merge::MergeInsertBuilder;
 
 pub mod datafusion;
 pub(crate) mod dataset;
-pub mod delete;
 pub mod merge;
-pub mod update;
 
 use crate::index::waiter::wait_for_index;
 pub use chrono::Duration;
-pub use delete::DeleteResult;
 use futures::future::{join_all, Either};
 pub use lance::dataset::optimize::CompactionOptions;
 pub use lance::dataset::refs::{TagContents, Tags as LanceTags};
@@ -92,7 +91,6 @@ use lance::dataset::statistics::DatasetStatisticsExt;
 use lance_index::frag_reuse::FRAG_REUSE_INDEX_NAME;
 pub use lance_index::optimize::OptimizeOptions;
 use serde_with::skip_serializing_none;
-pub use update::{UpdateBuilder, UpdateResult};
 
 /// Defines the type of column
 #[derive(Debug, Clone, Serialize, Deserialize)]
@@ -329,6 +327,72 @@ impl<T: IntoArrow> AddDataBuilder<T> {
     }
 }
 
+/// A builder for configuring an [`Table::update`] operation
+#[derive(Debug, Clone)]
+pub struct UpdateBuilder {
+    parent: Arc<dyn BaseTable>,
+    pub(crate) filter: Option<String>,
+    pub(crate) columns: Vec<(String, String)>,
+}
+
+impl UpdateBuilder {
+    fn new(parent: Arc<dyn BaseTable>) -> Self {
+        Self {
+            parent,
+            filter: None,
+            columns: Vec::new(),
+        }
+    }
+
+    /// Limits the update operation to rows matching the given filter
+    ///
+    /// If a row does not match the filter then it will be left unchanged.
+    pub fn only_if(mut self, filter: impl Into<String>) -> Self {
+        self.filter = Some(filter.into());
+        self
+    }
+
+    /// Specifies a column to update
+    ///
+    /// This method may be called multiple times to update multiple columns
+    ///
+    /// The `update_expr` should be an SQL expression explaining how to calculate
+    /// the new value for the column.  The expression will be evaluated against the
+    /// previous row's value.
+    ///
+    /// # Examples
+    ///
+    /// ```
+    /// # use lancedb::Table;
+    /// # async fn doctest_helper(tbl: Table) {
+    ///   let mut operation = tbl.update();
+    ///   // Increments the `bird_count` value by 1
+    ///   operation = operation.column("bird_count", "bird_count + 1");
+    ///   operation.execute().await.unwrap();
+    /// # }
+    /// ```
+    pub fn column(
+        mut self,
+        column_name: impl Into<String>,
+        update_expr: impl Into<String>,
+    ) -> Self {
+        self.columns.push((column_name.into(), update_expr.into()));
+        self
+    }
+
+    /// Executes the update operation.
+    /// Returns the update result
+    pub async fn execute(self) -> Result<UpdateResult> {
+        if self.columns.is_empty() {
+            Err(Error::InvalidInput {
+                message: "at least one column must be specified in an update operation".to_string(),
+            })
+        } else {
+            self.parent.clone().update(self).await
+        }
+    }
+}
+
 /// Filters that can be used to limit the rows returned by a query
 pub enum Filter {
     /// A SQL filter string
@@ -362,6 +426,17 @@ pub trait Tags: Send + Sync {
     async fn update(&mut self, tag: &str, version: u64) -> Result<()>;
 }
 
+#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize, Default)]
+pub struct UpdateResult {
+    #[serde(default)]
+    pub rows_updated: u64,
+    // The commit version associated with the operation.
+    // A version of `0` indicates compatibility with legacy servers that do not return
+    /// a commit version.
+    #[serde(default)]
+    pub version: u64,
+}
+
 #[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize, Default)]
 pub struct AddResult {
     // The commit version associated with the operation.
@@ -371,6 +446,15 @@ pub struct AddResult {
     pub version: u64,
 }
 
+#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize, Default)]
+pub struct DeleteResult {
+    // The commit version associated with the operation.
+    // A version of `0` indicates compatibility with legacy servers that do not return
+    /// a commit version.
+    #[serde(default)]
+    pub version: u64,
+}
+
 #[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize, Default)]
 pub struct MergeResult {
     // The commit version associated with the operation.
@@ -524,20 +608,10 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
     async fn list_versions(&self) -> Result<Vec<Version>>;
     /// Get the table definition.
     async fn table_definition(&self) -> Result<TableDefinition>;
-    /// Get the table URI (storage location)
-    async fn uri(&self) -> Result<String>;
+    /// Get the table URI
+    fn dataset_uri(&self) -> &str;
     /// Get the storage options used when opening this table, if any.
-    #[deprecated(since = "0.25.0", note = "Use initial_storage_options() instead")]
     async fn storage_options(&self) -> Option<HashMap<String, String>>;
-    /// Get the initial storage options that were passed in when opening this table.
-    ///
-    /// For dynamically refreshed options (e.g., credential vending), use [`Self::latest_storage_options`].
-    async fn initial_storage_options(&self) -> Option<HashMap<String, String>>;
-    /// Get the latest storage options, refreshing from provider if configured.
-    ///
-    /// Returns `Ok(Some(options))` if storage options are available (static or refreshed),
-    /// `Ok(None)` if no storage options were configured, or `Err(...)` if refresh failed.
-    async fn latest_storage_options(&self) -> Result<Option<HashMap<String, String>>>;
     /// Poll until the columns are fully indexed. Will return Error::Timeout if the columns
     /// are not fully indexed within the timeout.
     async fn wait_for_index(
@@ -547,19 +621,6 @@ pub trait BaseTable: std::fmt::Display + std::fmt::Debug + Send + Sync {
     ) -> Result<()>;
     /// Get statistics on the table
     async fn stats(&self) -> Result<TableStatistics>;
-    /// Create an ExecutionPlan for inserting data into the table.
-    ///
-    /// This is used by the DataFusion TableProvider implementation to support
-    /// INSERT INTO statements.
-    async fn create_insert_exec(
-        &self,
-        _input: Arc<dyn datafusion_physical_plan::ExecutionPlan>,
-        _write_params: WriteParams,
-    ) -> Result<Arc<dyn datafusion_physical_plan::ExecutionPlan>> {
-        Err(Error::NotSupported {
-            message: "create_insert_exec not implemented".to_string(),
-        })
-    }
 }
 
 /// A Table is a collection of strong typed Rows.
@@ -1256,43 +1317,20 @@ impl Table {
         self.inner.list_indices().await
     }
 
-    /// Get the table URI (storage location)
+    /// Get the underlying dataset URI
     ///
-    /// Returns the full storage location of the table (e.g., S3/GCS path).
-    /// For remote tables, this fetches the location from the server via describe.
-    pub async fn uri(&self) -> Result<String> {
-        self.inner.uri().await
+    /// Warning: This is an internal API and the return value is subject to change.
+    pub fn dataset_uri(&self) -> &str {
+        self.inner.dataset_uri()
     }
 
     /// Get the storage options used when opening this table, if any.
     ///
     /// Warning: This is an internal API and the return value is subject to change.
-    #[deprecated(since = "0.25.0", note = "Use initial_storage_options() instead")]
     pub async fn storage_options(&self) -> Option<HashMap<String, String>> {
-        #[allow(deprecated)]
         self.inner.storage_options().await
     }
 
-    /// Get the initial storage options that were passed in when opening this table.
-    ///
-    /// For dynamically refreshed options (e.g., credential vending), use [`Self::latest_storage_options`].
-    ///
-    /// Warning: This is an internal API and the return value is subject to change.
-    pub async fn initial_storage_options(&self) -> Option<HashMap<String, String>> {
-        self.inner.initial_storage_options().await
-    }
-
-    /// Get the latest storage options, refreshing from provider if configured.
-    ///
-    /// This method is useful for credential vending scenarios where storage options
-    /// may be refreshed dynamically. If no dynamic provider is configured, this
-    /// returns the initial static options.
-    ///
-    /// Warning: This is an internal API and the return value is subject to change.
-    pub async fn latest_storage_options(&self) -> Result<Option<HashMap<String, String>>> {
-        self.inner.latest_storage_options().await
-    }
-
     /// Get statistics about an index.
     /// Returns None if the index does not exist.
     pub async fn index_stats(
@@ -1386,9 +1424,7 @@ impl Table {
             })
             .collect::<Vec<_>>();
 
-        let unioned = UnionExec::try_new(projected_plans).map_err(|err| Error::Runtime {
-            message: err.to_string(),
-        })?;
+        let unioned = Arc::new(UnionExec::new(projected_plans));
         // We require 1 partition in the final output
         let repartitioned = RepartitionExec::try_new(
             unioned,
@@ -1629,14 +1665,18 @@ impl NativeTable {
 
         // Use DatasetBuilder::from_namespace which automatically fetches location
         // and storage options from the namespace
-        let builder = DatasetBuilder::from_namespace(namespace_client.clone(), table_id)
-            .await
-            .map_err(|e| match e {
-                lance::Error::Namespace { source, .. } => Error::Runtime {
-                    message: format!("Failed to get table info from namespace: {:?}", source),
-                },
-                source => Error::Lance { source },
-            })?;
+        let builder = DatasetBuilder::from_namespace(
+            namespace_client.clone(),
+            table_id,
+            false, // Don't ignore namespace storage options
+        )
+        .await
+        .map_err(|e| match e {
+            lance::Error::Namespace { source, .. } => Error::Runtime {
+                message: format!("Failed to get table info from namespace: {:?}", source),
+            },
+            source => Error::Lance { source },
+        })?;
 
         let dataset = builder
             .with_read_params(params)
@@ -1840,13 +1880,7 @@ impl NativeTable {
         let store_params = params
             .store_params
             .get_or_insert_with(ObjectStoreParams::default);
-        let accessor = match store_params.storage_options().cloned() {
-            Some(options) => {
-                StorageOptionsAccessor::with_initial_and_provider(options, storage_options_provider)
-            }
-            None => StorageOptionsAccessor::with_provider(storage_options_provider),
-        };
-        store_params.storage_options_accessor = Some(Arc::new(accessor));
+        store_params.storage_options_provider = Some(storage_options_provider);
 
         // Patch the params if we have a write store wrapper
         let params = match write_store_wrapper.clone() {
@@ -2022,7 +2056,7 @@ impl NativeTable {
             return provided;
         }
         let suggested = suggested_num_sub_vectors(dim);
-        if num_bits.is_some_and(|num_bits| num_bits == 4) && !suggested.is_multiple_of(2) {
+        if num_bits.is_some_and(|num_bits| num_bits == 4) && suggested % 2 != 0 {
             // num_sub_vectors must be even when 4 bits are used
             suggested + 1
         } else {
@@ -2370,7 +2404,7 @@ impl NativeTable {
                     with_row_id: Some(vq.base.with_row_id),
                     bypass_vector_index: Some(!vq.use_index),
                     full_text_query,
-                    ..Default::default()
+                    version: None,
                 })
             }
             AnyQuery::Query(q) => {
@@ -2432,11 +2466,18 @@ impl NativeTable {
                     columns,
                     prefilter: Some(q.prefilter),
                     offset: q.offset.map(|o| o as i32),
+                    ef: None,
+                    refine_factor: None,
+                    distance_type: None,
+                    nprobes: None,
                     vector_column: None, // No vector column for plain queries
                     with_row_id: Some(q.with_row_id),
                     bypass_vector_index: Some(true), // No vector index for plain queries
                     full_text_query,
-                    ..Default::default()
+                    version: None,
+                    fast_search: None,
+                    lower_bound: None,
+                    upper_bound: None,
                 })
             }
         }
@@ -2765,8 +2806,25 @@ impl BaseTable for NativeTable {
     }
 
     async fn update(&self, update: UpdateBuilder) -> Result<UpdateResult> {
-        // Delegate to the submodule implementation
-        update::execute_update(self, update).await
+        let dataset = self.dataset.get().await?.clone();
+        let mut builder = LanceUpdateBuilder::new(Arc::new(dataset));
+        if let Some(predicate) = update.filter {
+            builder = builder.update_where(&predicate)?;
+        }
+
+        for (column, value) in update.columns {
+            builder = builder.set(column, &value)?;
+        }
+
+        let operation = builder.build()?;
+        let res = operation.execute().await?;
+        self.dataset
+            .set_latest(res.new_dataset.as_ref().clone())
+            .await;
+        Ok(UpdateResult {
+            rows_updated: res.rows_updated,
+            version: res.new_dataset.version().version,
+        })
     }
 
     async fn create_plan(
@@ -3024,8 +3082,11 @@ impl BaseTable for NativeTable {
 
     /// Delete rows from the table
     async fn delete(&self, predicate: &str) -> Result<DeleteResult> {
-        // Delegate to the submodule implementation
-        delete::execute_delete(self, predicate).await
+        let mut dataset = self.dataset.get_mut().await?;
+        dataset.delete(predicate).await?;
+        Ok(DeleteResult {
+            version: dataset.version().version,
+        })
     }
 
     async fn tags(&self) -> Result<Box<dyn Tags + '_>> {
@@ -3169,25 +3230,16 @@ impl BaseTable for NativeTable {
         Ok(results.into_iter().flatten().collect())
     }
 
-    async fn uri(&self) -> Result<String> {
-        Ok(self.uri.clone())
+    fn dataset_uri(&self) -> &str {
+        self.uri.as_str()
     }
 
     async fn storage_options(&self) -> Option<HashMap<String, String>> {
-        self.initial_storage_options().await
-    }
-
-    async fn initial_storage_options(&self) -> Option<HashMap<String, String>> {
         self.dataset
             .get()
             .await
             .ok()
-            .and_then(|dataset| dataset.initial_storage_options().cloned())
-    }
-
-    async fn latest_storage_options(&self) -> Result<Option<HashMap<String, String>>> {
-        let dataset = self.dataset.get().await?;
-        Ok(dataset.latest_storage_options().await?.map(|o| o.0))
+            .and_then(|dataset| dataset.storage_options().cloned())
     }
 
     async fn index_stats(&self, index_name: &str) -> Result<Option<IndexStatistics>> {
@@ -3303,21 +3355,6 @@ impl BaseTable for NativeTable {
         };
         Ok(stats)
     }
-
-    async fn create_insert_exec(
-        &self,
-        input: Arc<dyn datafusion_physical_plan::ExecutionPlan>,
-        write_params: WriteParams,
-    ) -> Result<Arc<dyn datafusion_physical_plan::ExecutionPlan>> {
-        let ds = self.dataset.get().await?;
-        let dataset = Arc::new((*ds).clone());
-        Ok(Arc::new(datafusion::insert::InsertExec::new(
-            self.dataset.clone(),
-            dataset,
-            input,
-            write_params,
-        )))
-    }
 }
 
 #[skip_serializing_none]
@@ -3367,18 +3404,22 @@ pub struct FragmentSummaryStats {
 #[cfg(test)]
 #[allow(deprecated)]
 mod tests {
+    use std::iter;
     use std::sync::atomic::{AtomicBool, Ordering};
     use std::sync::Arc;
     use std::time::Duration;
 
     use arrow_array::{
         builder::{ListBuilder, StringBuilder},
-        Array, BooleanArray, FixedSizeListArray, Float32Array, Int32Array, LargeStringArray,
-        RecordBatch, RecordBatchIterator, RecordBatchReader, StringArray,
+        Array, BooleanArray, Date32Array, FixedSizeListArray, Float32Array, Float64Array,
+        Int32Array, Int64Array, LargeStringArray, RecordBatch, RecordBatchIterator,
+        RecordBatchReader, StringArray, TimestampMillisecondArray, TimestampNanosecondArray,
+        UInt32Array,
     };
     use arrow_array::{BinaryArray, LargeBinaryArray};
     use arrow_data::ArrayDataBuilder;
-    use arrow_schema::{DataType, Field, Schema};
+    use arrow_schema::{DataType, Field, Schema, TimeUnit};
+    use futures::TryStreamExt;
     use lance::dataset::WriteMode;
     use lance::io::{ObjectStoreParams, WrappingObjectStore};
     use lance::Dataset;
@@ -3390,6 +3431,7 @@ mod tests {
     use crate::connection::ConnectBuilder;
     use crate::index::scalar::{BTreeIndexBuilder, BitmapIndexBuilder};
     use crate::index::vector::{IvfHnswPqIndexBuilder, IvfHnswSqIndexBuilder};
+    use crate::query::{ExecutableQuery, QueryBase};
 
     #[tokio::test]
     async fn test_open() {
@@ -3611,6 +3653,306 @@ mod tests {
         assert_eq!(table.name(), "test");
     }
 
+    #[tokio::test]
+    async fn test_update_with_predicate() {
+        let tmp_dir = tempdir().unwrap();
+        let dataset_path = tmp_dir.path().join("test.lance");
+        let uri = dataset_path.to_str().unwrap();
+        let conn = connect(uri)
+            .read_consistency_interval(Duration::from_secs(0))
+            .execute()
+            .await
+            .unwrap();
+
+        let schema = Arc::new(Schema::new(vec![
+            Field::new("id", DataType::Int32, false),
+            Field::new("name", DataType::Utf8, false),
+        ]));
+
+        let record_batch_iter = RecordBatchIterator::new(
+            vec![RecordBatch::try_new(
+                schema.clone(),
+                vec![
+                    Arc::new(Int32Array::from_iter_values(0..10)),
+                    Arc::new(StringArray::from_iter_values(vec![
+                        "a", "b", "c", "d", "e", "f", "g", "h", "i", "j",
+                    ])),
+                ],
+            )
+            .unwrap()]
+            .into_iter()
+            .map(Ok),
+            schema.clone(),
+        );
+
+        let table = conn
+            .create_table("my_table", record_batch_iter)
+            .execute()
+            .await
+            .unwrap();
+
+        table
+            .update()
+            .only_if("id > 5")
+            .column("name", "'foo'")
+            .execute()
+            .await
+            .unwrap();
+
+        let mut batches = table
+            .query()
+            .select(Select::columns(&["id", "name"]))
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+
+        while let Some(batch) = batches.pop() {
+            let ids = batch
+                .column(0)
+                .as_any()
+                .downcast_ref::<Int32Array>()
+                .unwrap()
+                .iter()
+                .collect::<Vec<_>>();
+            let names = batch
+                .column(1)
+                .as_any()
+                .downcast_ref::<StringArray>()
+                .unwrap()
+                .iter()
+                .collect::<Vec<_>>();
+            for (i, name) in names.iter().enumerate() {
+                let id = ids[i].unwrap();
+                let name = name.unwrap();
+                if id > 5 {
+                    assert_eq!(name, "foo");
+                } else {
+                    assert_eq!(name, &format!("{}", (b'a' + id as u8) as char));
+                }
+            }
+        }
+    }
+
+    #[tokio::test]
+    async fn test_update_all_types() {
+        let tmp_dir = tempdir().unwrap();
+        let dataset_path = tmp_dir.path().join("test.lance");
+        let uri = dataset_path.to_str().unwrap();
+        let conn = connect(uri)
+            .read_consistency_interval(Duration::from_secs(0))
+            .execute()
+            .await
+            .unwrap();
+
+        let schema = Arc::new(Schema::new(vec![
+            Field::new("int32", DataType::Int32, false),
+            Field::new("int64", DataType::Int64, false),
+            Field::new("uint32", DataType::UInt32, false),
+            Field::new("string", DataType::Utf8, false),
+            Field::new("large_string", DataType::LargeUtf8, false),
+            Field::new("float32", DataType::Float32, false),
+            Field::new("float64", DataType::Float64, false),
+            Field::new("bool", DataType::Boolean, false),
+            Field::new("date32", DataType::Date32, false),
+            Field::new(
+                "timestamp_ns",
+                DataType::Timestamp(TimeUnit::Nanosecond, None),
+                false,
+            ),
+            Field::new(
+                "timestamp_ms",
+                DataType::Timestamp(TimeUnit::Millisecond, None),
+                false,
+            ),
+            Field::new(
+                "vec_f32",
+                DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2),
+                false,
+            ),
+            Field::new(
+                "vec_f64",
+                DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float64, true)), 2),
+                false,
+            ),
+        ]));
+
+        let record_batch_iter = RecordBatchIterator::new(
+            vec![RecordBatch::try_new(
+                schema.clone(),
+                vec![
+                    Arc::new(Int32Array::from_iter_values(0..10)),
+                    Arc::new(Int64Array::from_iter_values(0..10)),
+                    Arc::new(UInt32Array::from_iter_values(0..10)),
+                    Arc::new(StringArray::from_iter_values(vec![
+                        "a", "b", "c", "d", "e", "f", "g", "h", "i", "j",
+                    ])),
+                    Arc::new(LargeStringArray::from_iter_values(vec![
+                        "a", "b", "c", "d", "e", "f", "g", "h", "i", "j",
+                    ])),
+                    Arc::new(Float32Array::from_iter_values((0..10).map(|i| i as f32))),
+                    Arc::new(Float64Array::from_iter_values((0..10).map(|i| i as f64))),
+                    Arc::new(Into::<BooleanArray>::into(vec![
+                        true, false, true, false, true, false, true, false, true, false,
+                    ])),
+                    Arc::new(Date32Array::from_iter_values(0..10)),
+                    Arc::new(TimestampNanosecondArray::from_iter_values(0..10)),
+                    Arc::new(TimestampMillisecondArray::from_iter_values(0..10)),
+                    Arc::new(
+                        create_fixed_size_list(
+                            Float32Array::from_iter_values((0..20).map(|i| i as f32)),
+                            2,
+                        )
+                        .unwrap(),
+                    ),
+                    Arc::new(
+                        create_fixed_size_list(
+                            Float64Array::from_iter_values((0..20).map(|i| i as f64)),
+                            2,
+                        )
+                        .unwrap(),
+                    ),
+                ],
+            )
+            .unwrap()]
+            .into_iter()
+            .map(Ok),
+            schema.clone(),
+        );
+
+        let table = conn
+            .create_table("my_table", record_batch_iter)
+            .execute()
+            .await
+            .unwrap();
+
+        // check it can do update for each type
+        let updates: Vec<(&str, &str)> = vec![
+            ("string", "'foo'"),
+            ("large_string", "'large_foo'"),
+            ("int32", "1"),
+            ("int64", "1"),
+            ("uint32", "1"),
+            ("float32", "1.0"),
+            ("float64", "1.0"),
+            ("bool", "true"),
+            ("date32", "1"),
+            ("timestamp_ns", "1"),
+            ("timestamp_ms", "1"),
+            ("vec_f32", "[1.0, 1.0]"),
+            ("vec_f64", "[1.0, 1.0]"),
+        ];
+
+        let mut update_op = table.update();
+        for (column, value) in updates {
+            update_op = update_op.column(column, value);
+        }
+        update_op.execute().await.unwrap();
+
+        let mut batches = table
+            .query()
+            .select(Select::columns(&[
+                "string",
+                "large_string",
+                "int32",
+                "int64",
+                "uint32",
+                "float32",
+                "float64",
+                "bool",
+                "date32",
+                "timestamp_ns",
+                "timestamp_ms",
+                "vec_f32",
+                "vec_f64",
+            ]))
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        let batch = batches.pop().unwrap();
+
+        macro_rules! assert_column {
+            ($column:expr, $array_type:ty, $expected:expr) => {
+                let array = $column
+                    .as_any()
+                    .downcast_ref::<$array_type>()
+                    .unwrap()
+                    .iter()
+                    .collect::<Vec<_>>();
+                for v in array {
+                    assert_eq!(v, Some($expected));
+                }
+            };
+        }
+
+        assert_column!(batch.column(0), StringArray, "foo");
+        assert_column!(batch.column(1), LargeStringArray, "large_foo");
+        assert_column!(batch.column(2), Int32Array, 1);
+        assert_column!(batch.column(3), Int64Array, 1);
+        assert_column!(batch.column(4), UInt32Array, 1);
+        assert_column!(batch.column(5), Float32Array, 1.0);
+        assert_column!(batch.column(6), Float64Array, 1.0);
+        assert_column!(batch.column(7), BooleanArray, true);
+        assert_column!(batch.column(8), Date32Array, 1);
+        assert_column!(batch.column(9), TimestampNanosecondArray, 1);
+        assert_column!(batch.column(10), TimestampMillisecondArray, 1);
+
+        let array = batch
+            .column(11)
+            .as_any()
+            .downcast_ref::<FixedSizeListArray>()
+            .unwrap()
+            .iter()
+            .collect::<Vec<_>>();
+        for v in array {
+            let v = v.unwrap();
+            let f32array = v.as_any().downcast_ref::<Float32Array>().unwrap();
+            for v in f32array {
+                assert_eq!(v, Some(1.0));
+            }
+        }
+
+        let array = batch
+            .column(12)
+            .as_any()
+            .downcast_ref::<FixedSizeListArray>()
+            .unwrap()
+            .iter()
+            .collect::<Vec<_>>();
+        for v in array {
+            let v = v.unwrap();
+            let f64array = v.as_any().downcast_ref::<Float64Array>().unwrap();
+            for v in f64array {
+                assert_eq!(v, Some(1.0));
+            }
+        }
+    }
+
+    #[tokio::test]
+    async fn test_update_via_expr() {
+        let tmp_dir = tempdir().unwrap();
+        let dataset_path = tmp_dir.path().join("test.lance");
+        let uri = dataset_path.to_str().unwrap();
+        let conn = connect(uri)
+            .read_consistency_interval(Duration::from_secs(0))
+            .execute()
+            .await
+            .unwrap();
+        let tbl = conn
+            .create_table("my_table", make_test_batches())
+            .execute()
+            .await
+            .unwrap();
+        assert_eq!(1, tbl.count_rows(Some("i == 0".to_string())).await.unwrap());
+        tbl.update().column("i", "i+1").execute().await.unwrap();
+        assert_eq!(0, tbl.count_rows(Some("i == 0".to_string())).await.unwrap());
+    }
+
     #[derive(Default, Debug)]
     struct NoOpCacheWrapper {
         called: AtomicBool,
@@ -3679,7 +4021,7 @@ mod tests {
                 schema.clone(),
                 vec![
                     Arc::new(Int32Array::from_iter_values(offset..(offset + 10))),
-                    Arc::new(Int32Array::from_iter_values(std::iter::repeat_n(age, 10))),
+                    Arc::new(Int32Array::from_iter_values(iter::repeat(age).take(10))),
                 ],
             )],
             schema,
@@ -4814,8 +5156,8 @@ mod tests {
             ns_request
                 .columns
                 .as_ref()
-                .and_then(|c| c.column_names.as_ref()),
-            Some(&vec!["id".to_string()])
+                .and_then(|c| c.column_names.clone()),
+            Some(vec!["id".to_string()])
         );
         assert_eq!(ns_request.vector_column, Some("vector".to_string()));
         assert_eq!(ns_request.distance_type, Some("l2".to_string()));
@@ -4861,8 +5203,8 @@ mod tests {
             ns_request
                 .columns
                 .as_ref()
-                .and_then(|c| c.column_names.as_ref()),
-            Some(&vec!["id".to_string()])
+                .and_then(|c| c.column_names.clone()),
+            Some(vec!["id".to_string()])
         );
         assert_eq!(ns_request.with_row_id, Some(true));
         assert_eq!(ns_request.bypass_vector_index, Some(true));

rust/lancedb/src/table/datafusion.rs

@@ -3,7 +3,6 @@
 
 //! This module contains adapters to allow LanceDB tables to be used as DataFusion table providers.
 
-pub mod insert;
 pub mod udtf;
 
 use std::{collections::HashMap, sync::Arc};
@@ -14,12 +13,11 @@ use async_trait::async_trait;
 use datafusion_catalog::{Session, TableProvider};
 use datafusion_common::{DataFusionError, Result as DataFusionResult, Statistics};
 use datafusion_execution::{SendableRecordBatchStream, TaskContext};
-use datafusion_expr::{dml::InsertOp, Expr, TableProviderFilterPushDown, TableType};
+use datafusion_expr::{Expr, TableProviderFilterPushDown, TableType};
 use datafusion_physical_plan::{
     stream::RecordBatchStreamAdapter, DisplayAs, DisplayFormatType, ExecutionPlan, PlanProperties,
 };
 use futures::{TryFutureExt, TryStreamExt};
-use lance::dataset::{WriteMode, WriteParams};
 
 use super::{AnyQuery, BaseTable};
 use crate::{
@@ -252,33 +250,6 @@ impl TableProvider for BaseTableAdapter {
         // TODO
         None
     }
-
-    async fn insert_into(
-        &self,
-        _state: &dyn Session,
-        input: Arc<dyn ExecutionPlan>,
-        insert_op: InsertOp,
-    ) -> DataFusionResult<Arc<dyn ExecutionPlan>> {
-        let mode = match insert_op {
-            InsertOp::Append => WriteMode::Append,
-            InsertOp::Overwrite => WriteMode::Overwrite,
-            InsertOp::Replace => {
-                return Err(DataFusionError::NotImplemented(
-                    "Replace mode is not supported for LanceDB tables".to_string(),
-                ))
-            }
-        };
-
-        let write_params = WriteParams {
-            mode,
-            ..Default::default()
-        };
-
-        self.table
-            .create_insert_exec(input, write_params)
-            .await
-            .map_err(|e| DataFusionError::External(e.into()))
-    }
 }
 
 #[cfg(test)]

rust/lancedb/src/table/datafusion/insert.rs

@@ -1,446 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-//! DataFusion ExecutionPlan for inserting data into LanceDB tables.
-
-use std::any::Any;
-use std::sync::{Arc, LazyLock, Mutex};
-
-use arrow_array::{RecordBatch, UInt64Array};
-use arrow_schema::{DataType, Field, Schema as ArrowSchema, SchemaRef};
-use datafusion_common::{DataFusionError, Result as DataFusionResult};
-use datafusion_execution::{SendableRecordBatchStream, TaskContext};
-use datafusion_physical_expr::{EquivalenceProperties, Partitioning};
-use datafusion_physical_plan::execution_plan::{Boundedness, EmissionType};
-use datafusion_physical_plan::stream::RecordBatchStreamAdapter;
-use datafusion_physical_plan::{
-    DisplayAs, DisplayFormatType, ExecutionPlan, ExecutionPlanProperties, PlanProperties,
-};
-use lance::dataset::transaction::{Operation, Transaction};
-use lance::dataset::{CommitBuilder, InsertBuilder, WriteParams};
-use lance::Dataset;
-use lance_table::format::Fragment;
-
-use crate::table::dataset::DatasetConsistencyWrapper;
-
-pub(crate) static COUNT_SCHEMA: LazyLock<SchemaRef> = LazyLock::new(|| {
-    Arc::new(ArrowSchema::new(vec![Field::new(
-        "count",
-        DataType::UInt64,
-        false,
-    )]))
-});
-
-fn operation_fragments(operation: &Operation) -> &[Fragment] {
-    match operation {
-        Operation::Append { fragments } => fragments,
-        Operation::Overwrite { fragments, .. } => fragments,
-        _ => &[],
-    }
-}
-
-fn count_rows_from_operation(operation: &Operation) -> u64 {
-    operation_fragments(operation)
-        .iter()
-        .map(|f| f.num_rows().unwrap_or(0) as u64)
-        .sum()
-}
-
-fn operation_fragments_mut(operation: &mut Operation) -> &mut Vec<Fragment> {
-    match operation {
-        Operation::Append { fragments } => fragments,
-        Operation::Overwrite { fragments, .. } => fragments,
-        _ => panic!("Unsupported operation type for getting mutable fragments"),
-    }
-}
-
-fn merge_transactions(mut transactions: Vec<Transaction>) -> Option<Transaction> {
-    let mut first = transactions.pop()?;
-
-    for txn in transactions {
-        let first_fragments = operation_fragments_mut(&mut first.operation);
-        let txn_fragments = operation_fragments(&txn.operation);
-        first_fragments.extend_from_slice(txn_fragments);
-    }
-
-    Some(first)
-}
-
-/// ExecutionPlan for inserting data into a native LanceDB table.
-///
-/// This plan executes inserts by:
-/// 1. Each partition writes data independently using InsertBuilder::execute_uncommitted_stream
-/// 2. The last partition to complete commits all transactions atomically
-/// 3. Returns the count of inserted rows per partition
-#[derive(Debug)]
-pub struct InsertExec {
-    ds_wrapper: DatasetConsistencyWrapper,
-    dataset: Arc<Dataset>,
-    input: Arc<dyn ExecutionPlan>,
-    write_params: WriteParams,
-    properties: PlanProperties,
-    partial_transactions: Arc<Mutex<Vec<Transaction>>>,
-}
-
-impl InsertExec {
-    pub fn new(
-        ds_wrapper: DatasetConsistencyWrapper,
-        dataset: Arc<Dataset>,
-        input: Arc<dyn ExecutionPlan>,
-        write_params: WriteParams,
-    ) -> Self {
-        let schema = COUNT_SCHEMA.clone();
-        let num_partitions = input.output_partitioning().partition_count();
-        let properties = PlanProperties::new(
-            EquivalenceProperties::new(schema),
-            Partitioning::UnknownPartitioning(num_partitions),
-            EmissionType::Final,
-            Boundedness::Bounded,
-        );
-
-        Self {
-            ds_wrapper,
-            dataset,
-            input,
-            write_params,
-            properties,
-            partial_transactions: Arc::new(Mutex::new(Vec::with_capacity(num_partitions))),
-        }
-    }
-}
-
-impl DisplayAs for InsertExec {
-    fn fmt_as(&self, t: DisplayFormatType, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        match t {
-            DisplayFormatType::Default | DisplayFormatType::Verbose => {
-                write!(f, "InsertExec: mode={:?}", self.write_params.mode)
-            }
-            DisplayFormatType::TreeRender => {
-                write!(f, "InsertExec")
-            }
-        }
-    }
-}
-
-impl ExecutionPlan for InsertExec {
-    fn name(&self) -> &str {
-        Self::static_name()
-    }
-
-    fn as_any(&self) -> &dyn Any {
-        self
-    }
-
-    fn properties(&self) -> &PlanProperties {
-        &self.properties
-    }
-
-    fn children(&self) -> Vec<&Arc<dyn ExecutionPlan>> {
-        vec![&self.input]
-    }
-
-    fn maintains_input_order(&self) -> Vec<bool> {
-        vec![false]
-    }
-
-    fn benefits_from_input_partitioning(&self) -> Vec<bool> {
-        vec![false]
-    }
-
-    fn with_new_children(
-        self: Arc<Self>,
-        children: Vec<Arc<dyn ExecutionPlan>>,
-    ) -> DataFusionResult<Arc<dyn ExecutionPlan>> {
-        if children.len() != 1 {
-            return Err(DataFusionError::Internal(
-                "InsertExec requires exactly one child".to_string(),
-            ));
-        }
-        Ok(Arc::new(Self::new(
-            self.ds_wrapper.clone(),
-            self.dataset.clone(),
-            children[0].clone(),
-            self.write_params.clone(),
-        )))
-    }
-
-    fn execute(
-        &self,
-        partition: usize,
-        context: Arc<TaskContext>,
-    ) -> DataFusionResult<SendableRecordBatchStream> {
-        let input_stream = self.input.execute(partition, context)?;
-        let dataset = self.dataset.clone();
-        let write_params = self.write_params.clone();
-        let partial_transactions = self.partial_transactions.clone();
-        let total_partitions = self.input.output_partitioning().partition_count();
-        let ds_wrapper = self.ds_wrapper.clone();
-
-        let stream = futures::stream::once(async move {
-            let transaction = InsertBuilder::new(dataset.clone())
-                .with_params(&write_params)
-                .execute_uncommitted_stream(input_stream)
-                .await?;
-
-            let num_rows = count_rows_from_operation(&transaction.operation);
-
-            let to_commit = {
-                // Don't hold the lock over an await point.
-                let mut txns = partial_transactions.lock().unwrap();
-                txns.push(transaction);
-                if txns.len() == total_partitions {
-                    Some(std::mem::take(&mut *txns))
-                } else {
-                    None
-                }
-            };
-
-            if let Some(transactions) = to_commit {
-                if let Some(merged_txn) = merge_transactions(transactions) {
-                    let new_dataset = CommitBuilder::new(dataset.clone())
-                        .execute(merged_txn)
-                        .await?;
-                    ds_wrapper.set_latest(new_dataset).await;
-                }
-            }
-
-            Ok(RecordBatch::try_new(
-                COUNT_SCHEMA.clone(),
-                vec![Arc::new(UInt64Array::from(vec![num_rows]))],
-            )?)
-        });
-
-        Ok(Box::pin(RecordBatchStreamAdapter::new(
-            COUNT_SCHEMA.clone(),
-            stream,
-        )))
-    }
-}
-
-#[cfg(test)]
-mod tests {
-    use std::vec;
-
-    use super::*;
-    use arrow_array::{record_batch, Int32Array, RecordBatchIterator};
-    use datafusion::prelude::SessionContext;
-    use datafusion_catalog::MemTable;
-    use tempfile::tempdir;
-
-    use crate::connect;
-
-    #[tokio::test]
-    async fn test_insert_via_sql() {
-        let tmp_dir = tempdir().unwrap();
-        let uri = tmp_dir.path().to_str().unwrap();
-
-        let db = connect(uri).execute().await.unwrap();
-
-        // Create initial table
-        let batch = record_batch!(("id", Int32, [1, 2, 3])).unwrap();
-        let schema = batch.schema();
-        let reader = RecordBatchIterator::new(vec![Ok(batch)], schema);
-
-        let table = db
-            .create_table("test_insert", Box::new(reader))
-            .execute()
-            .await
-            .unwrap();
-
-        // Verify initial count
-        assert_eq!(table.count_rows(None).await.unwrap(), 3);
-
-        let ctx = SessionContext::new();
-        let provider =
-            crate::table::datafusion::BaseTableAdapter::try_new(table.base_table().clone())
-                .await
-                .unwrap();
-        ctx.register_table("test_insert", Arc::new(provider))
-            .unwrap();
-
-        ctx.sql("INSERT INTO test_insert VALUES (4), (5), (6)")
-            .await
-            .unwrap()
-            .collect()
-            .await
-            .unwrap();
-
-        // Verify final count
-        table.checkout_latest().await.unwrap();
-        assert_eq!(table.count_rows(None).await.unwrap(), 6);
-    }
-
-    #[tokio::test]
-    async fn test_insert_overwrite_via_sql() {
-        let tmp_dir = tempdir().unwrap();
-        let uri = tmp_dir.path().to_str().unwrap();
-
-        let db = connect(uri).execute().await.unwrap();
-
-        // Create initial table with 3 rows
-        let batch = record_batch!(("id", Int32, [1, 2, 3])).unwrap();
-        let schema = batch.schema();
-        let reader = RecordBatchIterator::new(vec![Ok(batch)], schema);
-
-        let table = db
-            .create_table("test_overwrite", Box::new(reader))
-            .execute()
-            .await
-            .unwrap();
-
-        assert_eq!(table.count_rows(None).await.unwrap(), 3);
-
-        let ctx = SessionContext::new();
-        let provider =
-            crate::table::datafusion::BaseTableAdapter::try_new(table.base_table().clone())
-                .await
-                .unwrap();
-        ctx.register_table("test_overwrite", Arc::new(provider))
-            .unwrap();
-
-        ctx.sql("INSERT OVERWRITE INTO test_overwrite VALUES (10), (20)")
-            .await
-            .unwrap()
-            .collect()
-            .await
-            .unwrap();
-
-        // Verify: should have 2 rows (overwritten, not appended)
-        table.checkout_latest().await.unwrap();
-        assert_eq!(table.count_rows(None).await.unwrap(), 2);
-    }
-
-    #[tokio::test]
-    async fn test_insert_empty_batch() {
-        let tmp_dir = tempdir().unwrap();
-        let uri = tmp_dir.path().to_str().unwrap();
-
-        let db = connect(uri).execute().await.unwrap();
-
-        // Create initial table
-        let schema = Arc::new(ArrowSchema::new(vec![Field::new(
-            "id",
-            DataType::Int32,
-            false,
-        )]));
-        let batches = vec![RecordBatch::try_new(
-            schema.clone(),
-            vec![Arc::new(Int32Array::from(vec![1, 2, 3]))],
-        )
-        .unwrap()];
-        let reader = RecordBatchIterator::new(batches.into_iter().map(Ok), schema.clone());
-
-        let table = db
-            .create_table("test_empty", Box::new(reader))
-            .execute()
-            .await
-            .unwrap();
-
-        assert_eq!(table.count_rows(None).await.unwrap(), 3);
-
-        let ctx = SessionContext::new();
-        let provider =
-            crate::table::datafusion::BaseTableAdapter::try_new(table.base_table().clone())
-                .await
-                .unwrap();
-        ctx.register_table("test_empty", Arc::new(provider))
-            .unwrap();
-
-        let source_schema = Arc::new(ArrowSchema::new(vec![Field::new(
-            "id",
-            DataType::Int32,
-            false,
-        )]));
-        // Empty batches
-        let source_reader = RecordBatchIterator::new(
-            std::iter::empty::<Result<RecordBatch, arrow_schema::ArrowError>>(),
-            source_schema,
-        );
-        let source_table = db
-            .create_table("empty_source", Box::new(source_reader))
-            .execute()
-            .await
-            .unwrap();
-        let source_provider =
-            crate::table::datafusion::BaseTableAdapter::try_new(source_table.base_table().clone())
-                .await
-                .unwrap();
-        ctx.register_table("empty_source", Arc::new(source_provider))
-            .unwrap();
-
-        // Execute INSERT with empty source
-        ctx.sql("INSERT INTO test_empty SELECT * FROM empty_source")
-            .await
-            .unwrap()
-            .collect()
-            .await
-            .unwrap();
-
-        // Verify: should still have 3 rows (nothing inserted)
-        table.checkout_latest().await.unwrap();
-        assert_eq!(table.count_rows(None).await.unwrap(), 3);
-    }
-
-    #[tokio::test]
-    async fn test_insert_multiple_batches() {
-        let tmp_dir = tempdir().unwrap();
-        let uri = tmp_dir.path().to_str().unwrap();
-
-        let db = connect(uri).execute().await.unwrap();
-
-        // Create initial table
-        let schema = Arc::new(ArrowSchema::new(vec![Field::new(
-            "id",
-            DataType::Int32,
-            true,
-        )]));
-        let batches =
-            vec![
-                RecordBatch::try_new(schema.clone(), vec![Arc::new(Int32Array::from(vec![1]))])
-                    .unwrap(),
-            ];
-        let reader = RecordBatchIterator::new(batches.into_iter().map(Ok), schema.clone());
-
-        let table = db
-            .create_table("test_multi_batch", Box::new(reader))
-            .execute()
-            .await
-            .unwrap();
-
-        let ctx = SessionContext::new();
-        let provider =
-            crate::table::datafusion::BaseTableAdapter::try_new(table.base_table().clone())
-                .await
-                .unwrap();
-        ctx.register_table("test_multi_batch", Arc::new(provider))
-            .unwrap();
-
-        // Memtable with multiple batches and multiple partitions
-        let source_table = MemTable::try_new(
-            schema.clone(),
-            vec![
-                // Partition 0
-                vec![
-                    record_batch!(("id", Int32, [2, 3])).unwrap(),
-                    record_batch!(("id", Int32, [4, 5])).unwrap(),
-                ],
-                // Partition 1
-                vec![record_batch!(("id", Int32, [6, 7, 8])).unwrap()],
-            ],
-        )
-        .unwrap();
-        ctx.register_table("multi_batch_source", Arc::new(source_table))
-            .unwrap();
-
-        ctx.sql("INSERT INTO test_multi_batch SELECT * FROM multi_batch_source")
-            .await
-            .unwrap()
-            .collect()
-            .await
-            .unwrap();
-
-        // Verify: should have 1 + 2 + 2 + 3 = 8 rows
-        table.checkout_latest().await.unwrap();
-        assert_eq!(table.count_rows(None).await.unwrap(), 8);
-    }
-}

rust/lancedb/src/table/delete.rs

@@ -1,161 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-use serde::{Deserialize, Serialize};
-
-use super::NativeTable;
-use crate::Result;
-
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize, Default)]
-pub struct DeleteResult {
-    // The commit version associated with the operation.
-    // A version of `0` indicates compatibility with legacy servers that do not return
-    /// a commit version.
-    #[serde(default)]
-    pub version: u64,
-}
-
-/// Internal implementation of the delete logic
-///
-/// This logic was moved from NativeTable::delete to keep table.rs clean.
-pub(crate) async fn execute_delete(table: &NativeTable, predicate: &str) -> Result<DeleteResult> {
-    // We access the dataset from the table. Since this is in the same module hierarchy (super),
-    // and 'dataset' is pub(crate), we can access it.
-    let mut dataset = table.dataset.get_mut().await?;
-
-    // Perform the actual delete on the Lance dataset
-    dataset.delete(predicate).await?;
-
-    // Return the result with the new version
-    Ok(DeleteResult {
-        version: dataset.version().version,
-    })
-}
-
-#[cfg(test)]
-mod tests {
-    use crate::connect;
-    use arrow_array::{record_batch, Int32Array, RecordBatch, RecordBatchIterator};
-    use arrow_schema::{DataType, Field, Schema};
-    use std::sync::Arc;
-
-    use crate::query::ExecutableQuery;
-    use futures::TryStreamExt;
-    #[tokio::test]
-    async fn test_delete_simple() {
-        let conn = connect("memory://").execute().await.unwrap();
-
-        // 1. Create a table with values 0 to 9
-        let schema = Arc::new(Schema::new(vec![Field::new("i", DataType::Int32, false)]));
-        let batch = RecordBatch::try_new(
-            schema.clone(),
-            vec![Arc::new(Int32Array::from_iter_values(0..10))],
-        )
-        .unwrap();
-
-        let table = conn
-            .create_table(
-                "test_delete",
-                RecordBatchIterator::new(vec![Ok(batch)], schema),
-            )
-            .execute()
-            .await
-            .unwrap();
-
-        // 2. Verify initial state
-        assert_eq!(table.count_rows(None).await.unwrap(), 10);
-
-        // 3. Execute Delete (removes values > 5)
-        table.delete("i > 5").await.unwrap();
-
-        // 4. Verify results
-        assert_eq!(table.count_rows(None).await.unwrap(), 6); // 0, 1, 2, 3, 4, 5 remain
-
-        // 5. Verify specific data consistency
-        let batches = table
-            .query()
-            .execute()
-            .await
-            .unwrap()
-            .try_collect::<Vec<_>>()
-            .await
-            .unwrap();
-        let batch = &batches[0];
-        let array = batch
-            .column(0)
-            .as_any()
-            .downcast_ref::<Int32Array>()
-            .unwrap();
-
-        // Ensure no value > 5 exists
-        for val in array.iter() {
-            assert!(val.unwrap() <= 5);
-        }
-    }
-    #[tokio::test]
-    async fn rows_removed_schema_same() {
-        let conn = connect("memory://").execute().await.unwrap();
-        let batch = record_batch!(
-            ("id", Int32, [1, 2, 3, 4, 5]),
-            ("name", Utf8, ["a", "b", "c", "d", "e"])
-        )
-        .unwrap();
-        let original_schema = batch.schema();
-
-        let table = conn
-            .create_table(
-                "test_delete_all",
-                RecordBatchIterator::new(vec![Ok(batch)], original_schema.clone()),
-            )
-            .execute()
-            .await
-            .unwrap();
-
-        table.delete("true").await.unwrap();
-
-        assert_eq!(table.count_rows(None).await.unwrap(), 0);
-
-        let current_schema = table.schema().await.unwrap();
-        //check if the original schema is the same as current
-        assert_eq!(current_schema, original_schema);
-    }
-
-    #[tokio::test]
-    async fn test_delete_false_increments_version() {
-        let conn = connect("memory://").execute().await.unwrap();
-
-        // Create a table with 5 rows
-        let batch = record_batch!(("id", Int32, [1, 2, 3, 4, 5])).unwrap();
-
-        let schema = batch.schema();
-
-        let table = conn
-            .create_table(
-                "test_delete_noop",
-                RecordBatchIterator::new(vec![Ok(batch)], schema),
-            )
-            .execute()
-            .await
-            .unwrap();
-
-        // Capture the initial state (Rows = 5, Version = 1)
-        let initial_rows = table.count_rows(None).await.unwrap();
-        let initial_version = table.version().await.unwrap();
-
-        assert_eq!(initial_rows, 5);
-        table.delete("false").await.unwrap();
-
-        // Rows should still be 5
-        let current_rows = table.count_rows(None).await.unwrap();
-        assert_eq!(
-            current_rows, initial_rows,
-            "Data should not change when predicate is false"
-        );
-
-        // version check
-        let current_version = table.version().await.unwrap();
-        assert!(
-            current_version > initial_version,
-            "Table version must increment after delete operation"
-        );
-    }
-}

rust/lancedb/src/table/update.rs

@@ -1,441 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-use std::sync::Arc;
-
-use lance::dataset::UpdateBuilder as LanceUpdateBuilder;
-use serde::{Deserialize, Serialize};
-
-use super::{BaseTable, NativeTable};
-use crate::Error;
-use crate::Result;
-
-/// The result of an update operation
-#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize, Default)]
-pub struct UpdateResult {
-    #[serde(default)]
-    pub rows_updated: u64,
-    /// The commit version associated with the operation.
-    #[serde(default)]
-    pub version: u64,
-}
-
-/// A builder for configuring a [`crate::table::Table::update`] operation
-#[derive(Debug, Clone)]
-pub struct UpdateBuilder {
-    parent: Arc<dyn BaseTable>,
-    pub(crate) filter: Option<String>,
-    pub(crate) columns: Vec<(String, String)>,
-}
-
-impl UpdateBuilder {
-    pub(crate) fn new(parent: Arc<dyn BaseTable>) -> Self {
-        Self {
-            parent,
-            filter: None,
-            columns: Vec::new(),
-        }
-    }
-
-    /// Limits the update operation to rows matching the given filter
-    ///
-    /// If a row does not match the filter then it will be left unchanged.
-    pub fn only_if(mut self, filter: impl Into<String>) -> Self {
-        self.filter = Some(filter.into());
-        self
-    }
-
-    /// Specifies a column to update
-    ///
-    /// This method may be called multiple times to update multiple columns
-    ///
-    /// The `update_expr` should be an SQL expression explaining how to calculate
-    /// the new value for the column.  The expression will be evaluated against the
-    /// previous row's value.
-    pub fn column(
-        mut self,
-        column_name: impl Into<String>,
-        update_expr: impl Into<String>,
-    ) -> Self {
-        self.columns.push((column_name.into(), update_expr.into()));
-        self
-    }
-
-    /// Executes the update operation.
-    pub async fn execute(self) -> Result<UpdateResult> {
-        if self.columns.is_empty() {
-            Err(Error::InvalidInput {
-                message: "at least one column must be specified in an update operation".to_string(),
-            })
-        } else {
-            self.parent.clone().update(self).await
-        }
-    }
-}
-
-/// Internal implementation of the update logic
-pub(crate) async fn execute_update(
-    table: &NativeTable,
-    update: UpdateBuilder,
-) -> Result<UpdateResult> {
-    // 1. Snapshot the current dataset
-    let dataset = table.dataset.get().await?.clone();
-
-    // 2. Initialize the Lance Core builder
-    let mut builder = LanceUpdateBuilder::new(Arc::new(dataset));
-
-    // 3. Apply the filter (WHERE clause)
-    if let Some(predicate) = update.filter {
-        builder = builder.update_where(&predicate)?;
-    }
-
-    // 4. Apply the columns (SET clause)
-    for (column, value) in update.columns {
-        builder = builder.set(column, &value)?;
-    }
-
-    // 5. Execute the operation (Write new files)
-    let operation = builder.build()?;
-    let res = operation.execute().await?;
-
-    // 6. Update the table's view of the latest version
-    table
-        .dataset
-        .set_latest(res.new_dataset.as_ref().clone())
-        .await;
-
-    Ok(UpdateResult {
-        rows_updated: res.rows_updated,
-        version: res.new_dataset.version().version,
-    })
-}
-
-#[cfg(test)]
-mod tests {
-    use crate::connect;
-    use crate::query::QueryBase;
-    use crate::query::{ExecutableQuery, Select};
-    use arrow_array::{
-        record_batch, Array, BooleanArray, Date32Array, FixedSizeListArray, Float32Array,
-        Float64Array, Int32Array, Int64Array, LargeStringArray, RecordBatch, RecordBatchIterator,
-        RecordBatchReader, StringArray, TimestampMillisecondArray, TimestampNanosecondArray,
-        UInt32Array,
-    };
-    use arrow_data::ArrayDataBuilder;
-    use arrow_schema::{ArrowError, DataType, Field, Schema, TimeUnit};
-    use futures::TryStreamExt;
-    use std::sync::Arc;
-    use std::time::Duration;
-
-    #[tokio::test]
-    async fn test_update_all_types() {
-        let conn = connect("memory://")
-            .read_consistency_interval(Duration::from_secs(0))
-            .execute()
-            .await
-            .unwrap();
-
-        let schema = Arc::new(Schema::new(vec![
-            Field::new("int32", DataType::Int32, false),
-            Field::new("int64", DataType::Int64, false),
-            Field::new("uint32", DataType::UInt32, false),
-            Field::new("string", DataType::Utf8, false),
-            Field::new("large_string", DataType::LargeUtf8, false),
-            Field::new("float32", DataType::Float32, false),
-            Field::new("float64", DataType::Float64, false),
-            Field::new("bool", DataType::Boolean, false),
-            Field::new("date32", DataType::Date32, false),
-            Field::new(
-                "timestamp_ns",
-                DataType::Timestamp(TimeUnit::Nanosecond, None),
-                false,
-            ),
-            Field::new(
-                "timestamp_ms",
-                DataType::Timestamp(TimeUnit::Millisecond, None),
-                false,
-            ),
-            Field::new(
-                "vec_f32",
-                DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2),
-                false,
-            ),
-            Field::new(
-                "vec_f64",
-                DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float64, true)), 2),
-                false,
-            ),
-        ]));
-
-        let record_batch_iter = RecordBatchIterator::new(
-            vec![RecordBatch::try_new(
-                schema.clone(),
-                vec![
-                    Arc::new(Int32Array::from_iter_values(0..10)),
-                    Arc::new(Int64Array::from_iter_values(0..10)),
-                    Arc::new(UInt32Array::from_iter_values(0..10)),
-                    Arc::new(StringArray::from_iter_values(vec![
-                        "a", "b", "c", "d", "e", "f", "g", "h", "i", "j",
-                    ])),
-                    Arc::new(LargeStringArray::from_iter_values(vec![
-                        "a", "b", "c", "d", "e", "f", "g", "h", "i", "j",
-                    ])),
-                    Arc::new(Float32Array::from_iter_values((0..10).map(|i| i as f32))),
-                    Arc::new(Float64Array::from_iter_values((0..10).map(|i| i as f64))),
-                    Arc::new(Into::<BooleanArray>::into(vec![
-                        true, false, true, false, true, false, true, false, true, false,
-                    ])),
-                    Arc::new(Date32Array::from_iter_values(0..10)),
-                    Arc::new(TimestampNanosecondArray::from_iter_values(0..10)),
-                    Arc::new(TimestampMillisecondArray::from_iter_values(0..10)),
-                    Arc::new(
-                        create_fixed_size_list(
-                            Float32Array::from_iter_values((0..20).map(|i| i as f32)),
-                            2,
-                        )
-                        .unwrap(),
-                    ),
-                    Arc::new(
-                        create_fixed_size_list(
-                            Float64Array::from_iter_values((0..20).map(|i| i as f64)),
-                            2,
-                        )
-                        .unwrap(),
-                    ),
-                ],
-            )
-            .unwrap()]
-            .into_iter()
-            .map(Ok),
-            schema.clone(),
-        );
-
-        let table = conn
-            .create_table("my_table", record_batch_iter)
-            .execute()
-            .await
-            .unwrap();
-
-        // check it can do update for each type
-        let updates: Vec<(&str, &str)> = vec![
-            ("string", "'foo'"),
-            ("large_string", "'large_foo'"),
-            ("int32", "1"),
-            ("int64", "1"),
-            ("uint32", "1"),
-            ("float32", "1.0"),
-            ("float64", "1.0"),
-            ("bool", "true"),
-            ("date32", "1"),
-            ("timestamp_ns", "1"),
-            ("timestamp_ms", "1"),
-            ("vec_f32", "[1.0, 1.0]"),
-            ("vec_f64", "[1.0, 1.0]"),
-        ];
-
-        let mut update_op = table.update();
-        for (column, value) in updates {
-            update_op = update_op.column(column, value);
-        }
-        update_op.execute().await.unwrap();
-
-        let mut batches = table
-            .query()
-            .select(Select::columns(&[
-                "string",
-                "large_string",
-                "int32",
-                "int64",
-                "uint32",
-                "float32",
-                "float64",
-                "bool",
-                "date32",
-                "timestamp_ns",
-                "timestamp_ms",
-                "vec_f32",
-                "vec_f64",
-            ]))
-            .execute()
-            .await
-            .unwrap()
-            .try_collect::<Vec<_>>()
-            .await
-            .unwrap();
-        let batch = batches.pop().unwrap();
-
-        macro_rules! assert_column {
-            ($column:expr, $array_type:ty, $expected:expr) => {
-                let array = $column
-                    .as_any()
-                    .downcast_ref::<$array_type>()
-                    .unwrap()
-                    .iter()
-                    .collect::<Vec<_>>();
-                for v in array {
-                    assert_eq!(v, Some($expected));
-                }
-            };
-        }
-
-        assert_column!(batch.column(0), StringArray, "foo");
-        assert_column!(batch.column(1), LargeStringArray, "large_foo");
-        assert_column!(batch.column(2), Int32Array, 1);
-        assert_column!(batch.column(3), Int64Array, 1);
-        assert_column!(batch.column(4), UInt32Array, 1);
-        assert_column!(batch.column(5), Float32Array, 1.0);
-        assert_column!(batch.column(6), Float64Array, 1.0);
-        assert_column!(batch.column(7), BooleanArray, true);
-        assert_column!(batch.column(8), Date32Array, 1);
-        assert_column!(batch.column(9), TimestampNanosecondArray, 1);
-        assert_column!(batch.column(10), TimestampMillisecondArray, 1);
-
-        let array = batch
-            .column(11)
-            .as_any()
-            .downcast_ref::<FixedSizeListArray>()
-            .unwrap()
-            .iter()
-            .collect::<Vec<_>>();
-        for v in array {
-            let v = v.unwrap();
-            let f32array = v.as_any().downcast_ref::<Float32Array>().unwrap();
-            for v in f32array {
-                assert_eq!(v, Some(1.0));
-            }
-        }
-
-        let array = batch
-            .column(12)
-            .as_any()
-            .downcast_ref::<FixedSizeListArray>()
-            .unwrap()
-            .iter()
-            .collect::<Vec<_>>();
-        for v in array {
-            let v = v.unwrap();
-            let f64array = v.as_any().downcast_ref::<Float64Array>().unwrap();
-            for v in f64array {
-                assert_eq!(v, Some(1.0));
-            }
-        }
-    }
-    ///Two helper functions
-    fn create_fixed_size_list<T: Array>(
-        values: T,
-        list_size: i32,
-    ) -> Result<FixedSizeListArray, ArrowError> {
-        let list_type = DataType::FixedSizeList(
-            Arc::new(Field::new("item", values.data_type().clone(), true)),
-            list_size,
-        );
-        let data = ArrayDataBuilder::new(list_type)
-            .len(values.len() / list_size as usize)
-            .add_child_data(values.into_data())
-            .build()
-            .unwrap();
-
-        Ok(FixedSizeListArray::from(data))
-    }
-
-    fn make_test_batches() -> impl RecordBatchReader + Send + Sync + 'static {
-        let schema = Arc::new(Schema::new(vec![Field::new("i", DataType::Int32, false)]));
-        RecordBatchIterator::new(
-            vec![RecordBatch::try_new(
-                schema.clone(),
-                vec![Arc::new(Int32Array::from_iter_values(0..10))],
-            )],
-            schema,
-        )
-    }
-
-    #[tokio::test]
-    async fn test_update_with_predicate() {
-        let conn = connect("memory://")
-            .read_consistency_interval(Duration::from_secs(0))
-            .execute()
-            .await
-            .unwrap();
-
-        let batch = record_batch!(
-            ("id", Int32, [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),
-            (
-                "name",
-                Utf8,
-                ["a", "b", "c", "d", "e", "f", "g", "h", "i", "j"]
-            )
-        )
-        .unwrap();
-
-        let schema = batch.schema();
-        // need the iterator for create table
-        let record_batch_iter = RecordBatchIterator::new(vec![Ok(batch)], schema);
-
-        let table = conn
-            .create_table("my_table", record_batch_iter)
-            .execute()
-            .await
-            .unwrap();
-
-        table
-            .update()
-            .only_if("id > 5")
-            .column("name", "'foo'")
-            .execute()
-            .await
-            .unwrap();
-
-        let mut batches = table
-            .query()
-            .select(Select::columns(&["id", "name"]))
-            .execute()
-            .await
-            .unwrap()
-            .try_collect::<Vec<_>>()
-            .await
-            .unwrap();
-
-        while let Some(batch) = batches.pop() {
-            let ids = batch
-                .column(0)
-                .as_any()
-                .downcast_ref::<Int32Array>()
-                .unwrap()
-                .iter()
-                .collect::<Vec<_>>();
-            let names = batch
-                .column(1)
-                .as_any()
-                .downcast_ref::<StringArray>()
-                .unwrap()
-                .iter()
-                .collect::<Vec<_>>();
-            for (i, name) in names.iter().enumerate() {
-                let id = ids[i].unwrap();
-                let name = name.unwrap();
-                if id > 5 {
-                    assert_eq!(name, "foo");
-                } else {
-                    assert_eq!(name, &format!("{}", (b'a' + id as u8) as char));
-                }
-            }
-        }
-    }
-
-    #[tokio::test]
-    async fn test_update_via_expr() {
-        let conn = connect("memory://")
-            .read_consistency_interval(Duration::from_secs(0))
-            .execute()
-            .await
-            .unwrap();
-        let tbl = conn
-            .create_table("my_table", make_test_batches())
-            .execute()
-            .await
-            .unwrap();
-        assert_eq!(1, tbl.count_rows(Some("i == 0".to_string())).await.unwrap());
-        tbl.update().column("i", "i+1").execute().await.unwrap();
-        assert_eq!(0, tbl.count_rows(Some("i == 0".to_string())).await.unwrap());
-    }
-}

rust/lancedb/src/test_utils/connection.rs

@@ -5,19 +5,16 @@
 
 use regex::Regex;
 use std::env;
-use std::process::Stdio;
-use tokio::io::{AsyncBufReadExt, BufReader};
-use tokio::process::{Child, ChildStdout, Command};
-use tokio::sync::mpsc;
+use std::io::{BufRead, BufReader};
+use std::process::{Child, ChildStdout, Command, Stdio};
 
 use crate::{connect, Connection};
-use anyhow::{anyhow, bail, Result};
+use anyhow::{bail, Result};
 use tempfile::{tempdir, TempDir};
 
 pub struct TestConnection {
     pub uri: String,
     pub connection: Connection,
-    pub is_remote: bool,
     _temp_dir: Option<TempDir>,
     _process: Option<TestProcess>,
 }
@@ -40,56 +37,6 @@ pub async fn new_test_connection() -> Result<TestConnection> {
     }
 }
 
-async fn spawn_stdout_reader(
-    mut stdout: BufReader<ChildStdout>,
-    port_sender: mpsc::Sender<anyhow::Result<String>>,
-) -> tokio::task::JoinHandle<()> {
-    let print_stdout = env::var("PRINT_LANCEDB_TEST_CONNECTION_SCRIPT_OUTPUT").is_ok();
-    tokio::spawn(async move {
-        let mut line = String::new();
-        let re = Regex::new(r"Query node now listening on 0.0.0.0:(.*)").unwrap();
-        loop {
-            line.clear();
-            let result = stdout.read_line(&mut line).await;
-            if let Err(err) = result {
-                port_sender
-                    .send(Err(anyhow!(
-                        "error while reading from process output: {}",
-                        err
-                    )))
-                    .await
-                    .unwrap();
-                return;
-            } else if result.unwrap() == 0 {
-                port_sender
-                    .send(Err(anyhow!(
-                        " hit EOF before reading port from process output."
-                    )))
-                    .await
-                    .unwrap();
-                return;
-            }
-            if re.is_match(&line) {
-                let caps = re.captures(&line).unwrap();
-                port_sender.send(Ok(caps[1].to_string())).await.unwrap();
-                break;
-            }
-        }
-        loop {
-            line.clear();
-            match stdout.read_line(&mut line).await {
-                Err(_) => return,
-                Ok(0) => return,
-                Ok(_size) => {
-                    if print_stdout {
-                        print!("{}", line);
-                    }
-                }
-            }
-        }
-    })
-}
-
 async fn new_remote_connection(script_path: &str) -> Result<TestConnection> {
     let temp_dir = tempdir()?;
     let data_path = temp_dir.path().to_str().unwrap().to_string();
@@ -110,25 +57,38 @@ async fn new_remote_connection(script_path: &str) -> Result<TestConnection> {
         child: child_result.unwrap(),
     };
     let stdout = BufReader::new(process.child.stdout.take().unwrap());
-    let (port_sender, mut port_receiver) = mpsc::channel(5);
-    let _reader = spawn_stdout_reader(stdout, port_sender).await;
-    let port = match port_receiver.recv().await {
-        None => bail!("Unable to determine the port number used by the phalanx process we spawned, because the reader thread was closed too soon."),
-        Some(Err(err)) => bail!("Unable to determine the port number used by the phalanx process we spawned, because of an error, {}", err),
-        Some(Ok(port)) => port,
-    };
+    let port = read_process_port(stdout)?;
     let uri = "db://test";
     let host_override = format!("http://localhost:{}", port);
     let connection = create_new_connection(uri, &host_override).await?;
     Ok(TestConnection {
         uri: uri.to_string(),
         connection,
-        is_remote: true,
         _temp_dir: Some(temp_dir),
         _process: Some(process),
     })
 }
 
+fn read_process_port(mut stdout: BufReader<ChildStdout>) -> Result<String> {
+    let mut line = String::new();
+    let re = Regex::new(r"Query node now listening on 0.0.0.0:(.*)").unwrap();
+    loop {
+        let result = stdout.read_line(&mut line);
+        if let Err(err) = result {
+            bail!(format!(
+                "read_process_port: error while reading from process output: {}",
+                err
+            ));
+        } else if result.unwrap() == 0 {
+            bail!("read_process_port: hit EOF before reading port from process output.");
+        }
+        if re.is_match(&line) {
+            let caps = re.captures(&line).unwrap();
+            return Ok(caps[1].to_string());
+        }
+    }
+}
+
 #[cfg(feature = "remote")]
 async fn create_new_connection(uri: &str, host_override: &str) -> crate::error::Result<Connection> {
     connect(uri)
@@ -154,7 +114,6 @@ async fn new_local_connection() -> Result<TestConnection> {
     Ok(TestConnection {
         uri: uri.to_string(),
         connection,
-        is_remote: false,
         _temp_dir: Some(temp_dir),
         _process: None,
     })

rust/lancedb/tests/embedding_registry_test.rs

@@ -4,6 +4,7 @@
 use std::{
     borrow::Cow,
     collections::{HashMap, HashSet},
+    iter::repeat,
     sync::Arc,
 };
 
@@ -267,10 +268,9 @@ fn create_some_records() -> Result<impl IntoArrow> {
             schema.clone(),
             vec![
                 Arc::new(Int32Array::from_iter_values(0..TOTAL as i32)),
-                Arc::new(StringArray::from_iter(std::iter::repeat_n(
-                    Some("hello world".to_string()),
-                    TOTAL,
-                ))),
+                Arc::new(StringArray::from_iter(
+                    repeat(Some("hello world".to_string())).take(TOTAL),
+                )),
             ],
         )
         .unwrap()]

rust/lancedb/tests/embeddings_parallel_test.rs

@@ -1,253 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
-use std::{
-    borrow::Cow,
-    sync::{
-        atomic::{AtomicUsize, Ordering},
-        Arc,
-    },
-    time::Duration,
-};
-
-use arrow::buffer::NullBuffer;
-use arrow_array::{
-    Array, FixedSizeListArray, Float32Array, RecordBatch, RecordBatchIterator, StringArray,
-};
-use arrow_schema::{DataType, Field, Schema};
-use lancedb::{
-    embeddings::{EmbeddingDefinition, EmbeddingFunction, MaybeEmbedded, WithEmbeddings},
-    Error, Result,
-};
-
-#[derive(Debug)]
-struct SlowMockEmbed {
-    name: String,
-    dim: usize,
-    delay_ms: u64,
-    call_count: Arc<AtomicUsize>,
-}
-
-impl SlowMockEmbed {
-    pub fn new(name: String, dim: usize, delay_ms: u64) -> Self {
-        Self {
-            name,
-            dim,
-            delay_ms,
-            call_count: Arc::new(AtomicUsize::new(0)),
-        }
-    }
-
-    pub fn get_call_count(&self) -> usize {
-        self.call_count.load(Ordering::SeqCst)
-    }
-}
-
-impl EmbeddingFunction for SlowMockEmbed {
-    fn name(&self) -> &str {
-        &self.name
-    }
-
-    fn source_type(&self) -> Result<Cow<'_, DataType>> {
-        Ok(Cow::Owned(DataType::Utf8))
-    }
-
-    fn dest_type(&self) -> Result<Cow<'_, DataType>> {
-        Ok(Cow::Owned(DataType::new_fixed_size_list(
-            DataType::Float32,
-            self.dim as _,
-            true,
-        )))
-    }
-
-    fn compute_source_embeddings(&self, source: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
-        // Simulate slow embedding computation
-        std::thread::sleep(Duration::from_millis(self.delay_ms));
-        self.call_count.fetch_add(1, Ordering::SeqCst);
-
-        let len = source.len();
-        let inner = Arc::new(Float32Array::from(vec![Some(1.0); len * self.dim]));
-        let field = Field::new("item", inner.data_type().clone(), false);
-        let arr = FixedSizeListArray::new(
-            Arc::new(field),
-            self.dim as _,
-            inner,
-            Some(NullBuffer::new_valid(len)),
-        );
-
-        Ok(Arc::new(arr))
-    }
-
-    fn compute_query_embeddings(&self, _input: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
-        unimplemented!()
-    }
-}
-
-fn create_test_batch() -> Result<RecordBatch> {
-    let schema = Arc::new(Schema::new(vec![Field::new("text", DataType::Utf8, false)]));
-    let text = StringArray::from(vec!["hello", "world"]);
-    RecordBatch::try_new(schema, vec![Arc::new(text)]).map_err(|e| Error::Runtime {
-        message: format!("Failed to create test batch: {}", e),
-    })
-}
-
-#[test]
-fn test_single_embedding_fast_path() {
-    // Single embedding should execute without spawning threads
-    let batch = create_test_batch().unwrap();
-    let schema = batch.schema();
-
-    let embed = Arc::new(SlowMockEmbed::new("test".to_string(), 2, 10));
-    let embedding_def = EmbeddingDefinition::new("text", "test", Some("embedding"));
-
-    let reader = RecordBatchIterator::new(vec![Ok(batch)], schema);
-    let embeddings = vec![(embedding_def, embed.clone() as Arc<dyn EmbeddingFunction>)];
-    let mut with_embeddings = WithEmbeddings::new(reader, embeddings);
-
-    let result = with_embeddings.next().unwrap().unwrap();
-    assert!(result.column_by_name("embedding").is_some());
-    assert_eq!(embed.get_call_count(), 1);
-}
-
-#[test]
-fn test_multiple_embeddings_parallel() {
-    // Multiple embeddings should execute in parallel
-    let batch = create_test_batch().unwrap();
-    let schema = batch.schema();
-
-    let embed1 = Arc::new(SlowMockEmbed::new("embed1".to_string(), 2, 100));
-    let embed2 = Arc::new(SlowMockEmbed::new("embed2".to_string(), 3, 100));
-    let embed3 = Arc::new(SlowMockEmbed::new("embed3".to_string(), 4, 100));
-
-    let def1 = EmbeddingDefinition::new("text", "embed1", Some("emb1"));
-    let def2 = EmbeddingDefinition::new("text", "embed2", Some("emb2"));
-    let def3 = EmbeddingDefinition::new("text", "embed3", Some("emb3"));
-
-    let reader = RecordBatchIterator::new(vec![Ok(batch)], schema);
-    let embeddings = vec![
-        (def1, embed1.clone() as Arc<dyn EmbeddingFunction>),
-        (def2, embed2.clone() as Arc<dyn EmbeddingFunction>),
-        (def3, embed3.clone() as Arc<dyn EmbeddingFunction>),
-    ];
-    let mut with_embeddings = WithEmbeddings::new(reader, embeddings);
-
-    let result = with_embeddings.next().unwrap().unwrap();
-
-    // Verify all embedding columns are present
-    assert!(result.column_by_name("emb1").is_some());
-    assert!(result.column_by_name("emb2").is_some());
-    assert!(result.column_by_name("emb3").is_some());
-
-    // Verify all embeddings were computed
-    assert_eq!(embed1.get_call_count(), 1);
-    assert_eq!(embed2.get_call_count(), 1);
-    assert_eq!(embed3.get_call_count(), 1);
-}
-
-#[test]
-fn test_embedding_column_order_preserved() {
-    // Verify that embedding columns are added in the same order as definitions
-    let batch = create_test_batch().unwrap();
-    let schema = batch.schema();
-
-    let embed1 = Arc::new(SlowMockEmbed::new("embed1".to_string(), 2, 10));
-    let embed2 = Arc::new(SlowMockEmbed::new("embed2".to_string(), 3, 10));
-    let embed3 = Arc::new(SlowMockEmbed::new("embed3".to_string(), 4, 10));
-
-    let def1 = EmbeddingDefinition::new("text", "embed1", Some("first"));
-    let def2 = EmbeddingDefinition::new("text", "embed2", Some("second"));
-    let def3 = EmbeddingDefinition::new("text", "embed3", Some("third"));
-
-    let reader = RecordBatchIterator::new(vec![Ok(batch)], schema);
-    let embeddings = vec![
-        (def1, embed1 as Arc<dyn EmbeddingFunction>),
-        (def2, embed2 as Arc<dyn EmbeddingFunction>),
-        (def3, embed3 as Arc<dyn EmbeddingFunction>),
-    ];
-    let mut with_embeddings = WithEmbeddings::new(reader, embeddings);
-
-    let result = with_embeddings.next().unwrap().unwrap();
-    let result_schema = result.schema();
-
-    // Original column is first
-    assert_eq!(result_schema.field(0).name(), "text");
-    // Embedding columns follow in order
-    assert_eq!(result_schema.field(1).name(), "first");
-    assert_eq!(result_schema.field(2).name(), "second");
-    assert_eq!(result_schema.field(3).name(), "third");
-}
-
-#[test]
-fn test_embedding_error_propagation() {
-    // Test that errors from embedding computation are properly propagated
-    #[derive(Debug)]
-    struct FailingEmbed {
-        name: String,
-    }
-
-    impl EmbeddingFunction for FailingEmbed {
-        fn name(&self) -> &str {
-            &self.name
-        }
-
-        fn source_type(&self) -> Result<Cow<'_, DataType>> {
-            Ok(Cow::Owned(DataType::Utf8))
-        }
-
-        fn dest_type(&self) -> Result<Cow<'_, DataType>> {
-            Ok(Cow::Owned(DataType::new_fixed_size_list(
-                DataType::Float32,
-                2,
-                true,
-            )))
-        }
-
-        fn compute_source_embeddings(&self, _source: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
-            Err(Error::Runtime {
-                message: "Intentional failure".to_string(),
-            })
-        }
-
-        fn compute_query_embeddings(&self, _input: Arc<dyn Array>) -> Result<Arc<dyn Array>> {
-            unimplemented!()
-        }
-    }
-
-    let batch = create_test_batch().unwrap();
-    let schema = batch.schema();
-
-    let embed = Arc::new(FailingEmbed {
-        name: "failing".to_string(),
-    });
-    let def = EmbeddingDefinition::new("text", "failing", Some("emb"));
-
-    let reader = RecordBatchIterator::new(vec![Ok(batch)], schema);
-    let embeddings = vec![(def, embed as Arc<dyn EmbeddingFunction>)];
-    let mut with_embeddings = WithEmbeddings::new(reader, embeddings);
-
-    let result = with_embeddings.next().unwrap();
-    assert!(result.is_err());
-    let err_msg = format!("{}", result.err().unwrap());
-    assert!(err_msg.contains("Intentional failure"));
-}
-
-#[test]
-fn test_maybe_embedded_with_no_embeddings() {
-    // Test that MaybeEmbedded::No variant works correctly
-    let batch = create_test_batch().unwrap();
-    let schema = batch.schema();
-
-    let reader = RecordBatchIterator::new(vec![Ok(batch.clone())], schema.clone());
-    let table_def = lancedb::table::TableDefinition {
-        schema: schema.clone(),
-        column_definitions: vec![lancedb::table::ColumnDefinition {
-            kind: lancedb::table::ColumnKind::Physical,
-        }],
-    };
-
-    let mut maybe_embedded = MaybeEmbedded::try_new(reader, table_def, None).unwrap();
-
-    let result = maybe_embedded.next().unwrap().unwrap();
-    assert_eq!(result.num_columns(), 1);
-    assert_eq!(result.column(0).as_ref(), batch.column(0).as_ref());
-}