feat(python): support blob modes in query to_pandas

Moves the Lance dependency bump process into an in-repository skill so
local agents and GitHub Actions share the same workflow definition.

The update workflow is now an explicit, optional-tag entrypoint;
latest-release resolution, duplicate PR handling, Java/Rust dependency
updates, and Sophon follow-up are documented in the skill and backed by
a small deterministic helper.

.agents/skills/README.md

@@ -0,0 +1,7 @@
+# Agent Skills
+
+This directory contains repo-scoped code agent skills for the LanceDB project.
+
+Each skill is a folder that contains a required `SKILL.md` and optional bundled resources.
+
+Codex discovers skills from `.agents/skills` in the current working directory and parent directories.

.agents/skills/lancedb-update-lance-dependency/SKILL.md

@@ -0,0 +1,98 @@
+---
+name: lancedb-update-lance-dependency
+description: Update LanceDB to a specific Lance release or tag. Use when bumping Lance dependencies in the lancedb repository, including Rust workspace Lance crates, Java lance-core, validation, branch creation, commit, push, and PR creation when requested.
+---
+
+# LanceDB Update Lance Dependency
+
+## Scope
+
+Use this skill in the `lancedb/lancedb` repository when updating the Lance dependency to a specific Lance version or tag.
+
+Inputs can be a version (`7.2.0-beta.1`), a tag (`v7.2.0-beta.1`), a tag ref (`refs/tags/v7.2.0-beta.1`), or `latest`.
+
+## Workflow
+
+1. Confirm the worktree status with `git status --short`.
+2. Resolve the target Lance version:
+
+   - If the input is `latest`, empty, or omitted, run:
+
+     ```bash
+     python3 ci/check_lance_release.py
+     ```
+
+     Parse the JSON output. If `needs_update` is not `true`, stop without creating a PR. Otherwise use `latest_tag`.
+
+   - If the input is explicit, use it directly.
+
+3. Compute update metadata without changing files:
+
+   ```bash
+   python3 ci/update_lance_dependency.py "$TAG_OR_VERSION" --metadata-only
+   ```
+
+   Before making changes, check for an existing open PR with the emitted `pr_title`:
+
+   ```bash
+   gh pr list --search "\"$PR_TITLE\" in:title" --state open --limit 1 --json number,url,title
+   ```
+
+   If a matching open PR exists, stop and report it instead of creating a duplicate.
+
+4. Run the deterministic update entrypoint:
+
+   ```bash
+   python3 ci/update_lance_dependency.py "$TAG_OR_VERSION"
+   ```
+
+   This updates the Rust workspace Lance dependencies through `ci/set_lance_version.py`, updates `java/pom.xml`, refreshes Cargo metadata, and prints JSON metadata containing `branch_name`, `commit_message`, and `pr_title`.
+
+5. Run validation:
+
+   ```bash
+   cargo clippy --quiet --workspace --tests --all-features -- -D warnings
+   cargo fmt --all --quiet
+   ```
+
+   Fix real diagnostics and rerun clippy until it succeeds. Do not skip warnings.
+
+6. Inspect `git status --short` and `git diff` to ensure only the Lance dependency update and required compatibility fixes are present.
+
+7. If the task only asks to prepare local changes, stop here and report the changed files and validation result.
+
+8. If the task asks to publish the update, create a branch using the printed `branch_name`, stage all relevant files, and commit using the printed `commit_message`. Do not amend or rewrite existing commits.
+
+9. Push to `origin`. Before creating the PR, check that the current token has push permission:
+
+   ```bash
+   gh api repos/lancedb/lancedb --jq .permissions.push
+   ```
+
+   If the remote branch already exists for the same generated branch name, delete the remote ref with `gh api -X DELETE repos/lancedb/lancedb/git/refs/heads/$BRANCH_NAME`, then push. Do not force-push.
+
+10. Create a PR targeting `main` with the printed `pr_title`. If there is no PR template, keep the body to two or three concise sentences: state the Lance dependency bump, note any required compatibility fixes, and link the triggering Lance tag or release.
+
+11. Read back the remote PR title after creation. If it is not a Conventional Commit title, fix it immediately.
+
+12. When running in GitHub Actions after creating the LanceDB PR, trigger the Sophon dependency update:
+
+    ```bash
+    gh workflow run codex-bump-lancedb-lance.yml \
+      --repo lancedb/sophon \
+      -f lance_ref="$LANCE_TAG" \
+      -f lancedb_ref="$BRANCH_NAME"
+    gh run list --repo lancedb/sophon --workflow codex-bump-lancedb-lance.yml --limit 1 --json databaseId,url,displayTitle
+    ```
+
+    Use the emitted metadata `tag` value as `LANCE_TAG`. Do this only after a new LanceDB PR has been created. If the update was skipped because no update is needed or an open PR already exists, do not trigger Sophon.
+
+## GitHub Actions
+
+When this skill is used from GitHub Actions, `TAG`, `GH_TOKEN`, and `GITHUB_TOKEN` may already be set. Resolve `latest` first when `TAG` is empty. Once an explicit tag or version is known, use:
+
+```bash
+python3 ci/update_lance_dependency.py "$TAG" --github-output "$GITHUB_OUTPUT"
+```
+
+Then use the emitted `branch_name`, `commit_message`, and `pr_title` values for branch, commit, and PR creation.

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.28.0-beta.11"
+current_version = "0.30.1-beta.0"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

.github/dependabot.yml

@@ -11,6 +11,11 @@ updates:
     schedule:
       interval: weekly
     open-pull-requests-limit: 10
+    # Only update Cargo.lock, never widen/raise the version requirements in
+    # Cargo.toml. The goal is keeping the lockfile (and the binaries we ship)
+    # current on security fixes, not forcing our library's consumers onto
+    # newer minimum versions.
+    versioning-strategy: lockfile-only
     groups:
       rust-minor-patch:
         update-types:

.github/workflows/build_windows_wheel/action.yml

@@ -29,7 +29,3 @@ runs:
         args: ${{ inputs.args }}
         docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/'"
         working-directory: python
-    - uses: actions/upload-artifact@v4
-      with:
-        name: windows-wheels
-        path: python\target\wheels

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

@@ -4,14 +4,16 @@ on:
   workflow_call:
     inputs:
       tag:
-        description: "Tag name from Lance"
-        required: true
+        description: "Tag name from Lance. If omitted, the skill will use the latest Lance release that needs an update."
+        required: false
+        default: ""
         type: string
   workflow_dispatch:
     inputs:
       tag:
-        description: "Tag name from Lance"
-        required: true
+        description: "Tag name from Lance. Leave empty to use the latest Lance release that needs an update."
+        required: false
+        default: ""
         type: string
 
 permissions:
@@ -25,7 +27,7 @@ jobs:
     steps:
       - name: Show inputs
         run: |
-          echo "tag = ${{ inputs.tag }}"
+          echo "tag = ${{ inputs.tag || 'latest' }}"
 
       - name: Checkout Repo LanceDB
         uses: actions/checkout@v4
@@ -71,65 +73,21 @@ jobs:
           OPENAI_API_KEY: ${{ secrets.CODEX_TOKEN }}
         run: |
           set -euo pipefail
-          VERSION="${TAG#refs/tags/}"
-          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
+          TARGET_TAG="${TAG:-latest}"
 
           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.
+          You are running inside the lancedb repository on a GitHub Actions runner.
 
-          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.
+          Use \$lancedb-update-lance-dependency with target "${TARGET_TAG}".
 
           Constraints:
-          - Use bash commands; avoid modifying GitHub workflow files other than through the scripted task above.
-          - Do not merge the PR.
-          - If any command fails, diagnose and fix the issue instead of aborting.
+          - Use env "GH_TOKEN" for GitHub operations.
+          - Do not merge the pull request.
+          - Do not force-push.
+          - Do not create a duplicate pull request if an open PR already exists for the target Lance version.
+          - If any command fails, diagnose and fix the root cause instead of aborting.
+          - After creating the PR, display the PR URL, "git status --short", and a concise summary of the commands run and their results.
           EOF
 
           printenv OPENAI_API_KEY | codex login --with-api-key
           codex --config shell_environment_policy.ignore_default_excludes=true exec --dangerously-bypass-approvals-and-sandbox "$(cat /tmp/codex-prompt.txt)"
-
-      - name: Trigger sophon dependency update
-        env:
-          TAG: ${{ inputs.tag }}
-          GH_TOKEN: ${{ secrets.ROBOT_TOKEN }}
-        run: |
-          set -euo pipefail
-          VERSION="${TAG#refs/tags/}"
-          VERSION="${VERSION#v}"
-          LANCEDB_BRANCH="codex/update-lance-${VERSION//[^a-zA-Z0-9]/-}"
-
-          echo "Triggering sophon workflow with:"
-          echo "  lance_ref: ${TAG#refs/tags/}"
-          echo "  lancedb_ref: ${LANCEDB_BRANCH}"
-
-          gh workflow run codex-bump-lancedb-lance.yml \
-            --repo lancedb/sophon \
-            -f lance_ref="${TAG#refs/tags/}" \
-            -f lancedb_ref="${LANCEDB_BRANCH}"
-
-      - name: Show latest sophon workflow run
-        env:
-          GH_TOKEN: ${{ secrets.ROBOT_TOKEN }}
-        run: |
-          set -euo pipefail
-          echo "Latest sophon workflow run:"
-          gh run list --repo lancedb/sophon --workflow codex-bump-lancedb-lance.yml --limit 1 --json databaseId,url,displayTitle

.github/workflows/lance-release-timer.yml

@@ -1,62 +0,0 @@
-name: Lance Release Timer
-
-on:
-  schedule:
-    - cron: "*/10 * * * *"
-  workflow_dispatch:
-
-permissions:
-  contents: read
-  actions: write
-
-concurrency:
-  group: lance-release-timer
-  cancel-in-progress: false
-
-jobs:
-  trigger-update:
-    runs-on: ubuntu-latest
-    steps:
-      - name: Checkout repository
-        uses: actions/checkout@v4
-
-      - name: Check for new Lance tag
-        id: check
-        env:
-          GH_TOKEN: ${{ secrets.ROBOT_TOKEN }}
-        run: |
-          python3 ci/check_lance_release.py --github-output "$GITHUB_OUTPUT"
-
-      - name: Look for existing PR
-        if: steps.check.outputs.needs_update == 'true'
-        id: pr
-        env:
-          GH_TOKEN: ${{ secrets.ROBOT_TOKEN }}
-        run: |
-          set -euo pipefail
-          TITLE="chore: update lance dependency to v${{ steps.check.outputs.latest_version }}"
-          COUNT=$(gh pr list --search "\"$TITLE\" in:title" --state open --limit 1 --json number --jq 'length')
-          if [ "$COUNT" -gt 0 ]; then
-            echo "Open PR already exists for $TITLE"
-            echo "pr_exists=true" >> "$GITHUB_OUTPUT"
-          else
-            echo "No existing PR for $TITLE"
-            echo "pr_exists=false" >> "$GITHUB_OUTPUT"
-          fi
-
-      - name: Trigger codex update workflow
-        if: steps.check.outputs.needs_update == 'true' && steps.pr.outputs.pr_exists != 'true'
-        env:
-          GH_TOKEN: ${{ secrets.ROBOT_TOKEN }}
-        run: |
-          set -euo pipefail
-          TAG=${{ steps.check.outputs.latest_tag }}
-          gh workflow run codex-update-lance-dependency.yml -f tag=refs/tags/$TAG
-
-      - name: Show latest codex workflow run
-        if: steps.check.outputs.needs_update == 'true' && steps.pr.outputs.pr_exists != 'true'
-        env:
-          GH_TOKEN: ${{ secrets.ROBOT_TOKEN }}
-        run: |
-          set -euo pipefail
-          gh run list --workflow codex-update-lance-dependency.yml --limit 1 --json databaseId,url,displayTitle

.github/workflows/nodejs.yml

@@ -157,7 +157,10 @@ jobs:
         npx jest --testEnvironment jest-environment-node-single-context --verbose
   macos:
     timeout-minutes: 30
-    runs-on: "macos-14"
+    # macos-15 ships a newer linker; the older macos-14 linker fails to insert
+    # branch islands when the debug cdylib's __text section exceeds the 128 MB
+    # AArch64 B/BL branch range.
+    runs-on: "macos-15"
     defaults:
       run:
         shell: bash

.github/workflows/pypi-publish.yml

@@ -8,6 +8,9 @@ on:
     # This should trigger a dry run (we skip the final publish step)
     paths:
       - .github/workflows/pypi-publish.yml
+      - .github/workflows/build_linux_wheel/action.yml
+      - .github/workflows/build_mac_wheel/action.yml
+      - .github/workflows/build_windows_wheel/action.yml
       - Cargo.toml # Change in dependency frequently breaks builds
       - Cargo.lock
 
@@ -21,32 +24,21 @@ jobs:
   linux:
     name: Python ${{ matrix.config.platform }} manylinux${{ matrix.config.manylinux }}
     timeout-minutes: 60
-    permissions:
-      id-token: write
-      contents: read
     strategy:
       matrix:
         config:
-          - platform: x86_64
-            manylinux: "2_17"
-            extra_args: ""
-            runner: ubuntu-22.04
           - platform: x86_64
             manylinux: "2_28"
             extra_args: "--features fp16kernels"
             runner: ubuntu-22.04
-          - platform: aarch64
-            manylinux: "2_17"
-            extra_args: ""
-            # For successful fat LTO builds, we need a large runner to avoid OOM errors.
-            runner: ubuntu-2404-8x-arm64
+          # For successful fat LTO builds, we need a large runner to avoid OOM errors.
           - platform: aarch64
             manylinux: "2_28"
             extra_args: "--features fp16kernels"
             runner: ubuntu-2404-8x-arm64
     runs-on: ${{ matrix.config.runner }}
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           fetch-depth: 0
           lfs: true
@@ -60,15 +52,14 @@ jobs:
           args: "--release --strip ${{ matrix.config.extra_args }}"
           arm-build: ${{ matrix.config.platform == 'aarch64' }}
           manylinux: ${{ matrix.config.manylinux }}
-      - uses: ./.github/workflows/upload_wheel
+      - uses: actions/upload-artifact@v7
         if: startsWith(github.ref, 'refs/tags/python-v')
         with:
-          fury_token: ${{ secrets.FURY_TOKEN }}
+          name: wheels-linux-${{ matrix.config.platform }}-${{ matrix.config.manylinux }}
+          path: target/wheels/lancedb-*.whl
+          if-no-files-found: error
   mac:
     timeout-minutes: 90
-    permissions:
-      id-token: write
-      contents: read
     runs-on: ${{ matrix.config.runner }}
     strategy:
       matrix:
@@ -78,7 +69,7 @@ jobs:
     env:
       MACOSX_DEPLOYMENT_TARGET: 10.15
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           fetch-depth: 0
           lfs: true
@@ -90,18 +81,21 @@ jobs:
         with:
           python-minor-version: 10
           args: "--release --strip --target ${{ matrix.config.target }} --features fp16kernels"
-      - uses: ./.github/workflows/upload_wheel
+      - uses: actions/upload-artifact@v7
         if: startsWith(github.ref, 'refs/tags/python-v')
         with:
-          fury_token: ${{ secrets.FURY_TOKEN }}
+          name: wheels-mac-${{ matrix.config.target }}
+          path: target/wheels/lancedb-*.whl
+          if-no-files-found: error
   windows:
-    timeout-minutes: 60
-    permissions:
-      id-token: write
-      contents: read
+    timeout-minutes: 90
     runs-on: windows-latest
+    env:
+      # link.exe is single-threaded and the long pole on Windows builds. Use
+      # rustc's bundled lld-link instead.
+      CARGO_TARGET_X86_64_PC_WINDOWS_MSVC_LINKER: rust-lld
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           fetch-depth: 0
           lfs: true
@@ -113,18 +107,70 @@ jobs:
         with:
           python-minor-version: 10
           args: "--release --strip"
-          vcpkg_token: ${{ secrets.VCPKG_GITHUB_PACKAGES }}
-      - uses: ./.github/workflows/upload_wheel
+      - uses: actions/upload-artifact@v7
         if: startsWith(github.ref, 'refs/tags/python-v')
         with:
-          fury_token: ${{ secrets.FURY_TOKEN }}
+          name: wheels-windows
+          path: target/wheels/lancedb-*.whl
+          if-no-files-found: error
+  publish:
+    name: Publish wheels
+    if: startsWith(github.ref, 'refs/tags/python-v')
+    needs: [linux, mac, windows]
+    runs-on: ubuntu-latest
+    permissions:
+      id-token: write
+      contents: read
+    steps:
+      - uses: actions/checkout@v6
+      - name: Download wheel artifacts
+        uses: actions/download-artifact@v8
+        with:
+          pattern: wheels-*
+          path: target/wheels
+          merge-multiple: true
+      - name: List wheels
+        run: ls -la target/wheels
+      - name: Choose repo
+        id: choose_repo
+        run: |
+          if [[ ${{ github.ref }} == *beta* ]]; then
+            echo "repo=fury" >> $GITHUB_OUTPUT
+          else
+            echo "repo=pypi" >> $GITHUB_OUTPUT
+          fi
+      - name: Publish to Fury
+        if: steps.choose_repo.outputs.repo == 'fury'
+        env:
+          FURY_TOKEN: ${{ secrets.FURY_TOKEN }}
+        run: |
+          shopt -s nullglob
+          WHEELS=(target/wheels/lancedb-*.whl)
+          if [[ ${#WHEELS[@]} -eq 0 ]]; then
+            echo "No wheels found in target/wheels/" >&2
+            exit 1
+          fi
+          for WHEEL in "${WHEELS[@]}"; do
+            echo "Uploading $WHEEL to Fury"
+            curl -f -F package=@"$WHEEL" "https://$FURY_TOKEN@push.fury.io/lancedb/"
+          done
+      # NOTE: pypa/gh-action-pypi-publish must be invoked directly from a
+      # workflow file, not from inside a composite action. When called from a
+      # composite, `github.action_repository` is empty (actions/runner#2473)
+      # and the action falls back to `github.repository`, producing a bogus
+      # `docker://ghcr.io/<repo>:<ref>` image reference that GHA tries to pull.
+      - name: Publish to PyPI
+        if: steps.choose_repo.outputs.repo == 'pypi'
+        uses: pypa/gh-action-pypi-publish@release/v1
+        with:
+          packages-dir: target/wheels/
   gh-release:
     if: startsWith(github.ref, 'refs/tags/python-v')
     runs-on: ubuntu-latest
     permissions:
       contents: write
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
         with:
           fetch-depth: 0
           lfs: true
@@ -187,13 +233,13 @@ jobs:
   report-failure:
     name: Report Workflow Failure
     runs-on: ubuntu-latest
-    needs: [linux, mac, windows]
+    needs: [linux, mac, windows, publish]
     permissions:
       contents: read
       issues: write
     if: always() && failure() && startsWith(github.ref, 'refs/tags/python-v')
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
       - uses: ./.github/actions/create-failure-issue
         with:
           job-results: ${{ toJSON(needs) }}

.github/workflows/python.yml

@@ -205,7 +205,7 @@ jobs:
       - name: Delete wheels
         run: rm -rf target/wheels
   pydantic1x:
-    timeout-minutes: 30
+    timeout-minutes: 60
     runs-on: "ubuntu-24.04"
     defaults:
       run:

.github/workflows/rust.yml

@@ -233,6 +233,26 @@ jobs:
           cargo update -p aws-sdk-sso --precise 1.62.0
           cargo update -p aws-sdk-ssooidc --precise 1.63.0
           cargo update -p aws-sdk-sts --precise 1.63.0
+          # aws-runtime/sigv4/credential-types/types and the aws-smithy-*
+          # crates bumped their MSRV to 1.91.1 in late 2026; pin to the last
+          # 1.91.0-compatible versions. The order matters — each downgrade
+          # only succeeds once everything that still pins it at a higher
+          # version has itself been downgraded.
+          cargo update -p aws-runtime --precise 1.5.12
+          cargo update -p aws-types --precise 1.3.9
+          cargo update -p aws-sigv4 --precise 1.3.5
+          cargo update -p aws-credential-types --precise 1.2.8
+          cargo update -p aws-smithy-checksums --precise 0.63.9
+          cargo update -p aws-smithy-runtime --precise 1.9.3
+          cargo update -p aws-smithy-http --precise 0.62.4
+          cargo update -p aws-smithy-eventstream --precise 0.60.12
+          cargo update -p aws-smithy-http-client --precise 1.1.3
+          cargo update -p aws-smithy-observability --precise 0.1.4
+          cargo update -p aws-smithy-query --precise 0.60.8
+          cargo update -p aws-smithy-runtime-api --precise 1.9.1
+          cargo update -p aws-smithy-async --precise 1.2.6
+          cargo update -p aws-smithy-types --precise 1.3.5
+          cargo update -p aws-smithy-xml --precise 0.60.11
           cargo update -p home --precise 0.5.9
       - name: cargo +${{ matrix.msrv }} check
         env:

.github/workflows/upload_wheel/action.yml

@@ -1,34 +0,0 @@
-name: upload-wheel
-
-description: "Upload wheels to Pypi"
-inputs:
-  fury_token:
-    required: true
-    description: "release token for the fury repo"
-
-runs:
-  using: "composite"
-  steps:
-  - name: Choose repo
-    shell: bash
-    id: choose_repo
-    run: |
-      if [[ ${{ github.ref }} == *beta* ]]; then
-        echo "repo=fury" >> $GITHUB_OUTPUT
-      else
-        echo "repo=pypi" >> $GITHUB_OUTPUT
-      fi
-  - name: Publish to Fury
-    if: steps.choose_repo.outputs.repo == 'fury'
-    shell: bash
-    env:
-      FURY_TOKEN: ${{ inputs.fury_token }}
-    run: |
-      WHEEL=$(ls target/wheels/lancedb-*.whl 2> /dev/null | head -n 1)
-      echo "Uploading $WHEEL to Fury"
-      curl -f -F package=@$WHEEL https://$FURY_TOKEN@push.fury.io/lancedb/
-  - name: Publish to PyPI
-    if: steps.choose_repo.outputs.repo == 'pypi'
-    uses: pypa/gh-action-pypi-publish@release/v1
-    with:
-      packages-dir: target/wheels/

AGENTS.md

@@ -17,9 +17,33 @@ Common commands:
 * Run tests: `cargo test --quiet --features remote --tests`
 * Run specific test: `cargo test --quiet --features remote -p <package_name> --test <test_name>`
 * Lint: `cargo clippy --quiet --features remote --tests --examples`
-* Format: `cargo fmt --all`
+* Format Rust: `cargo fmt --all`
+* Format Python: `ruff format .`
+* Lint Python: `ruff check .`
+* Bootstrap Python dev env: `cd python && uv run --extra tests --extra dev maturin develop --extras tests,dev`
+* Run Python tests: `cd python && uv run --extra tests pytest python/tests -vv --durations=10 -m "not slow and not s3_test"`
+* Run specific Python test: `cd python && uv run --extra tests pytest python/tests/<test_file>.py::<test_name> -q`
 
-Before committing changes, run formatting.
+For Python validation, prefer the uv-managed environment declared by `python/uv.lock`.
+Do not treat system `python`, global `pytest`, or missing editable-install errors as
+final blockers; bootstrap or enter the uv environment instead. If `lancedb._lancedb`
+is missing or stale, or if Rust/PyO3 binding code changed, rebuild the Python
+extension with the bootstrap command above before running tests.
+
+Before committing changes, run formatting for every language you touched. At minimum:
+
+* Rust changes: run `cargo fmt --all`.
+* Python changes: run `ruff format .` and `ruff check .` from the repository root,
+  and run targeted tests through `cd python && uv run ...`.
+* TypeScript changes: run the relevant `npm`/`pnpm` lint, format, build, and docs commands in `nodejs`.
+
+Before creating a PR, the exact value passed to `gh pr create --title` must follow
+Conventional Commits, such as `fix: support nested field paths in native index creation`
+or `feat(python): add dataset multiprocessing support`. Do not use a plain natural
+language summary like `Support nested field paths in native index creation` as the PR
+title. The semantic-release check uses the PR title and body as the merge commit message,
+so a non-conventional PR title will fail CI. After creating a PR, read the remote PR title
+back and fix it immediately if it is not conventional.
 
 ## Coding tips
 

Cargo.toml

@@ -13,20 +13,20 @@ categories = ["database-implementations"]
 rust-version = "1.91.0"
 
 [workspace.dependencies]
-lance = { "version" = "=7.0.0-beta.7", default-features = false, "tag" = "v7.0.0-beta.7", "git" = "https://github.com/lance-format/lance.git" }
-lance-core = { "version" = "=7.0.0-beta.7", "tag" = "v7.0.0-beta.7", "git" = "https://github.com/lance-format/lance.git" }
-lance-datagen = { "version" = "=7.0.0-beta.7", "tag" = "v7.0.0-beta.7", "git" = "https://github.com/lance-format/lance.git" }
-lance-file = { "version" = "=7.0.0-beta.7", "tag" = "v7.0.0-beta.7", "git" = "https://github.com/lance-format/lance.git" }
-lance-io = { "version" = "=7.0.0-beta.7", default-features = false, "tag" = "v7.0.0-beta.7", "git" = "https://github.com/lance-format/lance.git" }
-lance-index = { "version" = "=7.0.0-beta.7", "tag" = "v7.0.0-beta.7", "git" = "https://github.com/lance-format/lance.git" }
-lance-linalg = { "version" = "=7.0.0-beta.7", "tag" = "v7.0.0-beta.7", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace = { "version" = "=7.0.0-beta.7", "tag" = "v7.0.0-beta.7", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace-impls = { "version" = "=7.0.0-beta.7", default-features = false, "tag" = "v7.0.0-beta.7", "git" = "https://github.com/lance-format/lance.git" }
-lance-table = { "version" = "=7.0.0-beta.7", "tag" = "v7.0.0-beta.7", "git" = "https://github.com/lance-format/lance.git" }
-lance-testing = { "version" = "=7.0.0-beta.7", "tag" = "v7.0.0-beta.7", "git" = "https://github.com/lance-format/lance.git" }
-lance-datafusion = { "version" = "=7.0.0-beta.7", "tag" = "v7.0.0-beta.7", "git" = "https://github.com/lance-format/lance.git" }
-lance-encoding = { "version" = "=7.0.0-beta.7", "tag" = "v7.0.0-beta.7", "git" = "https://github.com/lance-format/lance.git" }
-lance-arrow = { "version" = "=7.0.0-beta.7", "tag" = "v7.0.0-beta.7", "git" = "https://github.com/lance-format/lance.git" }
+lance = { "version" = "=7.2.0-beta.3", default-features = false, "tag" = "v7.2.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-core = { "version" = "=7.2.0-beta.3", "tag" = "v7.2.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-datagen = { "version" = "=7.2.0-beta.3", "tag" = "v7.2.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-file = { "version" = "=7.2.0-beta.3", "tag" = "v7.2.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-io = { "version" = "=7.2.0-beta.3", default-features = false, "tag" = "v7.2.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-index = { "version" = "=7.2.0-beta.3", "tag" = "v7.2.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-linalg = { "version" = "=7.2.0-beta.3", "tag" = "v7.2.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace = { "version" = "=7.2.0-beta.3", "tag" = "v7.2.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace-impls = { "version" = "=7.2.0-beta.3", default-features = false, "tag" = "v7.2.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-table = { "version" = "=7.2.0-beta.3", "tag" = "v7.2.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-testing = { "version" = "=7.2.0-beta.3", "tag" = "v7.2.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-datafusion = { "version" = "=7.2.0-beta.3", "tag" = "v7.2.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-encoding = { "version" = "=7.2.0-beta.3", "tag" = "v7.2.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
+lance-arrow = { "version" = "=7.2.0-beta.3", "tag" = "v7.2.0-beta.3", "git" = "https://github.com/lance-format/lance.git" }
 ahash = "0.8"
 # Note that this one does not include pyarrow
 arrow = { version = "58.0.0", optional = false }

ci/check_lance_release.py

@@ -112,25 +112,25 @@ def fetch_remote_tags() -> List[TagInfo]:
             "api",
             "-X",
             "GET",
-            f"repos/{LANCE_REPO}/git/refs/tags",
-            "--paginate",
+            f"repos/{LANCE_REPO}/releases",
             "--jq",
-            ".[].ref",
+            ".[].tag_name",
+            "-F",
+            "per_page=20",
         ]
     )
     tags: List[TagInfo] = []
     for line in output.splitlines():
-        ref = line.strip()
-        if not ref.startswith("refs/tags/v"):
+        tag = line.strip()
+        if not tag.startswith("v"):
             continue
-        tag = ref.split("refs/tags/")[-1]
         version = tag.lstrip("v")
         try:
             tags.append(TagInfo(tag=tag, version=version, semver=parse_semver(version)))
         except ValueError:
             continue
     if not tags:
-        raise RuntimeError("No Lance tags could be parsed from GitHub API output")
+        raise RuntimeError("No Lance releases could be parsed from GitHub API output")
     return tags
 
 

ci/update_lance_dependency.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+"""Prepare a Lance dependency update for LanceDB."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import re
+import subprocess
+import sys
+from pathlib import Path
+from typing import Sequence
+
+try:
+    from check_lance_release import parse_semver
+except ModuleNotFoundError:
+    # Supports importing as ci.update_lance_dependency from tests or ad hoc checks.
+    from ci.check_lance_release import parse_semver  # type: ignore
+
+
+def normalize_version(raw: str) -> str:
+    value = raw.strip()
+    value = value.removeprefix("refs/tags/")
+    value = value.removeprefix("v")
+    try:
+        parse_semver(value)
+    except ValueError:
+        raise ValueError(f"Unsupported Lance version or tag: {raw}")
+    return value
+
+
+def normalized_tag(version: str) -> str:
+    return f"v{version}"
+
+
+def branch_name(version: str) -> str:
+    suffix = re.sub(r"[^a-zA-Z0-9]+", "-", version).strip("-")
+    suffix = re.sub(r"-+", "-", suffix)
+    return f"codex/update-lance-{suffix}"
+
+
+def commit_type(version: str) -> str:
+    prerelease = version.split("-", maxsplit=1)[1] if "-" in version else ""
+    return "chore" if "beta" in prerelease or "rc" in prerelease else "feat"
+
+
+def metadata_for(version: str) -> dict[str, str]:
+    kind = commit_type(version)
+    message = f"{kind}: update lance dependency to v{version}"
+    return {
+        "version": version,
+        "tag": normalized_tag(version),
+        "branch_name": branch_name(version),
+        "commit_type": kind,
+        "commit_message": message,
+        "pr_title": message,
+    }
+
+
+def run_command(cmd: Sequence[str], *, cwd: Path) -> None:
+    subprocess.run(cmd, cwd=cwd, check=True)
+
+
+def update_java_lance_core_version(repo_root: Path, version: str) -> None:
+    pom_path = repo_root / "java" / "pom.xml"
+    contents = pom_path.read_text(encoding="utf-8")
+    updated, count = re.subn(
+        r"(<lance-core\.version>)[^<]+(</lance-core\.version>)",
+        rf"\g<1>{version}\g<2>",
+        contents,
+        count=1,
+    )
+    if count != 1:
+        raise RuntimeError(
+            "Expected exactly one <lance-core.version> entry in java/pom.xml"
+        )
+    pom_path.write_text(updated, encoding="utf-8")
+
+
+def write_github_outputs(path: str | None, payload: dict[str, str]) -> None:
+    if not path:
+        return
+    with open(path, "a", encoding="utf-8") as output:
+        for key, value in payload.items():
+            output.write(f"{key}={value}\n")
+
+
+def main(argv: Sequence[str] | None = None) -> int:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "tag_or_version",
+        help="Lance tag or version, for example refs/tags/v7.2.0-beta.1 or 7.2.0",
+    )
+    parser.add_argument(
+        "--repo-root",
+        type=Path,
+        default=Path(__file__).resolve().parents[1],
+        help="Path to the lancedb repository root",
+    )
+    parser.add_argument(
+        "--github-output",
+        default=None,
+        help="Optional GitHub Actions output file to receive metadata fields",
+    )
+    parser.add_argument(
+        "--metadata-only",
+        action="store_true",
+        help="Only print derived metadata; do not modify dependency files",
+    )
+    args = parser.parse_args(argv)
+
+    repo_root = args.repo_root.resolve()
+    version = normalize_version(args.tag_or_version)
+    payload = metadata_for(version)
+
+    if not args.metadata_only:
+        run_command([sys.executable, "ci/set_lance_version.py", version], cwd=repo_root)
+        update_java_lance_core_version(repo_root, version)
+
+    write_github_outputs(args.github_output, payload)
+    print(json.dumps(payload, sort_keys=True))
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())

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>0.28.0-beta.11</version>
+    <version>0.30.1-beta.0</version>
 </dependency>
 ```
 

docs/src/js/classes/Connection.md

@@ -437,6 +437,39 @@ Open a table in the database.
 
 ***
 
+### renameTable()
+
+```ts
+abstract renameTable(
+   currentName,
+   newName,
+   options?): Promise<void>
+```
+
+Rename a table.
+
+Currently only supported by LanceDB Cloud. Local OSS connections and
+namespace-backed connections (via [connectNamespace](../functions/connectNamespace.md)) reject with
+a "not supported" error.
+
+#### Parameters
+
+* **currentName**: `string`
+    The current name of the table.
+
+* **newName**: `string`
+    The new name for the table.
+
+* **options?**: [`RenameTableOptions`](../interfaces/RenameTableOptions.md)
+    Optional namespace paths. When
+    `newNamespacePath` is omitted the table stays in `namespacePath`.
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+***
+
 ### tableNames()
 
 #### tableNames(options)

docs/src/js/classes/MergeInsertBuilder.md

@@ -76,6 +76,57 @@ the query optimizer chooses a suboptimal path.
 
 ***
 
+### useLsmWrite()
+
+```ts
+useLsmWrite(useLsmWrite): MergeInsertBuilder
+```
+
+Controls whether the merge uses the MemWAL LSM write path.
+
+By default (unset), a `mergeInsert` on a table with an LSM write spec is
+routed through Lance's MemWAL shard writer, and a table without one uses
+the standard path. Pass `false` to force the standard path even when a
+spec is set. Pass `true` to require a spec — `mergeInsert` rejects if none
+is installed.
+
+#### Parameters
+
+* **useLsmWrite**: `boolean`
+    Whether to use the LSM write path.
+
+#### Returns
+
+[`MergeInsertBuilder`](MergeInsertBuilder.md)
+
+***
+
+### validateSingleShard()
+
+```ts
+validateSingleShard(validateSingleShard): MergeInsertBuilder
+```
+
+Controls how an LSM merge checks that its input targets a single shard.
+
+When a table has an LSM write spec, every row in a `mergeInsert` call must
+route to the same shard. When `true` (the default), every row is inspected
+to verify this. When `false`, only the first row is inspected and the
+shard it routes to is used for the whole input — a faster path for callers
+that have already pre-sharded their input. Has no effect on tables without
+an LSM write spec.
+
+#### Parameters
+
+* **validateSingleShard**: `boolean`
+    Whether to check every row routes to one shard. Defaults to `true`.
+
+#### Returns
+
+[`MergeInsertBuilder`](MergeInsertBuilder.md)
+
+***
+
 ### whenMatchedUpdateAll()
 
 ```ts

docs/src/js/classes/Query.md

@@ -343,6 +343,30 @@ This is useful for pagination.
 
 ***
 
+### orderBy()
+
+```ts
+orderBy(ordering): this
+```
+
+Sort the results by the specified column(s).
+
+#### Parameters
+
+* **ordering**: [`ColumnOrdering`](../interfaces/ColumnOrdering.md) \| [`ColumnOrdering`](../interfaces/ColumnOrdering.md)[]
+
+#### Returns
+
+`this`
+
+This query builder.
+
+#### Inherited from
+
+`StandardQueryBase.orderBy`
+
+***
+
 ### outputSchema()
 
 ```ts

docs/src/js/classes/Scannable.md

@@ -0,0 +1,173 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / Scannable
+
+# Class: Scannable
+
+A data source that can be scanned as a stream of Arrow `RecordBatch`es.
+
+`Scannable` wraps the schema + optional row count + rescannable flag and
+a callback that yields batches one at a time. It is passed to consumers
+(e.g. `Table.add`, `createTable`, `mergeInsert` — follow-up work) that
+need to pull data without materializing the full dataset in JS memory.
+
+Batches cross the JS↔Rust boundary as Arrow IPC Stream messages; a fresh
+writer serializes each batch, and the Rust side decodes it with
+`arrow_ipc::reader::StreamReader`. One batch is in flight at a time.
+
+## Properties
+
+### numRows
+
+```ts
+readonly numRows: null | number;
+```
+
+***
+
+### rescannable
+
+```ts
+readonly rescannable: boolean;
+```
+
+***
+
+### schema
+
+```ts
+readonly schema: Schema<any>;
+```
+
+## Methods
+
+### fromFactory()
+
+```ts
+static fromFactory(
+   schema,
+   factory,
+   opts): Promise<Scannable>
+```
+
+Build a Scannable from an explicit schema and a factory that returns a
+fresh batch iterator on each call.
+
+The factory is invoked once per scan. Each iterator yields
+`RecordBatch`es matching the declared schema. Use this when you need
+direct control over the pull loop — for example, to wrap a streaming
+source whose batches are produced lazily.
+
+#### Parameters
+
+* **schema**: `Schema`&lt;`any`&gt;
+    The Arrow schema of the produced batches.
+
+* **factory**
+    Called at the start of each scan to produce a batch
+    iterator. Must be idempotent when `rescannable` is true.
+
+* **opts**: [`ScannableOptions`](../interfaces/ScannableOptions.md) = `{}`
+    Optional hints. `rescannable` defaults to `true`; set to
+    `false` if calling `factory()` twice would not reproduce the same data.
+
+#### Returns
+
+`Promise`&lt;[`Scannable`](Scannable.md)&gt;
+
+***
+
+### fromIterable()
+
+```ts
+static fromIterable(
+   schema,
+   iter,
+   opts): Promise<Scannable>
+```
+
+Build a Scannable from an iterable of `RecordBatch`es. `rescannable`
+defaults to `false`. Pass an explicit schema so the consumer can
+validate before any batch is pulled.
+
+`opts.rescannable: true` is honest for replayable iterables (Arrays,
+Sets, or custom iterables whose `[Symbol.iterator]()` returns a fresh
+iterator each call). It is rejected for one-shot iterables (generators,
+async generators, or already-an-iterator inputs) because their
+`[Symbol.iterator]()` returns the same exhausted object on the second
+scan. For replayable sources outside this shape, use
+`fromFactory(schema, () => createIter(), { rescannable: true })`.
+
+Note: when `opts.rescannable` is `true`, the constructor calls
+`[Symbol.iterator]()` once on the input to perform the structural check.
+
+#### Parameters
+
+* **schema**: `Schema`&lt;`any`&gt;
+
+* **iter**: `Iterable`&lt;`RecordBatch`&lt;`any`&gt;&gt; \| `AsyncIterable`&lt;`RecordBatch`&lt;`any`&gt;&gt;
+
+* **opts**: [`ScannableOptions`](../interfaces/ScannableOptions.md) = `{}`
+
+#### Returns
+
+`Promise`&lt;[`Scannable`](Scannable.md)&gt;
+
+***
+
+### fromRecordBatchReader()
+
+```ts
+static fromRecordBatchReader(reader, opts): Promise<Scannable>
+```
+
+Build a Scannable from an Arrow `RecordBatchReader`. A reader can only
+be consumed once; `rescannable` defaults to `false`.
+
+The reader must already be opened (via `.open()`) so its `.schema` is
+populated. `RecordBatchReader.from(...)` returns an unopened reader.
+
+`opts.rescannable: true` is rejected because `RecordBatchReader` is a
+self-iterator (its `[Symbol.iterator]()` returns itself), and this
+constructor does not call `reader.reset()` between scans, so a second
+scan would always see an exhausted reader. For genuinely replayable
+sources, use
+`fromFactory(schema, () => openReader(), { rescannable: true })`,
+which mints a fresh reader on each scan.
+
+#### Parameters
+
+* **reader**: `RecordBatchReader`&lt;`any`&gt;
+
+* **opts**: [`ScannableOptions`](../interfaces/ScannableOptions.md) = `{}`
+
+#### Returns
+
+`Promise`&lt;[`Scannable`](Scannable.md)&gt;
+
+***
+
+### fromTable()
+
+```ts
+static fromTable(table, opts): Promise<Scannable>
+```
+
+Build a Scannable from an in-memory Arrow `Table`. Always rescannable;
+the table's batches are replayed on each scan.
+
+The table's row count is authoritative: `opts.numRows` must either be
+omitted or equal to `table.numRows`. `opts.rescannable` of `false` is
+rejected because in-memory Tables are always rescannable.
+
+#### Parameters
+
+* **table**: `Table`&lt;`any`&gt;
+
+* **opts**: [`ScannableOptions`](../interfaces/ScannableOptions.md) = `{}`
+
+#### Returns
+
+`Promise`&lt;[`Scannable`](Scannable.md)&gt;

docs/src/js/classes/Table.md

@@ -187,6 +187,25 @@ Any attempt to use the table after it is closed will result in an error.
 
 ***
 
+### closeLsmWriters()
+
+```ts
+abstract closeLsmWriters(): Promise<void>
+```
+
+Drain and close any cached MemWAL shard writers held for this table.
+
+When an [LsmWriteSpec](../interfaces/LsmWriteSpec.md) is installed, `mergeInsert` opens MemWAL
+shard writers and caches them for reuse across calls. This closes them,
+flushing pending data; writers reopen lazily on the next `mergeInsert`.
+It is a no-op when no writers are cached.
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+***
+
 ### countRows()
 
 ```ts
@@ -690,6 +709,74 @@ of the given query
 
 ***
 
+### setLsmWriteSpec()
+
+```ts
+abstract setLsmWriteSpec(spec): Promise<void>
+```
+
+Install an [LsmWriteSpec](../interfaces/LsmWriteSpec.md) on this table, selecting Lance's MemWAL
+LSM-style write path for future `mergeInsert` calls.
+
+`LsmWriteSpec` chooses one of three sharding strategies via `specType`:
+
+- `"bucket"` — hash-bucket writes by the single-column unenforced primary
+  key (`column` and `numBuckets` required).
+- `"identity"` — shard by the raw value of a scalar `column`.
+- `"unsharded"` — route every write to a single shard.
+
+All variants require the table to have an unenforced primary key
+([Table#setUnenforcedPrimaryKey](Table.md#setunenforcedprimarykey)); bucket sharding additionally
+requires it to be the single column being bucketed.
+
+#### Parameters
+
+* **spec**: [`LsmWriteSpec`](../interfaces/LsmWriteSpec.md)
+    The sharding spec to install.
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+#### Example
+
+```ts
+await table.setUnenforcedPrimaryKey("id");
+await table.setLsmWriteSpec({
+  specType: "bucket",
+  column: "id",
+  numBuckets: 16,
+  maintainedIndexes: ["id_idx"],
+});
+```
+
+***
+
+### setUnenforcedPrimaryKey()
+
+```ts
+abstract setUnenforcedPrimaryKey(columns): Promise<void>
+```
+
+Set the unenforced primary key for this table to a single column.
+
+"Unenforced" means LanceDB does not check uniqueness on writes; the
+column is recorded in the schema as the primary key for use by features
+such as `merge_insert`. Only single-column primary keys are supported,
+and the key cannot be changed once set.
+
+#### Parameters
+
+* **columns**: `string` \| `string`[]
+    The primary key column. A one-element
+    array is also accepted; passing more than one column is rejected.
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+***
+
 ### stats()
 
 ```ts
@@ -793,6 +880,23 @@ Return the table as an arrow table
 
 ***
 
+### unsetLsmWriteSpec()
+
+```ts
+abstract unsetLsmWriteSpec(): Promise<void>
+```
+
+Remove the [LsmWriteSpec](../interfaces/LsmWriteSpec.md) from this table, reverting to the standard
+`mergeInsert` write path.
+
+Errors if no spec is currently set.
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+***
+
 ### update()
 
 #### update(opts)

docs/src/js/classes/VectorQuery.md

@@ -498,6 +498,30 @@ This is useful for pagination.
 
 ***
 
+### orderBy()
+
+```ts
+orderBy(ordering): this
+```
+
+Sort the results by the specified column(s).
+
+#### Parameters
+
+* **ordering**: [`ColumnOrdering`](../interfaces/ColumnOrdering.md) \| [`ColumnOrdering`](../interfaces/ColumnOrdering.md)[]
+
+#### Returns
+
+`this`
+
+This query builder.
+
+#### Inherited from
+
+`StandardQueryBase.orderBy`
+
+***
+
 ### outputSchema()
 
 ```ts

docs/src/js/globals.md

@@ -32,6 +32,7 @@
 - [PhraseQuery](classes/PhraseQuery.md)
 - [Query](classes/Query.md)
 - [QueryBase](classes/QueryBase.md)
+- [Scannable](classes/Scannable.md)
 - [Session](classes/Session.md)
 - [StaticHeaderProvider](classes/StaticHeaderProvider.md)
 - [Table](classes/Table.md)
@@ -50,6 +51,7 @@
 - [AlterColumnsResult](interfaces/AlterColumnsResult.md)
 - [ClientConfig](interfaces/ClientConfig.md)
 - [ColumnAlteration](interfaces/ColumnAlteration.md)
+- [ColumnOrdering](interfaces/ColumnOrdering.md)
 - [CompactionStats](interfaces/CompactionStats.md)
 - [ConnectNamespaceOptions](interfaces/ConnectNamespaceOptions.md)
 - [ConnectionOptions](interfaces/ConnectionOptions.md)
@@ -78,14 +80,17 @@
 - [IvfRqOptions](interfaces/IvfRqOptions.md)
 - [ListNamespacesOptions](interfaces/ListNamespacesOptions.md)
 - [ListNamespacesResponse](interfaces/ListNamespacesResponse.md)
+- [LsmWriteSpec](interfaces/LsmWriteSpec.md)
 - [MergeResult](interfaces/MergeResult.md)
 - [OpenTableOptions](interfaces/OpenTableOptions.md)
 - [OptimizeOptions](interfaces/OptimizeOptions.md)
 - [OptimizeStats](interfaces/OptimizeStats.md)
 - [QueryExecutionOptions](interfaces/QueryExecutionOptions.md)
 - [RemovalStats](interfaces/RemovalStats.md)
+- [RenameTableOptions](interfaces/RenameTableOptions.md)
 - [RestNamespaceConfig](interfaces/RestNamespaceConfig.md)
 - [RetryConfig](interfaces/RetryConfig.md)
+- [ScannableOptions](interfaces/ScannableOptions.md)
 - [ShuffleOptions](interfaces/ShuffleOptions.md)
 - [SplitCalculatedOptions](interfaces/SplitCalculatedOptions.md)
 - [SplitHashOptions](interfaces/SplitHashOptions.md)
@@ -100,6 +105,7 @@
 - [UpdateResult](interfaces/UpdateResult.md)
 - [Version](interfaces/Version.md)
 - [WriteExecutionOptions](interfaces/WriteExecutionOptions.md)
+- [WriteProgress](interfaces/WriteProgress.md)
 
 ## Type Aliases
 

docs/src/js/interfaces/AddDataOptions.md

@@ -19,3 +19,39 @@ mode: "append" | "overwrite";
 If "append" (the default) then the new data will be added to the table
 
 If "overwrite" then the new data will replace the existing data in the table.
+
+***
+
+### progress()
+
+```ts
+progress: (progress) => void;
+```
+
+Optional callback invoked periodically with write progress.
+
+The callback is fired once per batch written and once more with
+`done: true` when the write completes. Calls are dispatched
+asynchronously to the JS event loop and never block the write — a slow
+callback will queue events rather than back-pressure the writer.
+
+Errors thrown from the callback are logged with `console.warn` and
+swallowed — they do not abort the write.
+
+#### Parameters
+
+* **progress**: [`WriteProgress`](WriteProgress.md)
+
+#### Returns
+
+`void`
+
+#### Example
+
+```ts
+await table.add(data, {
+  progress: (p) => {
+    console.log(`${p.outputRows}/${p.totalRows ?? "?"} rows`);
+  },
+});
+```

docs/src/js/interfaces/ColumnOrdering.md

@@ -0,0 +1,31 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / ColumnOrdering
+
+# Interface: ColumnOrdering
+
+## Properties
+
+### ascending?
+
+```ts
+optional ascending: boolean;
+```
+
+***
+
+### columnName
+
+```ts
+columnName: string;
+```
+
+***
+
+### nullsFirst?
+
+```ts
+optional nullsFirst: boolean;
+```

docs/src/js/interfaces/ConnectionOptions.md

@@ -70,16 +70,20 @@ client used by manifest-enabled native connections.
 optional readConsistencyInterval: number;
 ```
 
-(For LanceDB OSS only): The interval, in seconds, at which to check for
-updates to the table from other processes. If None, then consistency is not
-checked. For performance reasons, this is the default. For strong
-consistency, set this to zero seconds. Then every read will check for
-updates from other processes. As a compromise, you can set this to a
-non-zero value for eventual consistency. If more than that interval
-has passed since the last check, then the table will be checked for updates.
-Note: this consistency only applies to read operations. Write operations are
+The interval, in seconds, at which to check for updates to the table
+from other processes. If None, then consistency is not checked. For
+performance reasons, this is the default. For strong consistency, set
+this to zero seconds. Then every read will check for updates from other
+processes. As a compromise, you can set this to a non-zero value for
+eventual consistency. If more than that interval has passed since the
+last check, then the table will be checked for updates. Note: this
+consistency only applies to read operations. Write operations are
 always consistent.
 
+Stronger consistency is not free. The smaller the interval, the more
+often each read pays the cost of checking for updates against object
+storage, raising per-read latency and cost.
+
 ***
 
 ### region?

docs/src/js/interfaces/LsmWriteSpec.md

@@ -0,0 +1,67 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / LsmWriteSpec
+
+# Interface: LsmWriteSpec
+
+Specification selecting Lance's MemWAL LSM-style write path for
+`mergeInsert`.
+
+`specType` is `"bucket"`, `"identity"`, or `"unsharded"`. For `"bucket"`,
+`column` and `numBuckets` are required; for `"identity"`, `column` is
+required and must be a deterministic function of the unenforced primary
+key (every row with a given primary key must always produce the same
+`column` value, or upserts of that key can land in different shards and a
+stale version can win).
+
+## Properties
+
+### column?
+
+```ts
+optional column: string;
+```
+
+Bucket and identity variants: the sharding column.
+
+***
+
+### maintainedIndexes?
+
+```ts
+optional maintainedIndexes: string[];
+```
+
+Names of indexes the MemWAL should keep up to date during writes.
+
+***
+
+### numBuckets?
+
+```ts
+optional numBuckets: number;
+```
+
+Bucket variant: the number of buckets, in `[1, 1024]`.
+
+***
+
+### specType
+
+```ts
+specType: "bucket" | "identity" | "unsharded";
+```
+
+One of `"bucket"`, `"identity"`, or `"unsharded"`.
+
+***
+
+### writerConfigDefaults?
+
+```ts
+optional writerConfigDefaults: Record<string, string>;
+```
+
+Default `ShardWriter` configuration recorded in the MemWAL index.

docs/src/js/interfaces/MergeResult.md

@@ -32,6 +32,14 @@ numInsertedRows: number;
 
 ***
 
+### numRows
+
+```ts
+numRows: number;
+```
+
+***
+
 ### numUpdatedRows
 
 ```ts

docs/src/js/interfaces/RenameTableOptions.md

@@ -0,0 +1,29 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / RenameTableOptions
+
+# Interface: RenameTableOptions
+
+## Properties
+
+### namespacePath?
+
+```ts
+optional namespacePath: string[];
+```
+
+The namespace path of the table being renamed. Defaults to the root
+namespace (`[]`) when omitted.
+
+***
+
+### newNamespacePath?
+
+```ts
+optional newNamespacePath: string[];
+```
+
+The namespace path to move the table to as part of the rename. When
+omitted the table stays in `namespacePath`.

docs/src/js/interfaces/ScannableOptions.md

@@ -0,0 +1,29 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / ScannableOptions
+
+# Interface: ScannableOptions
+
+## Properties
+
+### numRows?
+
+```ts
+optional numRows: number;
+```
+
+Hint about the number of rows. Not validated against the stream.
+
+***
+
+### rescannable?
+
+```ts
+optional rescannable: boolean;
+```
+
+Whether the source can be scanned more than once. Defaults to `true` for
+`fromTable` / `fromFactory` and `false` for `fromIterable` /
+`fromRecordBatchReader`.

docs/src/js/interfaces/WriteProgress.md

@@ -0,0 +1,84 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / WriteProgress
+
+# Interface: WriteProgress
+
+Progress snapshot for a write operation, delivered to the `progress`
+callback passed to [Table.add](../classes/Table.md#add).
+
+## Properties
+
+### activeTasks
+
+```ts
+activeTasks: number;
+```
+
+Number of parallel write tasks currently in flight.
+
+***
+
+### done
+
+```ts
+done: boolean;
+```
+
+`true` for the final callback; `false` otherwise.
+
+***
+
+### elapsedSeconds
+
+```ts
+elapsedSeconds: number;
+```
+
+Wall-clock seconds since the write started.
+
+***
+
+### outputBytes
+
+```ts
+outputBytes: number;
+```
+
+Number of bytes written so far.
+
+***
+
+### outputRows
+
+```ts
+outputRows: number;
+```
+
+Number of rows written so far.
+
+***
+
+### totalRows?
+
+```ts
+optional totalRows: number;
+```
+
+Total rows expected, when the input source reports it.
+
+Always set on the final callback (the one with `done: true`), falling
+back to the actual number of rows written when the source could not
+report a row count up front.
+
+***
+
+### totalTasks
+
+```ts
+totalTasks: number;
+```
+
+Total number of parallel write tasks (the write parallelism).

docs/src/python/python.md

@@ -166,6 +166,12 @@ lists the indices that LanceDb supports.
 
 ::: lancedb.index.IvfFlat
 
+::: lancedb.index.IvfSq
+
+::: lancedb.index.IvfRq
+
+::: lancedb.index.HnswFlat
+
 ::: lancedb.table.IndexStatistics
 
 ## Querying (Asynchronous)

java/lancedb-core/pom.xml

@@ -8,7 +8,7 @@
     <parent>
       <groupId>com.lancedb</groupId>
       <artifactId>lancedb-parent</artifactId>
-      <version>0.28.0-beta.11</version>
+      <version>0.30.1-beta.0</version>
       <relativePath>../pom.xml</relativePath>
     </parent>
 

java/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-parent</artifactId>
-    <version>0.28.0-beta.11</version>
+    <version>0.30.1-beta.0</version>
     <packaging>pom</packaging>
     <name>${project.artifactId}</name>
     <description>LanceDB Java SDK Parent POM</description>
@@ -28,7 +28,7 @@
     <properties>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
         <arrow.version>15.0.0</arrow.version>
-        <lance-core.version>7.0.0-beta.7</lance-core.version>
+        <lance-core.version>7.2.0-beta.1</lance-core.version>
         <spotless.skip>false</spotless.skip>
         <spotless.version>2.30.0</spotless.version>
         <spotless.java.googlejavaformat.version>1.7</spotless.java.googlejavaformat.version>

nodejs/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.28.0-beta.11"
+version = "0.30.1-beta.0"
 publish = false
 license.workspace = true
 description.workspace = true

nodejs/__test__/connection.test.ts

@@ -47,6 +47,14 @@ describe("given a connection", () => {
     await db.close();
     expect(db.isOpen()).toBe(false);
     await expect(db.tableNames()).rejects.toThrow("Connection is closed");
+    await expect(db.renameTable("a", "b")).rejects.toThrow(
+      "Connection is closed",
+    );
+  });
+
+  it("should report renameTable as unsupported on an OSS connection", async () => {
+    await db.createTable("a", [{ id: 1 }]);
+    await expect(db.renameTable("a", "b")).rejects.toThrow(/not supported/);
   });
   it("should be able to create a table from an object arg `createTable(options)`, or args `createTable(name, data, options)`", async () => {
     let tbl = await db.createTable("test", [{ id: 1 }, { id: 2 }]);
@@ -163,18 +171,22 @@ describe("given a connection", () => {
 
     let manifestDir =
       tmpDir.name + "/test_manifest_paths_v2_empty.lance/_versions";
-    readdirSync(manifestDir).forEach((file) => {
-      expect(file).toMatch(/^\d{20}\.manifest$/);
-    });
+    readdirSync(manifestDir)
+      .filter((f) => f.endsWith(".manifest"))
+      .forEach((file) => {
+        expect(file).toMatch(/^\d{20}\.manifest$/);
+      });
 
     table = (await db.createTable("test_manifest_paths_v2", [{ id: 1 }], {
       enableV2ManifestPaths: true,
     })) as LocalTable;
     expect(await table.usesV2ManifestPaths()).toBe(true);
     manifestDir = tmpDir.name + "/test_manifest_paths_v2.lance/_versions";
-    readdirSync(manifestDir).forEach((file) => {
-      expect(file).toMatch(/^\d{20}\.manifest$/);
-    });
+    readdirSync(manifestDir)
+      .filter((f) => f.endsWith(".manifest"))
+      .forEach((file) => {
+        expect(file).toMatch(/^\d{20}\.manifest$/);
+      });
   });
 
   it("should be able to migrate tables to the V2 manifest paths", async () => {
@@ -191,16 +203,20 @@ describe("given a connection", () => {
 
     const manifestDir =
       tmpDir.name + "/test_manifest_path_migration.lance/_versions";
-    readdirSync(manifestDir).forEach((file) => {
-      expect(file).toMatch(/^\d\.manifest$/);
-    });
+    readdirSync(manifestDir)
+      .filter((f) => f.endsWith(".manifest"))
+      .forEach((file) => {
+        expect(file).toMatch(/^\d\.manifest$/);
+      });
 
     await table.migrateManifestPathsV2();
     expect(await table.usesV2ManifestPaths()).toBe(true);
 
-    readdirSync(manifestDir).forEach((file) => {
-      expect(file).toMatch(/^\d{20}\.manifest$/);
-    });
+    readdirSync(manifestDir)
+      .filter((f) => f.endsWith(".manifest"))
+      .forEach((file) => {
+        expect(file).toMatch(/^\d{20}\.manifest$/);
+      });
   });
 });
 

nodejs/__test__/query.test.ts

@@ -109,3 +109,209 @@ describe("Query outputSchema", () => {
     expect(schema.fields.length).toBe(3);
   });
 });
+
+describe("Query orderBy", () => {
+  let tmpDir: tmp.DirResult;
+  let table: Table;
+
+  beforeEach(async () => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+    const db = await connect(tmpDir.name);
+
+    // Create table with numeric data for sorting
+    const schema = new Schema([
+      new Field("id", new Int64(), true),
+      new Field("score", new Float32(), true),
+      new Field("name", new Utf8(), true),
+    ]);
+
+    const data = makeArrowTable(
+      [
+        { id: 1n, score: 3.5, name: "charlie" },
+        { id: 2n, score: 1.2, name: "alice" },
+        { id: 3n, score: 2.8, name: "bob" },
+        { id: 4n, score: 0.5, name: "david" },
+        { id: 5n, score: 4.1, name: "eve" },
+      ],
+      { schema },
+    );
+    table = await db.createTable("test", data);
+  });
+
+  afterEach(() => {
+    tmpDir.removeCallback();
+  });
+
+  it("should sort by single column ascending", async () => {
+    const results = await table
+      .query()
+      .orderBy({ columnName: "score", ascending: true, nullsFirst: false })
+      .toArray();
+
+    expect(results.length).toBe(5);
+    // Verify ascending order
+    expect(results[0].score).toBeCloseTo(0.5, 0.001);
+    expect(results[1].score).toBeCloseTo(1.2, 0.001);
+    expect(results[2].score).toBeCloseTo(2.8, 0.001);
+    expect(results[3].score).toBeCloseTo(3.5, 0.001);
+    expect(results[4].score).toBeCloseTo(4.1, 0.001);
+  });
+
+  it("should sort by single column descending", async () => {
+    const results = await table
+      .query()
+      .orderBy({ columnName: "score", ascending: false, nullsFirst: false })
+      .toArray();
+
+    expect(results.length).toBe(5);
+    // Verify descending order
+    expect(results[0].score).toBeCloseTo(4.1, 0.001);
+    expect(results[1].score).toBeCloseTo(3.5, 0.001);
+    expect(results[2].score).toBeCloseTo(2.8, 0.001);
+    expect(results[3].score).toBeCloseTo(1.2, 0.001);
+    expect(results[4].score).toBeCloseTo(0.5, 0.001);
+  });
+
+  it("should use ascending as default direction", async () => {
+    const results = await table
+      .query()
+      .orderBy({ columnName: "score" })
+      .toArray();
+
+    expect(results.length).toBe(5);
+    // Verify ascending order (default)
+    expect(results[0].score).toBeCloseTo(0.5, 0.001);
+    expect(results[1].score).toBeCloseTo(1.2, 0.001);
+    expect(results[2].score).toBeCloseTo(2.8, 0.001);
+    expect(results[3].score).toBeCloseTo(3.5, 0.001);
+    expect(results[4].score).toBeCloseTo(4.1, 0.001);
+  });
+
+  it("should sort by string column", async () => {
+    const results = await table
+      .query()
+      .orderBy({ columnName: "name" })
+      .toArray();
+
+    expect(results.length).toBe(5);
+    // Verify alphabetical order
+    expect(results[0].name).toBe("alice");
+    expect(results[1].name).toBe("bob");
+    expect(results[2].name).toBe("charlie");
+    expect(results[3].name).toBe("david");
+    expect(results[4].name).toBe("eve");
+  });
+
+  it("should support method chaining with where", async () => {
+    const results = await table
+      .query()
+      .where("score > 2.0")
+      .orderBy({ columnName: "score" })
+      .toArray();
+    expect(results.length).toBe(3);
+    // Verify filtered and sorted
+    expect(results[0].score).toBeCloseTo(2.8, 0.001);
+    expect(results[1].score).toBeCloseTo(3.5, 0.001);
+    expect(results[2].score).toBeCloseTo(4.1, 0.001);
+  });
+
+  it("should support method chaining with limit", async () => {
+    const results = await table
+      .query()
+      .orderBy({ columnName: "score", ascending: false })
+      .limit(3)
+      .toArray();
+
+    expect(results.length).toBe(3);
+    // Verify top 3 in descending order
+    expect(results[0].score).toBeCloseTo(4.1, 0.001);
+    expect(results[1].score).toBeCloseTo(3.5, 0.001);
+    expect(results[2].score).toBeCloseTo(2.8, 0.001);
+  });
+
+  it("should support method chaining with offset", async () => {
+    const results = await table
+      .query()
+      .orderBy({ columnName: "score" })
+      .offset(2)
+      .limit(2)
+      .toArray();
+
+    expect(results.length).toBe(2);
+    // Verify results skip first 2 and take next 2
+    expect(results[0].score).toBeCloseTo(2.8, 0.001);
+    expect(results[1].score).toBeCloseTo(3.5, 0.001);
+  });
+
+  it("should support method chaining with select", async () => {
+    const results = await table
+      .query()
+      .orderBy({ columnName: "name" })
+      .select(["name", "score"])
+      .toArray();
+
+    expect(results.length).toBe(5);
+    // Verify only selected columns are present
+    expect(Object.keys(results[0])).toEqual(["name", "score"]);
+    expect(Object.keys(results[4])).toEqual(["name", "score"]);
+    // Verify sorted by name
+    expect(results[0].name).toBe("alice");
+    expect(results[4].name).toBe("eve");
+  });
+
+  it("should support complex method chaining", async () => {
+    const results = await table
+      .query()
+      .where("score > 1.0")
+      .orderBy({ columnName: "score", ascending: false })
+      .limit(3)
+      .select(["id", "score", "name"])
+      .toArray();
+
+    expect(results.length).toBe(3);
+    // Verify filtered, sorted, limited, and projected
+    expect(results[0].score).toBeCloseTo(4.1, 0.001);
+    expect(results[1].score).toBeCloseTo(3.5, 0.001);
+    expect(results[2].score).toBeCloseTo(2.8, 0.001);
+    expect(Object.keys(results[0])).toEqual(["id", "score", "name"]);
+  });
+
+  it("should support multi-column ordering and null placement", async () => {
+    const schema = new Schema([
+      new Field("group", new Int64(), true),
+      new Field("score", new Float32(), true),
+      new Field("name", new Utf8(), true),
+    ]);
+
+    const data = makeArrowTable(
+      [
+        { group: 1n, score: null, name: "z" },
+        { group: 1n, score: 1.0, name: "b" },
+        { group: 1n, score: 1.0, name: "a" },
+        { group: 2n, score: 0.5, name: "c" },
+      ],
+      { schema },
+    );
+    const nullTable = await (await connect(tmpDir.name)).createTable(
+      "test_multi_order",
+      data,
+      { mode: "overwrite" },
+    );
+
+    const results = await nullTable
+      .query()
+      .orderBy([
+        { columnName: "group", ascending: true, nullsFirst: false },
+        { columnName: "score", ascending: true, nullsFirst: true },
+        { columnName: "name", ascending: true, nullsFirst: false },
+      ])
+      .toArray();
+
+    expect(results.map((r) => [r.group, r.score, r.name])).toEqual([
+      [1n, null, "z"],
+      [1n, 1.0, "a"],
+      [1n, 1.0, "b"],
+      [2n, 0.5, "c"],
+    ]);
+  });
+});

nodejs/__test__/remote.test.ts

@@ -617,4 +617,68 @@ describe("remote connection", () => {
       );
     });
   });
+
+  describe("renameTable", () => {
+    async function captureRenameRequest(
+      call: (db: Connection) => Promise<void>,
+    ): Promise<{ url: string; body: Record<string, unknown> }> {
+      let captured: { url: string; body: Record<string, unknown> } | undefined;
+      await withMockDatabase((req, res) => {
+        let raw = "";
+        req.on("data", (chunk) => {
+          raw += chunk;
+        });
+        req.on("end", () => {
+          captured = {
+            url: req.url ?? "",
+            body: raw ? JSON.parse(raw) : {},
+          };
+          res.writeHead(200, { "Content-Type": "application/json" }).end("");
+        });
+      }, call);
+      if (!captured) {
+        throw new Error("mock server never saw a request");
+      }
+      return captured;
+    }
+
+    it("sends rename request for a table in the root namespace", async () => {
+      const { url, body } = await captureRenameRequest(async (db) => {
+        await db.renameTable("table1", "table2");
+      });
+      expect(url).toBe("/v1/table/table1/rename/");
+      // biome-ignore lint/style/useNamingConvention: snake_case mandated by the server wire format
+      expect(body).toEqual({ new_table_name: "table2" });
+    });
+
+    it("omits new_namespace when only the current namespace is supplied", async () => {
+      // Safe-default check: passing namespacePath alone must not send
+      // `new_namespace`, so the server keeps the table in its current
+      // namespace instead of silently moving it to root.
+      const { url, body } = await captureRenameRequest(async (db) => {
+        await db.renameTable("table1", "table2", {
+          namespacePath: ["ns1"],
+        });
+      });
+      expect(url).toBe("/v1/table/ns1$table1/rename/");
+      // biome-ignore lint/style/useNamingConvention: snake_case mandated by the server wire format
+      expect(body).toEqual({ new_table_name: "table2" });
+    });
+
+    it("includes new_namespace in the body for a cross-namespace rename", async () => {
+      const { url, body } = await captureRenameRequest(async (db) => {
+        await db.renameTable("table1", "table2", {
+          namespacePath: ["ns1"],
+          newNamespacePath: ["ns2"],
+        });
+      });
+      expect(url).toBe("/v1/table/ns1$table1/rename/");
+      expect(body).toEqual({
+        // biome-ignore lint/style/useNamingConvention: snake_case mandated by the server wire format
+        new_table_name: "table2",
+        // biome-ignore lint/style/useNamingConvention: snake_case mandated by the server wire format
+        new_namespace: ["ns2"],
+      });
+    });
+  });
 });

nodejs/__test__/scannable.test.ts

@@ -0,0 +1,438 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import {
+  Field,
+  Float16,
+  Int32,
+  type RecordBatch,
+  RecordBatchReader,
+  Schema,
+  tableToIPC,
+} from "apache-arrow";
+import { makeArrowTable, makeEmptyTable } from "../lancedb/arrow";
+import { Scannable } from "../lancedb/scannable";
+
+function makeTable() {
+  return makeArrowTable(
+    [
+      { id: 1, name: "a" },
+      { id: 2, name: "b" },
+      { id: 3, name: "c" },
+    ],
+    { vectorColumns: {} },
+  );
+}
+
+async function makeReader(): Promise<RecordBatchReader> {
+  // `RecordBatchReader.from()` returns an unopened reader; `.schema` is only
+  // populated after `.open()`. Opening sync readers is synchronous.
+  const reader = RecordBatchReader.from(tableToIPC(makeTable()));
+  return reader.open() as RecordBatchReader;
+}
+
+describe("Scannable", () => {
+  describe("fromTable", () => {
+    test("reflects schema, numRows, and defaults rescannable=true", async () => {
+      const table = makeTable();
+      const scannable = await Scannable.fromTable(table);
+
+      expect(scannable.schema).toBe(table.schema);
+      expect(scannable.numRows).toBe(table.numRows);
+      expect(scannable.rescannable).toBe(true);
+    });
+
+    test("throws when opts.numRows does not match table.numRows", async () => {
+      await expect(
+        Scannable.fromTable(makeTable(), { numRows: 42 }),
+      ).rejects.toThrow(/does not match table\.numRows/);
+    });
+
+    test("throws when opts.rescannable is false", async () => {
+      await expect(
+        Scannable.fromTable(makeTable(), { rescannable: false }),
+      ).rejects.toThrow(/always rescannable/);
+    });
+  });
+
+  describe("fromRecordBatchReader", () => {
+    test("reflects schema and defaults numRows=null, rescannable=false", async () => {
+      const reader = await makeReader();
+      const scannable = await Scannable.fromRecordBatchReader(reader);
+
+      expect(scannable.schema).toBe(reader.schema);
+      expect(scannable.numRows).toBeNull();
+      expect(scannable.rescannable).toBe(false);
+    });
+
+    test("honors numRows override", async () => {
+      const scannable = await Scannable.fromRecordBatchReader(
+        await makeReader(),
+        { numRows: 3 },
+      );
+
+      expect(scannable.numRows).toBe(3);
+      expect(scannable.rescannable).toBe(false);
+    });
+
+    test("rescannable: false explicit does not throw", async () => {
+      const reader = await makeReader();
+      const scannable = await Scannable.fromRecordBatchReader(reader, {
+        rescannable: false,
+      });
+      expect(scannable.rescannable).toBe(false);
+    });
+
+    test("throws when opts.rescannable is true", async () => {
+      const reader = await makeReader();
+      await expect(
+        Scannable.fromRecordBatchReader(reader, { rescannable: true }),
+      ).rejects.toThrow(/does not accept rescannable/);
+    });
+
+    test("throws when opts.rescannable is true even alongside numRows", async () => {
+      const reader = await makeReader();
+      await expect(
+        Scannable.fromRecordBatchReader(reader, {
+          numRows: 3,
+          rescannable: true,
+        }),
+      ).rejects.toThrow(/does not accept rescannable/);
+    });
+  });
+
+  describe("fromIterable", () => {
+    test("accepts a sync iterable of batches", async () => {
+      const table = makeTable();
+      const scannable = await Scannable.fromIterable(
+        table.schema,
+        table.batches,
+      );
+
+      expect(scannable.schema).toBe(table.schema);
+      expect(scannable.numRows).toBeNull();
+      expect(scannable.rescannable).toBe(false);
+    });
+
+    test("accepts an async iterable of batches", async () => {
+      const table = makeTable();
+      async function* generator(): AsyncGenerator<RecordBatch> {
+        for (const batch of table.batches) {
+          yield batch;
+        }
+      }
+
+      const scannable = await Scannable.fromIterable(table.schema, generator());
+      expect(scannable.schema).toBe(table.schema);
+      expect(scannable.rescannable).toBe(false);
+    });
+
+    describe("rescannable: true detection", () => {
+      // Replayable inputs: [Symbol.iterator]() / [Symbol.asyncIterator]()
+      // returns a fresh iterator each call. Must NOT throw.
+
+      test("Array passes (fresh ArrayIterator each call)", async () => {
+        const table = makeTable();
+        const scannable = await Scannable.fromIterable(
+          table.schema,
+          table.batches,
+          { rescannable: true },
+        );
+        expect(scannable.rescannable).toBe(true);
+      });
+
+      test("Set passes (fresh SetIterator each call)", async () => {
+        const table = makeTable();
+        const set = new Set<RecordBatch>(table.batches);
+        const scannable = await Scannable.fromIterable(table.schema, set, {
+          rescannable: true,
+        });
+        expect(scannable.rescannable).toBe(true);
+      });
+
+      test("custom Iterable returning a fresh iterator passes", async () => {
+        const table = makeTable();
+        const replayable: Iterable<RecordBatch> = {
+          [Symbol.iterator]() {
+            return table.batches[Symbol.iterator]();
+          },
+        };
+        const scannable = await Scannable.fromIterable(
+          table.schema,
+          replayable,
+          { rescannable: true },
+        );
+        expect(scannable.rescannable).toBe(true);
+      });
+
+      test("object with generator method passes (fresh generator each call)", async () => {
+        const table = makeTable();
+        const replayable: Iterable<RecordBatch> = {
+          *[Symbol.iterator]() {
+            for (const batch of table.batches) yield batch;
+          },
+        };
+        const scannable = await Scannable.fromIterable(
+          table.schema,
+          replayable,
+          { rescannable: true },
+        );
+        expect(scannable.rescannable).toBe(true);
+      });
+
+      test("empty Array passes (replayable degenerate case)", async () => {
+        const schema = makeTable().schema;
+        const scannable = await Scannable.fromIterable(
+          schema,
+          [] as RecordBatch[],
+          { rescannable: true },
+        );
+        expect(scannable.rescannable).toBe(true);
+      });
+
+      // One-shot inputs: [Symbol.iterator]() / [Symbol.asyncIterator]()
+      // returns the same object, or the input is already-an-iterator.
+      // Must throw with a /one-shot/ message.
+
+      test("sync generator throws", async () => {
+        const table = makeTable();
+        function* generator(): Generator<RecordBatch> {
+          for (const batch of table.batches) yield batch;
+        }
+        await expect(
+          Scannable.fromIterable(table.schema, generator(), {
+            rescannable: true,
+          }),
+        ).rejects.toThrow(/one-shot/);
+      });
+
+      test("async generator throws", async () => {
+        const table = makeTable();
+        async function* generator(): AsyncGenerator<RecordBatch> {
+          for (const batch of table.batches) yield batch;
+        }
+        await expect(
+          Scannable.fromIterable(table.schema, generator(), {
+            rescannable: true,
+          }),
+        ).rejects.toThrow(/one-shot/);
+      });
+
+      test("empty generator throws (one-shot degenerate case)", async () => {
+        const schema = makeTable().schema;
+        function* generator(): Generator<RecordBatch> {
+          // intentionally empty; yields nothing.
+        }
+        await expect(
+          Scannable.fromIterable(schema, generator(), { rescannable: true }),
+        ).rejects.toThrow(/one-shot/);
+      });
+
+      test("custom self-iterator throws", async () => {
+        const table = makeTable();
+        const batches = table.batches;
+        let i = 0;
+        const oneShot: Iterable<RecordBatch> & Iterator<RecordBatch> = {
+          [Symbol.iterator]() {
+            return this;
+          },
+          next() {
+            if (i >= batches.length) {
+              return { done: true, value: undefined };
+            }
+            return { done: false, value: batches[i++] };
+          },
+        };
+        await expect(
+          Scannable.fromIterable(table.schema, oneShot, { rescannable: true }),
+        ).rejects.toThrow(/one-shot/);
+      });
+
+      test("Array.values() (IterableIterator) throws", async () => {
+        const table = makeTable();
+        const iter = table.batches.values();
+        await expect(
+          Scannable.fromIterable(table.schema, iter, { rescannable: true }),
+        ).rejects.toThrow(/one-shot/);
+      });
+
+      test("raw iterator (only `.next`) throws", async () => {
+        const table = makeTable();
+        const batches = table.batches;
+        let i = 0;
+        const rawIter = {
+          next(): IteratorResult<RecordBatch> {
+            if (i >= batches.length) {
+              return { done: true, value: undefined };
+            }
+            return { done: false, value: batches[i++] };
+          },
+        };
+        await expect(
+          Scannable.fromIterable(
+            table.schema,
+            rawIter as unknown as Iterable<RecordBatch>,
+            { rescannable: true },
+          ),
+        ).rejects.toThrow(/one-shot/);
+      });
+
+      // Edge: null/undefined must not crash the detection helper. The
+      // null check belongs to `normalizeIterator` and only fires when a
+      // scan starts.
+
+      test("null input does not crash detection at construction", async () => {
+        const schema = makeTable().schema;
+        await expect(
+          Scannable.fromIterable(
+            schema,
+            null as unknown as Iterable<RecordBatch>,
+            {
+              rescannable: true,
+            },
+          ),
+        ).resolves.toBeDefined();
+      });
+
+      test("undefined input does not crash detection at construction", async () => {
+        const schema = makeTable().schema;
+        await expect(
+          Scannable.fromIterable(
+            schema,
+            undefined as unknown as Iterable<RecordBatch>,
+            { rescannable: true },
+          ),
+        ).resolves.toBeDefined();
+      });
+
+      // Default (rescannable omitted) skips the check entirely, so even
+      // pathological inputs construct without throwing here.
+
+      test("rescannable omitted skips detection entirely (generator passes)", async () => {
+        const table = makeTable();
+        function* generator(): Generator<RecordBatch> {
+          for (const batch of table.batches) yield batch;
+        }
+        const scannable = await Scannable.fromIterable(
+          table.schema,
+          generator(),
+        );
+        expect(scannable.rescannable).toBe(false);
+      });
+
+      test("rescannable: false explicit skips detection entirely (generator passes)", async () => {
+        const table = makeTable();
+        function* generator(): Generator<RecordBatch> {
+          for (const batch of table.batches) yield batch;
+        }
+        const scannable = await Scannable.fromIterable(
+          table.schema,
+          generator(),
+          { rescannable: false },
+        );
+        expect(scannable.rescannable).toBe(false);
+      });
+    });
+  });
+
+  describe("fromFactory", () => {
+    test("defaults rescannable=true and does not invoke the factory eagerly", async () => {
+      const table = makeTable();
+      const factory = jest.fn(() => table.batches);
+
+      const scannable = await Scannable.fromFactory(table.schema, factory);
+
+      expect(scannable.schema).toBe(table.schema);
+      expect(scannable.rescannable).toBe(true);
+      expect(factory).not.toHaveBeenCalled();
+    });
+
+    test("honors rescannable and numRows overrides", async () => {
+      const table = makeTable();
+      const scannable = await Scannable.fromFactory(
+        table.schema,
+        () => table.batches,
+        { numRows: 7, rescannable: false },
+      );
+
+      expect(scannable.numRows).toBe(7);
+      expect(scannable.rescannable).toBe(false);
+    });
+  });
+
+  describe("validation", () => {
+    test("throws when numRows is negative", async () => {
+      await expect(
+        Scannable.fromFactory(makeTable().schema, () => [], { numRows: -1 }),
+      ).rejects.toThrow(/non-negative/);
+    });
+
+    test("throws when numRows is not an integer", async () => {
+      await expect(
+        Scannable.fromFactory(makeTable().schema, () => [], { numRows: 3.5 }),
+      ).rejects.toThrow(/integer/);
+    });
+  });
+
+  describe("native handle", () => {
+    test("exposes a native handle via inner", async () => {
+      const scannable = await Scannable.fromTable(makeTable());
+      expect(scannable.inner).toBeDefined();
+      expect(typeof scannable.inner).toBe("object");
+      expect(scannable.inner).not.toBeNull();
+    });
+  });
+
+  // Schema-variety construction tests. Each asserts that construction
+  // succeeds against a richer Arrow schema, which transitively exercises
+  // schema serialization and the Rust-side `ipc_file_to_schema` for types
+  // beyond flat primitives.
+  describe("schema variety", () => {
+    test("accepts an empty table", async () => {
+      const schema = new Schema([new Field("id", new Int32(), true)]);
+      const table = makeEmptyTable(schema);
+      const scannable = await Scannable.fromTable(table);
+
+      expect(scannable.numRows).toBe(0);
+      expect(scannable.schema).toBe(table.schema);
+    });
+
+    test("accepts nested struct and list columns", async () => {
+      const table = makeArrowTable(
+        [
+          { id: 1, point: { x: 0, y: 0 }, tags: ["a", "b"] },
+          { id: 2, point: { x: 1, y: 2 }, tags: ["c"] },
+        ],
+        { vectorColumns: {} },
+      );
+      const scannable = await Scannable.fromTable(table);
+
+      expect(scannable.schema).toBe(table.schema);
+      expect(scannable.numRows).toBe(2);
+    });
+
+    test("accepts a FixedSizeList (vector) column", async () => {
+      const table = makeArrowTable(
+        [
+          { id: 1, vec: [1, 2, 3] },
+          { id: 2, vec: [4, 5, 6] },
+        ],
+        { vectorColumns: { vec: { type: new Float16() } } },
+      );
+      const scannable = await Scannable.fromTable(table);
+
+      expect(scannable.schema).toBe(table.schema);
+      expect(scannable.numRows).toBe(2);
+    });
+
+    test("accepts a table with many columns", async () => {
+      const row: Record<string, number> = {};
+      for (let i = 0; i < 50; i++) row[`c${i}`] = i;
+      const table = makeArrowTable([row, row], { vectorColumns: {} });
+      const scannable = await Scannable.fromTable(table);
+
+      expect(scannable.schema.fields.length).toBe(50);
+      expect(scannable.numRows).toBe(2);
+    });
+  });
+});

nodejs/__test__/table.test.ts

@@ -28,6 +28,7 @@ import {
   List,
   Schema,
   SchemaLike,
+  Struct,
   Type,
   Uint8,
   Utf8,
@@ -115,6 +116,48 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
       await expect(table.countRows()).resolves.toBe(1);
     });
 
+    it("should invoke the progress callback", async () => {
+      const events: import("../lancedb").WriteProgress[] = [];
+      await table.add([{ id: 1 }, { id: 2 }, { id: 3 }], {
+        progress: (p) => events.push(p),
+      });
+
+      expect(events.length).toBeGreaterThan(0);
+      const last = events[events.length - 1];
+      expect(last.done).toBe(true);
+      // Earlier callbacks must have done=false.
+      for (const ev of events.slice(0, -1)) {
+        expect(ev.done).toBe(false);
+      }
+      // outputRows reflects the rows added in this call, not table size.
+      expect(last.outputRows).toBe(3);
+      // The input source (an array) reports a row count, so totalRows is set.
+      expect(last.totalRows).toBe(3);
+      // outputRows is monotonic.
+      for (let i = 1; i < events.length; i++) {
+        expect(events[i].outputRows).toBeGreaterThanOrEqual(
+          events[i - 1].outputRows,
+        );
+      }
+    });
+
+    it("should swallow errors thrown from the progress callback", async () => {
+      const warn = jest
+        .spyOn(console, "warn")
+        .mockImplementation(() => undefined);
+      try {
+        const res = await table.add([{ id: 1 }, { id: 2 }], {
+          progress: () => {
+            throw new Error("callback bomb");
+          },
+        });
+        expect(res.version).toBeGreaterThan(0);
+        expect(warn).toHaveBeenCalled();
+      } finally {
+        warn.mockRestore();
+      }
+    });
+
     it("should let me close the table", async () => {
       expect(table.isOpen()).toBe(true);
       table.close();
@@ -738,6 +781,113 @@ describe("When creating an index", () => {
     expect(indices2.length).toBe(0);
   });
 
+  it("should create and search a nested vector index", async () => {
+    const db = await connect(tmpDir.name);
+    const nestedSchema = new Schema([
+      new Field("id", new Int32(), true),
+      new Field(
+        "image",
+        new Struct([
+          new Field(
+            "embedding",
+            new FixedSizeList(2, new Field("item", new Float32(), true)),
+            true,
+          ),
+        ]),
+        true,
+      ),
+    ]);
+    const nestedTable = await db.createTable(
+      "nested_vector",
+      makeArrowTable(
+        Array.from({ length: 300 }, (_, id) => ({
+          id,
+          image: { embedding: [id, id + 1] },
+        })),
+        { schema: nestedSchema },
+      ),
+    );
+
+    await nestedTable.createIndex("image.embedding", {
+      name: "image_embedding_idx",
+    });
+    const indices = await nestedTable.listIndices();
+    expect(indices).toContainEqual({
+      name: "image_embedding_idx",
+      indexType: "IvfPq",
+      columns: ["image.embedding"],
+    });
+
+    const explicit = await nestedTable
+      .query()
+      .nearestTo([0.0, 1.0])
+      .column("image.embedding")
+      .limit(1)
+      .toArray();
+    const inferred = await nestedTable
+      .query()
+      .nearestTo([0.0, 1.0])
+      .limit(1)
+      .toArray();
+    expect(inferred[0].id).toEqual(explicit[0].id);
+  });
+
+  it("should report multiple nested vector candidates", async () => {
+    const db = await connect(tmpDir.name);
+    const nestedSchema = new Schema([
+      new Field(
+        "image",
+        new Struct([
+          new Field(
+            "embedding",
+            new FixedSizeList(2, new Field("item", new Float32(), true)),
+            true,
+          ),
+        ]),
+        true,
+      ),
+      new Field(
+        "text",
+        new Struct([
+          new Field(
+            "embedding",
+            new FixedSizeList(2, new Field("item", new Float32(), true)),
+            true,
+          ),
+        ]),
+        true,
+      ),
+    ]);
+    const nestedTable = await db.createTable(
+      "multiple_nested_vectors",
+      makeArrowTable(
+        [
+          {
+            image: { embedding: [0.0, 1.0] },
+            text: { embedding: [2.0, 3.0] },
+          },
+        ],
+        { schema: nestedSchema },
+      ),
+    );
+
+    await expect(
+      nestedTable.query().nearestTo([0.0, 1.0]).limit(1).toArray(),
+    ).rejects.toThrow(/image\.embedding.*text\.embedding/);
+  });
+
+  it("should report when no default vector column exists", async () => {
+    const db = await connect(tmpDir.name);
+    const noVectorTable = await db.createTable(
+      "no_vector",
+      makeArrowTable([{ id: 0, label: "cat" }]),
+    );
+
+    await expect(
+      noVectorTable.query().nearestTo([0.0, 1.0]).limit(1).toArray(),
+    ).rejects.toThrow(/No vector column/);
+  });
+
   it("should wait for index readiness", async () => {
     // Create an index and then wait for it to be ready
     await tbl.createIndex("vec");
@@ -2348,3 +2498,224 @@ describe("when creating a table with Float32Array vectors", () => {
     expect((fsl.children[0].type as Float32).precision).toBe(1);
   });
 });
+
+describe("setUnenforcedPrimaryKey", () => {
+  let tmpDir: tmp.DirResult;
+
+  beforeEach(() => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  });
+  afterEach(() => tmpDir.removeCallback());
+
+  it("sets a single-column primary key (string or one-element array)", async () => {
+    const conn = await connect(tmpDir.name);
+    const schema = new arrow.Schema([
+      new arrow.Field("id", new arrow.Int64(), false),
+    ]);
+    const t1 = await conn.createEmptyTable("t1", schema);
+    await t1.setUnenforcedPrimaryKey("id");
+
+    const t2 = await conn.createEmptyTable("t2", schema);
+    await t2.setUnenforcedPrimaryKey(["id"]);
+  });
+
+  it("rejects a compound primary key", async () => {
+    const conn = await connect(tmpDir.name);
+    const table = await conn.createEmptyTable(
+      "t",
+      new arrow.Schema([
+        new arrow.Field("id", new arrow.Int64(), false),
+        new arrow.Field("name", new arrow.Utf8(), false),
+      ]),
+    );
+    await expect(
+      table.setUnenforcedPrimaryKey(["id", "name"]),
+    ).rejects.toThrow();
+  });
+
+  it("rejects changing the primary key once set", async () => {
+    const conn = await connect(tmpDir.name);
+    const table = await conn.createEmptyTable(
+      "t",
+      new arrow.Schema([
+        new arrow.Field("id", new arrow.Int64(), false),
+        new arrow.Field("name", new arrow.Utf8(), false),
+      ]),
+    );
+    await table.setUnenforcedPrimaryKey("id");
+    await expect(table.setUnenforcedPrimaryKey("name")).rejects.toThrow();
+    await expect(table.setUnenforcedPrimaryKey("id")).rejects.toThrow();
+  });
+});
+
+describe("setLsmWriteSpec / unsetLsmWriteSpec", () => {
+  let tmpDir: tmp.DirResult;
+
+  beforeEach(() => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  });
+  afterEach(() => tmpDir.removeCallback());
+
+  async function makeTable(conn: Connection): Promise<Table> {
+    return await conn.createEmptyTable(
+      "t",
+      new arrow.Schema([new arrow.Field("id", new arrow.Int64(), false)]),
+    );
+  }
+
+  it("installs and removes a bucket spec", async () => {
+    const conn = await connect(tmpDir.name);
+    const table = await makeTable(conn);
+
+    await table.setUnenforcedPrimaryKey("id");
+    await table.setLsmWriteSpec({
+      specType: "bucket",
+      column: "id",
+      numBuckets: 4,
+    });
+    await table.unsetLsmWriteSpec();
+    // A second unset errors — there is no spec left to remove.
+    await expect(table.unsetLsmWriteSpec()).rejects.toThrow();
+    // A fresh spec can be installed after unset.
+    await table.setLsmWriteSpec({
+      specType: "bucket",
+      column: "id",
+      numBuckets: 8,
+    });
+  });
+
+  it("installs an unsharded spec", async () => {
+    const conn = await connect(tmpDir.name);
+    const table = await makeTable(conn);
+
+    await table.setUnenforcedPrimaryKey("id");
+    await table.setLsmWriteSpec({ specType: "unsharded" });
+    await table.unsetLsmWriteSpec();
+  });
+
+  it("installs an identity spec", async () => {
+    const conn = await connect(tmpDir.name);
+    const table = await makeTable(conn);
+
+    await table.setUnenforcedPrimaryKey("id");
+    await table.setLsmWriteSpec({ specType: "identity", column: "id" });
+    await table.unsetLsmWriteSpec();
+  });
+
+  it("rejects an invalid spec", async () => {
+    const conn = await connect(tmpDir.name);
+    const table = await makeTable(conn);
+
+    await table.setUnenforcedPrimaryKey("id");
+    // num_buckets out of range.
+    await expect(
+      table.setLsmWriteSpec({
+        specType: "bucket",
+        column: "id",
+        numBuckets: 0,
+      }),
+    ).rejects.toThrow();
+    // Column mismatch.
+    await expect(
+      table.setLsmWriteSpec({
+        specType: "bucket",
+        column: "missing",
+        numBuckets: 4,
+      }),
+    ).rejects.toThrow();
+  });
+});
+
+describe("LSM merge insert", () => {
+  let tmpDir: tmp.DirResult;
+
+  beforeEach(() => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  });
+  afterEach(() => tmpDir.removeCallback());
+
+  async function bucketTable(conn: Connection): Promise<Table> {
+    // The primary key column must be non-nullable.
+    const table = await conn.createEmptyTable(
+      "t",
+      new arrow.Schema([
+        new arrow.Field("id", new arrow.Utf8(), false),
+        new arrow.Field("value", new arrow.Float64(), true),
+      ]),
+    );
+    await table.add([
+      { id: "a", value: 1 },
+      { id: "b", value: 2 },
+    ]);
+    await table.setUnenforcedPrimaryKey("id");
+    // numBuckets = 1: every row routes to the single bucket.
+    await table.setLsmWriteSpec({
+      specType: "bucket",
+      column: "id",
+      numBuckets: 1,
+    });
+    return table;
+  }
+
+  it("routes merge_insert through the shard writer", async () => {
+    const conn = await connect(tmpDir.name);
+    const table = await bucketTable(conn);
+
+    const res = await table
+      .mergeInsert("id")
+      .whenMatchedUpdateAll()
+      .whenNotMatchedInsertAll()
+      .execute([
+        { id: "c", value: 3 },
+        { id: "d", value: 4 },
+      ]);
+    // LSM path: rows go to the MemWAL, so only numRows is populated.
+    expect(res.numRows).toBe(2);
+    expect(res.version).toBe(0);
+    expect(res.numInsertedRows).toBe(0);
+
+    await table.closeLsmWriters();
+  });
+
+  it("falls back to the standard path with useLsmWrite(false)", async () => {
+    const conn = await connect(tmpDir.name);
+    const table = await bucketTable(conn);
+
+    const res = await table
+      .mergeInsert("id")
+      .whenNotMatchedInsertAll()
+      .useLsmWrite(false)
+      .execute([
+        { id: "b", value: 9 },
+        { id: "e", value: 5 },
+      ]);
+    // Standard path commits: id="e" inserted ("b" already exists).
+    expect(res.numInsertedRows).toBe(1);
+    expect(await table.countRows()).toBe(3);
+  });
+
+  it("supports validateSingleShard(false)", async () => {
+    const conn = await connect(tmpDir.name);
+    const table = await bucketTable(conn);
+
+    const res = await table
+      .mergeInsert("id")
+      .whenMatchedUpdateAll()
+      .whenNotMatchedInsertAll()
+      .validateSingleShard(false)
+      .execute([{ id: "f", value: 6 }]);
+    expect(res.numRows).toBe(1);
+  });
+
+  it("rejects a non-upsert merge under an LSM spec", async () => {
+    const conn = await connect(tmpDir.name);
+    const table = await bucketTable(conn);
+
+    await expect(
+      table
+        .mergeInsert("id")
+        .whenNotMatchedInsertAll()
+        .execute([{ id: "g", value: 7 }]),
+    ).rejects.toThrow();
+  });
+});

nodejs/examples/filtering.test.ts

@@ -38,5 +38,14 @@ test("filtering examples", async () => {
     // --8<-- [start:sql_search]
     await tbl.query().where("id = 10").limit(10).toArray();
     // --8<-- [end:sql_search]
+
+    // --8<-- [start:orderby_search]
+    await tbl
+      .query()
+      .where("id > 10")
+      .orderBy({ columnName: "id", ascending: false })
+      .limit(5)
+      .toArray();
+    // --8<-- [end:orderby_search]
   });
 });

nodejs/lancedb/arrow.ts

@@ -1291,6 +1291,18 @@ export async function fromRecordBatchToBuffer(
   return Buffer.from(await writer.toUint8Array());
 }
 
+/**
+ * Create a buffer containing a single record batch using the Arrow IPC Stream
+ * serialization. Each call produces a self-contained Stream message (schema +
+ * batch + EOS) suitable for incremental decode by `arrow_ipc::reader::StreamReader`.
+ */
+export async function fromRecordBatchToStreamBuffer(
+  batch: RecordBatch,
+): Promise<Buffer> {
+  const writer = RecordBatchStreamWriter.writeAll([batch]);
+  return Buffer.from(await writer.toUint8Array());
+}
+
 /**
  * Serialize an Arrow Table into a buffer using the Arrow IPC Stream serialization
  *

nodejs/lancedb/connection.ts

@@ -144,6 +144,19 @@ export interface DropNamespaceOptions {
   behavior?: "restrict" | "cascade";
 }
 
+export interface RenameTableOptions {
+  /**
+   * The namespace path of the table being renamed. Defaults to the root
+   * namespace (`[]`) when omitted.
+   */
+  namespacePath?: string[];
+  /**
+   * The namespace path to move the table to as part of the rename. When
+   * omitted the table stays in `namespacePath`.
+   */
+  newNamespacePath?: string[];
+}
+
 /**
  * A LanceDB Connection that allows you to open tables and create new ones.
  *
@@ -391,6 +404,24 @@ export abstract class Connection {
       isShallow?: boolean;
     },
   ): Promise<Table>;
+
+  /**
+   * Rename a table.
+   *
+   * Currently only supported by LanceDB Cloud. Local OSS connections and
+   * namespace-backed connections (via {@link connectNamespace}) reject with
+   * a "not supported" error.
+   *
+   * @param {string} currentName - The current name of the table.
+   * @param {string} newName - The new name for the table.
+   * @param {RenameTableOptions} options - Optional namespace paths. When
+   *   `newNamespacePath` is omitted the table stays in `namespacePath`.
+   */
+  abstract renameTable(
+    currentName: string,
+    newName: string,
+    options?: RenameTableOptions,
+  ): Promise<void>;
 }
 
 /** @hideconstructor */
@@ -651,6 +682,19 @@ export class LocalConnection extends Connection {
       options?.behavior,
     );
   }
+
+  async renameTable(
+    currentName: string,
+    newName: string,
+    options?: RenameTableOptions,
+  ): Promise<void> {
+    return this.inner.renameTable(
+      currentName,
+      newName,
+      options?.namespacePath ?? [],
+      options?.newNamespacePath,
+    );
+  }
 }
 
 /**

nodejs/lancedb/index.ts

@@ -71,6 +71,7 @@ export {
   CreateNamespaceResponse,
   DropNamespaceResponse,
   DescribeNamespaceResponse,
+  RenameTableOptions,
 } from "./connection";
 
 export { Session } from "./native.js";
@@ -82,6 +83,7 @@ export {
   VectorQuery,
   TakeQuery,
   QueryExecutionOptions,
+  ColumnOrdering,
   FullTextSearchOptions,
   RecordBatchIterator,
   FullTextQuery,
@@ -112,6 +114,8 @@ export {
   UpdateOptions,
   OptimizeOptions,
   Version,
+  WriteProgress,
+  LsmWriteSpec,
   ColumnAlteration,
 } from "./table";
 
@@ -126,6 +130,7 @@ export { MergeInsertBuilder, WriteExecutionOptions } from "./merge";
 
 export * as embedding from "./embedding";
 export { permutationBuilder, PermutationBuilder } from "./permutation";
+export { Scannable, ScannableOptions } from "./scannable";
 export * as rerankers from "./rerankers";
 export {
   SchemaLike,

nodejs/lancedb/merge.ts

@@ -87,6 +87,41 @@ export class MergeInsertBuilder {
       this.#schema,
     );
   }
+  /**
+   * Controls whether the merge uses the MemWAL LSM write path.
+   *
+   * By default (unset), a `mergeInsert` on a table with an LSM write spec is
+   * routed through Lance's MemWAL shard writer, and a table without one uses
+   * the standard path. Pass `false` to force the standard path even when a
+   * spec is set. Pass `true` to require a spec — `mergeInsert` rejects if none
+   * is installed.
+   *
+   * @param useLsmWrite - Whether to use the LSM write path.
+   */
+  useLsmWrite(useLsmWrite: boolean): MergeInsertBuilder {
+    return new MergeInsertBuilder(
+      this.#native.useLsmWrite(useLsmWrite),
+      this.#schema,
+    );
+  }
+  /**
+   * Controls how an LSM merge checks that its input targets a single shard.
+   *
+   * When a table has an LSM write spec, every row in a `mergeInsert` call must
+   * route to the same shard. When `true` (the default), every row is inspected
+   * to verify this. When `false`, only the first row is inspected and the
+   * shard it routes to is used for the whole input — a faster path for callers
+   * that have already pre-sharded their input. Has no effect on tables without
+   * an LSM write spec.
+   *
+   * @param validateSingleShard - Whether to check every row routes to one shard. Defaults to `true`.
+   */
+  validateSingleShard(validateSingleShard: boolean): MergeInsertBuilder {
+    return new MergeInsertBuilder(
+      this.#native.validateSingleShard(validateSingleShard),
+      this.#schema,
+    );
+  }
   /**
    * Executes the merge insert operation
    *

nodejs/lancedb/query.ts

@@ -79,6 +79,12 @@ export interface QueryExecutionOptions {
   timeoutMs?: number;
 }
 
+export interface ColumnOrdering {
+  columnName: string;
+  ascending?: boolean;
+  nullsFirst?: boolean;
+}
+
 /**
  * Options that control the behavior of a full text search
  */
@@ -417,6 +423,21 @@ export class StandardQueryBase<
     return this;
   }
 
+  /**
+   * Sort the results by the specified column(s).
+   * @returns This query builder.
+   */
+  orderBy(ordering: ColumnOrdering | ColumnOrdering[]): this {
+    const orderings = Array.isArray(ordering) ? ordering : [ordering];
+    const normalized = orderings.map((o) => ({
+      columnName: o.columnName,
+      ascending: o.ascending ?? true,
+      nullsFirst: o.nullsFirst ?? false,
+    }));
+    this.doCall((inner) => inner.orderBy(normalized));
+    return this;
+  }
+
   /**
    * Skip searching un-indexed data. This can make search faster, but will miss
    * any data that is not yet indexed.

nodejs/lancedb/scannable.ts

@@ -0,0 +1,274 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import {
+  Table as ArrowTable,
+  RecordBatch,
+  RecordBatchReader,
+  Schema,
+} from "apache-arrow";
+import {
+  fromRecordBatchToStreamBuffer,
+  fromTableToBuffer,
+  makeEmptyTable,
+} from "./arrow";
+import { NapiScannable } from "./native.js";
+
+export interface ScannableOptions {
+  /** Hint about the number of rows. Not validated against the stream. */
+  numRows?: number;
+  /**
+   * Whether the source can be scanned more than once. Defaults to `true` for
+   * `fromTable` / `fromFactory` and `false` for `fromIterable` /
+   * `fromRecordBatchReader`.
+   */
+  rescannable?: boolean;
+}
+
+/**
+ * A data source that can be scanned as a stream of Arrow `RecordBatch`es.
+ *
+ * `Scannable` wraps the schema + optional row count + rescannable flag and
+ * a callback that yields batches one at a time. It is passed to consumers
+ * (e.g. `Table.add`, `createTable`, `mergeInsert` — follow-up work) that
+ * need to pull data without materializing the full dataset in JS memory.
+ *
+ * Batches cross the JS↔Rust boundary as Arrow IPC Stream messages; a fresh
+ * writer serializes each batch, and the Rust side decodes it with
+ * `arrow_ipc::reader::StreamReader`. One batch is in flight at a time.
+ */
+export class Scannable {
+  readonly schema: Schema;
+  readonly numRows: number | null;
+  readonly rescannable: boolean;
+
+  /** @hidden */
+  private readonly native: NapiScannable;
+
+  private constructor(
+    native: NapiScannable,
+    schema: Schema,
+    numRows: number | null,
+    rescannable: boolean,
+  ) {
+    this.native = native;
+    this.schema = schema;
+    this.numRows = numRows;
+    this.rescannable = rescannable;
+  }
+
+  /** @hidden Access the native handle for passing through to Rust consumers. */
+  get inner(): NapiScannable {
+    return this.native;
+  }
+
+  /**
+   * Build a Scannable from an explicit schema and a factory that returns a
+   * fresh batch iterator on each call.
+   *
+   * The factory is invoked once per scan. Each iterator yields
+   * `RecordBatch`es matching the declared schema. Use this when you need
+   * direct control over the pull loop — for example, to wrap a streaming
+   * source whose batches are produced lazily.
+   *
+   * @param schema - The Arrow schema of the produced batches.
+   * @param factory - Called at the start of each scan to produce a batch
+   *   iterator. Must be idempotent when `rescannable` is true.
+   * @param opts - Optional hints. `rescannable` defaults to `true`; set to
+   *   `false` if calling `factory()` twice would not reproduce the same data.
+   */
+  static async fromFactory(
+    schema: Schema,
+    factory: () =>
+      | AsyncIterable<RecordBatch>
+      | Iterable<RecordBatch>
+      | AsyncIterator<RecordBatch>
+      | Iterator<RecordBatch>,
+    opts: ScannableOptions = {},
+  ): Promise<Scannable> {
+    const numRows = opts.numRows ?? null;
+    if (numRows != null && !Number.isInteger(numRows)) {
+      throw new TypeError("numRows must be an integer");
+    }
+    const rescannable = opts.rescannable ?? true;
+
+    let iter: AsyncIterator<RecordBatch> | Iterator<RecordBatch> | null = null;
+    const getNextBatch = async (isStart: boolean): Promise<Buffer | null> => {
+      // `isStart` is true on the first pull of every new scan_as_stream.
+      // Drop any cached iterator so factory() is re-invoked for the next scan
+      if (isStart) {
+        iter = null;
+      }
+      if (iter === null) {
+        iter = normalizeIterator(factory());
+      }
+      const result = await iter.next();
+      if (result.done) {
+        iter = null;
+        return null;
+      }
+      return fromRecordBatchToStreamBuffer(result.value);
+    };
+
+    const schemaBuf = await fromTableToBuffer(makeEmptyTable(schema));
+    const native = new NapiScannable(
+      schemaBuf,
+      numRows,
+      rescannable,
+      getNextBatch,
+    );
+    return new Scannable(native, schema, numRows, rescannable);
+  }
+
+  /**
+   * Build a Scannable from an in-memory Arrow `Table`. Always rescannable;
+   * the table's batches are replayed on each scan.
+   *
+   * The table's row count is authoritative: `opts.numRows` must either be
+   * omitted or equal to `table.numRows`. `opts.rescannable` of `false` is
+   * rejected because in-memory Tables are always rescannable.
+   */
+  static async fromTable(
+    table: ArrowTable,
+    opts: ScannableOptions = {},
+  ): Promise<Scannable> {
+    if (opts.numRows != null && opts.numRows !== table.numRows) {
+      throw new TypeError(
+        `opts.numRows (${opts.numRows}) does not match table.numRows (${table.numRows}). ` +
+          `The table's row count is authoritative; omit numRows or pass the matching value.`,
+      );
+    }
+    if (opts.rescannable === false) {
+      throw new TypeError(
+        `fromTable does not accept rescannable: false. ` +
+          `In-memory Arrow Tables are always rescannable; omit the option or pass true.`,
+      );
+    }
+    return Scannable.fromFactory(table.schema, () => table.batches, {
+      numRows: table.numRows,
+      rescannable: true,
+    });
+  }
+
+  /**
+   * Build a Scannable from an iterable of `RecordBatch`es. `rescannable`
+   * defaults to `false`. Pass an explicit schema so the consumer can
+   * validate before any batch is pulled.
+   *
+   * `opts.rescannable: true` is honest for replayable iterables (Arrays,
+   * Sets, or custom iterables whose `[Symbol.iterator]()` returns a fresh
+   * iterator each call). It is rejected for one-shot iterables (generators,
+   * async generators, or already-an-iterator inputs) because their
+   * `[Symbol.iterator]()` returns the same exhausted object on the second
+   * scan. For replayable sources outside this shape, use
+   * `fromFactory(schema, () => createIter(), { rescannable: true })`.
+   *
+   * Note: when `opts.rescannable` is `true`, the constructor calls
+   * `[Symbol.iterator]()` once on the input to perform the structural check.
+   */
+  static async fromIterable(
+    schema: Schema,
+    iter: AsyncIterable<RecordBatch> | Iterable<RecordBatch>,
+    opts: ScannableOptions = {},
+  ): Promise<Scannable> {
+    if (opts.rescannable === true && isOneShotIterable(iter)) {
+      throw new TypeError(
+        `fromIterable: rescannable: true is not honest for one-shot iterables ` +
+          `(generators, async generators, or iterators where [Symbol.iterator]() ` +
+          `returns the same object). The source would be exhausted after the first scan. ` +
+          `Use fromFactory(schema, () => createIter(), { rescannable: true }) for sources ` +
+          `where each call mints a fresh iterator.`,
+      );
+    }
+    return Scannable.fromFactory(schema, () => iter, {
+      numRows: opts.numRows,
+      rescannable: opts.rescannable ?? false,
+    });
+  }
+
+  /**
+   * Build a Scannable from an Arrow `RecordBatchReader`. A reader can only
+   * be consumed once; `rescannable` defaults to `false`.
+   *
+   * The reader must already be opened (via `.open()`) so its `.schema` is
+   * populated. `RecordBatchReader.from(...)` returns an unopened reader.
+   *
+   * `opts.rescannable: true` is rejected because `RecordBatchReader` is a
+   * self-iterator (its `[Symbol.iterator]()` returns itself), and this
+   * constructor does not call `reader.reset()` between scans, so a second
+   * scan would always see an exhausted reader. For genuinely replayable
+   * sources, use
+   * `fromFactory(schema, () => openReader(), { rescannable: true })`,
+   * which mints a fresh reader on each scan.
+   */
+  static async fromRecordBatchReader(
+    reader: RecordBatchReader,
+    opts: ScannableOptions = {},
+  ): Promise<Scannable> {
+    if (opts.rescannable === true) {
+      throw new TypeError(
+        `fromRecordBatchReader does not accept rescannable: true. ` +
+          `RecordBatchReader is a self-iterator (its [Symbol.iterator]() ` +
+          `returns itself) and would be exhausted after the first scan. ` +
+          `Use fromFactory(schema, () => openReader(), { rescannable: true }) ` +
+          `for sources where each call mints a fresh reader.`,
+      );
+    }
+    return Scannable.fromFactory(reader.schema, () => reader, {
+      numRows: opts.numRows,
+      rescannable: false,
+    });
+  }
+}
+
+function normalizeIterator<T>(
+  source: AsyncIterable<T> | Iterable<T> | AsyncIterator<T> | Iterator<T>,
+): AsyncIterator<T> | Iterator<T> {
+  if (source == null) {
+    throw new TypeError("Scannable factory returned null/undefined");
+  }
+  if (
+    typeof (source as AsyncIterable<T>)[Symbol.asyncIterator] === "function"
+  ) {
+    return (source as AsyncIterable<T>)[Symbol.asyncIterator]();
+  }
+  if (typeof (source as Iterable<T>)[Symbol.iterator] === "function") {
+    return (source as Iterable<T>)[Symbol.iterator]();
+  }
+  // Already an iterator (has `.next`).
+  if (typeof (source as Iterator<T>).next === "function") {
+    return source as Iterator<T>;
+  }
+  throw new TypeError("Scannable factory returned a non-iterable value");
+}
+
+// A "self-iterator" returns the same object from `[Symbol.iterator]()` /
+// `[Symbol.asyncIterator]()`. Generators behave this way, so they exhaust
+// after one pass. Replayable iterables (Array, Set, custom) return a fresh
+// iterator each call. Detection mirrors `normalizeIterator`'s ordering so
+// classification matches scan-time behavior.
+function isOneShotIterable(
+  source: AsyncIterable<unknown> | Iterable<unknown>,
+): boolean {
+  // null/undefined are not one-shot in any meaningful sense; let
+  // `normalizeIterator` raise the actual error at scan time.
+  if (source == null) return false;
+  const ref = source as unknown;
+  if (
+    typeof (source as AsyncIterable<unknown>)[Symbol.asyncIterator] ===
+    "function"
+  ) {
+    const it = (source as AsyncIterable<unknown>)[
+      Symbol.asyncIterator
+    ]() as unknown;
+    return it === ref;
+  }
+  if (typeof (source as Iterable<unknown>)[Symbol.iterator] === "function") {
+    const it = (source as Iterable<unknown>)[Symbol.iterator]() as unknown;
+    return it === ref;
+  }
+  // Already-an-iterator (has `.next` but no `Symbol.iterator`) is by
+  // definition one-shot.
+  if (typeof (source as { next?: unknown }).next === "function") return true;
+  return false;
+}

nodejs/lancedb/table.ts

@@ -46,6 +46,33 @@ import { sanitizeType } from "./sanitize";
 import { IntoSql, toSQL } from "./util";
 export { IndexConfig } from "./native";
 
+/**
+ * Progress snapshot for a write operation, delivered to the `progress`
+ * callback passed to {@link Table.add}.
+ */
+export interface WriteProgress {
+  /** Number of rows written so far. */
+  outputRows: number;
+  /** Number of bytes written so far. */
+  outputBytes: number;
+  /**
+   * Total rows expected, when the input source reports it.
+   *
+   * Always set on the final callback (the one with `done: true`), falling
+   * back to the actual number of rows written when the source could not
+   * report a row count up front.
+   */
+  totalRows?: number;
+  /** Wall-clock seconds since the write started. */
+  elapsedSeconds: number;
+  /** Number of parallel write tasks currently in flight. */
+  activeTasks: number;
+  /** Total number of parallel write tasks (the write parallelism). */
+  totalTasks: number;
+  /** `true` for the final callback; `false` otherwise. */
+  done: boolean;
+}
+
 /**
  * Options for adding data to a table.
  */
@@ -56,6 +83,28 @@ export interface AddDataOptions {
    * If "overwrite" then the new data will replace the existing data in the table.
    */
   mode: "append" | "overwrite";
+
+  /**
+   * Optional callback invoked periodically with write progress.
+   *
+   * The callback is fired once per batch written and once more with
+   * `done: true` when the write completes. Calls are dispatched
+   * asynchronously to the JS event loop and never block the write — a slow
+   * callback will queue events rather than back-pressure the writer.
+   *
+   * Errors thrown from the callback are logged with `console.warn` and
+   * swallowed — they do not abort the write.
+   *
+   * @example
+   * ```ts
+   * await table.add(data, {
+   *   progress: (p) => {
+   *     console.log(`${p.outputRows}/${p.totalRows ?? "?"} rows`);
+   *   },
+   * });
+   * ```
+   */
+  progress: (progress: WriteProgress) => void;
 }
 
 export interface UpdateOptions {
@@ -106,6 +155,30 @@ export interface Version {
   metadata: Record<string, string>;
 }
 
+/**
+ * Specification selecting Lance's MemWAL LSM-style write path for
+ * `mergeInsert`.
+ *
+ * `specType` is `"bucket"`, `"identity"`, or `"unsharded"`. For `"bucket"`,
+ * `column` and `numBuckets` are required; for `"identity"`, `column` is
+ * required and must be a deterministic function of the unenforced primary
+ * key (every row with a given primary key must always produce the same
+ * `column` value, or upserts of that key can land in different shards and a
+ * stale version can win).
+ */
+export interface LsmWriteSpec {
+  /** One of `"bucket"`, `"identity"`, or `"unsharded"`. */
+  specType: "bucket" | "identity" | "unsharded";
+  /** Bucket and identity variants: the sharding column. */
+  column?: string;
+  /** Bucket variant: the number of buckets, in `[1, 1024]`. */
+  numBuckets?: number;
+  /** Names of indexes the MemWAL should keep up to date during writes. */
+  maintainedIndexes?: string[];
+  /** Default `ShardWriter` configuration recorded in the MemWAL index. */
+  writerConfigDefaults?: Record<string, string>;
+}
+
 /**
  * A Table is a collection of Records in a LanceDB Database.
  *
@@ -449,6 +522,64 @@ export abstract class Table {
    * containing the new version number of the table after dropping the columns.
    */
   abstract dropColumns(columnNames: string[]): Promise<DropColumnsResult>;
+  /**
+   * Set the unenforced primary key for this table to a single column.
+   *
+   * "Unenforced" means LanceDB does not check uniqueness on writes; the
+   * column is recorded in the schema as the primary key for use by features
+   * such as `merge_insert`. Only single-column primary keys are supported,
+   * and the key cannot be changed once set.
+   * @param {string | string[]} columns The primary key column. A one-element
+   * array is also accepted; passing more than one column is rejected.
+   * @returns {Promise<void>}
+   */
+  abstract setUnenforcedPrimaryKey(columns: string | string[]): Promise<void>;
+  /**
+   * Install an {@link LsmWriteSpec} on this table, selecting Lance's MemWAL
+   * LSM-style write path for future `mergeInsert` calls.
+   *
+   * `LsmWriteSpec` chooses one of three sharding strategies via `specType`:
+   *
+   * - `"bucket"` — hash-bucket writes by the single-column unenforced primary
+   *   key (`column` and `numBuckets` required).
+   * - `"identity"` — shard by the raw value of a scalar `column`.
+   * - `"unsharded"` — route every write to a single shard.
+   *
+   * All variants require the table to have an unenforced primary key
+   * ({@link Table#setUnenforcedPrimaryKey}); bucket sharding additionally
+   * requires it to be the single column being bucketed.
+   * @param {LsmWriteSpec} spec The sharding spec to install.
+   * @returns {Promise<void>}
+   * @example
+   * ```ts
+   * await table.setUnenforcedPrimaryKey("id");
+   * await table.setLsmWriteSpec({
+   *   specType: "bucket",
+   *   column: "id",
+   *   numBuckets: 16,
+   *   maintainedIndexes: ["id_idx"],
+   * });
+   * ```
+   */
+  abstract setLsmWriteSpec(spec: LsmWriteSpec): Promise<void>;
+  /**
+   * Remove the {@link LsmWriteSpec} from this table, reverting to the standard
+   * `mergeInsert` write path.
+   *
+   * Errors if no spec is currently set.
+   * @returns {Promise<void>}
+   */
+  abstract unsetLsmWriteSpec(): Promise<void>;
+  /**
+   * Drain and close any cached MemWAL shard writers held for this table.
+   *
+   * When an {@link LsmWriteSpec} is installed, `mergeInsert` opens MemWAL
+   * shard writers and caches them for reuse across calls. This closes them,
+   * flushing pending data; writers reopen lazily on the next `mergeInsert`.
+   * It is a no-op when no writers are cached.
+   * @returns {Promise<void>}
+   */
+  abstract closeLsmWriters(): Promise<void>;
   /** Retrieve the version of the table */
 
   abstract version(): Promise<number>;
@@ -636,7 +767,20 @@ export class LocalTable extends Table {
     const schema = await this.schema();
 
     const buffer = await fromDataToBuffer(data, undefined, schema);
-    return await this.inner.add(buffer, mode);
+    // Wrap the user callback so a thrown error doesn't surface as an
+    // unhandled exception (the callback fires from a napi threadsafe
+    // function — exceptions there crash the process).
+    const userProgress = options?.progress;
+    const progress = userProgress
+      ? (p: WriteProgress) => {
+          try {
+            userProgress(p);
+          } catch (e) {
+            console.warn("Table.add progress callback threw:", e);
+          }
+        }
+      : undefined;
+    return await this.inner.add(buffer, mode, progress);
   }
 
   async update(
@@ -897,6 +1041,23 @@ export class LocalTable extends Table {
     return await this.inner.dropColumns(columnNames);
   }
 
+  async setUnenforcedPrimaryKey(columns: string | string[]): Promise<void> {
+    const cols = typeof columns === "string" ? [columns] : columns;
+    return await this.inner.setUnenforcedPrimaryKey(cols);
+  }
+
+  async setLsmWriteSpec(spec: LsmWriteSpec): Promise<void> {
+    return await this.inner.setLsmWriteSpec(spec);
+  }
+
+  async unsetLsmWriteSpec(): Promise<void> {
+    return await this.inner.unsetLsmWriteSpec();
+  }
+
+  async closeLsmWriters(): Promise<void> {
+    return await this.inner.closeLsmWriters();
+  }
+
   async version(): Promise<number> {
     return await this.inner.version();
   }

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.28.0-beta.11",
+	"version": "0.30.1-beta.0",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.28.0-beta.11",
+	"version": "0.30.1-beta.0",
 	"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": "0.28.0-beta.11",
+	"version": "0.30.1-beta.0",
 	"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": "0.28.0-beta.11",
+	"version": "0.30.1-beta.0",
 	"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": "0.28.0-beta.11",
+	"version": "0.30.1-beta.0",
 	"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": "0.28.0-beta.11",
+  "version": "0.30.1-beta.0",
   "os": [
     "win32"
   ],

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.28.0-beta.11",
+	"version": "0.30.1-beta.0",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package.json

@@ -11,7 +11,7 @@
     "ann"
   ],
   "private": false,
-  "version": "0.28.0-beta.11",
+  "version": "0.30.1-beta.0",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",

nodejs/src/connection.rs

@@ -459,4 +459,23 @@ impl Connection {
             transaction_id: resp.transaction_id,
         })
     }
+
+    /// Rename a table. `current_namespace_path` and `new_namespace_path` default to
+    /// the root namespace when omitted; the caller is expected to either pass both
+    /// or pass neither.
+    #[napi(catch_unwind)]
+    pub async fn rename_table(
+        &self,
+        current_name: String,
+        new_name: String,
+        current_namespace_path: Option<Vec<String>>,
+        new_namespace_path: Option<Vec<String>>,
+    ) -> napi::Result<()> {
+        let cur_ns = current_namespace_path.unwrap_or_default();
+        let new_ns = new_namespace_path.unwrap_or_default();
+        self.get_inner()?
+            .rename_table(&current_name, &new_name, &cur_ns, &new_ns)
+            .await
+            .default_error()
+    }
 }

nodejs/src/lib.rs

@@ -16,6 +16,7 @@ pub mod permutation;
 mod query;
 pub mod remote;
 mod rerankers;
+mod scannable;
 mod session;
 mod table;
 mod util;
@@ -23,15 +24,19 @@ mod util;
 #[napi(object)]
 #[derive(Debug)]
 pub struct ConnectionOptions {
-    /// (For LanceDB OSS only): The interval, in seconds, at which to check for
-    /// updates to the table from other processes. If None, then consistency is not
-    /// checked. For performance reasons, this is the default. For strong
-    /// consistency, set this to zero seconds. Then every read will check for
-    /// updates from other processes. As a compromise, you can set this to a
-    /// non-zero value for eventual consistency. If more than that interval
-    /// has passed since the last check, then the table will be checked for updates.
-    /// Note: this consistency only applies to read operations. Write operations are
+    /// The interval, in seconds, at which to check for updates to the table
+    /// from other processes. If None, then consistency is not checked. For
+    /// performance reasons, this is the default. For strong consistency, set
+    /// this to zero seconds. Then every read will check for updates from other
+    /// processes. As a compromise, you can set this to a non-zero value for
+    /// eventual consistency. If more than that interval has passed since the
+    /// last check, then the table will be checked for updates. Note: this
+    /// consistency only applies to read operations. Write operations are
     /// always consistent.
+    ///
+    /// Stronger consistency is not free. The smaller the interval, the more
+    /// often each read pays the cost of checking for updates against object
+    /// storage, raising per-read latency and cost.
     pub read_consistency_interval: Option<f64>,
     /// (For LanceDB OSS only): configuration for object storage.
     ///

nodejs/src/merge.rs

@@ -50,6 +50,20 @@ impl NativeMergeInsertBuilder {
         this
     }
 
+    #[napi]
+    pub fn use_lsm_write(&self, use_lsm_write: bool) -> Self {
+        let mut this = self.clone();
+        this.inner.use_lsm_write(use_lsm_write);
+        this
+    }
+
+    #[napi]
+    pub fn validate_single_shard(&self, validate_single_shard: bool) -> Self {
+        let mut this = self.clone();
+        this.inner.validate_single_shard(validate_single_shard);
+        this
+    }
+
     #[napi(catch_unwind)]
     pub async fn execute(&self, buf: Buffer) -> napi::Result<MergeResult> {
         let data = ipc_file_to_batches(buf.to_vec())

nodejs/src/query.rs

@@ -3,6 +3,12 @@
 
 use std::sync::Arc;
 
+use crate::error::NapiErrorExt;
+use crate::error::convert_error;
+use crate::iterator::RecordBatchIterator;
+use crate::rerankers::RerankHybridCallbackArgs;
+use crate::rerankers::Reranker;
+use crate::util::{parse_distance_type, schema_to_buffer};
 use arrow_array::{
     Array, Float16Array as ArrowFloat16Array, Float32Array as ArrowFloat32Array,
     Float64Array as ArrowFloat64Array, UInt8Array as ArrowUInt8Array,
@@ -19,16 +25,27 @@ use lancedb::query::QueryBase;
 use lancedb::query::QueryExecutionOptions;
 use lancedb::query::Select;
 use lancedb::query::TakeQuery as LanceDbTakeQuery;
-use lancedb::query::VectorQuery as LanceDbVectorQuery;
+use lancedb::query::{ColumnOrdering as LanceDbColumnOrdering, VectorQuery as LanceDbVectorQuery};
 use napi::bindgen_prelude::*;
 use napi_derive::napi;
 
-use crate::error::NapiErrorExt;
-use crate::error::convert_error;
-use crate::iterator::RecordBatchIterator;
-use crate::rerankers::RerankHybridCallbackArgs;
-use crate::rerankers::Reranker;
-use crate::util::{parse_distance_type, schema_to_buffer};
+#[napi(object)]
+pub struct ColumnOrdering {
+    pub ascending: bool,
+    pub nulls_first: bool,
+    pub column_name: String,
+}
+
+impl From<ColumnOrdering> for LanceDbColumnOrdering {
+    fn from(value: ColumnOrdering) -> Self {
+        match (value.ascending, value.nulls_first) {
+            (true, true) => Self::asc_nulls_first(value.column_name),
+            (true, false) => Self::asc_nulls_last(value.column_name),
+            (false, true) => Self::desc_nulls_first(value.column_name),
+            (false, false) => Self::desc_nulls_last(value.column_name),
+        }
+    }
+}
 
 fn bytes_to_arrow_array(data: Uint8Array, dtype: String) -> napi::Result<Arc<dyn Array>> {
     let buf = arrow_buffer::Buffer::from(data.to_vec());
@@ -128,6 +145,18 @@ impl Query {
         self.inner = self.inner.clone().with_row_id();
     }
 
+    #[napi]
+    pub fn order_by(&mut self, ordering: Option<Vec<ColumnOrdering>>) -> napi::Result<()> {
+        let ordering = ordering.map(|ordering| {
+            ordering
+                .into_iter()
+                .map(LanceDbColumnOrdering::from)
+                .collect()
+        });
+        self.inner = self.inner.clone().order_by(ordering);
+        Ok(())
+    }
+
     #[napi(catch_unwind)]
     pub async fn output_schema(&self) -> napi::Result<Buffer> {
         let schema = self.inner.output_schema().await.default_error()?;
@@ -328,6 +357,18 @@ impl VectorQuery {
         Ok(())
     }
 
+    #[napi]
+    pub fn order_by(&mut self, ordering: Option<Vec<ColumnOrdering>>) -> napi::Result<()> {
+        let ordering = ordering.map(|ordering| {
+            ordering
+                .into_iter()
+                .map(LanceDbColumnOrdering::from)
+                .collect()
+        });
+        self.inner = self.inner.clone().order_by(ordering);
+        Ok(())
+    }
+
     #[napi(catch_unwind)]
     pub async fn output_schema(&self) -> napi::Result<Buffer> {
         let schema = self.inner.output_schema().await.default_error()?;

nodejs/src/scannable.rs

@@ -0,0 +1,253 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+//! NodeJS binding for the [`lancedb::data::scannable::Scannable`] trait.
+//!
+//! The JS side supplies a `getNextBatch(isStart)` callback that returns the
+//! next Arrow `RecordBatch` encoded as a self-contained Arrow IPC Stream
+//! message (schema message + record batch message + EOS marker) wrapped in a
+//! `Buffer`, or `null` when the stream is exhausted. The Rust side parses
+//! each buffer with `arrow_ipc::reader::StreamReader`, validates every
+//! standalone batch stream against the declared schema, and yields decoded
+//! `RecordBatch`es as a [`SendableRecordBatchStream`].
+//!
+//! `isStart` is `true` on the first `getNextBatch` call of each new
+//! `scan_as_stream` and `false` thereafter. JS uses it to drop any cached
+//! iterator and re-invoke its factory at scan boundaries, so retries
+//! triggered by mid-stream failures restart at batch 0.
+
+use std::io::Cursor;
+use std::sync::Arc;
+
+use arrow_array::RecordBatch;
+use arrow_ipc::reader::StreamReader;
+use arrow_schema::SchemaRef;
+use futures::stream::once;
+use lancedb::arrow::{SendableRecordBatchStream, SimpleRecordBatchStream};
+use lancedb::data::scannable::Scannable as LanceScannable;
+use lancedb::ipc::ipc_file_to_schema;
+use lancedb::{Error, Result as LanceResult};
+use napi::bindgen_prelude::*;
+use napi::threadsafe_function::ThreadsafeFunction;
+use napi_derive::napi;
+
+/// Threadsafe handle to the JS `getNextBatch` callback. The callback takes a
+/// single boolean `isStart` (`true` on the first call of each new scan) and
+/// returns a Promise that resolves to a `Buffer` containing one IPC Stream
+/// message, or `null` at end-of-stream.
+type GetNextBatchFn = ThreadsafeFunction<bool, Promise<Option<Buffer>>, bool, Status, false>;
+
+/// A Rust-side view of a JS-constructed `Scannable`.
+///
+/// Held in JS as the return value of the `Scannable` class constructor. When
+/// passed to a consumer that accepts `impl lancedb::data::scannable::Scannable`,
+/// the consumer invokes `scan_as_stream()` to pull batches through the JS
+/// callback.
+#[napi]
+pub struct NapiScannable {
+    schema: SchemaRef,
+    num_rows: Option<usize>,
+    rescannable: bool,
+    // `ThreadsafeFunction` is not `Clone`; wrap in `Arc` so the stream
+    // returned by `scan_as_stream` can own a handle independent of `self`.
+    get_next_batch: Arc<GetNextBatchFn>,
+    // Tracks whether a scan has already started; used to enforce one-shot
+    // semantics on non-rescannable sources.
+    scanned: bool,
+}
+
+#[napi]
+impl NapiScannable {
+    /// Construct a new `NapiScannable`.
+    ///
+    /// - `schema_buf` — Arrow IPC File buffer carrying only the schema (no batches).
+    /// - `num_rows` — optional row count hint; not validated against the stream.
+    /// - `rescannable` — whether `get_next_batch` may be re-driven after the
+    ///   scan completes.
+    /// - `get_next_batch` -- JS callback that yields the next batch as an Arrow
+    ///   IPC Stream message wrapped in a `Buffer`, or `null` at EOF. The
+    ///   `isStart` argument is `true` on the first call of each new scan;
+    ///   JS uses it to discard any cached iterator before pulling.
+    #[napi(constructor)]
+    pub fn new(
+        schema_buf: Buffer,
+        num_rows: Option<i64>,
+        rescannable: bool,
+        get_next_batch: Function<bool, Promise<Option<Buffer>>>,
+    ) -> napi::Result<Self> {
+        let schema = ipc_file_to_schema(schema_buf.to_vec())
+            .map_err(|e| napi::Error::from_reason(format!("Invalid schema buffer: {}", e)))?;
+        let num_rows = num_rows
+            .map(|n| {
+                usize::try_from(n)
+                    .map_err(|_| napi::Error::from_reason("num_rows must be non-negative"))
+            })
+            .transpose()?;
+        let get_next_batch = Arc::new(get_next_batch.build_threadsafe_function().build()?);
+        Ok(Self {
+            schema,
+            num_rows,
+            rescannable,
+            get_next_batch,
+            scanned: false,
+        })
+    }
+}
+
+impl std::fmt::Debug for NapiScannable {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        f.debug_struct("NapiScannable")
+            .field("schema", &self.schema)
+            .field("num_rows", &self.num_rows)
+            .field("rescannable", &self.rescannable)
+            .finish()
+    }
+}
+
+impl LanceScannable for NapiScannable {
+    fn schema(&self) -> SchemaRef {
+        self.schema.clone()
+    }
+
+    fn scan_as_stream(&mut self) -> SendableRecordBatchStream {
+        let schema = self.schema.clone();
+
+        // One-shot enforcement for non-rescannable sources: return a stream
+        // whose first item is an error.
+        if self.scanned && !self.rescannable {
+            let err_stream = once(async {
+                Err(Error::InvalidInput {
+                    message: "Scannable has already been consumed (non-rescannable source)"
+                        .to_string(),
+                })
+            });
+            return Box::pin(SimpleRecordBatchStream::new(err_stream, schema));
+        }
+        self.scanned = true;
+
+        let tsfn = Arc::clone(&self.get_next_batch);
+        let declared_schema = schema.clone();
+
+        // State threaded through the unfold. `is_first_pull` starts true so
+        // the first call into JS signals a new-scan boundary; JS uses it to
+        // reset any cached iterator before factory()-ing a fresh one.
+        let initial = State {
+            tsfn,
+            batch_index: 0,
+            declared_schema,
+            errored: false,
+            is_first_pull: true,
+        };
+
+        let stream = futures::stream::unfold(initial, |mut state| async move {
+            if state.errored {
+                return None;
+            }
+
+            // Pull the next IPC Stream buffer from JS. `is_first_pull` is
+            // consumed here and cleared so subsequent pulls continue the
+            // same scan rather than restarting it.
+            let is_start = state.is_first_pull;
+            state.is_first_pull = false;
+            let buf = match pull_next(&state.tsfn, is_start).await {
+                Ok(Some(buf)) => buf,
+                Ok(None) => return None,
+                Err(e) => {
+                    state.errored = true;
+                    return Some((Err(e), state));
+                }
+            };
+
+            match decode_one_batch(buf.as_ref(), &state.declared_schema) {
+                Ok(batch) => {
+                    state.batch_index += 1;
+                    Some((Ok(batch), state))
+                }
+                Err(e) => {
+                    let tagged = Error::Runtime {
+                        message: format!(
+                            "[scannable/rust-bridge] failure at batch index {}: {}",
+                            state.batch_index, e
+                        ),
+                    };
+                    state.errored = true;
+                    Some((Err(tagged), state))
+                }
+            }
+        });
+
+        Box::pin(SimpleRecordBatchStream::new(stream, schema))
+    }
+
+    fn num_rows(&self) -> Option<usize> {
+        self.num_rows
+    }
+
+    fn rescannable(&self) -> bool {
+        self.rescannable
+    }
+}
+
+struct State {
+    tsfn: Arc<GetNextBatchFn>,
+    batch_index: usize,
+    declared_schema: SchemaRef,
+    errored: bool,
+    /// True for the very first pull of a new scan. Forwarded to JS so the
+    /// callback can drop any cached iterator and call its factory fresh,
+    /// which makes rescannable sources restart at batch 0 even when the
+    /// previous scan ended mid-stream.
+    is_first_pull: bool,
+}
+
+/// Invoke the JS callback and await its Promise. `is_start` is forwarded to
+/// the JS side as the `isStart` argument so it can reset its iterator at the
+/// scan boundary. Errors on the JS side surface here as rejected promises
+/// and are tunneled back as `lancedb::Error::Runtime`.
+async fn pull_next(tsfn: &GetNextBatchFn, is_start: bool) -> LanceResult<Option<Buffer>> {
+    let promise = tsfn
+        .call_async(is_start)
+        .await
+        .map_err(|e| Error::Runtime {
+            message: format!(
+                "[scannable/js-factory] napi error status={}, reason={}",
+                e.status, e.reason
+            ),
+        })?;
+    promise.await.map_err(|e| Error::Runtime {
+        message: format!(
+            "[scannable/js-iterator] napi error status={}, reason={}",
+            e.status, e.reason
+        ),
+    })
+}
+
+/// Decode one IPC Stream buffer (schema + batch + EOS) into a `RecordBatch`.
+/// Each buffer is a standalone IPC stream, so every decoded stream schema must
+/// match the one declared at construction.
+fn decode_one_batch(buf: &[u8], declared: &SchemaRef) -> LanceResult<RecordBatch> {
+    let reader = StreamReader::try_new(Cursor::new(buf), None).map_err(|e| Error::Runtime {
+        message: format!("failed to open IPC stream reader: {}", e),
+    })?;
+
+    let actual = reader.schema();
+    if actual.as_ref() != declared.as_ref() {
+        return Err(Error::InvalidInput {
+            message: format!(
+                "declared schema does not match stream schema: declared={:?} actual={:?}",
+                declared, actual
+            ),
+        });
+    }
+
+    let mut iter = reader;
+    let batch = iter
+        .next()
+        .ok_or_else(|| Error::Runtime {
+            message: "IPC stream contained schema but no record batch".to_string(),
+        })?
+        .map_err(|e| Error::Runtime {
+            message: format!("failed to decode record batch: {}", e),
+        })?;
+    Ok(batch)
+}

nodejs/src/table.rs

@@ -9,6 +9,7 @@ use lancedb::table::{
     OptimizeAction, OptimizeOptions, Table as LanceDbTable,
 };
 use napi::bindgen_prelude::*;
+use napi::threadsafe_function::{ThreadsafeFunction, ThreadsafeFunctionCallMode};
 use napi_derive::napi;
 
 use crate::error::NapiErrorExt;
@@ -67,8 +68,16 @@ impl Table {
         schema_to_buffer(&schema)
     }
 
-    #[napi(catch_unwind)]
-    pub async fn add(&self, buf: Buffer, mode: String) -> napi::Result<AddResult> {
+    #[napi(
+        catch_unwind,
+        ts_args_type = "buf: Buffer, mode: string, progressCallback?: (progress: WriteProgressInfo) => void"
+    )]
+    pub async fn add(
+        &self,
+        buf: Buffer,
+        mode: String,
+        progress_callback: Option<ProgressFn>,
+    ) -> napi::Result<AddResult> {
         let batches = ipc_file_to_batches(buf.to_vec())
             .map_err(|e| napi::Error::from_reason(format!("Failed to read IPC file: {}", e)))?;
         let batches = batches
@@ -92,6 +101,19 @@ impl Table {
             return Err(napi::Error::from_reason(format!("Invalid mode: {}", mode)));
         };
 
+        if let Some(tsfn) = progress_callback {
+            op = op.progress(move |p| {
+                // NonBlocking: dispatch onto the JS event loop without
+                // blocking the writer thread.  With napi-rs's default
+                // unbounded queue, events are not dropped — a slow JS
+                // callback will just queue them.
+                tsfn.call(
+                    WriteProgressInfo::from(p),
+                    ThreadsafeFunctionCallMode::NonBlocking,
+                );
+            });
+        }
+
         let res = op.execute().await.default_error()?;
         Ok(res.into())
     }
@@ -344,6 +366,36 @@ impl Table {
         Ok(res.into())
     }
 
+    #[napi(catch_unwind)]
+    pub async fn set_unenforced_primary_key(&self, columns: Vec<String>) -> napi::Result<()> {
+        self.inner_ref()?
+            .set_unenforced_primary_key(columns)
+            .await
+            .default_error()
+    }
+
+    #[napi(catch_unwind)]
+    pub async fn set_lsm_write_spec(&self, spec: LsmWriteSpec) -> napi::Result<()> {
+        let native_spec = lancedb::table::LsmWriteSpec::try_from(spec)?;
+        self.inner_ref()?
+            .set_lsm_write_spec(native_spec)
+            .await
+            .default_error()
+    }
+
+    #[napi(catch_unwind)]
+    pub async fn unset_lsm_write_spec(&self) -> napi::Result<()> {
+        self.inner_ref()?
+            .unset_lsm_write_spec()
+            .await
+            .default_error()
+    }
+
+    #[napi(catch_unwind)]
+    pub async fn close_lsm_writers(&self) -> napi::Result<()> {
+        self.inner_ref()?.close_lsm_writers().await.default_error()
+    }
+
     #[napi(catch_unwind)]
     pub async fn version(&self) -> napi::Result<i64> {
         self.inner_ref()?
@@ -538,6 +590,63 @@ impl From<lancedb::index::IndexConfig> for IndexConfig {
     }
 }
 
+/// Specification selecting Lance's MemWAL LSM-style write path for
+/// `mergeInsert`.
+///
+/// `specType` must be `"bucket"`, `"identity"`, or `"unsharded"`. For
+/// `"bucket"`, `column` and `numBuckets` are required; for `"identity"`,
+/// `column` is required.
+#[napi(object)]
+#[derive(Clone, Debug)]
+pub struct LsmWriteSpec {
+    /// One of `"bucket"`, `"identity"`, or `"unsharded"`.
+    pub spec_type: String,
+    /// Bucket and identity variants: the sharding column.
+    pub column: Option<String>,
+    /// Bucket variant: the number of buckets, in `[1, 1024]`.
+    pub num_buckets: Option<u32>,
+    /// Names of indexes the MemWAL should keep up to date during writes.
+    pub maintained_indexes: Option<Vec<String>>,
+    /// Default `ShardWriter` configuration recorded in the MemWAL index.
+    pub writer_config_defaults: Option<HashMap<String, String>>,
+}
+
+impl TryFrom<LsmWriteSpec> for lancedb::table::LsmWriteSpec {
+    type Error = napi::Error;
+
+    fn try_from(value: LsmWriteSpec) -> napi::Result<Self> {
+        let maintained = value.maintained_indexes.unwrap_or_default();
+        let writer_config_defaults = value.writer_config_defaults.unwrap_or_default();
+        let spec = match value.spec_type.as_str() {
+            "bucket" => {
+                let column = value.column.ok_or_else(|| {
+                    napi::Error::from_reason("LsmWriteSpec bucket requires `column`")
+                })?;
+                let num_buckets = value.num_buckets.ok_or_else(|| {
+                    napi::Error::from_reason("LsmWriteSpec bucket requires `numBuckets`")
+                })?;
+                Self::bucket(column, num_buckets)
+            }
+            "identity" => {
+                let column = value.column.ok_or_else(|| {
+                    napi::Error::from_reason("LsmWriteSpec identity requires `column`")
+                })?;
+                Self::identity(column)
+            }
+            "unsharded" => Self::unsharded(),
+            other => {
+                return Err(napi::Error::from_reason(format!(
+                    "LsmWriteSpec `specType` must be 'bucket', 'identity', or 'unsharded', got '{}'",
+                    other
+                )));
+            }
+        };
+        Ok(spec
+            .with_maintained_indexes(maintained)
+            .with_writer_config_defaults(writer_config_defaults))
+    }
+}
+
 /// Statistics about a compaction operation.
 #[napi(object)]
 #[derive(Clone, Debug)]
@@ -572,6 +681,44 @@ pub struct OptimizeStats {
     pub prune: RemovalStats,
 }
 
+/// Progress snapshot for a write operation, delivered to the JS callback
+/// passed to `Table.add`.
+#[napi(object)]
+#[derive(Clone, Debug)]
+pub struct WriteProgressInfo {
+    /// Number of rows written so far.
+    pub output_rows: i64,
+    /// Number of bytes written so far.
+    pub output_bytes: i64,
+    /// Total rows expected, if the input source reports it.
+    /// Always set on the final callback (where `done` is `true`).
+    pub total_rows: Option<i64>,
+    /// Wall-clock seconds since monitoring started.
+    pub elapsed_seconds: f64,
+    /// Number of parallel write tasks currently in flight.
+    pub active_tasks: i64,
+    /// Total number of parallel write tasks (the write parallelism).
+    pub total_tasks: i64,
+    /// `true` for the final callback; `false` otherwise.
+    pub done: bool,
+}
+
+impl From<&lancedb::table::write_progress::WriteProgress> for WriteProgressInfo {
+    fn from(p: &lancedb::table::write_progress::WriteProgress) -> Self {
+        Self {
+            output_rows: p.output_rows() as i64,
+            output_bytes: p.output_bytes() as i64,
+            total_rows: p.total_rows().map(|n| n as i64),
+            elapsed_seconds: p.elapsed().as_secs_f64(),
+            active_tasks: p.active_tasks() as i64,
+            total_tasks: p.total_tasks() as i64,
+            done: p.done(),
+        }
+    }
+}
+
+type ProgressFn = ThreadsafeFunction<WriteProgressInfo, (), WriteProgressInfo, Status, false>;
+
 ///  A definition of a column alteration. The alteration changes the column at
 /// `path` to have the new name `name`, to be nullable if `nullable` is true,
 /// and to have the data type `data_type`. At least one of `rename` or `nullable`
@@ -798,6 +945,7 @@ pub struct MergeResult {
     pub num_updated_rows: i64,
     pub num_deleted_rows: i64,
     pub num_attempts: i64,
+    pub num_rows: i64,
 }
 
 impl From<lancedb::table::MergeResult> for MergeResult {
@@ -808,6 +956,7 @@ impl From<lancedb::table::MergeResult> for MergeResult {
             num_updated_rows: value.num_updated_rows as i64,
             num_deleted_rows: value.num_deleted_rows as i64,
             num_attempts: value.num_attempts as i64,
+            num_rows: value.num_rows as i64,
         }
     }
 }

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.31.0-beta.11"
+current_version = "0.33.1-beta.0"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

python/AGENTS.md

@@ -4,16 +4,26 @@ code is in the `src/` directory and the Python bindings are in the `lancedb/` di
 
 Common commands:
 
+* Bootstrap dev env: `uv run --extra tests --extra dev maturin develop --extras tests,dev`
 * Build: `make develop`
 * Format: `make format`
 * Lint: `make check`
 * Fix lints: `make fix`
-* Test: `make test`
-* Doc test: `make doctest`
+* Test: `uv run --extra tests pytest python/tests -vv --durations=10 -m "not slow and not s3_test"`
+* Run specific test: `uv run --extra tests pytest python/tests/<test_file>.py::<test_name> -q`
+* Doc test: `uv run --extra tests pytest --doctest-modules python/lancedb`
+
+Use the uv-managed environment declared by `uv.lock` for Python validation. Do
+not treat system `python`, global `pytest`, or missing editable-install errors
+as final blockers; bootstrap or enter the uv environment instead. `make test`
+and `make doctest` assume the development environment is already prepared.
 
 Before committing changes, run lints and then formatting.
 
-When you change the Rust code, you will need to recompile the Python bindings: `make develop`.
+When you change the Rust code, PyO3 binding code, or see a missing/stale
+`lancedb._lancedb`, recompile the Python bindings with
+`uv run --extra tests --extra dev maturin develop --extras tests,dev` before
+running tests.
 
 When you export new types from Rust to Python, you must manually update `python/lancedb/_lancedb.pyi`
 with the corresponding type hints. You can run `pyright` to check for type errors in the Python code.

python/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.31.0-beta.11"
+version = "0.33.1-beta.0"
 publish = false
 edition.workspace = true
 description = "Python bindings for LanceDB"
@@ -19,6 +19,7 @@ arrow = { version = "58.0.0", features = ["pyarrow"] }
 async-trait = "0.1"
 bytes = "1"
 lancedb = { path = "../rust/lancedb", default-features = false }
+datafusion-common.workspace = true
 lance-core.workspace = true
 lance-namespace.workspace = true
 lance-namespace-impls.workspace = true

python/python/lancedb/__init__.py

@@ -94,7 +94,6 @@ def connect(
     host_override: str, optional
         The override url for LanceDB Cloud.
     read_consistency_interval: timedelta, default None
-        (For LanceDB OSS only)
         The interval at which to check for updates to the table from other
         processes. If None, then consistency is not checked. For performance
         reasons, this is the default. For strong consistency, set this to
@@ -104,6 +103,10 @@ def connect(
         the last check, then the table will be checked for updates. Note: this
         consistency only applies to read operations. Write operations are
         always consistent.
+
+        Stronger consistency is not free. The smaller the interval, the more
+        often each read pays the cost of checking for updates against object
+        storage, raising per-read latency and cost.
     client_config: ClientConfig or dict, optional
         Configuration options for the LanceDB Cloud HTTP client. If a dict, then
         the keys are the attributes of the ClientConfig class. If None, then the
@@ -147,6 +150,13 @@ def connect(
     >>> db = lancedb.connect("s3://my-bucket/lancedb",
     ...                      storage_options={"aws_access_key_id": "***"})
 
+    For tests and temporary data, use an in-memory database:
+
+    >>> db = lancedb.connect("memory://")
+
+    In-memory databases are not persisted. Tables are dropped when the last
+    connection or table handle referencing them is closed.
+
     Connect to LanceDB cloud:
 
     >>> db = lancedb.connect("db://my_database", api_key="ldb_...",
@@ -210,6 +220,7 @@ def connect(
             request_thread_pool=request_thread_pool,
             client_config=client_config,
             storage_options=storage_options,
+            read_consistency_interval=read_consistency_interval,
             **kwargs,
         )
     _check_s3_bucket_with_dots(str(uri), storage_options)
@@ -304,6 +315,15 @@ def deserialize_conn(
             manifest_enabled=parsed.get("manifest_enabled", False),
             namespace_client_properties=parsed.get("namespace_client_properties"),
         )
+    elif connection_type == "remote":
+        return RemoteDBConnection(
+            parsed["db_url"],
+            parsed["api_key"],
+            parsed.get("region", "us-east-1"),
+            host_override=parsed.get("host_override"),
+            client_config=parsed.get("client_config"),
+            storage_options=storage_options,
+        )
     else:
         raise ValueError(f"Unknown connection_type: {connection_type}")
 
@@ -336,7 +356,6 @@ async def connect_async(
     host_override: str, optional
         The override url for LanceDB Cloud.
     read_consistency_interval: timedelta, default None
-        (For LanceDB OSS only)
         The interval at which to check for updates to the table from other
         processes. If None, then consistency is not checked. For performance
         reasons, this is the default. For strong consistency, set this to
@@ -346,6 +365,10 @@ async def connect_async(
         the last check, then the table will be checked for updates. Note: this
         consistency only applies to read operations. Write operations are
         always consistent.
+
+        Stronger consistency is not free. The smaller the interval, the more
+        often each read pays the cost of checking for updates against object
+        storage, raising per-read latency and cost.
     client_config: ClientConfig or dict, optional
         Configuration options for the LanceDB Cloud HTTP client. If a dict, then
         the keys are the attributes of the ClientConfig class. If None, then the
@@ -378,6 +401,8 @@ async def connect_async(
     ...     db = await lancedb.connect_async("s3://my-bucket/lancedb",
     ...                                      storage_options={
     ...                                          "aws_access_key_id": "***"})
+    ...     # For tests and temporary data, use an in-memory database
+    ...     db = await lancedb.connect_async("memory://")
     ...     # Connect to LanceDB cloud
     ...     db = await lancedb.connect_async("db://my_database", api_key="ldb_...",
     ...                                      client_config={

python/python/lancedb/_lancedb.pyi

@@ -51,7 +51,7 @@ class PyExpr:
     def to_sql(self) -> str: ...
 
 def expr_col(name: str) -> PyExpr: ...
-def expr_lit(value: Union[bool, int, float, str]) -> PyExpr: ...
+def expr_lit(value: Union[bool, int, float, str, bytes]) -> PyExpr: ...
 def expr_func(name: str, args: List[PyExpr]) -> PyExpr: ...
 
 class Session:
@@ -217,6 +217,10 @@ class Table:
     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]]: ...
+    async def set_unenforced_primary_key(self, columns: List[str]) -> None: ...
+    async def set_lsm_write_spec(self, spec: LsmWriteSpec) -> None: ...
+    async def unset_lsm_write_spec(self) -> None: ...
+    async def close_lsm_writers(self) -> None: ...
     @property
     def tags(self) -> Tags: ...
     def query(self) -> Query: ...
@@ -255,6 +259,11 @@ class RecordBatchStream:
     def __aiter__(self) -> "RecordBatchStream": ...
     async def __anext__(self) -> pa.RecordBatch: ...
 
+class ColumnOrdering(TypedDict):
+    column_name: str
+    ascending: bool
+    nulls_first: bool
+
 class Query:
     def where(self, filter: str): ...
     def where_expr(self, expr: PyExpr): ...
@@ -268,6 +277,7 @@ class Query:
     def postfilter(self): ...
     def nearest_to(self, query_vec: pa.Array) -> VectorQuery: ...
     def nearest_to_text(self, query: dict) -> FTSQuery: ...
+    def order_by(self, ordering: Optional[List[ColumnOrdering]]): ...
     async def output_schema(self) -> pa.Schema: ...
     async def execute(
         self, max_batch_length: Optional[int], timeout: Optional[timedelta]
@@ -296,6 +306,7 @@ class FTSQuery:
     def get_query(self) -> str: ...
     def add_query_vector(self, query_vec: pa.Array) -> None: ...
     def nearest_to(self, query_vec: pa.Array) -> HybridQuery: ...
+    def order_by(self, ordering: Optional[List[ColumnOrdering]]): ...
     async def output_schema(self) -> pa.Schema: ...
     async def execute(
         self, max_batch_length: Optional[int], timeout: Optional[timedelta]
@@ -321,6 +332,7 @@ class VectorQuery:
     def maximum_nprobes(self, maximum_nprobes: int): ...
     def bypass_vector_index(self): ...
     def nearest_to_text(self, query: dict) -> HybridQuery: ...
+    def order_by(self, ordering: Optional[List[ColumnOrdering]]): ...
     def to_query_request(self) -> PyQueryRequest: ...
 
 class HybridQuery:
@@ -339,6 +351,7 @@ class HybridQuery:
     def minimum_nprobes(self, minimum_nprobes: int): ...
     def maximum_nprobes(self, maximum_nprobes: int): ...
     def bypass_vector_index(self): ...
+    def order_by(self, ordering: Optional[List[ColumnOrdering]]): ...
     def to_vector_query(self) -> VectorQuery: ...
     def to_fts_query(self) -> FTSQuery: ...
     def get_limit(self) -> int: ...
@@ -368,6 +381,7 @@ class PyQueryRequest:
     bypass_vector_index: Optional[bool]
     postfilter: Optional[bool]
     norm: Optional[str]
+    order_by: Optional[List[ColumnOrdering]]
 
 class CompactionStats:
     fragments_removed: int
@@ -407,6 +421,38 @@ class MergeResult:
     num_inserted_rows: int
     num_deleted_rows: int
     num_attempts: int
+    num_rows: int
+
+class LsmWriteSpec:
+    """Specification selecting Lance's MemWAL LSM-style write path for
+    `merge_insert`."""
+
+    @staticmethod
+    def bucket(column: str, num_buckets: int) -> "LsmWriteSpec": ...
+    @staticmethod
+    def identity(column: str) -> "LsmWriteSpec": ...
+    @staticmethod
+    def unsharded() -> "LsmWriteSpec": ...
+    def with_maintained_indexes(self, indexes: List[str]) -> "LsmWriteSpec":
+        """Return a copy of this spec asking the MemWAL to keep the named
+        indexes up to date as rows are appended."""
+        ...
+    def with_writer_config_defaults(self, defaults: Dict[str, str]) -> "LsmWriteSpec":
+        """Return a copy of this spec recording the given default
+        `ShardWriter` configuration in the MemWAL index."""
+        ...
+    @property
+    def spec_type(self) -> str:
+        """One of 'bucket', 'identity', or 'unsharded'."""
+        ...
+    @property
+    def column(self) -> Optional[str]: ...
+    @property
+    def num_buckets(self) -> Optional[int]: ...
+    @property
+    def maintained_indexes(self) -> List[str]: ...
+    @property
+    def writer_config_defaults(self) -> Dict[str, str]: ...
 
 class AddColumnsResult:
     version: int

python/python/lancedb/db.py

@@ -8,7 +8,17 @@ from abc import abstractmethod
 from datetime import timedelta
 from pathlib import Path
 import sys
-from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Literal, Optional, Union
+from typing import (
+    TYPE_CHECKING,
+    Any,
+    Dict,
+    Generator,
+    Iterable,
+    List,
+    Literal,
+    Optional,
+    Union,
+)
 
 if sys.version_info >= (3, 12):
     from typing import override
@@ -313,7 +323,7 @@ class DBConnection(EnforceOverrides):
         >>> data = [{"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7},
         ...         {"vector": [0.2, 1.8], "lat": 40.1, "long":  -74.1}]
         >>> db.create_table("my_table", data)
-        LanceTable(name='my_table', version=1, ...)
+        LanceTable(name='my_table', ...)
         >>> db["my_table"].head()
         pyarrow.Table
         vector: fixed_size_list<item: float>[2]
@@ -334,7 +344,7 @@ class DBConnection(EnforceOverrides):
         ...    "long": [-122.7, -74.1]
         ... })
         >>> db.create_table("table2", data)
-        LanceTable(name='table2', version=1, ...)
+        LanceTable(name='table2', ...)
         >>> db["table2"].head()
         pyarrow.Table
         vector: fixed_size_list<item: float>[2]
@@ -357,7 +367,7 @@ class DBConnection(EnforceOverrides):
         ...   pa.field("long", pa.float32())
         ... ])
         >>> db.create_table("table3", data, schema = custom_schema)
-        LanceTable(name='table3', version=1, ...)
+        LanceTable(name='table3', ...)
         >>> db["table3"].head()
         pyarrow.Table
         vector: fixed_size_list<item: float>[2]
@@ -391,7 +401,7 @@ class DBConnection(EnforceOverrides):
         ...     pa.field("price", pa.float32()),
         ... ])
         >>> db.create_table("table4", make_batches(), schema=schema)
-        LanceTable(name='table4', version=1, ...)
+        LanceTable(name='table4', ...)
 
         """
         raise NotImplementedError
@@ -568,15 +578,15 @@ class LanceDBConnection(DBConnection):
     >>> db = lancedb.connect("./.lancedb")
     >>> db.create_table("my_table", data=[{"vector": [1.1, 1.2], "b": 2},
     ...                                   {"vector": [0.5, 1.3], "b": 4}])
-    LanceTable(name='my_table', version=1, ...)
+    LanceTable(name='my_table', ...)
     >>> db.create_table("another_table", data=[{"vector": [0.4, 0.4], "b": 6}])
-    LanceTable(name='another_table', version=1, ...)
+    LanceTable(name='another_table', ...)
     >>> sorted(db.table_names())
     ['another_table', 'my_table']
     >>> len(db)
     2
     >>> db["my_table"]
-    LanceTable(name='my_table', version=1, ...)
+    LanceTable(name='my_table', ...)
     >>> "my_table" in db
     True
     >>> db.drop_table("my_table")
@@ -847,11 +857,20 @@ class LanceDBConnection(DBConnection):
             )
         )
 
+    def _all_table_names(self) -> Generator[str, None, None]:
+        page_token = None
+        while True:
+            response = self.list_tables(page_token=page_token)
+            yield from response.tables
+            page_token = response.page_token
+            if not page_token:
+                return
+
     def __len__(self) -> int:
-        return len(self.table_names())
+        return sum(1 for _ in self._all_table_names())
 
     def __contains__(self, name: str) -> bool:
-        return name in self.table_names()
+        return name in self._all_table_names()
 
     @override
     def create_table(

python/python/lancedb/expr.py

@@ -63,7 +63,7 @@ def _coerce(value: "ExprLike") -> "Expr":
 
 
 # Type alias used in annotations.
-ExprLike = Union["Expr", bool, int, float, str]
+ExprLike = Union["Expr", bool, int, float, str, bytes]
 
 
 class Expr:
@@ -261,13 +261,13 @@ def col(name: str) -> Expr:
     return Expr(expr_col(name))
 
 
-def lit(value: Union[bool, int, float, str]) -> Expr:
+def lit(value: Union[bool, int, float, str, bytes]) -> Expr:
     """Create a literal (constant) value expression.
 
     Parameters
     ----------
     value:
-        A Python ``bool``, ``int``, ``float``, or ``str``.
+        A Python ``bool``, ``int``, ``float``, ``str``, or ``bytes``.
 
     Examples
     --------

python/python/lancedb/index.py

@@ -281,6 +281,9 @@ class HnswPq:
     m: int = 20
     ef_construction: int = 300
     target_partition_size: Optional[int] = None
+    # Name of the accelerator (e.g. "cuda") to use for IVF training. When set,
+    # create_index() dispatches to pylance to build the index on the accelerator.
+    accelerator: Optional[str] = None
 
 
 @dataclass
@@ -386,6 +389,9 @@ class HnswSq:
     m: int = 20
     ef_construction: int = 300
     target_partition_size: Optional[int] = None
+    # Name of the accelerator (e.g. "cuda") to use for IVF training. When set,
+    # create_index() dispatches to pylance to build the index on the accelerator.
+    accelerator: Optional[str] = None
 
 
 @dataclass
@@ -579,6 +585,9 @@ class IvfFlat:
     max_iterations: int = 50
     sample_rate: int = 256
     target_partition_size: Optional[int] = None
+    # Name of the accelerator (e.g. "cuda") to use for IVF training. When set,
+    # create_index() dispatches to pylance to build the index on the accelerator.
+    accelerator: Optional[str] = None
 
 
 @dataclass
@@ -609,6 +618,9 @@ class IvfSq:
     max_iterations: int = 50
     sample_rate: int = 256
     target_partition_size: Optional[int] = None
+    # Name of the accelerator (e.g. "cuda") to use for IVF training. When set,
+    # create_index() dispatches to pylance to build the index on the accelerator.
+    accelerator: Optional[str] = None
 
 
 @dataclass
@@ -739,6 +751,9 @@ class IvfPq:
     max_iterations: int = 50
     sample_rate: int = 256
     target_partition_size: Optional[int] = None
+    # Name of the accelerator (e.g. "cuda") to use for IVF training. When set,
+    # create_index() dispatches to pylance to build the index on the accelerator.
+    accelerator: Optional[str] = None
 
 
 @dataclass
@@ -792,6 +807,9 @@ class IvfRq:
     max_iterations: int = 50
     sample_rate: int = 256
     target_partition_size: Optional[int] = None
+    # Name of the accelerator (e.g. "cuda") to use for IVF training. When set,
+    # create_index() dispatches to pylance to build the index on the accelerator.
+    accelerator: Optional[str] = None
 
 
 __all__ = [

python/python/lancedb/merge.py

@@ -34,6 +34,8 @@ class LanceMergeInsertBuilder(object):
         self._when_not_matched_by_source_condition = None
         self._timeout = None
         self._use_index = True
+        self._use_lsm_write = None
+        self._validate_single_shard = None
 
     def when_matched_update_all(
         self, *, where: Optional[str] = None
@@ -96,6 +98,46 @@ class LanceMergeInsertBuilder(object):
         self._use_index = use_index
         return self
 
+    def use_lsm_write(self, use_lsm_write: bool) -> LanceMergeInsertBuilder:
+        """
+        Controls whether the merge uses the MemWAL LSM write path.
+
+        By default (unset), a `merge_insert` on a table with an LSM write spec
+        is routed through Lance's MemWAL shard writer, and a table without one
+        uses the standard path. Pass `False` to force the standard path even
+        when a spec is set. Pass `True` to require a spec — `merge_insert`
+        raises an error if none is installed.
+
+        Parameters
+        ----------
+        use_lsm_write: bool
+            Whether to use the LSM write path.
+        """
+        self._use_lsm_write = use_lsm_write
+        return self
+
+    def validate_single_shard(
+        self, validate_single_shard: bool
+    ) -> LanceMergeInsertBuilder:
+        """
+        Controls how an LSM merge checks that its input targets a single shard.
+
+        When a table has an LSM write spec, every row in a `merge_insert` call
+        must route to the same shard. When `True` (the default), every row is
+        inspected to verify this. When `False`, only the first row is inspected
+        and the shard it routes to is used for the whole input — a faster path
+        for callers that have already pre-sharded their input.
+
+        Has no effect on tables without an LSM write spec.
+
+        Parameters
+        ----------
+        validate_single_shard: bool
+            Whether to check every row routes to one shard. Defaults to `True`.
+        """
+        self._validate_single_shard = validate_single_shard
+        return self
+
     def execute(
         self,
         new_data: DATA,

python/python/lancedb/namespace_utils.py

@@ -6,22 +6,44 @@
 from typing import Optional
 
 
+_CREATE_NAMESPACE_MODES = frozenset({"create", "exist_ok", "overwrite"})
+_DROP_NAMESPACE_MODES = frozenset({"SKIP", "FAIL"})
+_DROP_NAMESPACE_BEHAVIORS = frozenset({"RESTRICT", "CASCADE"})
+
+
 def _normalize_create_namespace_mode(mode: Optional[str]) -> Optional[str]:
     """Normalize create namespace mode to lowercase (API expects lowercase)."""
     if mode is None:
         return None
-    return mode.lower()
+    normalized = mode.lower()
+    if normalized not in _CREATE_NAMESPACE_MODES:
+        raise ValueError(
+            f"Invalid create namespace mode {mode!r}: "
+            f"expected one of 'create', 'exist_ok', 'overwrite'"
+        )
+    return normalized
 
 
 def _normalize_drop_namespace_mode(mode: Optional[str]) -> Optional[str]:
     """Normalize drop namespace mode to uppercase (API expects uppercase)."""
     if mode is None:
         return None
-    return mode.upper()
+    normalized = mode.upper()
+    if normalized not in _DROP_NAMESPACE_MODES:
+        raise ValueError(
+            f"Invalid drop namespace mode {mode!r}: expected one of 'skip', 'fail'"
+        )
+    return normalized
 
 
 def _normalize_drop_namespace_behavior(behavior: Optional[str]) -> Optional[str]:
     """Normalize drop namespace behavior to uppercase (API expects uppercase)."""
     if behavior is None:
         return None
-    return behavior.upper()
+    normalized = behavior.upper()
+    if normalized not in _DROP_NAMESPACE_BEHAVIORS:
+        raise ValueError(
+            f"Invalid drop namespace behavior {behavior!r}: "
+            f"expected one of 'restrict', 'cascade'"
+        )
+    return normalized

python/python/lancedb/permutation.py

@@ -3,12 +3,13 @@
 
 import copy
 import json
+import os
 
 from deprecation import deprecated
 import pyarrow as pa
 
 from ._lancedb import async_permutation_builder, PermutationReader
-from .table import LanceTable
+from .table import LanceTable, Table
 from .background_loop import LOOP
 from .util import batch_to_tensor, batch_to_tensor_rows
 from typing import Any, Callable, Iterator, Literal, Optional, TYPE_CHECKING, Union
@@ -354,6 +355,49 @@ class Transforms:
 DEFAULT_BATCH_SIZE = 100
 
 
+def _table_to_pickle_state(table: Table) -> dict[str, Any]:
+    from .remote.table import RemoteTable
+
+    if isinstance(table, RemoteTable):
+        return {
+            "kind": "remote",
+            "table": table,
+        }
+
+    if not isinstance(table, LanceTable):
+        raise ValueError(f"Cannot pickle table of type {type(table)!r}")
+
+    base_uri = table._conn.uri
+    if base_uri.startswith("memory://"):
+        return {
+            "kind": "memory",
+            "name": table.name,
+            "data": table.to_arrow(),
+        }
+
+    return {
+        "kind": "local",
+        "name": table.name,
+        "uri": base_uri,
+        "namespace": table._namespace_path,
+        "storage_options": table._conn.storage_options,
+    }
+
+
+def _table_from_pickle_state(state: dict[str, Any]) -> Table:
+    from . import connect
+
+    kind = state["kind"]
+    if kind == "remote":
+        return state["table"]
+    if kind == "memory":
+        return connect("memory://").create_table(state["name"], state["data"])
+    if kind == "local":
+        db = connect(state["uri"], storage_options=state["storage_options"])
+        return db.open_table(state["name"], namespace_path=state["namespace"] or None)
+    raise ValueError(f"Unknown table pickle state kind: {kind}")
+
+
 class Permutation:
     """
     A Permutation is a view of a dataset that can be used as input to model training
@@ -369,15 +413,15 @@ class Permutation:
 
     def __init__(
         self,
-        base_table: LanceTable,
-        permutation_table: Optional[LanceTable],
+        base_table: Table,
+        permutation_table: Optional[Table],
         split: int,
         selection: dict[str, str],
         batch_size: int,
         transform_fn: Callable[pa.RecordBatch, Any],
         offset: Optional[int] = None,
         limit: Optional[int] = None,
-        connection_factory: Optional[Callable[[str], LanceTable]] = None,
+        connection_factory: Optional[Callable[[str], Table]] = None,
         _reader: Optional[PermutationReader] = None,
     ):
         """
@@ -397,6 +441,7 @@ class Permutation:
         if _reader is None:
             _reader = LOOP.run(self._build_reader())
         self.reader: PermutationReader = _reader
+        self._pid = os.getpid()
 
     async def _build_reader(self) -> PermutationReader:
         reader = await PermutationReader.from_tables(
@@ -428,29 +473,25 @@ class Permutation:
         return new
 
     def with_connection_factory(
-        self, connection_factory: Callable[[str], LanceTable]
+        self, connection_factory: Callable[[str], Table]
     ) -> "Permutation":
         """
         Creates a new permutation that will use ``connection_factory`` to reopen
         the base table when this permutation is unpickled in a worker process.
 
-        The factory is a callable that takes a single argument — the base table
-        name — and returns a [LanceTable]. It must be picklable; the worker
+        The factory is a callable that takes a single argument, the base table
+        name, and returns a LanceDB table. It must be picklable; the worker
         will pickle it via standard ``pickle`` and call it to recover the base
         table. Picklable callables in practice means top-level (module-level)
         functions, ``functools.partial`` of such functions, or instances of
         picklable classes implementing ``__call__``. Lambdas and closures over
         local variables don't pickle with the default protocol.
 
-        Setting a factory is necessary when the URI alone is not enough to
-        re-open the connection — most importantly for LanceDB Cloud (``db://``)
-        connections, where ``api_key`` and ``region`` aren't recoverable from
-        the connection object after construction.
-
-        For local file or cloud-storage paths the factory is optional: if not
-        set, ``__getstate__`` falls back to capturing
-        ``(uri, storage_options, namespace_path)`` and re-opening via
-        ``lancedb.connect(uri, storage_options=...)``.
+        A factory is optional for normal local and remote LanceDB connections:
+        if not set, ``__getstate__`` captures the table's own picklable reopen
+        state. Use a factory when that default state is not enough, for example
+        when credentials should be loaded from the worker environment instead
+        of being embedded in the pickle.
 
         Examples
         --------
@@ -508,7 +549,7 @@ class Permutation:
         return new
 
     @classmethod
-    def identity(cls, table: LanceTable) -> "Permutation":
+    def identity(cls, table: Table) -> "Permutation":
         """
         Creates an identity permutation for the given table.
         """
@@ -517,8 +558,8 @@ class Permutation:
     @classmethod
     def from_tables(
         cls,
-        base_table: LanceTable,
-        permutation_table: Optional[LanceTable] = None,
+        base_table: Table,
+        permutation_table: Optional[Table] = None,
         split: Optional[Union[str, int]] = None,
     ) -> "Permutation":
         """
@@ -594,11 +635,10 @@ class Permutation:
 
         The base table is captured either via a user-supplied
         ``connection_factory`` (see [with_connection_factory]) or, as a
-        fallback, by introspecting ``(uri, storage_options, namespace_path)``
-        on the connection. The permutation table — always an in-memory
-        LanceDB table — is captured as a pyarrow Table (which pickles via
-        Arrow IPC natively). The reader is dropped from the wire format;
-        ``__setstate__`` rebuilds it from the restored tables.
+        fallback, by the table's own picklable reopen state. The permutation
+        table is captured as a pyarrow Table (which pickles via Arrow IPC
+        natively). The reader is dropped from the wire format and rebuilt
+        lazily on first use.
         """
         permutation_data: Optional[pa.Table] = None
         if self.permutation_table is not None:
@@ -622,39 +662,9 @@ class Permutation:
             # namespace from the existing connection.
             return common
 
-        # URI-introspection fallback: only viable for native (OSS) connections
-        # where (uri, storage_options) is enough to reopen. Remote / cloud
-        # connections don't expose recoverable api_key / region — those users
-        # must call with_connection_factory().
-        try:
-            base_uri = self.base_table._conn.uri
-            storage_options = self.base_table._conn.storage_options
-        except AttributeError as e:
-            raise ValueError(
-                "Cannot pickle this Permutation: the base table's connection "
-                "does not expose a uri/storage_options, which usually means it "
-                "is a remote (LanceDB Cloud) connection. Call "
-                "Permutation.with_connection_factory(...) first to provide a "
-                "picklable callable that re-opens the base table from a worker "
-                "process."
-            ) from e
-
-        if base_uri.startswith("memory://"):
-            # In-memory base tables don't exist in any worker process by
-            # default, so dump the entire base table into the pickle. This
-            # can be expensive for large datasets — users with large
-            # in-memory base tables should either persist them or set a
-            # connection_factory.
-            return {
-                **common,
-                "base_table_data": self.base_table.to_arrow(),
-            }
-
         return {
             **common,
-            "base_table_uri": base_uri,
-            "base_table_namespace": self.base_table._namespace_path,
-            "base_table_storage_options": storage_options,
+            "base_table_state": _table_to_pickle_state(self.base_table),
         }
 
     def __setstate__(self, state: dict[str, Any]) -> None:
@@ -663,6 +673,8 @@ class Permutation:
         connection_factory = state["connection_factory"]
         if connection_factory is not None:
             base_table = connection_factory(state["base_table_name"])
+        elif "base_table_state" in state:
+            base_table = _table_from_pickle_state(state["base_table_state"])
         elif "base_table_data" in state:
             # In-memory base table inlined into the pickle; rebuild the same
             # way we rebuild the in-memory permutation table.
@@ -680,7 +692,7 @@ class Permutation:
                 namespace_path=state["base_table_namespace"] or None,
             )
 
-        permutation_table: Optional[LanceTable] = None
+        permutation_table: Optional[Table] = None
         if state["permutation_data"] is not None:
             mem_db = connect("memory://")
             permutation_table = mem_db.create_table(
@@ -696,10 +708,28 @@ class Permutation:
         self.offset = state["offset"]
         self.limit = state["limit"]
         self.connection_factory = connection_factory
+        self.reader = None
+        self._pid = None
+
+    def _ensure_open(self) -> None:
+        pid = os.getpid()
+        if self.reader is not None and getattr(self, "_pid", None) == pid:
+            return
+        # The reader owns Rust-side table handles. Rebuild it after unpickle or
+        # fork even though the Python table wrappers reopen themselves.
+        if hasattr(self.base_table, "_ensure_open"):
+            self.base_table._ensure_open()
+        if self.permutation_table is not None and hasattr(
+            self.permutation_table, "_ensure_open"
+        ):
+            self.permutation_table._ensure_open()
         self.reader = LOOP.run(self._build_reader())
+        self._pid = pid
 
     @property
     def schema(self) -> pa.Schema:
+        self._ensure_open()
+
         async def do_output_schema():
             return await self.reader.output_schema(self.selection)
 
@@ -717,6 +747,7 @@ class Permutation:
         """
         The number of rows in the permutation
         """
+        self._ensure_open()
         return self.reader.count_rows()
 
     @property
@@ -875,6 +906,7 @@ class Permutation:
         If skip_last_batch is True, the last batch will be skipped if it is not a
         multiple of batch_size.
         """
+        self._ensure_open()
 
         async def get_iter():
             return await self.reader.read(self.selection, batch_size=batch_size)
@@ -968,22 +1000,33 @@ class Permutation:
         new.transform_fn = transform
         return new
 
+    def take_offsets(self, offsets: list[int]) -> Any:
+        """
+        Take rows from the permutation by offset
+
+        The returned value is passed through the permutation's current transform,
+        so `with_format` and `with_transform` affect this method in the same way
+        they affect iteration.
+        """
+        self._ensure_open()
+
+        async def do_take_offsets():
+            return await self.reader.take_offsets(offsets, selection=self.selection)
+
+        batch = LOOP.run(do_take_offsets())
+        return self.transform_fn(batch)
+
     def __getitem__(self, index: int) -> Any:
         """
         Returns a single row from the permutation by offset
         """
-        return self.__getitems__([index])
+        return self.take_offsets([index])
 
     def __getitems__(self, indices: list[int]) -> Any:
         """
         Returns rows from the permutation by offset
         """
-
-        async def do_getitems():
-            return await self.reader.take_offsets(indices, selection=self.selection)
-
-        batch = LOOP.run(do_getitems())
-        return self.transform_fn(batch)
+        return self.take_offsets(indices)
 
     @deprecated(details="Use with_skip instead")
     def skip(self, skip: int) -> "Permutation":
@@ -1001,9 +1044,11 @@ class Permutation:
         """
         Skip the first `skip` rows of the permutation
         """
+        self._ensure_open()
         new = copy.copy(self)
         new.offset = skip
         new.reader = LOOP.run(new._build_reader())
+        new._pid = os.getpid()
         return new
 
     @deprecated(details="Use with_take instead")
@@ -1022,9 +1067,11 @@ class Permutation:
         """
         Limit the permutation to `limit` rows (following any `skip`)
         """
+        self._ensure_open()
         new = copy.copy(self)
         new.limit = limit
         new.reader = LOOP.run(new._build_reader())
+        new._pid = os.getpid()
         return new
 
     @deprecated(details="Use with_repeat instead")

python/python/lancedb/query.py

@@ -3,12 +3,14 @@
 
 from __future__ import annotations
 
+import asyncio
 from abc import ABC, abstractmethod
 from concurrent.futures import ThreadPoolExecutor
-from enum import Enum
 from datetime import timedelta
+from enum import Enum
 from typing import (
     TYPE_CHECKING,
+    Any,
     Dict,
     List,
     Literal,
@@ -17,44 +19,51 @@ from typing import (
     Type,
     TypeVar,
     Union,
-    Any,
 )
 
-import asyncio
 import deprecation
 import numpy as np
 import pyarrow as pa
 import pyarrow.compute as pc
 import pydantic
+from typing_extensions import Annotated
 
-from lancedb.pydantic import PYDANTIC_VERSION
+from lancedb._lancedb import fts_query_to_json
 from lancedb.background_loop import LOOP
+from lancedb.pydantic import PYDANTIC_VERSION
 
 from . import __version__
 from .arrow import AsyncRecordBatchReader
 from .dependencies import pandas as pd
+from .expr import Expr
 from .rerankers.base import Reranker
 from .rerankers.rrf import RRFReranker
 from .rerankers.util import check_reranker_result
 from .util import flatten_columns
-from .expr import Expr
-from lancedb._lancedb import fts_query_to_json
-from typing_extensions import Annotated
+
+BlobMode = Literal["lazy", "bytes", "descriptions"]
+
+_BLOB_MODE_TO_HANDLING = {
+    "lazy": "blobs_descriptions",
+    "bytes": "all_binary",
+    "descriptions": "blobs_descriptions",
+}
 
 if TYPE_CHECKING:
     import sys
+
     import PIL
     import polars as pl
 
-    from ._lancedb import Query as LanceQuery
     from ._lancedb import FTSQuery as LanceFTSQuery
     from ._lancedb import HybridQuery as LanceHybridQuery
-    from ._lancedb import VectorQuery as LanceVectorQuery
-    from ._lancedb import TakeQuery as LanceTakeQuery
     from ._lancedb import PyQueryRequest
+    from ._lancedb import Query as LanceQuery
+    from ._lancedb import TakeQuery as LanceTakeQuery
+    from ._lancedb import VectorQuery as LanceVectorQuery
     from .common import VEC
     from .pydantic import LanceModel
-    from .table import Table
+    from .table import AsyncTable, Table
 
     if sys.version_info >= (3, 11):
         from typing import Self
@@ -64,6 +73,147 @@ if TYPE_CHECKING:
 T = TypeVar("T", bound="LanceModel")
 
 
+def _validate_blob_mode(blob_mode: BlobMode) -> None:
+    if blob_mode not in _BLOB_MODE_TO_HANDLING:
+        modes = ", ".join(repr(mode) for mode in _BLOB_MODE_TO_HANDLING)
+        raise ValueError(f"blob_mode must be one of {modes}, got {blob_mode!r}")
+
+
+def _field_is_blob(field: pa.Field) -> bool:
+    metadata = field.metadata or {}
+    return metadata.get(b"lance-encoding:blob") == b"true" or (
+        metadata.get("lance-encoding:blob") == "true"
+    )
+
+
+def _schema_has_blob_field(schema: pa.Schema) -> bool:
+    return any(_field_is_blob(field) for field in schema)
+
+
+def _blob_mode_requires_native_pandas(blob_mode: BlobMode, schema: pa.Schema) -> bool:
+    return blob_mode in ("lazy", "bytes") and _schema_has_blob_field(schema)
+
+
+def _unsupported_blob_pandas_error(reason: str) -> RuntimeError:
+    return RuntimeError(
+        "blob_mode='lazy' and blob_mode='bytes' require Lance native pandas "
+        f"conversion for queries that return blob columns, but {reason}. "
+        "Use blob_mode='descriptions' or remove blob columns from the projection."
+    )
+
+
+def _query_is_plain_scan(query: Query) -> bool:
+    return (
+        query.vector is None
+        and query.full_text_query is None
+        and not query.postfilter
+        and not query.order_by
+    )
+
+
+def _filter_to_sql(filter: Optional[Union[str, Expr]]) -> Optional[str]:
+    if filter is None:
+        return None
+    if isinstance(filter, Expr):
+        return filter.to_sql()
+    return filter
+
+
+def _projection_to_scanner_kwargs(
+    columns: Optional[
+        Union[
+            List[str], List[Tuple[str, Union[str, Expr]]], Dict[str, Union[str, Expr]]
+        ]
+    ],
+) -> Dict[str, Any]:
+    if columns is None:
+        return {}
+    if isinstance(columns, list):
+        if all(isinstance(column, str) for column in columns):
+            return {"columns": columns}
+        if all(isinstance(column, tuple) and len(column) == 2 for column in columns):
+            return {
+                "columns": {
+                    name: expr.to_sql() if isinstance(expr, Expr) else expr
+                    for name, expr in columns
+                }
+            }
+        # Let Lance raise the detailed projection validation error.
+        return {"columns": columns}
+
+    projection = {}
+    for name, expr in columns.items():
+        if isinstance(expr, Expr):
+            expr = expr.to_sql()
+        projection[name] = expr
+    return {"columns": projection}
+
+
+def _scanner_kwargs_for_query(query: Query, blob_mode: BlobMode) -> Dict[str, Any]:
+    kwargs = {
+        **_projection_to_scanner_kwargs(query.columns),
+        "filter": _filter_to_sql(query.filter),
+        "limit": query.limit,
+        "offset": query.offset,
+        "with_row_id": query.with_row_id,
+        "fast_search": query.fast_search,
+        "blob_handling": _BLOB_MODE_TO_HANDLING[blob_mode],
+    }
+    return {key: value for key, value in kwargs.items() if value is not None}
+
+
+def _ensure_lazy_blob_frame(
+    df: "pd.DataFrame", schema: pa.Schema, blob_mode: BlobMode
+) -> "pd.DataFrame":
+    if blob_mode != "lazy" or not _schema_has_blob_field(schema) or len(df) == 0:
+        return df
+
+    for field in schema:
+        if not _field_is_blob(field) or field.name not in df.columns:
+            continue
+        value = df[field.name].iloc[0]
+        if value is not None and not hasattr(value, "readall"):
+            raise _unsupported_blob_pandas_error(
+                "the Lance scanner did not return lazy blob files"
+            )
+    return df
+
+
+def _scanner_to_pandas(scanner: Any, blob_mode: BlobMode, **kwargs) -> "pd.DataFrame":
+    schema = getattr(scanner, "projected_schema", None)
+    if schema is None:
+        schema = getattr(scanner, "schema", None)
+    if schema is None:
+        schema = getattr(scanner, "dataset_schema", None)
+    if callable(schema):
+        schema = schema()
+    if hasattr(scanner, "to_pandas"):
+        try:
+            df = scanner.to_pandas(blob_mode=blob_mode, **kwargs)
+        except TypeError as err:
+            message = str(err)
+            if "blob_mode" not in message and "unexpected keyword" not in message:
+                raise
+            df = scanner.to_pandas(**kwargs)
+        if schema is not None:
+            return _ensure_lazy_blob_frame(df, schema, blob_mode)
+        return df
+
+    if hasattr(scanner, "to_pyarrow"):
+        reader = scanner.to_pyarrow()
+        tbl = reader.read_all()
+    elif hasattr(scanner, "to_table"):
+        tbl = scanner.to_table()
+    else:
+        reader = scanner.to_reader()
+        tbl = reader.read_all()
+    if blob_mode == "lazy" and _schema_has_blob_field(tbl.schema):
+        raise _unsupported_blob_pandas_error(
+            "the Lance scanner does not expose to_pandas"
+        )
+    return tbl.to_pandas(**kwargs)
+
+
 # Pydantic validation function for vector queries
 def ensure_vector_query(
     val: Any,
@@ -92,6 +242,12 @@ def ensure_vector_query(
         return val
 
 
+class ColumnOrdering(pydantic.BaseModel):
+    column_name: str
+    ascending: bool = True
+    nulls_first: bool = False
+
+
 class FullTextQueryType(str, Enum):
     MATCH = "match"
     MATCH_PHRASE = "match_phrase"
@@ -504,6 +660,8 @@ class Query(pydantic.BaseModel):
     # Bypass the vector index and use a brute force search
     bypass_vector_index: Optional[bool] = None
 
+    order_by: Optional[List[ColumnOrdering]] = None
+
     @classmethod
     def from_inner(cls, req: PyQueryRequest) -> Self:
         query = cls()
@@ -524,6 +682,8 @@ class Query(pydantic.BaseModel):
         query.refine_factor = req.refine_factor
         query.bypass_vector_index = req.bypass_vector_index
         query.postfilter = req.postfilter
+        if req.order_by is not None:
+            query.order_by = [ColumnOrdering(**o) for o in req.order_by]
         if req.full_text_search is not None:
             query.full_text_query = FullTextSearchQuery(
                 columns=None,
@@ -572,9 +732,22 @@ class LanceQueryBuilder(ABC):
             If "auto", the query type is inferred based on the query.
         vector_column_name: str
             The name of the vector column to use for vector search.
+        ordering_field_name: Optional[str]
+            .. deprecated:: 0.27.0
+                Use ``order_by()`` method instead.
+        fts_columns: Optional[Union[str, List[str]]]
+            The columns to search in for full text search.
         fast_search: bool
             Skip flat search of unindexed data.
         """
+        if ordering_field_name is not None:
+            import warnings
+
+            warnings.warn(
+                "ordering_field_name is deprecated, use .order_by() method instead.",
+                DeprecationWarning,
+                stacklevel=2,
+            )
         # Check hybrid search first as it supports empty query pattern
         if query_type == "hybrid":
             # hybrid fts and vector query
@@ -671,6 +844,7 @@ class LanceQueryBuilder(ABC):
         self._text = None
         self._ef = None
         self._bypass_vector_index = None
+        self._order_by = None
 
     @deprecation.deprecated(
         deprecated_in="0.3.1",
@@ -693,7 +867,9 @@ class LanceQueryBuilder(ABC):
         self,
         flatten: Optional[Union[int, bool]] = None,
         *,
+        blob_mode: BlobMode = "lazy",
         timeout: Optional[timedelta] = None,
+        **kwargs,
     ) -> "pd.DataFrame":
         """
         Execute the query and return the results as a pandas DataFrame.
@@ -711,9 +887,32 @@ class LanceQueryBuilder(ABC):
         timeout: Optional[timedelta]
             The maximum time to wait for the query to complete.
             If None, wait indefinitely.
+        blob_mode: str, default "lazy"
+            Controls how blob columns are returned for plain scan queries.
+            Vector, FTS, hybrid, and other non-native query shapes keep the
+            existing Arrow conversion path and only support blob descriptions.
+        **kwargs
+            Forwarded to pyarrow.Table.to_pandas after query execution and
+            optional flattening.
         """
+        _validate_blob_mode(blob_mode)
+        native_error = None
         tbl = flatten_columns(self.to_arrow(timeout=timeout), flatten)
-        return tbl.to_pandas()
+        if _blob_mode_requires_native_pandas(blob_mode, tbl.schema):
+            if flatten is None and timeout is None:
+                try:
+                    df = self._plain_scan_to_pandas(blob_mode, **kwargs)
+                    if df is not None:
+                        return df
+                except Exception as err:
+                    native_error = err
+            reason = (
+                "this query shape cannot use Lance native pandas conversion"
+                if native_error is None
+                else str(native_error)
+            )
+            raise _unsupported_blob_pandas_error(reason) from native_error
+        return tbl.to_pandas(**kwargs)
 
     @abstractmethod
     def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
@@ -947,6 +1146,24 @@ class LanceQueryBuilder(ABC):
         """  # noqa: E501
         return self._table._explain_plan(self.to_query_object(), verbose=verbose)
 
+    def order_by(self, ordering: Optional[List[ColumnOrdering]]) -> Self:
+        """
+        Set the ordering for the results.
+
+        Parameters
+        ----------
+        ordering: Optional[List[ColumnOrdering]]
+            The ordering to use for the results.  If None, then the default ordering
+            will be used.
+
+        Returns
+        -------
+        LanceQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        self._order_by = ordering
+        return self
+
     def analyze_plan(self) -> str:
         """
         Run the query and return its execution plan with runtime metrics.
@@ -1039,6 +1256,19 @@ class LanceQueryBuilder(ABC):
         """
         raise NotImplementedError
 
+    def _plain_scan_to_pandas(
+        self,
+        blob_mode: BlobMode,
+        **kwargs,
+    ) -> Optional["pd.DataFrame"]:
+        query = self.to_query_object()
+        if not _query_is_plain_scan(query):
+            return None
+
+        dataset = self._table.to_lance()
+        scanner = dataset.scanner(**_scanner_kwargs_for_query(query, blob_mode))
+        return _scanner_to_pandas(scanner, blob_mode, **kwargs)
+
     @abstractmethod
     def to_query_object(self) -> Query:
         """Return a serializable representation of the query
@@ -1314,6 +1544,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
             fast_search=self._fast_search,
             ef=self._ef,
             bypass_vector_index=self._bypass_vector_index,
+            order_by=self._order_by,
         )
 
     def to_batches(
@@ -1465,7 +1696,9 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
         super().__init__(table)
         self._query = query
         self._phrase_query = False
-        self.ordering_field_name = ordering_field_name
+        # Deprecated compatibility parameter. Native FTS ordering is now
+        # configured through order_by(); LanceQueryBuilder.create emits the warning.
+        _ = ordering_field_name
         self._reranker = None
         self._fast_search = fast_search
         if isinstance(fts_columns, str):
@@ -1514,6 +1747,7 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
             ),
             offset=self._offset,
             fast_search=self._fast_search,
+            order_by=self._order_by,
         )
 
     def output_schema(self) -> pa.Schema:
@@ -1579,6 +1813,7 @@ class LanceEmptyQueryBuilder(LanceQueryBuilder):
             limit=self._limit,
             with_row_id=self._with_row_id,
             offset=self._offset,
+            order_by=self._order_by,
         )
 
     def output_schema(self) -> pa.Schema:
@@ -2155,7 +2390,11 @@ class AsyncQueryBase(object):
     Base class for all async queries (take, scan, vector, fts, hybrid)
     """
 
-    def __init__(self, inner: Union[LanceQuery, LanceVectorQuery, LanceTakeQuery]):
+    def __init__(
+        self,
+        inner: Union[LanceQuery, LanceVectorQuery, LanceTakeQuery],
+        table: Optional["AsyncTable"] = None,
+    ):
         """
         Construct an AsyncQueryBase
 
@@ -2163,6 +2402,7 @@ class AsyncQueryBase(object):
         [AsyncTable.query][lancedb.table.AsyncTable.query] method to create a query.
         """
         self._inner = inner
+        self._table = table
 
     def to_query_object(self) -> Query:
         """
@@ -2305,6 +2545,9 @@ class AsyncQueryBase(object):
         self,
         flatten: Optional[Union[int, bool]] = None,
         timeout: Optional[timedelta] = None,
+        *,
+        blob_mode: BlobMode = "lazy",
+        **kwargs,
     ) -> "pd.DataFrame":
         """
         Execute the query and collect the results into a pandas DataFrame.
@@ -2337,10 +2580,48 @@ class AsyncQueryBase(object):
             The maximum time to wait for the query to complete.
             If not specified, no timeout is applied. If the query does not
             complete within the specified time, an error will be raised.
+        blob_mode: str, default "lazy"
+            Controls how blob columns are returned for plain scan queries.
+            Vector, FTS, hybrid, and other non-native query shapes keep the
+            existing Arrow conversion path and only support blob descriptions.
+        **kwargs
+            Forwarded to pyarrow.Table.to_pandas after query execution and
+            optional flattening.
         """
-        return (
-            flatten_columns(await self.to_arrow(timeout=timeout), flatten)
-        ).to_pandas()
+        _validate_blob_mode(blob_mode)
+        native_error = None
+        tbl = flatten_columns(await self.to_arrow(timeout=timeout), flatten)
+        if _blob_mode_requires_native_pandas(blob_mode, tbl.schema):
+            if flatten is None and timeout is None:
+                try:
+                    df = await self._plain_scan_to_pandas(blob_mode, **kwargs)
+                    if df is not None:
+                        return df
+                except Exception as err:
+                    native_error = err
+            reason = (
+                "this query shape cannot use Lance native pandas conversion"
+                if native_error is None
+                else str(native_error)
+            )
+            raise _unsupported_blob_pandas_error(reason) from native_error
+        return tbl.to_pandas(**kwargs)
+
+    async def _plain_scan_to_pandas(
+        self,
+        blob_mode: BlobMode,
+        **kwargs,
+    ) -> Optional["pd.DataFrame"]:
+        if self._table is None:
+            return None
+
+        query = self.to_query_object()
+        if not _query_is_plain_scan(query):
+            return None
+
+        dataset = await self._table._to_lance()
+        scanner = dataset.scanner(**_scanner_kwargs_for_query(query, blob_mode))
+        return _scanner_to_pandas(scanner, blob_mode, **kwargs)
 
     async def to_polars(
         self,
@@ -2447,14 +2728,18 @@ class AsyncStandardQuery(AsyncQueryBase):
     Base class for "standard" async queries (all but take currently)
     """
 
-    def __init__(self, inner: Union[LanceQuery, LanceVectorQuery]):
+    def __init__(
+        self,
+        inner: Union[LanceQuery, LanceVectorQuery],
+        table: Optional["AsyncTable"] = None,
+    ):
         """
         Construct an AsyncStandardQuery
 
         This method is not intended to be called directly.  Instead, use the
         [AsyncTable.query][lancedb.table.AsyncTable.query] method to create a query.
         """
-        super().__init__(inner)
+        super().__init__(inner, table)
 
     def where(self, predicate: Union[str, Expr]) -> Self:
         """
@@ -2502,6 +2787,27 @@ class AsyncStandardQuery(AsyncQueryBase):
         self._inner.offset(offset)
         return self
 
+    def order_by(self, ordering: Optional[List[ColumnOrdering]]) -> Self:
+        """
+        Set the ordering for the results.
+
+        Parameters
+        ----------
+        ordering: Optional[List[ColumnOrdering]]
+            The ordering to use for the results.  If None, then the default ordering
+            will be used.
+        """
+        if ordering is None:
+            self._inner.order_by(None)
+        else:
+            self._inner.order_by(
+                [
+                    o.model_dump() if hasattr(o, "model_dump") else o.dict()
+                    for o in ordering
+                ]
+            )
+        return self
+
     def fast_search(self) -> Self:
         """
         Skip searching un-indexed data.
@@ -2539,14 +2845,14 @@ class AsyncStandardQuery(AsyncQueryBase):
 
 
 class AsyncQuery(AsyncStandardQuery):
-    def __init__(self, inner: LanceQuery):
+    def __init__(self, inner: LanceQuery, table: Optional["AsyncTable"] = None):
         """
         Construct an AsyncQuery
 
         This method is not intended to be called directly.  Instead, use the
         [AsyncTable.query][lancedb.table.AsyncTable.query] method to create a query.
         """
-        super().__init__(inner)
+        super().__init__(inner, table)
         self._inner = inner
 
     @classmethod
@@ -2630,10 +2936,11 @@ class AsyncQuery(AsyncStandardQuery):
             new_self = self._inner.nearest_to(query_vectors[0])
             for v in query_vectors[1:]:
                 new_self.add_query_vector(v)
-            return AsyncVectorQuery(new_self)
+            return AsyncVectorQuery(new_self, self._table)
         else:
             return AsyncVectorQuery(
-                self._inner.nearest_to(AsyncQuery._query_vec_to_array(query_vector))
+                self._inner.nearest_to(AsyncQuery._query_vec_to_array(query_vector)),
+                self._table,
             )
 
     def nearest_to_text(
@@ -2666,17 +2973,18 @@ class AsyncQuery(AsyncStandardQuery):
 
         if isinstance(query, str):
             return AsyncFTSQuery(
-                self._inner.nearest_to_text({"query": query, "columns": columns})
+                self._inner.nearest_to_text({"query": query, "columns": columns}),
+                self._table,
             )
         # FullTextQuery object
-        return AsyncFTSQuery(self._inner.nearest_to_text({"query": query}))
+        return AsyncFTSQuery(self._inner.nearest_to_text({"query": query}), self._table)
 
 
 class AsyncFTSQuery(AsyncStandardQuery):
     """A query for full text search for LanceDB."""
 
-    def __init__(self, inner: LanceFTSQuery):
-        super().__init__(inner)
+    def __init__(self, inner: LanceFTSQuery, table: Optional["AsyncTable"] = None):
+        super().__init__(inner, table)
         self._inner = inner
         self._reranker = None
 
@@ -2758,10 +3066,11 @@ class AsyncFTSQuery(AsyncStandardQuery):
             new_self = self._inner.nearest_to(query_vectors[0])
             for v in query_vectors[1:]:
                 new_self.add_query_vector(v)
-            return AsyncHybridQuery(new_self)
+            return AsyncHybridQuery(new_self, self._table)
         else:
             return AsyncHybridQuery(
-                self._inner.nearest_to(AsyncQuery._query_vec_to_array(query_vector))
+                self._inner.nearest_to(AsyncQuery._query_vec_to_array(query_vector)),
+                self._table,
             )
 
     async def to_batches(
@@ -2952,7 +3261,7 @@ class AsyncVectorQueryBase:
 
 
 class AsyncVectorQuery(AsyncStandardQuery, AsyncVectorQueryBase):
-    def __init__(self, inner: LanceVectorQuery):
+    def __init__(self, inner: LanceVectorQuery, table: Optional["AsyncTable"] = None):
         """
         Construct an AsyncVectorQuery
 
@@ -2962,7 +3271,7 @@ class AsyncVectorQuery(AsyncStandardQuery, AsyncVectorQueryBase):
         a vector query.  Or you can use
         [AsyncTable.vector_search][lancedb.table.AsyncTable.vector_search]
         """
-        super().__init__(inner)
+        super().__init__(inner, table)
         self._inner = inner
         self._reranker = None
         self._query_string = None
@@ -3016,10 +3325,13 @@ class AsyncVectorQuery(AsyncStandardQuery, AsyncVectorQueryBase):
 
         if isinstance(query, str):
             return AsyncHybridQuery(
-                self._inner.nearest_to_text({"query": query, "columns": columns})
+                self._inner.nearest_to_text({"query": query, "columns": columns}),
+                self._table,
             )
         # FullTextQuery object
-        return AsyncHybridQuery(self._inner.nearest_to_text({"query": query}))
+        return AsyncHybridQuery(
+            self._inner.nearest_to_text({"query": query}), self._table
+        )
 
     async def to_batches(
         self,
@@ -3046,8 +3358,8 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
     in the `rerank` method to convert the scores to ranks and then normalize them.
     """
 
-    def __init__(self, inner: LanceHybridQuery):
-        super().__init__(inner)
+    def __init__(self, inner: LanceHybridQuery, table: Optional["AsyncTable"] = None):
+        super().__init__(inner, table)
         self._inner = inner
         self._norm = "score"
         self._reranker = RRFReranker()
@@ -3088,8 +3400,8 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
         max_batch_length: Optional[int] = None,
         timeout: Optional[timedelta] = None,
     ) -> AsyncRecordBatchReader:
-        fts_query = AsyncFTSQuery(self._inner.to_fts_query())
-        vec_query = AsyncVectorQuery(self._inner.to_vector_query())
+        fts_query = AsyncFTSQuery(self._inner.to_fts_query(), self._table)
+        vec_query = AsyncVectorQuery(self._inner.to_vector_query(), self._table)
 
         # save the row ID choice that was made on the query builder and force it
         # to actually fetch the row ids because we need this for reranking
@@ -3189,8 +3501,15 @@ class AsyncTakeQuery(AsyncQueryBase):
     Builder for parameterizing and executing take queries.
     """
 
-    def __init__(self, inner: LanceTakeQuery):
-        super().__init__(inner)
+    def __init__(self, inner: LanceTakeQuery, table: Optional["AsyncTable"] = None):
+        super().__init__(inner, table)
+
+    async def _plain_scan_to_pandas(
+        self,
+        blob_mode: BlobMode,
+        **kwargs,
+    ) -> Optional["pd.DataFrame"]:
+        return None
 
 
 class BaseQueryBuilder(object):
@@ -3272,16 +3591,18 @@ class BaseQueryBuilder(object):
             If not specified, no timeout is applied. If the query does not
             complete within the specified time, an error will be raised.
         """
-        async_iter = LOOP.run(self._inner.execute(max_batch_length, timeout))
+        async_reader = LOOP.run(
+            self._inner.to_batches(max_batch_length=max_batch_length, timeout=timeout)
+        )
 
         def iter_sync():
             try:
                 while True:
-                    yield LOOP.run(async_iter.__anext__())
+                    yield LOOP.run(async_reader.__anext__())
             except StopAsyncIteration:
                 return
 
-        return pa.RecordBatchReader.from_batches(async_iter.schema, iter_sync())
+        return pa.RecordBatchReader.from_batches(async_reader.schema, iter_sync())
 
     def to_arrow(self, timeout: Optional[timedelta] = None) -> pa.Table:
         """
@@ -3321,6 +3642,9 @@ class BaseQueryBuilder(object):
         self,
         flatten: Optional[Union[int, bool]] = None,
         timeout: Optional[timedelta] = None,
+        *,
+        blob_mode: BlobMode = "lazy",
+        **kwargs,
     ) -> "pd.DataFrame":
         """
         Execute the query and collect the results into a pandas DataFrame.
@@ -3353,8 +3677,15 @@ class BaseQueryBuilder(object):
             The maximum time to wait for the query to complete.
             If not specified, no timeout is applied. If the query does not
             complete within the specified time, an error will be raised.
+        blob_mode: str, default "lazy"
+            Controls how blob columns are returned for plain scan queries.
+        **kwargs
+            Forwarded to pyarrow.Table.to_pandas after query execution and
+            optional flattening.
         """
-        return LOOP.run(self._inner.to_pandas(flatten, timeout))
+        return LOOP.run(
+            self._inner.to_pandas(flatten, timeout, blob_mode=blob_mode, **kwargs)
+        )
 
     def to_polars(
         self,

python/python/lancedb/remote/db.py

@@ -3,6 +3,7 @@
 
 
 from datetime import timedelta
+import json
 import logging
 from concurrent.futures import ThreadPoolExecutor
 import sys
@@ -17,7 +18,7 @@ else:
 
 # Remove this import to fix circular dependency
 # from lancedb import connect_async
-from lancedb.remote import ClientConfig
+from lancedb.remote import ClientConfig, RetryConfig, TimeoutConfig, TlsConfig
 import pyarrow as pa
 
 from ..common import DATA
@@ -36,6 +37,64 @@ from ..table import Table
 from ..util import validate_table_name
 
 
+def _duration_seconds(value: Optional[timedelta]) -> Optional[float]:
+    return value.total_seconds() if value is not None else None
+
+
+def _timeout_config_to_dict(
+    config: Optional[TimeoutConfig],
+) -> Optional[dict[str, Any]]:
+    if config is None:
+        return None
+    return {
+        "timeout": _duration_seconds(config.timeout),
+        "connect_timeout": _duration_seconds(config.connect_timeout),
+        "read_timeout": _duration_seconds(config.read_timeout),
+        "pool_idle_timeout": _duration_seconds(config.pool_idle_timeout),
+    }
+
+
+def _retry_config_to_dict(config: RetryConfig) -> dict[str, Any]:
+    return {
+        "retries": config.retries,
+        "connect_retries": config.connect_retries,
+        "read_retries": config.read_retries,
+        "backoff_factor": config.backoff_factor,
+        "backoff_jitter": config.backoff_jitter,
+        "statuses": config.statuses,
+    }
+
+
+def _tls_config_to_dict(config: Optional[TlsConfig]) -> Optional[dict[str, Any]]:
+    if config is None:
+        return None
+    return {
+        "cert_file": config.cert_file,
+        "key_file": config.key_file,
+        "ssl_ca_cert": config.ssl_ca_cert,
+        "assert_hostname": config.assert_hostname,
+    }
+
+
+def _client_config_to_dict(config: ClientConfig) -> dict[str, Any]:
+    if config.header_provider is not None:
+        raise ValueError(
+            "Cannot serialize a remote connection with a header_provider. "
+            "Use static api_key/extra_headers or provide a worker-side "
+            "connection factory instead."
+        )
+    return {
+        "user_agent": config.user_agent,
+        "retry_config": _retry_config_to_dict(config.retry_config),
+        "timeout_config": _timeout_config_to_dict(config.timeout_config),
+        "extra_headers": config.extra_headers,
+        "id_delimiter": config.id_delimiter,
+        "tls_config": _tls_config_to_dict(config.tls_config),
+        "header_provider": None,
+        "user_id": config.user_id,
+    }
+
+
 class RemoteDBConnection(DBConnection):
     """A connection to a remote LanceDB database."""
 
@@ -50,6 +109,7 @@ class RemoteDBConnection(DBConnection):
         connection_timeout: Optional[float] = None,
         read_timeout: Optional[float] = None,
         storage_options: Optional[Dict[str, str]] = None,
+        read_consistency_interval: Optional[timedelta] = None,
     ):
         """Connect to a remote LanceDB database."""
         if isinstance(client_config, dict):
@@ -88,6 +148,11 @@ class RemoteDBConnection(DBConnection):
         parsed = urlparse(db_url)
         if parsed.scheme != "db":
             raise ValueError(f"Invalid scheme: {parsed.scheme}, only accepts db://")
+        self.db_url = db_url
+        self.api_key = api_key
+        self.region = region
+        self.host_override = host_override
+        self.storage_options = storage_options
         self.db_name = parsed.netloc
 
         self.client_config = client_config
@@ -103,12 +168,27 @@ class RemoteDBConnection(DBConnection):
                 host_override=host_override,
                 client_config=client_config,
                 storage_options=storage_options,
+                read_consistency_interval=read_consistency_interval,
             )
         )
 
     def __repr__(self) -> str:
         return f"RemoteConnect(name={self.db_name})"
 
+    @override
+    def serialize(self) -> str:
+        return json.dumps(
+            {
+                "connection_type": "remote",
+                "db_url": self.db_url,
+                "api_key": self.api_key,
+                "region": self.region,
+                "host_override": self.host_override,
+                "client_config": _client_config_to_dict(self.client_config),
+                "storage_options": self.storage_options,
+            }
+        )
+
     @override
     def list_namespaces(
         self,
@@ -329,7 +409,12 @@ class RemoteDBConnection(DBConnection):
             )
 
         table = LOOP.run(self._conn.open_table(name, namespace_path=namespace_path))
-        return RemoteTable(table, self.db_name)
+        return RemoteTable(
+            table,
+            self.db_name,
+            connection_state=self.serialize,
+            namespace_path=namespace_path,
+        )
 
     def clone_table(
         self,
@@ -378,7 +463,12 @@ class RemoteDBConnection(DBConnection):
                 is_shallow=is_shallow,
             )
         )
-        return RemoteTable(table, self.db_name)
+        return RemoteTable(
+            table,
+            self.db_name,
+            connection_state=self.serialize,
+            namespace_path=target_namespace_path,
+        )
 
     @override
     def create_table(
@@ -523,7 +613,12 @@ class RemoteDBConnection(DBConnection):
                 fill_value=fill_value,
             )
         )
-        return RemoteTable(table, self.db_name)
+        return RemoteTable(
+            table,
+            self.db_name,
+            connection_state=self.serialize,
+            namespace_path=namespace_path,
+        )
 
     @override
     def drop_table(self, name: str, namespace_path: Optional[List[str]] = None):

python/python/lancedb/remote/table.py

@@ -2,11 +2,25 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 from datetime import timedelta
+import deprecation
 import logging
 from functools import cached_property
-from typing import Any, Callable, Dict, Iterable, List, Optional, Union, Literal
+import os
+from typing import (
+    Any,
+    Callable,
+    Dict,
+    Iterable,
+    List,
+    Optional,
+    Union,
+    Literal,
+    overload,
+)
 import warnings
 
+from lancedb import __version__
+
 from lancedb._lancedb import (
     AddColumnsResult,
     AddResult,
@@ -14,6 +28,7 @@ from lancedb._lancedb import (
     DeleteResult,
     DropColumnsResult,
     IndexConfig,
+    LsmWriteSpec,
     MergeResult,
     UpdateResult,
 )
@@ -31,6 +46,7 @@ from lancedb.index import (
     LabelList,
 )
 from lancedb.remote.db import LOOP
+from lancedb.table import IndexConfigType, KNOWN_METRICS
 import pyarrow as pa
 
 from lancedb.common import DATA, VEC, VECTOR_COLUMN_NAME
@@ -39,7 +55,7 @@ from lancedb.embeddings import EmbeddingFunctionRegistry
 from lancedb.table import _normalize_progress
 
 from ..query import LanceVectorQueryBuilder, LanceQueryBuilder, LanceTakeQueryBuilder
-from ..table import AsyncTable, IndexStatistics, Query, Table, Tags
+from ..table import AsyncTable, BlobMode, IndexStatistics, Query, Table, Tags
 from ..types import BaseTokenizerType
 
 
@@ -48,14 +64,80 @@ class RemoteTable(Table):
         self,
         table: AsyncTable,
         db_name: str,
+        *,
+        connection_state: Optional[Union[str, Callable[[], str]]] = None,
+        namespace_path: Optional[List[str]] = None,
     ):
-        self._table = table
+        self._table_handle = table
+        self._name = table.name
         self.db_name = db_name
+        self._connection_state = connection_state
+        self._namespace_path = list(namespace_path or [])
+        self._checkout_version: Optional[int] = None
+        self._pid = os.getpid()
+
+    def _serialized_connection_state(self) -> str:
+        if self._connection_state is None:
+            raise RuntimeError(
+                "Cannot reopen this remote table because it does not carry "
+                "serialized connection state"
+            )
+        if callable(self._connection_state):
+            self._connection_state = self._connection_state()
+        return self._connection_state
+
+    @property
+    def _table(self) -> AsyncTable:
+        self._ensure_open()
+        assert self._table_handle is not None
+        return self._table_handle
+
+    @_table.setter
+    def _table(self, table: AsyncTable) -> None:
+        self._table_handle = table
+        self._name = table.name
+        self._pid = os.getpid()
+
+    def _ensure_open(self) -> None:
+        pid = os.getpid()
+        if self._table_handle is not None and self._pid == pid:
+            return
+
+        # Pickle clears the handle; fork inherits a handle created in the
+        # parent process. In both cases reopen before touching the Rust client.
+        from lancedb import deserialize_conn
+
+        db = deserialize_conn(self._serialized_connection_state(), for_worker=True)
+        table = db.open_table(self._name, namespace_path=self._namespace_path)
+        if self._checkout_version is not None:
+            table.checkout(self._checkout_version)
+
+        self._table_handle = table._table
+        self.db_name = table.db_name
+        self._pid = pid
+
+    def __getstate__(self) -> dict:
+        return {
+            "connection_state": self._serialized_connection_state(),
+            "db_name": self.db_name,
+            "name": self.name,
+            "namespace_path": self._namespace_path,
+            "checkout_version": self._checkout_version,
+        }
+
+    def __setstate__(self, state: dict) -> None:
+        self._table_handle = None
+        self._name = state["name"]
+        self.db_name = state["db_name"]
+        self._connection_state = state["connection_state"]
+        self._namespace_path = state["namespace_path"]
+        self._checkout_version = state["checkout_version"]
+        self._pid = None
 
     @property
     def name(self) -> str:
         """The name of the table"""
-        return self._table.name
+        return self._name
 
     def __repr__(self) -> str:
         return f"RemoteTable({self.db_name}.{self.name})"
@@ -100,18 +182,24 @@ class RemoteTable(Table):
         """to_arrow() is not yet supported on LanceDB cloud."""
         raise NotImplementedError("to_arrow() is not yet supported on LanceDB cloud.")
 
-    def to_pandas(self):
+    def to_pandas(self, blob_mode: BlobMode = "lazy", **kwargs):
         """to_pandas() is not yet supported on LanceDB cloud."""
         raise NotImplementedError("to_pandas() is not yet supported on LanceDB cloud.")
 
     def checkout(self, version: Union[int, str]):
-        return LOOP.run(self._table.checkout(version))
+        result = LOOP.run(self._table.checkout(version))
+        self._checkout_version = self.version
+        return result
 
     def checkout_latest(self):
-        return LOOP.run(self._table.checkout_latest())
+        result = LOOP.run(self._table.checkout_latest())
+        self._checkout_version = None
+        return result
 
     def restore(self, version: Optional[Union[int, str]] = None):
-        return LOOP.run(self._table.restore(version))
+        result = LOOP.run(self._table.restore(version))
+        self._checkout_version = None
+        return result
 
     def list_indices(self) -> Iterable[IndexConfig]:
         """List all the indices on the table"""
@@ -121,6 +209,11 @@ class RemoteTable(Table):
         """List all the stats of a specified index"""
         return LOOP.run(self._table.index_stats(index_uuid))
 
+    @deprecation.deprecated(
+        deprecated_in="0.25.0",
+        current_version=__version__,
+        details="Use create_index() with config=BTree()/Bitmap()/LabelList() instead.",
+    )
     def create_scalar_index(
         self,
         column: str,
@@ -130,7 +223,12 @@ class RemoteTable(Table):
         wait_timeout: Optional[timedelta] = None,
         name: Optional[str] = None,
     ):
-        """Creates a scalar index
+        """Creates a scalar index.
+
+        .. deprecated:: 0.25.0
+            Use :meth:`create_index` with a BTree, Bitmap, or LabelList config instead.
+            Example: ``table.create_index("column", config=BTree())``
+
         Parameters
         ----------
         column : str
@@ -161,6 +259,11 @@ class RemoteTable(Table):
             )
         )
 
+    @deprecation.deprecated(
+        deprecated_in="0.25.0",
+        current_version=__version__,
+        details="Use create_index() with config=FTS() instead.",
+    )
     def create_fts_index(
         self,
         column: str,
@@ -181,6 +284,12 @@ class RemoteTable(Table):
         prefix_only: bool = False,
         name: Optional[str] = None,
     ):
+        """Create a full-text search index on a column.
+
+        .. deprecated:: 0.25.0
+            Use :meth:`create_index` with an FTS config instead.
+            Example: ``table.create_index("text_column", config=FTS())``
+        """
         config = FTS(
             with_position=with_position,
             base_tokenizer=base_tokenizer,
@@ -204,9 +313,43 @@ class RemoteTable(Table):
             )
         )
 
+    # New unified API overload
+    @overload
     def create_index(
         self,
-        metric="l2",
+        column: str,
+        /,
+        *,
+        config: IndexConfigType,
+        wait_timeout: Optional[timedelta] = ...,
+        name: Optional[str] = ...,
+        train: bool = ...,
+    ) -> None: ...
+
+    # Legacy API overload (deprecated)
+    @overload
+    def create_index(
+        self,
+        metric: Literal["l2", "cosine", "dot", "hamming"] = ...,
+        vector_column_name: str = ...,
+        index_cache_size: Optional[int] = ...,
+        num_partitions: Optional[int] = ...,
+        num_sub_vectors: Optional[int] = ...,
+        replace: Optional[bool] = ...,
+        accelerator: Optional[str] = ...,
+        index_type: Literal[
+            "VECTOR", "IVF_FLAT", "IVF_SQ", "IVF_PQ", "IVF_HNSW_SQ", "IVF_HNSW_PQ"
+        ] = ...,
+        wait_timeout: Optional[timedelta] = ...,
+        *,
+        num_bits: int = ...,
+        name: Optional[str] = ...,
+        train: bool = ...,
+    ) -> None: ...
+
+    def create_index(
+        self,
+        metric: str = "l2",
         vector_column_name: str = VECTOR_COLUMN_NAME,
         index_cache_size: Optional[int] = None,
         num_partitions: Optional[int] = None,
@@ -217,89 +360,113 @@ class RemoteTable(Table):
         wait_timeout: Optional[timedelta] = None,
         *,
         num_bits: int = 8,
+        config: Optional[IndexConfigType] = None,
         name: Optional[str] = None,
         train: bool = True,
     ):
-        """Create an index on the table.
+        """Create an index on a column.
 
-        Parameters
-        ----------
-        metric : str
-            The metric to use for the index. Default is "l2".
-        vector_column_name : str
-            The name of the vector column. Default is "vector".
+        This method supports both the new unified API and the legacy API
+        for backwards compatibility. The new API takes the column name as the
+        first positional argument and an index configuration object via
+        ``config``; the legacy API takes the distance metric as the first
+        argument plus separate ``vector_column_name`` / ``num_partitions`` /
+        etc. parameters, and emits a ``DeprecationWarning``.
 
         Examples
         --------
-        >>> import lancedb
-        >>> import uuid
-        >>> from lancedb.schema import vector
-        >>> db = lancedb.connect("db://...", api_key="...", # doctest: +SKIP
-        ...                      region="...") # doctest: +SKIP
-        >>> table_name = uuid.uuid4().hex
-        >>> schema = pa.schema(
-        ...     [
-        ...             pa.field("id", pa.uint32(), False),
-        ...            pa.field("vector", vector(128), False),
-        ...             pa.field("s", pa.string(), False),
-        ...     ]
+        New API (recommended):
+
+        >>> table.create_index(  # doctest: +SKIP
+        ...     "vector", config=IvfPq(distance_type="l2")
         ... )
-        >>> table = db.create_table( # doctest: +SKIP
-        ...     table_name, # doctest: +SKIP
-        ...     schema=schema, # doctest: +SKIP
+        >>> table.create_index("category", config=BTree())  # doctest: +SKIP
+        >>> table.create_index("content", config=FTS())  # doctest: +SKIP
+
+        Legacy API (deprecated):
+
+        >>> table.create_index(  # doctest: +SKIP
+        ...     "l2", vector_column_name="vector"
         ... )
-        >>> table.create_index("l2", "vector") # doctest: +SKIP
         """
+        # Detect whether this is a legacy API call
+        is_legacy = self._is_legacy_create_index_call(
+            metric,
+            config,
+            num_partitions,
+            num_sub_vectors,
+            vector_column_name,
+            accelerator,
+            index_cache_size,
+            replace,
+        )
 
-        if accelerator is not None:
-            logging.warning(
-                "GPU accelerator is not yet supported on LanceDB cloud."
-                "If you have 100M+ vectors to index,"
-                "please contact us at contact@lancedb.com"
-            )
-        if replace is not None:
-            logging.warning(
-                "replace is not supported on LanceDB cloud."
-                "Existing indexes will always be replaced."
+        if is_legacy:
+            warnings.warn(
+                "The create_index() API with metric/num_partitions parameters is "
+                "deprecated and will be removed in a future version. "
+                "Please migrate to the new unified API:\n"
+                "  # Old (deprecated):\n"
+                "  table.create_index('l2', vector_column_name='my_vector')\n"
+                "  # New (recommended):\n"
+                "  table.create_index('my_vector', config=IvfPq(distance_type='l2'))",
+                DeprecationWarning,
+                stacklevel=2,
             )
 
-        index_type = index_type.upper()
-        if index_type == "VECTOR" or index_type == "IVF_PQ":
-            config = IvfPq(
-                distance_type=metric,
-                num_partitions=num_partitions,
-                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":
-            raise ValueError(
-                "IVF_HNSW_PQ is not supported on LanceDB cloud."
-                "Please use IVF_HNSW_SQ instead."
-            )
-        elif index_type == "IVF_HNSW_SQ":
-            config = HnswSq(distance_type=metric, num_partitions=num_partitions)
-        elif index_type == "IVF_HNSW_FLAT":
-            config = HnswFlat(distance_type=metric, num_partitions=num_partitions)
-        elif index_type == "IVF_FLAT":
-            config = IvfFlat(distance_type=metric, num_partitions=num_partitions)
+            column = vector_column_name
+
+            if accelerator is not None:
+                logging.warning(
+                    "GPU accelerator is not yet supported on LanceDB cloud."
+                    "If you have 100M+ vectors to index,"
+                    "please contact us at contact@lancedb.com"
+                )
+            if replace is not None:
+                logging.warning(
+                    "replace is not supported on LanceDB cloud."
+                    "Existing indexes will always be replaced."
+                )
+
+            idx_type = index_type.upper()
+            if idx_type == "VECTOR" or idx_type == "IVF_PQ":
+                config = IvfPq(
+                    distance_type=metric,
+                    num_partitions=num_partitions,
+                    num_sub_vectors=num_sub_vectors,
+                    num_bits=num_bits,
+                )
+            elif idx_type == "IVF_RQ":
+                config = IvfRq(
+                    distance_type=metric,
+                    num_partitions=num_partitions,
+                    num_bits=num_bits,
+                )
+            elif idx_type == "IVF_SQ":
+                config = IvfSq(distance_type=metric, num_partitions=num_partitions)
+            elif idx_type == "IVF_HNSW_PQ":
+                raise ValueError(
+                    "IVF_HNSW_PQ is not supported on LanceDB cloud."
+                    "Please use IVF_HNSW_SQ instead."
+                )
+            elif idx_type == "IVF_HNSW_SQ":
+                config = HnswSq(distance_type=metric, num_partitions=num_partitions)
+            elif idx_type == "IVF_HNSW_FLAT":
+                config = HnswFlat(distance_type=metric, num_partitions=num_partitions)
+            elif idx_type == "IVF_FLAT":
+                config = IvfFlat(distance_type=metric, num_partitions=num_partitions)
+            else:
+                raise ValueError(
+                    f"Unknown vector index type: {idx_type}. Valid options are"
+                    " 'IVF_FLAT', 'IVF_PQ', 'IVF_RQ', 'IVF_SQ',"
+                    " 'IVF_HNSW_PQ', 'IVF_HNSW_SQ', 'IVF_HNSW_FLAT'"
+                )
         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_HNSW_FLAT'"
-            )
+            column = metric
 
         LOOP.run(
             self._table.create_index(
-                vector_column_name,
+                column,
                 config=config,
                 wait_timeout=wait_timeout,
                 name=name,
@@ -307,6 +474,37 @@ class RemoteTable(Table):
             )
         )
 
+    def _is_legacy_create_index_call(
+        self,
+        first_arg: str,
+        config: Optional[IndexConfigType],
+        num_partitions: Optional[int],
+        num_sub_vectors: Optional[int],
+        vector_column_name: str,
+        accelerator: Optional[str],
+        index_cache_size: Optional[int],
+        replace: Optional[bool],
+    ) -> bool:
+        """Detect if this is a legacy create_index call."""
+        if config is not None:
+            return False
+        if any(
+            x is not None
+            for x in (
+                num_partitions,
+                num_sub_vectors,
+                accelerator,
+                index_cache_size,
+                replace,
+            )
+        ):
+            return True
+        if vector_column_name != VECTOR_COLUMN_NAME:
+            return True
+        if first_arg.lower() in KNOWN_METRICS:
+            return True
+        return False
+
     def add(
         self,
         data: DATA,
@@ -655,6 +853,22 @@ class RemoteTable(Table):
     def drop_columns(self, columns: Iterable[str]) -> DropColumnsResult:
         return LOOP.run(self._table.drop_columns(columns))
 
+    def set_unenforced_primary_key(self, columns: Union[str, Iterable[str]]) -> None:
+        """Not supported on LanceDB Cloud."""
+        return LOOP.run(self._table.set_unenforced_primary_key(columns))
+
+    def set_lsm_write_spec(self, spec: "LsmWriteSpec") -> None:
+        """Not supported on LanceDB Cloud."""
+        return LOOP.run(self._table.set_lsm_write_spec(spec))
+
+    def unset_lsm_write_spec(self) -> None:
+        """Not supported on LanceDB Cloud."""
+        return LOOP.run(self._table.unset_lsm_write_spec())
+
+    def close_lsm_writers(self) -> None:
+        """No-op on LanceDB Cloud (no local shard writers)."""
+        return LOOP.run(self._table.close_lsm_writers())
+
     def drop_index(self, index_name: str):
         return LOOP.run(self._table.drop_index(index_name))
 

python/python/lancedb/rerankers/linear_combination.py

@@ -102,8 +102,15 @@ class LinearCombinationReranker(Reranker):
 
         combined_list = []
         for row_id, result in results.items():
+            # Convert vector distance to a relevance score in [0, 1] where
+            # higher is better.  Missing vector entries are penalised with
+            # `_invert_score(fill)` = 1 - fill (= 0.0 for the default fill=1).
             vector_score = self._invert_score(result.get("_distance", fill))
-            fts_score = result.get("_score", fill)
+            # FTS scores (BM25) are already in a "higher = more relevant" space.
+            # Missing FTS entries are penalised symmetrically: we use
+            # `1 - fill` so that the same `fill` value drives both missing-vector
+            # and missing-FTS penalties in the same direction.
+            fts_score = result.get("_score", 1 - fill)
             result["_relevance_score"] = self._combine_score(vector_score, fts_score)
             combined_list.append(result)
 
@@ -123,8 +130,12 @@ class LinearCombinationReranker(Reranker):
         return tbl
 
     def _combine_score(self, vector_score, fts_score):
-        # these scores represent distance
-        return 1 - (self.weight * vector_score + (1 - self.weight) * fts_score)
+        # Both vector_score (inverted distance) and fts_score are in a
+        # "higher = more relevant" space.  A straight weighted average gives
+        # higher _relevance_score to better matches, as expected.
+        # Previously this returned `1 - (...)` which inverted the final
+        # ranking so that the *least* relevant document ranked first.
+        return self.weight * vector_score + (1 - self.weight) * fts_score
 
     def _invert_score(self, dist: float):
         # Invert the score between relevance and distance

python/python/lancedb/table.py

@@ -87,6 +87,20 @@ from .util import (
 )
 from .index import lang_mapping
 
+BlobMode = Literal["lazy", "bytes", "descriptions"]
+
+
+def _field_is_blob(field: pa.Field) -> bool:
+    metadata = field.metadata or {}
+    return metadata.get(b"lance-encoding:blob") == b"true" or (
+        metadata.get("lance-encoding:blob") == "true"
+    )
+
+
+def _schema_has_blob_field(schema: pa.Schema) -> bool:
+    return any(_field_is_blob(field) for field in schema)
+
+
 _MODEL_BACKED_TOKENIZER_PREFIXES = ("jieba", "lindera")
 _MODEL_BACKED_TOKENIZER_ERRORS = (
     "unknown base tokenizer",
@@ -154,6 +168,7 @@ if TYPE_CHECKING:
         AlterColumnsResult,
         DeleteResult,
         DropColumnsResult,
+        LsmWriteSpec,
         MergeResult,
         UpdateResult,
     )
@@ -171,6 +186,24 @@ if TYPE_CHECKING:
         DistanceType,
     )
 
+# Type alias for index configuration objects
+IndexConfigType = Union[
+    IvfFlat,
+    IvfPq,
+    IvfSq,
+    IvfRq,
+    HnswFlat,
+    HnswPq,
+    HnswSq,
+    BTree,
+    Bitmap,
+    LabelList,
+    FTS,
+]
+
+# Known distance metrics for legacy API detection
+KNOWN_METRICS = {"l2", "cosine", "dot", "hamming"}
+
 
 def _into_pyarrow_reader(
     data, schema: Optional[pa.Schema] = None
@@ -759,14 +792,22 @@ class Table(ABC):
         """
         raise NotImplementedError
 
-    def to_pandas(self) -> "pandas.DataFrame":
+    def to_pandas(self, blob_mode: BlobMode = "lazy", **kwargs) -> "pandas.DataFrame":
         """Return the table as a pandas DataFrame.
 
+        Parameters
+        ----------
+        blob_mode: str, default "lazy"
+            Controls how blob columns are returned for backends that support
+            Lance blob-aware pandas conversion.
+        **kwargs
+            Forwarded to PyArrow / Lance pandas conversion.
+
         Returns
         -------
         pd.DataFrame
         """
-        return self.to_arrow().to_pandas()
+        return self.to_arrow().to_pandas(**kwargs)
 
     @abstractmethod
     def to_arrow(self) -> pa.Table:
@@ -796,11 +837,49 @@ class Table(ABC):
         """
         raise NotImplementedError
 
+    # New unified API overload
+    @overload
     def create_index(
         self,
-        metric="l2",
-        num_partitions=256,
-        num_sub_vectors=96,
+        column: str,
+        /,
+        *,
+        config: IndexConfigType,
+        replace: bool = ...,
+        wait_timeout: Optional[timedelta] = ...,
+        name: Optional[str] = ...,
+        train: bool = ...,
+    ) -> None: ...
+
+    # Legacy API overload (deprecated)
+    @overload
+    def create_index(
+        self,
+        metric: Literal["l2", "cosine", "dot", "hamming"] = ...,
+        num_partitions: Optional[int] = ...,
+        num_sub_vectors: Optional[int] = ...,
+        vector_column_name: str = ...,
+        replace: bool = ...,
+        accelerator: Optional[str] = ...,
+        index_cache_size: Optional[int] = ...,
+        *,
+        index_type: VectorIndexType = ...,
+        wait_timeout: Optional[timedelta] = ...,
+        num_bits: int = ...,
+        max_iterations: int = ...,
+        sample_rate: int = ...,
+        m: int = ...,
+        ef_construction: int = ...,
+        name: Optional[str] = ...,
+        train: bool = ...,
+        target_partition_size: Optional[int] = ...,
+    ) -> None: ...
+
+    def create_index(
+        self,
+        metric: DistanceType = "l2",
+        num_partitions: Optional[int] = None,
+        num_sub_vectors: Optional[int] = None,
         vector_column_name: str = VECTOR_COLUMN_NAME,
         replace: bool = True,
         accelerator: Optional[str] = None,
@@ -813,46 +892,53 @@ class Table(ABC):
         sample_rate: int = 256,
         m: int = 20,
         ef_construction: int = 300,
+        config: Optional[IndexConfigType] = None,
         name: Optional[str] = None,
         train: bool = True,
         target_partition_size: Optional[int] = None,
     ):
-        """Create an index on the table.
+        """Create an index on a column.
+
+        This method supports both the new unified API and the legacy API
+        for backwards compatibility. The new API takes the column name as the
+        first positional argument and an index configuration object via
+        ``config``; the legacy API takes the distance metric as the first
+        argument plus separate ``vector_column_name`` / ``num_partitions`` /
+        etc. parameters, and emits a ``DeprecationWarning``.
 
         Parameters
         ----------
-        metric: str, default "l2"
-            The distance metric to use when creating the index.
-            Valid values are "l2", "cosine", "dot", or "hamming".
-            l2 is euclidean distance.
-            Hamming is available only for binary vectors.
-        num_partitions: int, default 256
-            The number of IVF partitions to use when creating the index.
-            Default is 256.
-        num_sub_vectors: int, default 96
-            The number of PQ sub-vectors to use when creating the index.
-            Default is 96.
-        vector_column_name: str, default "vector"
-            The vector column name to create the index.
-        replace: bool, default True
-            - If True, replace the existing index if it exists.
+        metric : str
+            For new API: the column name to index.
+            For legacy API: the distance metric ("l2", "cosine", "dot", "hamming").
+        config : IndexConfigType, optional
+            The index configuration object. If provided, uses the new unified API.
+            Can be one of: IvfFlat, IvfPq, IvfSq, IvfRq, HnswPq, HnswSq,
+            BTree, Bitmap, LabelList, FTS.
+        replace : bool, default True
+            Whether to replace an existing index on this column.
+        wait_timeout : timedelta, optional
+            Timeout to wait for async indexing to complete.
+        name : str, optional
+            Custom name for the index.
+        train : bool, default True
+            Whether to train the index with existing data.
 
-            - If False, raise an error if duplicate index exists.
-        accelerator: str, default None
-            If set, use the given accelerator to create the index.
-            Only support "cuda" for now.
-        index_cache_size : int, optional
-            The size of the index cache in number of entries. Default value is 256.
-        num_bits: int
-            The number of bits to encode sub-vectors. Only used with the IVF_PQ index.
-            Only 4 and 8 are supported.
-        wait_timeout: timedelta, optional
-            The timeout to wait if indexing is asynchronous.
-        name: str, optional
-            The name of the index. If not provided, a default name will be generated.
-        train: bool, default True
-            Whether to train the index with existing data. Vector indices always train
-            with existing data.
+        Examples
+        --------
+        New API (recommended):
+
+        >>> table.create_index(  # doctest: +SKIP
+        ...     "vector", config=IvfPq(distance_type="l2")
+        ... )
+        >>> table.create_index("category", config=BTree())  # doctest: +SKIP
+        >>> table.create_index("content", config=FTS())  # doctest: +SKIP
+
+        Legacy API (deprecated):
+
+        >>> table.create_index(  # doctest: +SKIP
+        ...     "l2", vector_column_name="vector"
+        ... )
         """
         raise NotImplementedError
 
@@ -1177,7 +1263,7 @@ class Table(ABC):
         ...      .when_not_matched_insert_all() \\
         ...      .execute(new_data)
         >>> res
-        MergeResult(version=2, num_updated_rows=2, num_inserted_rows=1, num_deleted_rows=0, num_attempts=1)
+        MergeResult(version=2, num_updated_rows=2, num_inserted_rows=1, num_deleted_rows=0, num_attempts=1, num_rows=3)
         >>> # The order of new rows is non-deterministic since we use
         >>> # a hash-join as part of this operation and so we sort here
         >>> table.to_arrow().sort_by("a").to_pandas()
@@ -2167,7 +2253,7 @@ class LanceTable(Table):
         return LOOP.run(self._table.count_rows(filter))
 
     def __repr__(self) -> str:
-        val = f"{self.__class__.__name__}(name={self.name!r}, version={self.version}"
+        val = f"{self.__class__.__name__}(name={self.name!r}"
         if self._conn.read_consistency_interval is not None:
             val += ", read_consistency_interval={!r}".format(
                 self._conn.read_consistency_interval
@@ -2182,14 +2268,31 @@ class LanceTable(Table):
         """Return the first n rows of the table."""
         return LOOP.run(self._table.head(n))
 
-    def to_pandas(self) -> "pd.DataFrame":
+    def to_pandas(self, blob_mode: BlobMode = "lazy", **kwargs) -> "pd.DataFrame":
         """Return the table as a pandas DataFrame.
 
+        Parameters
+        ----------
+        blob_mode: str, default "lazy"
+            Controls how Lance blob columns are returned.
+        **kwargs
+            Forwarded to Lance pandas conversion.
+
         Returns
         -------
         pd.DataFrame
         """
-        return self.to_arrow().to_pandas()
+        if blob_mode == "descriptions" or not _schema_has_blob_field(self.schema):
+            return self.to_arrow().to_pandas(**kwargs)
+
+        if (
+            blob_mode == "lazy"
+            and self._namespace_client is None
+            and get_uri_scheme(self._dataset_path) == "memory"
+        ):
+            return self.to_arrow().to_pandas(**kwargs)
+
+        return self.to_lance().to_pandas(blob_mode=blob_mode, **kwargs)
 
     def to_arrow(self) -> pa.Table:
         """Return the table as a pyarrow Table.
@@ -2226,11 +2329,51 @@ class LanceTable(Table):
             dataset, allow_pyarrow_filter=False, batch_size=batch_size
         )
 
+    # New unified API overload
+    @overload
     def create_index(
         self,
-        metric: DistanceType = "l2",
-        num_partitions=None,
-        num_sub_vectors=None,
+        column: str,
+        /,
+        *,
+        config: IndexConfigType,
+        replace: bool = ...,
+        wait_timeout: Optional[timedelta] = ...,
+        name: Optional[str] = ...,
+        train: bool = ...,
+    ) -> None: ...
+
+    # Legacy API overload (deprecated)
+    @overload
+    def create_index(
+        self,
+        metric: Literal["l2", "cosine", "dot", "hamming"] = ...,
+        num_partitions: Optional[int] = ...,
+        num_sub_vectors: Optional[int] = ...,
+        vector_column_name: str = ...,
+        replace: bool = ...,
+        accelerator: Optional[str] = ...,
+        index_cache_size: Optional[int] = ...,
+        num_bits: int = ...,
+        index_type: Literal[
+            "IVF_FLAT", "IVF_SQ", "IVF_PQ", "IVF_RQ", "IVF_HNSW_SQ", "IVF_HNSW_PQ"
+        ] = ...,
+        max_iterations: int = ...,
+        sample_rate: int = ...,
+        m: int = ...,
+        ef_construction: int = ...,
+        *,
+        wait_timeout: Optional[timedelta] = ...,
+        name: Optional[str] = ...,
+        train: bool = ...,
+        target_partition_size: Optional[int] = ...,
+    ) -> None: ...
+
+    def create_index(
+        self,
+        metric: str = "l2",
+        num_partitions: Optional[int] = None,
+        num_sub_vectors: Optional[int] = None,
         vector_column_name: str = VECTOR_COLUMN_NAME,
         replace: bool = True,
         accelerator: Optional[str] = None,
@@ -2250,47 +2393,232 @@ class LanceTable(Table):
         m: int = 20,
         ef_construction: int = 300,
         *,
+        config: Optional[IndexConfigType] = None,
+        wait_timeout: Optional[timedelta] = None,
         name: Optional[str] = None,
         train: bool = True,
         target_partition_size: Optional[int] = None,
     ):
-        """Create an index on the table."""
-        if accelerator is not None:
-            # accelerator is only supported through pylance.
-            self.to_lance().create_index(
-                column=vector_column_name,
-                index_type=index_type,
+        """Create an index on a column.
+
+        This method supports both the new unified API and the legacy API
+        for backwards compatibility. The new API takes the column name as the
+        first positional argument and an index configuration object via
+        ``config``; the legacy API takes the distance metric as the first
+        argument plus separate ``vector_column_name`` / ``num_partitions`` /
+        etc. parameters, and emits a ``DeprecationWarning``.
+
+        Parameters
+        ----------
+        metric : str
+            For new API: the column name to index.
+            For legacy API: the distance metric ("l2", "cosine", "dot", "hamming").
+        config : IndexConfigType, optional
+            The index configuration object. If provided, uses the new unified API.
+            Can be one of: IvfFlat, IvfPq, IvfSq, IvfRq, HnswPq, HnswSq,
+            BTree, Bitmap, LabelList, FTS.
+        replace : bool, default True
+            Whether to replace an existing index on this column.
+        wait_timeout : timedelta, optional
+            Timeout to wait for async indexing to complete.
+        name : str, optional
+            Custom name for the index.
+        train : bool, default True
+            Whether to train the index with existing data.
+
+        Examples
+        --------
+        New API (recommended):
+
+        >>> table.create_index(  # doctest: +SKIP
+        ...     "vector", config=IvfPq(distance_type="l2")
+        ... )
+        >>> table.create_index("category", config=BTree())  # doctest: +SKIP
+        >>> table.create_index("content", config=FTS())  # doctest: +SKIP
+
+        Legacy API (deprecated):
+
+        >>> table.create_index(  # doctest: +SKIP
+        ...     "l2", vector_column_name="vector"
+        ... )
+        """
+        # Detect whether this is a legacy API call
+        is_legacy = self._is_legacy_create_index_call(
+            metric,
+            config,
+            num_partitions,
+            num_sub_vectors,
+            vector_column_name,
+            accelerator,
+            index_cache_size,
+        )
+
+        if is_legacy:
+            warnings.warn(
+                "The create_index() API with metric/num_partitions parameters is "
+                "deprecated and will be removed in a future version. "
+                "Please migrate to the new unified API:\n"
+                "  # Old (deprecated):\n"
+                "  table.create_index('l2', vector_column_name='my_vector')\n"
+                "  # New (recommended):\n"
+                "  table.create_index('my_vector', config=IvfPq(distance_type='l2'))",
+                DeprecationWarning,
+                stacklevel=2,
+            )
+
+            # Legacy API: first arg is the distance metric
+            column = vector_column_name
+
+            # Build config from legacy parameters
+            config = self._build_vector_config_from_legacy_params(
                 metric=metric,
+                index_type=index_type,
                 num_partitions=num_partitions,
                 num_sub_vectors=num_sub_vectors,
-                replace=replace,
-                accelerator=accelerator,
-                index_cache_size=index_cache_size,
                 num_bits=num_bits,
+                max_iterations=max_iterations,
+                sample_rate=sample_rate,
                 m=m,
                 ef_construction=ef_construction,
                 target_partition_size=target_partition_size,
+                accelerator=accelerator,
             )
-            self.checkout_latest()
-            return
-        elif index_type == "IVF_FLAT":
-            config = IvfFlat(
+
+            # Handle accelerator through pylance
+            if accelerator is not None:
+                self.to_lance().create_index(
+                    column=column,
+                    index_type=index_type,
+                    metric=metric,
+                    num_partitions=num_partitions,
+                    num_sub_vectors=num_sub_vectors,
+                    replace=replace,
+                    accelerator=accelerator,
+                    index_cache_size=index_cache_size,
+                    num_bits=num_bits,
+                    m=m,
+                    ef_construction=ef_construction,
+                    target_partition_size=target_partition_size,
+                )
+                self.checkout_latest()
+                return
+        else:
+            # New API: metric is the column name
+            column = metric
+
+            # Check if config has accelerator set and dispatch to pylance
+            if config is not None and hasattr(config, "accelerator"):
+                acc = getattr(config, "accelerator", None)
+                if acc is not None:
+                    # Dispatch to pylance for GPU acceleration
+                    index_type_map = {
+                        "IvfFlat": "IVF_FLAT",
+                        "IvfSq": "IVF_SQ",
+                        "IvfPq": "IVF_PQ",
+                        "IvfRq": "IVF_RQ",
+                        "HnswPq": "IVF_HNSW_PQ",
+                        "HnswSq": "IVF_HNSW_SQ",
+                    }
+                    cfg_type = type(config).__name__
+                    lance_index_type = index_type_map.get(cfg_type, "IVF_PQ")
+
+                    self.to_lance().create_index(
+                        column=column,
+                        index_type=lance_index_type,
+                        metric=getattr(config, "distance_type", "l2"),
+                        num_partitions=getattr(config, "num_partitions", None),
+                        num_sub_vectors=getattr(config, "num_sub_vectors", None),
+                        replace=replace,
+                        accelerator=acc,
+                        num_bits=getattr(config, "num_bits", 8),
+                        m=getattr(config, "m", 20),
+                        ef_construction=getattr(config, "ef_construction", 300),
+                        target_partition_size=getattr(
+                            config, "target_partition_size", None
+                        ),
+                    )
+                    self.checkout_latest()
+                    return
+
+        return LOOP.run(
+            self._table.create_index(
+                column,
+                replace=replace,
+                config=config,
+                wait_timeout=wait_timeout,
+                name=name,
+                train=train,
+            )
+        )
+
+    def _is_legacy_create_index_call(
+        self,
+        first_arg: str,
+        config: Optional[IndexConfigType],
+        num_partitions: Optional[int],
+        num_sub_vectors: Optional[int],
+        vector_column_name: str,
+        accelerator: Optional[str],
+        index_cache_size: Optional[int],
+    ) -> bool:
+        """Detect if this is a legacy create_index call."""
+        # If config is provided, it's definitely the new API
+        if config is not None:
+            return False
+
+        # If old-style parameters were explicitly set, it's legacy
+        if any(
+            x is not None
+            for x in (num_partitions, num_sub_vectors, accelerator, index_cache_size)
+        ):
+            return True
+
+        # If vector_column_name differs from default, it's legacy
+        if vector_column_name != VECTOR_COLUMN_NAME:
+            return True
+
+        # If first arg is a known metric, assume legacy
+        if first_arg.lower() in KNOWN_METRICS:
+            return True
+
+        # Otherwise assume new API
+        return False
+
+    def _build_vector_config_from_legacy_params(
+        self,
+        metric: str,
+        index_type: str,
+        num_partitions: Optional[int],
+        num_sub_vectors: Optional[int],
+        num_bits: int,
+        max_iterations: int,
+        sample_rate: int,
+        m: int,
+        ef_construction: int,
+        target_partition_size: Optional[int],
+        accelerator: Optional[str],
+    ) -> IndexConfigType:
+        """Build an index config object from legacy parameters."""
+        if index_type == "IVF_FLAT":
+            return IvfFlat(
                 distance_type=metric,
                 num_partitions=num_partitions,
                 max_iterations=max_iterations,
                 sample_rate=sample_rate,
                 target_partition_size=target_partition_size,
+                accelerator=accelerator,
             )
         elif index_type == "IVF_SQ":
-            config = IvfSq(
+            return IvfSq(
                 distance_type=metric,
                 num_partitions=num_partitions,
                 max_iterations=max_iterations,
                 sample_rate=sample_rate,
                 target_partition_size=target_partition_size,
+                accelerator=accelerator,
             )
         elif index_type == "IVF_PQ":
-            config = IvfPq(
+            return IvfPq(
                 distance_type=metric,
                 num_partitions=num_partitions,
                 num_sub_vectors=num_sub_vectors,
@@ -2298,18 +2626,20 @@ class LanceTable(Table):
                 max_iterations=max_iterations,
                 sample_rate=sample_rate,
                 target_partition_size=target_partition_size,
+                accelerator=accelerator,
             )
         elif index_type == "IVF_RQ":
-            config = IvfRq(
+            return IvfRq(
                 distance_type=metric,
                 num_partitions=num_partitions,
                 num_bits=num_bits,
                 max_iterations=max_iterations,
                 sample_rate=sample_rate,
                 target_partition_size=target_partition_size,
+                accelerator=accelerator,
             )
         elif index_type == "IVF_HNSW_PQ":
-            config = HnswPq(
+            return HnswPq(
                 distance_type=metric,
                 num_partitions=num_partitions,
                 num_sub_vectors=num_sub_vectors,
@@ -2319,9 +2649,10 @@ class LanceTable(Table):
                 m=m,
                 ef_construction=ef_construction,
                 target_partition_size=target_partition_size,
+                accelerator=accelerator,
             )
         elif index_type == "IVF_HNSW_SQ":
-            config = HnswSq(
+            return HnswSq(
                 distance_type=metric,
                 num_partitions=num_partitions,
                 max_iterations=max_iterations,
@@ -2329,9 +2660,10 @@ class LanceTable(Table):
                 m=m,
                 ef_construction=ef_construction,
                 target_partition_size=target_partition_size,
+                accelerator=accelerator,
             )
         elif index_type == "IVF_HNSW_FLAT":
-            config = HnswFlat(
+            return HnswFlat(
                 distance_type=metric,
                 num_partitions=num_partitions,
                 max_iterations=max_iterations,
@@ -2343,16 +2675,6 @@ class LanceTable(Table):
         else:
             raise ValueError(f"Unknown index type {index_type}")
 
-        return LOOP.run(
-            self._table.create_index(
-                vector_column_name,
-                replace=replace,
-                config=config,
-                name=name,
-                train=train,
-            )
-        )
-
     def drop_index(self, name: str) -> None:
         """
         Drops an index from the table
@@ -2452,6 +2774,11 @@ class LanceTable(Table):
         """
         return LOOP.run(self._table.latest_storage_options())
 
+    @deprecation.deprecated(
+        deprecated_in="0.25.0",
+        current_version=__version__,
+        details="Use create_index() with config=BTree()/Bitmap()/LabelList() instead.",
+    )
     def create_scalar_index(
         self,
         column: str,
@@ -2460,6 +2787,12 @@ class LanceTable(Table):
         index_type: ScalarIndexType = "BTREE",
         name: Optional[str] = None,
     ):
+        """Create a scalar index on a column.
+
+        .. deprecated:: 0.25.0
+            Use :meth:`create_index` with a BTree, Bitmap, or LabelList config instead.
+            Example: ``table.create_index("column", config=BTree())``
+        """
         if index_type == "BTREE":
             config = BTree()
         elif index_type == "BITMAP":
@@ -2472,6 +2805,11 @@ class LanceTable(Table):
             self._table.create_index(column, replace=replace, config=config, name=name)
         )
 
+    @deprecation.deprecated(
+        deprecated_in="0.25.0",
+        current_version=__version__,
+        details="Use create_index() with config=FTS() instead.",
+    )
     def create_fts_index(
         self,
         field_names: Union[str, List[str]],
@@ -2495,6 +2833,12 @@ class LanceTable(Table):
         prefix_only: bool = False,
         name: Optional[str] = None,
     ):
+        """Create a full-text search index on a column.
+
+        .. deprecated:: 0.25.0
+            Use :meth:`create_index` with an FTS config instead.
+            Example: ``table.create_index("text_column", config=FTS())``
+        """
         self._ensure_no_legacy_fts_index()
 
         if use_tantivy:
@@ -2518,11 +2862,6 @@ class LanceTable(Table):
                 "at a time. To search over multiple text fields, create a "
                 "separate FTS index for each field."
             )
-        if "." in field_names:
-            raise ValueError(
-                "Native FTS indexes can only be created on top-level fields. "
-                f"Received nested field path: {field_names!r}."
-            )
 
         if tokenizer_name is None:
             tokenizer_configs = {
@@ -3263,6 +3602,26 @@ class LanceTable(Table):
     def drop_columns(self, columns: Iterable[str]) -> DropColumnsResult:
         return LOOP.run(self._table.drop_columns(columns))
 
+    def set_unenforced_primary_key(self, columns: Union[str, Iterable[str]]) -> None:
+        """Set the unenforced primary key. See
+        [`AsyncTable.set_unenforced_primary_key`][lancedb.AsyncTable.set_unenforced_primary_key]."""
+        return LOOP.run(self._table.set_unenforced_primary_key(columns))
+
+    def set_lsm_write_spec(self, spec: "LsmWriteSpec") -> None:
+        """Install an LsmWriteSpec. See
+        [`AsyncTable.set_lsm_write_spec`][lancedb.AsyncTable.set_lsm_write_spec]."""
+        return LOOP.run(self._table.set_lsm_write_spec(spec))
+
+    def unset_lsm_write_spec(self) -> None:
+        """Remove the LsmWriteSpec. See
+        [`AsyncTable.unset_lsm_write_spec`][lancedb.AsyncTable.unset_lsm_write_spec]."""
+        return LOOP.run(self._table.unset_lsm_write_spec())
+
+    def close_lsm_writers(self) -> None:
+        """Close cached MemWAL shard writers. See
+        [`AsyncTable.close_lsm_writers`][lancedb.AsyncTable.close_lsm_writers]."""
+        return LOOP.run(self._table.close_lsm_writers())
+
     def uses_v2_manifest_paths(self) -> bool:
         """
         Check if the table is using the new v2 manifest paths.
@@ -3808,6 +4167,79 @@ class AsyncTable:
         Any attempt to use the table after it has been closed will raise an error."""
         return self._inner.close()
 
+    async def set_unenforced_primary_key(
+        self, columns: Union[str, Iterable[str]]
+    ) -> None:
+        """Set the unenforced primary key for this table to the given
+        ordered list of columns.
+
+        "Unenforced" means LanceDB does not check uniqueness on writes; the
+        columns are recorded in the schema as the primary key so that
+        features such as `merge_insert` can use them. Calling this again
+        replaces any previously-set primary key.
+
+        Parameters
+        ----------
+        columns : str or Iterable[str]
+            Either a single column name (single-column key) or an ordered
+            iterable of column names (composite key). Each column dtype
+            must be one of: int32, int64, utf8, large_utf8, binary,
+            large_binary, fixed_size_binary.
+        """
+        if isinstance(columns, str):
+            columns = [columns]
+        else:
+            columns = list(columns)
+        await self._inner.set_unenforced_primary_key(columns)
+
+    async def set_lsm_write_spec(self, spec: "LsmWriteSpec") -> None:
+        """Install an LsmWriteSpec on this table.
+
+        The spec selects Lance's MemWAL LSM-style write path for future
+        `merge_insert` calls. ``LsmWriteSpec`` chooses one of three sharding
+        strategies:
+
+        - ``LsmWriteSpec.bucket(column, num_buckets)`` — hash-bucket writes by
+          the single-column unenforced primary key.
+        - ``LsmWriteSpec.identity(column)`` — shard by the raw value of a
+          scalar column.
+        - ``LsmWriteSpec.unsharded()`` — route every write to a single shard.
+
+        All variants require the table to have an unenforced primary key set
+        via [`set_unenforced_primary_key`]; bucket sharding additionally
+        requires it to be the single column being bucketed.
+
+        Parameters
+        ----------
+        spec : LsmWriteSpec
+            The sharding spec to install.
+
+        Examples
+        --------
+        >>> from lancedb._lancedb import LsmWriteSpec
+        >>> # table.set_unenforced_primary_key("id")
+        >>> # table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 16))
+        """
+        await self._inner.set_lsm_write_spec(spec)
+
+    async def unset_lsm_write_spec(self) -> None:
+        """Remove the LsmWriteSpec from this table.
+
+        Reverts to the standard `merge_insert` write path. Errors if no spec
+        is currently set.
+        """
+        await self._inner.unset_lsm_write_spec()
+
+    async def close_lsm_writers(self) -> None:
+        """Drain and close any cached MemWAL shard writers for this table.
+
+        When an LSM write spec is installed, `merge_insert` opens MemWAL shard
+        writers and caches them for reuse across calls. This closes them,
+        flushing pending data; writers reopen lazily on the next
+        `merge_insert`. It is a no-op when no writers are cached.
+        """
+        await self._inner.close_lsm_writers()
+
     @property
     def name(self) -> str:
         """The name of the table."""
@@ -3864,16 +4296,46 @@ class AsyncTable:
         can be executed with methods like [to_arrow][lancedb.query.AsyncQuery.to_arrow],
         [to_pandas][lancedb.query.AsyncQuery.to_pandas] and more.
         """
-        return AsyncQuery(self._inner.query())
+        return AsyncQuery(self._inner.query(), self)
 
-    async def to_pandas(self) -> "pd.DataFrame":
+    async def _to_lance(self, **kwargs) -> lance.LanceDataset:
+        try:
+            import lance
+        except ImportError:
+            raise ImportError(
+                "The lance library is required to use this function. "
+                "Please install with `pip install pylance`."
+            )
+
+        return lance.dataset(
+            await self.uri(),
+            version=await self.version(),
+            storage_options=await self.latest_storage_options(),
+            **kwargs,
+        )
+
+    async def to_pandas(self, blob_mode: BlobMode = "lazy", **kwargs) -> "pd.DataFrame":
         """Return the table as a pandas DataFrame.
 
+        Parameters
+        ----------
+        blob_mode: str, default "lazy"
+            Controls how Lance blob columns are returned.
+        **kwargs
+            Forwarded to PyArrow / Lance pandas conversion.
+
         Returns
         -------
         pd.DataFrame
         """
-        return (await self.to_arrow()).to_pandas()
+        if blob_mode == "descriptions" or not _schema_has_blob_field(
+            await self.schema()
+        ):
+            return (await self.to_arrow()).to_pandas(**kwargs)
+
+        if blob_mode == "lazy" and get_uri_scheme(await self.uri()) == "memory":
+            return (await self.to_arrow()).to_pandas(**kwargs)
+        return (await self._to_lance()).to_pandas(blob_mode=blob_mode, **kwargs)
 
     async def to_arrow(self) -> pa.Table:
         """Return the table as a pyarrow Table.
@@ -4233,7 +4695,7 @@ class AsyncTable:
         ...      .when_not_matched_insert_all() \\
         ...      .execute(new_data)
         >>> res
-        MergeResult(version=2, num_updated_rows=2, num_inserted_rows=1, num_deleted_rows=0, num_attempts=1)
+        MergeResult(version=2, num_updated_rows=2, num_inserted_rows=1, num_deleted_rows=0, num_attempts=1, num_rows=3)
         >>> # The order of new rows is non-deterministic since we use
         >>> # a hash-join as part of this operation and so we sort here
         >>> table.to_arrow().sort_by("a").to_pandas()
@@ -4512,6 +4974,8 @@ class AsyncTable:
             async_query = async_query.fast_search()
         if query.with_row_id:
             async_query = async_query.with_row_id()
+        if query.order_by:
+            async_query = async_query.order_by(query.order_by)
 
         if query.vector:
             async_query = async_query.nearest_to(query.vector).distance_range(
@@ -4611,6 +5075,8 @@ class AsyncTable:
                 when_not_matched_by_source_condition=merge._when_not_matched_by_source_condition,
                 timeout=merge._timeout,
                 use_index=merge._use_index,
+                use_lsm_write=merge._use_lsm_write,
+                validate_single_shard=merge._validate_single_shard,
             ),
         )
 
@@ -4904,7 +5370,7 @@ class AsyncTable:
         pa.RecordBatch
             A record batch containing the rows at the given offsets.
         """
-        return AsyncTakeQuery(self._inner.take_offsets(offsets))
+        return AsyncTakeQuery(self._inner.take_offsets(offsets), self)
 
     def take_row_ids(self, row_ids: list[int]) -> AsyncTakeQuery:
         """
@@ -4933,7 +5399,7 @@ class AsyncTable:
         AsyncTakeQuery
             A query object that can be executed to get the rows.
         """
-        return AsyncTakeQuery(self._inner.take_row_ids(row_ids))
+        return AsyncTakeQuery(self._inner.take_row_ids(row_ids), self)
 
     @property
     def tags(self) -> AsyncTags:

python/python/lancedb/util.py

@@ -10,7 +10,7 @@ import pathlib
 import warnings
 from datetime import date, datetime
 from functools import singledispatch
-from typing import Tuple, Union, Optional, Any
+from typing import Tuple, Union, Optional, Any, List
 from urllib.parse import urlparse
 
 import numpy as np
@@ -189,7 +189,33 @@ def flatten_columns(tbl: pa.Table, flatten: Optional[Union[int, bool]] = None):
     return tbl
 
 
-def inf_vector_column_query(schema: pa.Schema) -> str:
+def _format_field_path(path: List[str]) -> str:
+    def format_segment(segment: str) -> str:
+        if all(char.isalnum() or char == "_" for char in segment):
+            return segment
+        return f"`{segment.replace('`', '``')}`"
+
+    return ".".join(format_segment(segment) for segment in path)
+
+
+def _iter_vector_columns(
+    field: pa.Field, path: List[str], dim: Optional[int] = None
+) -> List[str]:
+    field_path = [*path, field.name]
+    if is_vector_column(field.type):
+        vector_dim = infer_vector_column_dim(field.type)
+        if dim is None or vector_dim == dim:
+            return [_format_field_path(field_path)]
+        return []
+    if pa.types.is_struct(field.type):
+        columns = []
+        for idx in range(field.type.num_fields):
+            columns.extend(_iter_vector_columns(field.type.field(idx), field_path, dim))
+        return columns
+    return []
+
+
+def inf_vector_column_query(schema: pa.Schema, dim: Optional[int] = None) -> str:
     """
     Get the vector column name
 
@@ -202,26 +228,21 @@ def inf_vector_column_query(schema: pa.Schema) -> str:
     -------
     str: the vector column name.
     """
-    vector_col_name = ""
-    vector_col_count = 0
-    for field_name in schema.names:
-        field = schema.field(field_name)
-        if is_vector_column(field.type):
-            vector_col_count += 1
-            if vector_col_count > 1:
-                raise ValueError(
-                    "Schema has more than one vector column. "
-                    "Please specify the vector column name "
-                    "for vector search"
-                )
-            elif vector_col_count == 1:
-                vector_col_name = field_name
-    if vector_col_count == 0:
+    vector_col_names = []
+    for field in schema:
+        vector_col_names.extend(_iter_vector_columns(field, [], dim))
+    if len(vector_col_names) > 1:
+        raise ValueError(
+            "Schema has more than one vector column. "
+            "Please specify the vector column name "
+            f"for vector search. Candidates: {vector_col_names}"
+        )
+    if len(vector_col_names) == 0:
         raise ValueError(
             "There is no vector column in the data. "
             "Please specify the vector column name for vector search"
         )
-    return vector_col_name
+    return vector_col_names[0]
 
 
 def is_vector_column(data_type: pa.DataType) -> bool:
@@ -247,6 +268,29 @@ def is_vector_column(data_type: pa.DataType) -> bool:
     return False
 
 
+def infer_vector_column_dim(data_type: pa.DataType) -> Optional[int]:
+    if pa.types.is_fixed_size_list(data_type):
+        return data_type.list_size
+    if pa.types.is_list(data_type):
+        return infer_vector_column_dim(data_type.value_type)
+    return None
+
+
+def _query_vector_dim(query: Optional[Any]) -> Optional[int]:
+    if query is None:
+        return None
+    if isinstance(query, np.ndarray):
+        if query.ndim == 0:
+            return None
+        return query.shape[-1]
+    if isinstance(query, list) and query:
+        first = query[0]
+        if isinstance(first, (list, tuple, np.ndarray)):
+            return len(first)
+        return len(query)
+    return None
+
+
 def infer_vector_column_name(
     schema: pa.Schema,
     query_type: str,
@@ -262,7 +306,9 @@ def infer_vector_column_name(
 
     if query is not None or query_type == "hybrid":
         try:
-            vector_column_name = inf_vector_column_query(schema)
+            vector_column_name = inf_vector_column_query(
+                schema, dim=_query_vector_dim(query)
+            )
         except Exception as e:
             raise e
 

python/python/tests/docs/test_merge_insert.py

@@ -57,7 +57,7 @@ async def test_upsert_async(mem_db_async):
     await table.count_rows()  # 3
     res
     # MergeResult(version=2, num_updated_rows=1,
-    # num_inserted_rows=1, num_deleted_rows=0)
+    # num_inserted_rows=1, num_deleted_rows=0, num_rows=2)
     # --8<-- [end:upsert_basic_async]
     assert await table.count_rows() == 3
     assert res.version == 2
@@ -86,7 +86,7 @@ def test_insert_if_not_exists(mem_db):
     table.count_rows()  # 3
     res
     # MergeResult(version=2, num_updated_rows=0,
-    # num_inserted_rows=1, num_deleted_rows=0)
+    # num_inserted_rows=1, num_deleted_rows=0, num_rows=1)
     # --8<-- [end:insert_if_not_exists]
     assert table.count_rows() == 3
     assert res.version == 2
@@ -116,7 +116,7 @@ async def test_insert_if_not_exists_async(mem_db_async):
     await table.count_rows()  # 3
     res
     # MergeResult(version=2, num_updated_rows=0,
-    # num_inserted_rows=1, num_deleted_rows=0)
+    # num_inserted_rows=1, num_deleted_rows=0, num_rows=1)
     # --8<-- [end:insert_if_not_exists]
     assert await table.count_rows() == 3
     assert res.version == 2
@@ -150,7 +150,7 @@ def test_replace_range(mem_db):
     table.count_rows("doc_id = 1")  # 1
     res
     # MergeResult(version=2, num_updated_rows=1,
-    # num_inserted_rows=0, num_deleted_rows=1)
+    # num_inserted_rows=0, num_deleted_rows=1, num_rows=1)
     # --8<-- [end:insert_if_not_exists]
     assert table.count_rows("doc_id = 1") == 1
     assert res.version == 2
@@ -185,7 +185,7 @@ async def test_replace_range_async(mem_db_async):
     await table.count_rows("doc_id = 1")  # 1
     res
     # MergeResult(version=2, num_updated_rows=1,
-    # num_inserted_rows=0, num_deleted_rows=1)
+    # num_inserted_rows=0, num_deleted_rows=1, num_rows=1)
     # --8<-- [end:insert_if_not_exists]
     assert await table.count_rows("doc_id = 1") == 1
     assert res.version == 2

python/python/tests/models/lindera/ipadic/config.yml

@@ -1,4 +1,3 @@
 segmenter:
   mode: "normal"
-  dictionary:
-    path: "./python/tests/models/lindera/ipadic/main"
+  dictionary: "./python/tests/models/lindera/ipadic/main"

python/python/tests/test_db.py

@@ -6,6 +6,7 @@ import re
 import sys
 from datetime import timedelta
 import os
+from types import SimpleNamespace
 
 import lancedb
 import numpy as np
@@ -188,6 +189,43 @@ def test_table_names(tmp_db: lancedb.DBConnection):
     assert len(result) == 3
 
 
+def test_db_contains_and_len_include_all_table_name_pages(tmp_db: lancedb.DBConnection):
+    for idx in range(20):
+        tmp_db.create_table(f"table_{idx}", data=[{"id": idx}])
+
+    assert len(tmp_db) == 20
+    for idx in range(20):
+        assert f"table_{idx}" in tmp_db
+    assert "does_not_exist" not in tmp_db
+
+
+def test_db_contains_stops_after_matching_table_page(
+    tmp_db: lancedb.DBConnection, monkeypatch
+):
+    calls = []
+    pages = {
+        None: SimpleNamespace(tables=["table_0", "table_1"], page_token="next"),
+        "next": SimpleNamespace(tables=["table_2"], page_token=None),
+    }
+
+    def list_tables(*, page_token=None, **_kwargs):
+        calls.append(page_token)
+        return pages[page_token]
+
+    monkeypatch.setattr(tmp_db, "list_tables", list_tables)
+
+    assert "table_1" in tmp_db
+    assert calls == [None]
+
+    calls.clear()
+    assert "table_2" in tmp_db
+    assert calls == [None, "next"]
+
+    calls.clear()
+    assert len(tmp_db) == 3
+    assert calls == [None, "next"]
+
+
 @pytest.mark.asyncio
 async def test_table_names_async(tmp_path):
     db = lancedb.connect(tmp_path)
@@ -428,7 +466,8 @@ async def test_create_table_v2_manifest_paths_async(tmp_path):
     assert await tbl.uses_v2_manifest_paths()
     manifests_dir = tmp_path / "test_v2_manifest_paths.lance" / "_versions"
     for manifest in os.listdir(manifests_dir):
-        assert re.match(r"\d{20}\.manifest", manifest)
+        if manifest.endswith(".manifest"):
+            assert re.match(r"\d{20}\.manifest", manifest)
 
     # Start a table in V1 mode then migrate
     tbl = await db_no_v2_paths.create_table(
@@ -438,13 +477,15 @@ async def test_create_table_v2_manifest_paths_async(tmp_path):
     assert not await tbl.uses_v2_manifest_paths()
     manifests_dir = tmp_path / "test_v2_migration.lance" / "_versions"
     for manifest in os.listdir(manifests_dir):
-        assert re.match(r"\d\.manifest", manifest)
+        if manifest.endswith(".manifest"):
+            assert re.match(r"\d\.manifest", manifest)
 
     await tbl.migrate_manifest_paths_v2()
     assert await tbl.uses_v2_manifest_paths()
 
     for manifest in os.listdir(manifests_dir):
-        assert re.match(r"\d{20}\.manifest", manifest)
+        if manifest.endswith(".manifest"):
+            assert re.match(r"\d{20}\.manifest", manifest)
 
 
 @pytest.mark.asyncio
@@ -914,6 +955,29 @@ def test_local_namespace_operations(tmp_path):
     assert db.list_namespaces().namespaces == []
 
 
+def test_create_namespace_invalid_mode_raises(tmp_path):
+    """Unrecognized create namespace modes raise a clear error."""
+    db = lancedb.connect(tmp_path)
+    with pytest.raises(ValueError, match="Invalid create namespace mode"):
+        db.create_namespace(["child"], mode="frobnicate")
+
+
+def test_drop_namespace_invalid_mode_raises(tmp_path):
+    """Unrecognized drop namespace modes raise a clear error."""
+    db = lancedb.connect(tmp_path)
+    db.create_namespace(["child"])
+    with pytest.raises(ValueError, match="Invalid drop namespace mode"):
+        db.drop_namespace(["child"], mode="frobnicate")
+
+
+def test_drop_namespace_invalid_behavior_raises(tmp_path):
+    """Unrecognized drop namespace behaviors raise a clear error."""
+    db = lancedb.connect(tmp_path)
+    db.create_namespace(["child"])
+    with pytest.raises(ValueError, match="Invalid drop namespace behavior"):
+        db.drop_namespace(["child"], behavior="frobnicate")
+
+
 def test_clone_table_latest_version(tmp_path):
     """Test cloning a table with the latest version (default behavior)"""
     import os

python/python/tests/test_fts.py

@@ -29,6 +29,7 @@ from lancedb.query import (
     MultiMatchQuery,
     PhraseQuery,
     BooleanQuery,
+    ColumnOrdering,
     Occur,
     LanceFtsQueryBuilder,
 )
@@ -116,8 +117,7 @@ def lindera_ipadic(language_model_home):
     config_path.write_text(
         "segmenter:\n"
         '  mode: "normal"\n'
-        "  dictionary:\n"
-        f'    path: "{extracted_model.resolve().as_posix()}"\n',
+        f'  dictionary: "{extracted_model.resolve().as_posix()}"\n',
         encoding="utf-8",
     )
 
@@ -215,11 +215,12 @@ def test_reject_legacy_tantivy_index(table):
 
 @pytest.mark.parametrize("with_position", [True, False])
 def test_create_inverted_index(table, with_position):
-    table.create_fts_index(
-        "text",
-        with_position=with_position,
-        name="custom_fts_index",
-    )
+    with pytest.warns(DeprecationWarning, match="create_fts_index"):
+        table.create_fts_index(
+            "text",
+            with_position=with_position,
+            name="custom_fts_index",
+        )
     indices = table.list_indices()
     fts_indices = [i for i in indices if i.index_type == "FTS"]
     assert any(i.name == "custom_fts_index" for i in fts_indices)
@@ -500,6 +501,36 @@ async def test_search_fts_specify_column_async(async_table):
         pass
 
 
+def test_search_order_by_descending(table):
+    table.create_fts_index("text")
+    rows = (
+        table.search("puppy")
+        .order_by([ColumnOrdering(column_name="count", ascending=False)])
+        .limit(20)
+        .select(["text", "count"])
+        .to_list()
+    )
+
+    for r in rows:
+        assert "puppy" in r["text"]
+    assert sorted(rows, key=lambda x: x["count"], reverse=True) == rows
+
+
+def test_search_order_by_ascending(table):
+    table.create_fts_index("text")
+    rows = (
+        table.search("puppy")
+        .order_by([ColumnOrdering(column_name="count", ascending=True)])
+        .limit(20)
+        .select(["text", "count"])
+        .to_list()
+    )
+
+    for r in rows:
+        assert "puppy" in r["text"]
+    assert sorted(rows, key=lambda x: x["count"]) == rows
+
+
 def test_create_index_from_table(tmp_path, table):
     table.create_fts_index("text")
     df = table.search("puppy").limit(5).select(["text"]).to_pandas()
@@ -533,8 +564,111 @@ def test_create_index_multiple_columns(tmp_path, table):
 
 
 def test_nested_schema(tmp_path, table):
-    with pytest.raises(ValueError, match="top-level fields"):
-        table.create_fts_index("nested.text")
+    table.create_fts_index("nested.text", with_position=True)
+    indices = table.list_indices()
+    assert len(indices) == 1
+    assert indices[0].index_type == "FTS"
+    assert indices[0].columns == ["nested.text"]
+
+    results = (
+        table.search("puppy", query_type="fts", fts_columns="nested.text")
+        .limit(5)
+        .to_list()
+    )
+    assert len(results) > 0
+    assert all("puppy" in row["nested"]["text"] for row in results)
+
+    results = table.search(MatchQuery("puppy", "nested.text")).limit(5).to_list()
+    assert len(results) > 0
+    assert all("puppy" in row["nested"]["text"] for row in results)
+
+    phrase_results = (
+        table.search(PhraseQuery("puppy runs", "nested.text")).limit(5).to_list()
+    )
+    assert len(phrase_results) > 0
+    assert all("puppy runs" in row["nested"]["text"] for row in phrase_results)
+
+    hybrid_results = (
+        table.search(query_type="hybrid", fts_columns="nested.text")
+        .vector([0 for _ in range(128)])
+        .text("puppy")
+        .limit(5)
+        .to_list()
+    )
+    assert len(hybrid_results) > 0
+
+
+@pytest.mark.asyncio
+async def test_nested_schema_async(async_table):
+    await async_table.create_index("nested.text", config=FTS(with_position=True))
+    indices = await async_table.list_indices()
+    assert len(indices) == 1
+    assert indices[0].index_type == "FTS"
+    assert indices[0].columns == ["nested.text"]
+
+    results = await (
+        async_table.query()
+        .nearest_to_text("puppy", columns="nested.text")
+        .limit(5)
+        .to_list()
+    )
+    assert len(results) > 0
+    assert all("puppy" in row["nested"]["text"] for row in results)
+
+    results = await (
+        async_table.query()
+        .nearest_to_text(MatchQuery("puppy", "nested.text"))
+        .limit(5)
+        .to_list()
+    )
+    assert len(results) > 0
+    assert all("puppy" in row["nested"]["text"] for row in results)
+
+    phrase_results = await (
+        async_table.query()
+        .nearest_to_text(PhraseQuery("puppy runs", "nested.text"))
+        .limit(5)
+        .to_list()
+    )
+    assert len(phrase_results) > 0
+    assert all("puppy runs" in row["nested"]["text"] for row in phrase_results)
+
+    hybrid_results = await (
+        async_table.query()
+        .nearest_to([0 for _ in range(128)])
+        .nearest_to_text("puppy", columns="nested.text")
+        .limit(5)
+        .to_list()
+    )
+    assert len(hybrid_results) > 0
+
+
+def test_nested_schema_rejects_invalid_fts_fields(tmp_path):
+    db = ldb.connect(tmp_path)
+    data = pa.table(
+        {
+            "payload": pa.array(
+                [
+                    {"text": "puppy runs", "count": 1},
+                    {"text": "car drives", "count": 2},
+                ]
+            ),
+            "vector": pa.array(
+                [[0.1, 0.1], [0.2, 0.2]],
+                type=pa.list_(pa.float32(), list_size=2),
+            ),
+        }
+    )
+    table = db.create_table("test", data=data)
+
+    with pytest.raises(ValueError, match="FTS index cannot be created.*payload"):
+        table.create_fts_index("payload")
+
+    with pytest.raises(ValueError, match="FTS index cannot be created.*count"):
+        table.create_fts_index("payload.count")
+
+    with pytest.raises(ValueError, match="Field path `payload.missing` not found"):
+        table.create_fts_index("payload.missing")
 
 
 def test_search_index_with_filter(table):

python/python/tests/test_index.py

@@ -105,6 +105,46 @@ async def test_create_scalar_index(some_table: AsyncTable):
     assert len(indices) == 0
 
 
+@pytest.mark.asyncio
+async def test_create_nested_scalar_index_lists_canonical_paths(db_async):
+    metadata_type = pa.struct(
+        [
+            pa.field("user_id", pa.int32()),
+            pa.field("user.id", pa.int32()),
+        ]
+    )
+    data = pa.Table.from_arrays(
+        [
+            pa.array([1, 2, 3], type=pa.int32()),
+            pa.array(
+                [
+                    {"user_id": 10, "user.id": 100},
+                    {"user_id": 20, "user.id": 200},
+                    {"user_id": 30, "user.id": 300},
+                ],
+                type=metadata_type,
+            ),
+        ],
+        names=["user_id", "metadata"],
+    )
+    table = await db_async.create_table("nested_scalar_index", data)
+
+    await table.create_index("user_id", config=BTree(), name="top_user_id_idx")
+    await table.create_index(
+        "metadata.user_id", config=BTree(), name="nested_user_id_idx"
+    )
+    await table.create_index(
+        "metadata.`user.id`", config=BTree(), name="escaped_user_id_idx"
+    )
+
+    columns_by_name = {
+        index.name: index.columns for index in await table.list_indices()
+    }
+    assert columns_by_name["top_user_id_idx"] == ["user_id"]
+    assert columns_by_name["nested_user_id_idx"] == ["metadata.user_id"]
+    assert columns_by_name["escaped_user_id_idx"] == ["metadata.`user.id`"]
+
+
 @pytest.mark.asyncio
 async def test_create_fixed_size_binary_index(some_table: AsyncTable):
     await some_table.create_index("fsb", config=BTree())
@@ -122,12 +162,13 @@ async def test_create_bitmap_index(some_table: AsyncTable):
     await some_table.create_index("data", config=Bitmap())
     indices = await some_table.list_indices()
     assert len(indices) == 3
+    # list_indices returns indices in alphabetical order by name
     assert indices[0].index_type == "Bitmap"
-    assert indices[0].columns == ["id"]
+    assert indices[0].columns == ["data"]
     assert indices[1].index_type == "Bitmap"
-    assert indices[1].columns == ["is_active"]
+    assert indices[1].columns == ["id"]
     assert indices[2].index_type == "Bitmap"
-    assert indices[2].columns == ["data"]
+    assert indices[2].columns == ["is_active"]
 
     index_name = indices[0].name
     stats = await some_table.index_stats(index_name)

python/python/tests/test_lsm_write_spec.py

@@ -0,0 +1,138 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+"""Tests for installing and clearing an LsmWriteSpec via
+`Table.set_lsm_write_spec` / `Table.unset_lsm_write_spec`.
+"""
+
+from datetime import timedelta
+
+import lancedb
+import pyarrow as pa
+import pytest
+from lancedb._lancedb import LsmWriteSpec
+
+SCHEMA = pa.schema(
+    [
+        pa.field("id", pa.utf8(), nullable=False),
+        pa.field("v", pa.int32(), nullable=False),
+    ]
+)
+
+
+def _batch(ids, vs):
+    return pa.RecordBatch.from_arrays(
+        [pa.array(ids, type=pa.utf8()), pa.array(vs, type=pa.int32())],
+        schema=SCHEMA,
+    )
+
+
+def _reader(ids, vs):
+    return pa.RecordBatchReader.from_batches(SCHEMA, [_batch(ids, vs)])
+
+
+def _make_table(tmp_path):
+    db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
+    table = db.create_table("t", _reader(["seed"], [0]))
+    return db, table
+
+
+def test_set_lsm_write_spec_validates(tmp_path):
+    _db, table = _make_table(tmp_path)
+
+    # Out-of-range num_buckets.
+    with pytest.raises(Exception, match="num_buckets"):
+        table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 0))
+    with pytest.raises(Exception, match="num_buckets"):
+        table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 1025))
+
+    # Happy path then mutation rejected.
+    table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 4))
+    with pytest.raises(Exception, match="mutation"):
+        table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 8))
+
+
+def test_unset_lsm_write_spec(tmp_path):
+    _db, table = _make_table(tmp_path)
+
+    # unset errors when no spec is set.
+    with pytest.raises(Exception, match="no LSM write spec"):
+        table.unset_lsm_write_spec()
+
+    # Install a spec, then remove it; afterwards a fresh spec can be set.
+    table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 4))
+    table.unset_lsm_write_spec()
+    # A second unset errors — there is no spec left to remove.
+    with pytest.raises(Exception, match="no LSM write spec"):
+        table.unset_lsm_write_spec()
+    table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 8))
+
+
+def test_set_unsharded_spec(tmp_path):
+    _db, table = _make_table(tmp_path)
+    # Lance MemWAL still requires a primary key on the dataset; Unsharded
+    # just skips per-row hashing.
+    table.set_unenforced_primary_key("id")
+    table.set_lsm_write_spec(LsmWriteSpec.unsharded())
+    table.unset_lsm_write_spec()
+
+
+def test_lsm_write_spec_repr():
+    s = LsmWriteSpec.bucket("id", 4)
+    assert s.spec_type == "bucket"
+    assert s.column == "id"
+    assert s.num_buckets == 4
+    assert s.maintained_indexes == []
+    assert "bucket" in repr(s)
+    assert "id" in repr(s)
+    assert "4" in repr(s)
+
+    u = LsmWriteSpec.unsharded()
+    assert u.spec_type == "unsharded"
+    assert u.column is None
+    assert u.num_buckets is None
+    assert "unsharded" in repr(u)
+
+
+def test_lsm_write_spec_with_maintained_indexes():
+    s = LsmWriteSpec.bucket("id", 4).with_maintained_indexes(["idx_a", "idx_b"])
+    assert s.maintained_indexes == ["idx_a", "idx_b"]
+
+
+@pytest.mark.asyncio
+async def test_async_set_unset_lsm_write_spec(tmp_path):
+    db = await lancedb.connect_async(
+        tmp_path, read_consistency_interval=timedelta(seconds=0)
+    )
+    table = await db.create_table(
+        "t",
+        pa.RecordBatchReader.from_batches(SCHEMA, [_batch(["seed"], [0])]),
+    )
+
+    await table.set_unenforced_primary_key("id")
+    await table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 4))
+    await table.unset_lsm_write_spec()
+    # A second unset errors.
+    with pytest.raises(Exception, match="no LSM write spec"):
+        await table.unset_lsm_write_spec()
+
+
+def test_set_identity_spec(tmp_path):
+    _db, table = _make_table(tmp_path)
+    # Identity sharding still requires an unenforced primary key on the
+    # table; it shards by the raw value of the given column.
+    table.set_unenforced_primary_key("id")
+    table.set_lsm_write_spec(LsmWriteSpec.identity("v"))
+    table.unset_lsm_write_spec()
+
+
+def test_lsm_write_spec_identity_and_writer_config_defaults():
+    s = LsmWriteSpec.identity("v")
+    assert s.spec_type == "identity"
+    assert s.column == "v"
+    assert s.num_buckets is None
+    assert "identity" in repr(s)
+
+    s = s.with_writer_config_defaults({"durable_write": "false"})
+    assert s.writer_config_defaults == {"durable_write": "false"}
+    assert "durable_write" in repr(s)

python/python/tests/test_merge_insert_lsm.py

@@ -0,0 +1,196 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+"""Tests for the MemWAL LSM ``merge_insert`` dispatch."""
+
+from datetime import timedelta
+
+import lancedb
+import pyarrow as pa
+import pytest
+from lancedb._lancedb import LsmWriteSpec
+
+SCHEMA = pa.schema(
+    [
+        pa.field("id", pa.int64(), nullable=False),
+        pa.field("value", pa.int64(), nullable=False),
+    ]
+)
+
+REGION_SCHEMA = pa.schema(
+    [
+        pa.field("id", pa.int64(), nullable=False),
+        pa.field("region", pa.utf8(), nullable=False),
+    ]
+)
+
+
+def _reader(ids):
+    batch = pa.RecordBatch.from_arrays(
+        [
+            pa.array(ids, type=pa.int64()),
+            pa.array(list(range(len(ids))), type=pa.int64()),
+        ],
+        schema=SCHEMA,
+    )
+    return pa.RecordBatchReader.from_batches(SCHEMA, [batch])
+
+
+def _region_reader(rows):
+    batch = pa.RecordBatch.from_arrays(
+        [
+            pa.array([row[0] for row in rows], type=pa.int64()),
+            pa.array([row[1] for row in rows], type=pa.utf8()),
+        ],
+        schema=REGION_SCHEMA,
+    )
+    return pa.RecordBatchReader.from_batches(REGION_SCHEMA, [batch])
+
+
+def _bucket_table(tmp_path):
+    """A table with ``id`` as the primary key and a single-bucket LSM spec."""
+    db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
+    table = db.create_table("t", _reader([1, 2, 3]))
+    table.set_unenforced_primary_key("id")
+    # num_buckets = 1: every row routes to the single bucket.
+    table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 1))
+    return table
+
+
+def test_lsm_merge_insert_bucket(tmp_path):
+    table = _bucket_table(tmp_path)
+    # Empty `on` defaults to the primary key.
+    result = (
+        table.merge_insert([])
+        .when_matched_update_all()
+        .when_not_matched_insert_all()
+        .execute(_reader([3, 4, 5]))
+    )
+    # LSM path: rows go to the MemWAL, so only num_rows is populated.
+    assert result.num_rows == 3
+    assert result.version == 0
+    assert result.num_inserted_rows == 0
+    assert result.num_updated_rows == 0
+
+
+def test_lsm_merge_insert_unsharded(tmp_path):
+    db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
+    table = db.create_table("t", _reader([1, 2, 3]))
+    table.set_unenforced_primary_key("id")
+    table.set_lsm_write_spec(LsmWriteSpec.unsharded())
+    result = (
+        table.merge_insert("id")
+        .when_matched_update_all()
+        .when_not_matched_insert_all()
+        .execute(_reader([10, 11, 12, 13]))
+    )
+    assert result.num_rows == 4
+
+
+def test_lsm_merge_insert_identity(tmp_path):
+    db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
+    table = db.create_table("t", _region_reader([(1, "us"), (2, "us")]))
+    table.set_unenforced_primary_key("id")
+    table.set_lsm_write_spec(LsmWriteSpec.identity("region"))
+    # All rows share one identity value, so they route to one shard.
+    result = (
+        table.merge_insert([])
+        .when_matched_update_all()
+        .when_not_matched_insert_all()
+        .execute(_region_reader([(3, "us"), (4, "us")]))
+    )
+    assert result.num_rows == 2
+
+
+def test_lsm_merge_insert_use_lsm_write_false(tmp_path):
+    table = _bucket_table(tmp_path)  # rows id = 1, 2, 3
+    # use_lsm_write(False) opts out: the standard path runs and commits.
+    result = (
+        table.merge_insert("id")
+        .when_not_matched_insert_all()
+        .use_lsm_write(False)
+        .execute(_reader([3, 4, 5]))
+    )
+    assert result.num_inserted_rows == 2
+    assert table.count_rows() == 5
+
+
+def test_lsm_merge_insert_validate_single_shard_off(tmp_path):
+    table = _bucket_table(tmp_path)
+    result = (
+        table.merge_insert([])
+        .when_matched_update_all()
+        .when_not_matched_insert_all()
+        .validate_single_shard(False)
+        .execute(_reader([6, 7, 8]))
+    )
+    assert result.num_rows == 3
+
+
+def test_lsm_merge_insert_use_lsm_write_true_requires_spec(tmp_path):
+    # A table with a primary key but no LSM write spec installed.
+    db = lancedb.connect(tmp_path, read_consistency_interval=timedelta(seconds=0))
+    table = db.create_table("t", _reader([1, 2, 3]))
+    table.set_unenforced_primary_key("id")
+    with pytest.raises(Exception, match="use_lsm_write"):
+        (
+            table.merge_insert("id")
+            .when_matched_update_all()
+            .when_not_matched_insert_all()
+            .use_lsm_write(True)
+            .execute(_reader([4]))
+        )
+
+
+def test_lsm_merge_insert_rejects_on_not_primary_key(tmp_path):
+    table = _bucket_table(tmp_path)
+    with pytest.raises(Exception, match="primary key"):
+        (
+            table.merge_insert("value")
+            .when_matched_update_all()
+            .when_not_matched_insert_all()
+            .execute(_reader([1]))
+        )
+
+
+def test_lsm_merge_insert_rejects_non_upsert(tmp_path):
+    table = _bucket_table(tmp_path)
+    # Insert-only (no when_matched_update_all) is not the upsert shape.
+    with pytest.raises(Exception, match="upsert"):
+        table.merge_insert([]).when_not_matched_insert_all().execute(_reader([4]))
+
+
+def test_lsm_close_writers(tmp_path):
+    table = _bucket_table(tmp_path)
+    (
+        table.merge_insert([])
+        .when_matched_update_all()
+        .when_not_matched_insert_all()
+        .execute(_reader([7, 8]))
+    )
+    table.close_lsm_writers()
+    # The writer reopens lazily on the next merge_insert.
+    result = (
+        table.merge_insert([])
+        .when_matched_update_all()
+        .when_not_matched_insert_all()
+        .execute(_reader([9]))
+    )
+    assert result.num_rows == 1
+
+
+@pytest.mark.asyncio
+async def test_async_lsm_merge_insert(tmp_path):
+    db = await lancedb.connect_async(
+        tmp_path, read_consistency_interval=timedelta(seconds=0)
+    )
+    table = await db.create_table("t", _reader([1, 2, 3]))
+    await table.set_unenforced_primary_key("id")
+    await table.set_lsm_write_spec(LsmWriteSpec.bucket("id", 1))
+
+    builder = (
+        table.merge_insert([]).when_matched_update_all().when_not_matched_insert_all()
+    )
+    result = await builder.execute(_reader([3, 4, 5]))
+    assert result.num_rows == 3
+    await table.close_lsm_writers()

python/python/tests/test_namespace.py

@@ -76,6 +76,35 @@ class TestNamespaceConnection:
         assert len(result) == 0
         assert list(result.columns) == ["id", "vector", "text"]
 
+    def test_table_to_pandas_blob_lazy_through_namespace(self):
+        """Namespace-backed tables should use Lance blob-aware pandas conversion."""
+        pytest.importorskip("lance")
+        db = lancedb.connect_namespace("dir", {"root": self.temp_dir})
+        db.create_namespace(["test_ns"])
+        data = pa.table(
+            {
+                "id": pa.array([1, 2], pa.int64()),
+                "blob": pa.array([b"hello", b"world"], pa.large_binary()),
+            },
+            schema=pa.schema(
+                [
+                    pa.field("id", pa.int64()),
+                    pa.field(
+                        "blob",
+                        pa.large_binary(),
+                        metadata={"lance-encoding:blob": "true"},
+                    ),
+                ]
+            ),
+        )
+
+        table = db.create_table("blob_table", data, namespace_path=["test_ns"])
+        df = table.to_pandas(blob_mode="lazy").sort_values("id")
+
+        blob = df["blob"].iloc[0]
+        assert hasattr(blob, "readall")
+        assert blob.readall() == b"hello"
+
     def test_open_table_through_namespace(self):
         """Test opening an existing table through namespace."""
         db = lancedb.connect_namespace("dir", {"root": self.temp_dir})

python/python/tests/test_permutation.py

@@ -1080,3 +1080,29 @@ def test_getitems_invalid_offset(some_permutation: Permutation):
     """Test __getitems__ with an out-of-range offset raises an error."""
     with pytest.raises(Exception):
         some_permutation.__getitems__([999999])
+
+
+def test_take_offsets(some_permutation: Permutation):
+    result = some_permutation.take_offsets([0, 1, 2])
+
+    assert isinstance(result, list)
+    assert "id" in result[0]
+    assert "value" in result[0]
+    assert len(result) == 3
+
+
+def test_take_offsets_empty_identity_permutation(mem_db):
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(10), "value": range(10)})
+    )
+    permutation = Permutation.identity(tbl)
+
+    result = permutation.take_offsets([])
+
+    assert result == []
+
+
+def test_take_offsets_empty_permutation(some_permutation: Permutation):
+    result = some_permutation.take_offsets([])
+
+    assert result == []

python/python/tests/test_primary_key.py

@@ -0,0 +1,79 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+"""Tests for Table.set_unenforced_primary_key."""
+
+from datetime import timedelta
+
+import lancedb
+import pyarrow as pa
+import pytest
+
+
+def _empty_table(path, schema):
+    db = lancedb.connect(path, read_consistency_interval=timedelta(seconds=0))
+    return db.create_table("t", schema=schema)
+
+
+def test_set_unenforced_primary_key_accepts_string_or_one_element_list(tmp_path):
+    schema = pa.schema([pa.field("id", pa.int64(), nullable=False)])
+
+    # Bare string.
+    table = _empty_table(tmp_path / "s", schema)
+    table.set_unenforced_primary_key("id")
+
+    # One-element list.
+    table = _empty_table(tmp_path / "l", schema)
+    table.set_unenforced_primary_key(["id"])
+
+
+def test_set_unenforced_primary_key_rejects_compound_and_empty(tmp_path):
+    table = _empty_table(
+        tmp_path,
+        pa.schema(
+            [
+                pa.field("a", pa.utf8(), nullable=False),
+                pa.field("b", pa.int64(), nullable=False),
+            ]
+        ),
+    )
+    # Compound keys are not supported.
+    with pytest.raises(Exception, match="compound"):
+        table.set_unenforced_primary_key(["a", "b"])
+    # Empty input.
+    with pytest.raises(Exception, match="required"):
+        table.set_unenforced_primary_key([])
+
+
+def test_set_unenforced_primary_key_is_immutable(tmp_path):
+    table = _empty_table(
+        tmp_path,
+        pa.schema(
+            [
+                pa.field("a", pa.utf8(), nullable=False),
+                pa.field("b", pa.int64(), nullable=False),
+            ]
+        ),
+    )
+    table.set_unenforced_primary_key("a")
+    # The primary key cannot be changed or re-set once installed.
+    with pytest.raises(Exception, match="already set"):
+        table.set_unenforced_primary_key("b")
+    with pytest.raises(Exception, match="already set"):
+        table.set_unenforced_primary_key("a")
+
+
+def test_set_unenforced_primary_key_validates(tmp_path):
+    table = _empty_table(
+        tmp_path / "t", pa.schema([pa.field("id", pa.utf8(), nullable=False)])
+    )
+    # Unknown column.
+    with pytest.raises(Exception, match="not found"):
+        table.set_unenforced_primary_key("nonexistent")
+
+    # Unsupported dtype (Float32 not in the supported set).
+    bad = _empty_table(
+        tmp_path / "bad", pa.schema([pa.field("id", pa.float32(), nullable=False)])
+    )
+    with pytest.raises(Exception, match="not supported"):
+        bad.set_unenforced_primary_key("id")

python/python/tests/test_query.py

@@ -25,6 +25,7 @@ from lancedb.query import (
     AsyncHybridQuery,
     AsyncQueryBase,
     AsyncVectorQuery,
+    ColumnOrdering,
     LanceVectorQueryBuilder,
     MatchQuery,
     PhraseQuery,
@@ -38,6 +39,35 @@ from utils import exception_output
 from importlib.util import find_spec
 
 
+def _blob_query_data():
+    return pa.table(
+        {
+            "id": pa.array([1, 2, 3, 4], pa.int64()),
+            "tag": pa.array(["drop", "keep", "keep", "keep"], pa.utf8()),
+            "vector": pa.array(
+                [[1.0, 0.0], [2.0, 0.0], [3.0, 0.0], [4.0, 0.0]],
+                type=pa.list_(pa.float32(), list_size=2),
+            ),
+            "blob": pa.array([b"one", b"two", b"three", b"four"], pa.large_binary()),
+        },
+        schema=pa.schema(
+            [
+                pa.field("id", pa.int64()),
+                pa.field("tag", pa.utf8()),
+                pa.field("vector", pa.list_(pa.float32(), list_size=2)),
+                pa.field(
+                    "blob", pa.large_binary(), metadata={"lance-encoding:blob": "true"}
+                ),
+            ]
+        ),
+    )
+
+
+def _assert_lazy_blob(value, expected: bytes):
+    assert hasattr(value, "readall")
+    assert value.readall() == expected
+
+
 @pytest.fixture(scope="module")
 def table(tmpdir_factory) -> lancedb.table.Table:
     tmp_path = str(tmpdir_factory.mktemp("data"))
@@ -164,6 +194,178 @@ def test_offset(table):
     assert len(results_with_offset.to_pandas()) == 1
 
 
+@pytest.mark.asyncio
+async def test_query_to_pandas_kwargs(table, table_async):
+    sync_df = (
+        LanceVectorQueryBuilder(table, [0, 0], "vector")
+        .select(["id"])
+        .limit(1)
+        .to_pandas(split_blocks=True)
+    )
+    assert sync_df["id"].tolist() == [1]
+
+    async_df = await (
+        table_async.query().select(["id"]).limit(2).to_pandas(split_blocks=True)
+    )
+    assert async_df["id"].tolist() == [1, 2]
+
+
+@pytest.mark.parametrize("blob_mode", ["lazy", "bytes", "descriptions"])
+def test_plain_scan_query_to_pandas_blob_modes(tmp_db, blob_mode):
+    pytest.importorskip("lance")
+    table = tmp_db.create_table(
+        f"test_query_to_pandas_blob_{blob_mode}", _blob_query_data()
+    )
+
+    df = (
+        table.search()
+        .select(["id", "blob"])
+        .where("id = 1")
+        .to_pandas(blob_mode=blob_mode)
+    )
+
+    assert df["id"].tolist() == [1]
+    if blob_mode == "lazy":
+        _assert_lazy_blob(df["blob"].iloc[0], b"one")
+    elif blob_mode == "bytes":
+        assert df["blob"].tolist() == [b"one"]
+    else:
+        first = df["blob"].iloc[0]
+        assert first != b"one"
+        assert not hasattr(first, "readall")
+
+
+def test_plain_scan_query_to_pandas_blob_projection(tmp_db):
+    pytest.importorskip("lance")
+    table = tmp_db.create_table(
+        "test_query_to_pandas_blob_projection", _blob_query_data()
+    )
+
+    df = (
+        table.search()
+        .where("id >= 2")
+        .select({"id_alias": "id", "payload": "blob", "double_id": "id * 2"})
+        .limit(2)
+        .offset(1)
+        .to_pandas(blob_mode="bytes")
+    )
+
+    assert df["id_alias"].tolist() == [3, 4]
+    assert df["payload"].tolist() == [b"three", b"four"]
+    assert df["double_id"].tolist() == [6, 8]
+
+
+@pytest.mark.asyncio
+async def test_async_plain_scan_query_to_pandas_blob_projection(tmp_db_async):
+    pytest.importorskip("lance")
+    table = await tmp_db_async.create_table(
+        "test_async_query_to_pandas_blob_projection", _blob_query_data()
+    )
+
+    lazy_df = await (
+        table.query().where("id = 1").select(["id", "blob"]).to_pandas(blob_mode="lazy")
+    )
+    assert lazy_df["id"].tolist() == [1]
+    _assert_lazy_blob(lazy_df["blob"].iloc[0], b"one")
+
+    bytes_df = await (
+        table.query()
+        .where("id >= 2")
+        .select({"id_alias": "id", "payload": "blob", "double_id": "id * 2"})
+        .limit(2)
+        .offset(1)
+        .to_pandas(blob_mode="bytes")
+    )
+    assert bytes_df["id_alias"].tolist() == [3, 4]
+    assert bytes_df["payload"].tolist() == [b"three", b"four"]
+    assert bytes_df["double_id"].tolist() == [6, 8]
+
+    desc_df = await (
+        table.query()
+        .where("id = 1")
+        .select(["blob"])
+        .to_pandas(blob_mode="descriptions")
+    )
+    first = desc_df["blob"].iloc[0]
+    assert first != b"one"
+    assert not hasattr(first, "readall")
+
+
+def test_vector_query_to_pandas_blob_mode_requires_native_path(tmp_db):
+    pytest.importorskip("lance")
+    table = tmp_db.create_table("test_vector_query_blob_mode", _blob_query_data())
+
+    with pytest.raises(RuntimeError, match="Lance native pandas conversion"):
+        table.search([1.0, 0.0]).select(["blob", "vector"]).limit(1).to_pandas(
+            blob_mode="lazy"
+        )
+
+
+def test_order_by_plain_query(mem_db):
+    table = mem_db.create_table(
+        "test_order_by",
+        pa.table(
+            {
+                "group": [1, 1, 1, 2],
+                "score": [None, 1.0, 1.0, 0.5],
+                "name": ["z", "b", "a", "c"],
+            }
+        ),
+    )
+
+    res = (
+        table.search()
+        .order_by(
+            [
+                ColumnOrdering(column_name="group", ascending=True, nulls_first=False),
+                ColumnOrdering(column_name="score", ascending=True, nulls_first=True),
+                ColumnOrdering(column_name="name", ascending=True, nulls_first=False),
+            ]
+        )
+        .to_arrow()
+    )
+
+    assert res.select(["group", "score", "name"]).to_pylist() == [
+        {"group": 1, "score": None, "name": "z"},
+        {"group": 1, "score": 1.0, "name": "a"},
+        {"group": 1, "score": 1.0, "name": "b"},
+        {"group": 2, "score": 0.5, "name": "c"},
+    ]
+
+
+@pytest.mark.asyncio
+async def test_order_by_async_query(mem_db_async: AsyncConnection):
+    table = await mem_db_async.create_table(
+        "test_order_by_async",
+        pa.table(
+            {
+                "group": [1, 1, 1, 2],
+                "score": [None, 1.0, 1.0, 0.5],
+                "name": ["z", "b", "a", "c"],
+            }
+        ),
+    )
+
+    res = await (
+        table.query()
+        .order_by(
+            [
+                ColumnOrdering(column_name="group", ascending=True, nulls_first=False),
+                ColumnOrdering(column_name="score", ascending=True, nulls_first=True),
+                ColumnOrdering(column_name="name", ascending=True, nulls_first=False),
+            ]
+        )
+        .to_arrow()
+    )
+
+    assert res.select(["group", "score", "name"]).to_pylist() == [
+        {"group": 1, "score": None, "name": "z"},
+        {"group": 1, "score": 1.0, "name": "a"},
+        {"group": 1, "score": 1.0, "name": "b"},
+        {"group": 2, "score": 0.5, "name": "c"},
+    ]
+
+
 def test_query_builder(table):
     rs = (
         LanceVectorQueryBuilder(table, [0, 0], "vector")
@@ -1430,6 +1632,37 @@ def test_take_queries(tmp_path):
     ]
 
 
+def test_take_queries_to_batches(tmp_path):
+    # Regression test for the sync take-query path: `to_batches` previously
+    # raised ``AttributeError: 'AsyncTakeQuery' object has no attribute
+    # 'execute'`` because the inherited ``BaseQueryBuilder.to_batches`` called
+    # ``execute`` on the async wrapper instead of the native query.
+    db = lancedb.connect(tmp_path)
+    data = pa.table({"idx": list(range(100)), "label": [str(i) for i in range(100)]})
+    table = db.create_table("test", data)
+
+    # Take by offset → to_batches
+    rs = list(table.take_offsets([5, 2, 17]).to_batches())
+    assert all(isinstance(b, pa.RecordBatch) for b in rs)
+    assert sum(b.num_rows for b in rs) == 3
+    assert sorted(v for b in rs for v in b.column("idx").to_pylist()) == [2, 5, 17]
+
+    # Take by row id → to_batches
+    rs = list(table.take_row_ids([5, 2, 17]).to_batches())
+    assert all(isinstance(b, pa.RecordBatch) for b in rs)
+    assert sum(b.num_rows for b in rs) == 3
+    assert sorted(v for b in rs for v in b.column("idx").to_pylist()) == [2, 5, 17]
+
+    # Take with select projection → to_batches preserves the projection
+    rs = list(table.take_row_ids([5, 2, 17]).select(["label"]).to_batches())
+    assert all(b.schema.names == ["label"] for b in rs)
+    assert sorted(v for b in rs for v in b.column("label").to_pylist()) == [
+        "17",
+        "2",
+        "5",
+    ]
+
+
 def test_getitems(tmp_path):
     db = lancedb.connect(tmp_path)
     data = pa.table(

python/python/tests/test_remote_db.py

@@ -1,12 +1,13 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
-import re
 from concurrent.futures import ThreadPoolExecutor
 import contextlib
 from datetime import timedelta
 import http.server
 import json
 import multiprocessing as mp
+import pickle
+import re
 import sys
 import threading
 import time
@@ -16,6 +17,7 @@ from packaging.version import Version
 
 import lancedb
 from lancedb.conftest import MockTextEmbeddingFunction
+from lancedb.query import ColumnOrdering
 from lancedb.remote import ClientConfig
 from lancedb.remote.errors import HttpError, RetryError
 import pytest
@@ -170,6 +172,155 @@ def test_table_len_sync():
         assert len(table) == 1
 
 
+def test_remote_connection_serializes():
+    def handler(request):
+        request.send_response(200)
+        request.send_header("Content-Type", "application/json")
+        request.end_headers()
+        request.wfile.write(b'{"tables": []}')
+
+    with mock_lancedb_connection(handler) as db:
+        serialized = json.loads(db.serialize())
+        assert isinstance(serialized["client_config"], dict)
+        restored = lancedb.deserialize_conn(db.serialize())
+        assert restored.table_names() == []
+
+
+def test_remote_table_is_picklable():
+    def handler(request):
+        request.close_connection = True
+        if request.path == "/v1/table/test/describe/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            payload = json.dumps(
+                {
+                    "version": 1,
+                    "schema": {
+                        "fields": [
+                            {"name": "id", "type": {"type": "int64"}, "nullable": False}
+                        ]
+                    },
+                }
+            )
+            request.wfile.write(payload.encode())
+        elif request.path == "/v1/table/test/count_rows/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(b"3")
+        else:
+            request.send_response(404)
+            request.end_headers()
+
+    with mock_lancedb_connection(handler) as db:
+        table = db.open_table("test")
+        restored = pickle.loads(pickle.dumps(table))
+        assert restored.count_rows() == 3
+
+
+def test_remote_table_open_does_not_require_picklable_client_config():
+    from lancedb.remote import HeaderProvider
+
+    class LocalHeaderProvider(HeaderProvider):
+        def get_headers(self):
+            return {"X-Test-Header": "present"}
+
+    def handler(request):
+        request.close_connection = True
+        assert request.headers.get("X-Test-Header") == "present"
+        if request.path == "/v1/table/test/describe/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(b'{"version": 1, "schema": {"fields": []}}')
+        elif request.path == "/v1/table/test/count_rows/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(b"3")
+        else:
+            request.send_response(404)
+            request.end_headers()
+
+    with http.server.HTTPServer(
+        ("localhost", 0), make_mock_http_handler(handler)
+    ) as server:
+        port = server.server_address[1]
+        handle = threading.Thread(target=server.serve_forever)
+        handle.start()
+        try:
+            db = lancedb.connect(
+                "db://dev",
+                api_key="fake",
+                host_override=f"http://localhost:{port}",
+                client_config={
+                    "retry_config": {"retries": 0},
+                    "timeout_config": {"connect_timeout": 2, "read_timeout": 2},
+                    "header_provider": LocalHeaderProvider(),
+                },
+            )
+            table = db.open_table("test")
+            assert table.count_rows() == 3
+            with pytest.raises(ValueError, match="header_provider"):
+                pickle.dumps(table)
+        finally:
+            server.shutdown()
+            handle.join()
+
+
+def test_remote_permutation_is_picklable():
+    from lancedb.permutation import Permutation
+
+    rows = list(range(10))
+
+    def handler(request):
+        request.close_connection = True
+        if request.path == "/v1/table/test/describe/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            payload = json.dumps(
+                {
+                    "version": 1,
+                    "schema": {
+                        "fields": [
+                            {"name": "a", "type": {"type": "int64"}, "nullable": False}
+                        ]
+                    },
+                }
+            )
+            request.wfile.write(payload.encode())
+        elif request.path == "/v1/table/test/count_rows/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(str(len(rows)).encode())
+        elif request.path == "/v1/table/test/query/":
+            content_len = int(request.headers.get("Content-Length"))
+            body = json.loads(request.rfile.read(content_len))
+            if "filter" in body:
+                match = re.search(r"_rowoffset in \((.*?)\)", body["filter"])
+                offsets = [int(offset.strip()) for offset in match.group(1).split(",")]
+            else:
+                offsets = rows
+            table = pa.table({"a": [rows[offset] for offset in offsets]})
+
+            request.send_response(200)
+            request.send_header("Content-Type", "application/vnd.apache.arrow.file")
+            request.end_headers()
+            with pa.ipc.new_file(request.wfile, schema=table.schema) as writer:
+                writer.write_table(table)
+        else:
+            request.send_response(404)
+            request.end_headers()
+
+    with mock_lancedb_connection(handler) as db:
+        permutation = Permutation.identity(db.open_table("test"))
+        restored = pickle.loads(pickle.dumps(permutation))
+        assert restored.__getitems__([0, 2, 4]) == [{"a": 0}, {"a": 2}, {"a": 4}]
+
+
 def test_create_table_exist_ok():
     def handler(request):
         if request.path == "/v1/table/test/create/?mode=exist_ok":
@@ -268,6 +419,25 @@ def test_table_unimplemented_functions():
             table.to_pandas()
 
 
+def test_table_to_pandas_not_supported():
+    def handler(request):
+        if request.path == "/v1/table/test/create/?mode=create":
+            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}])
+        with pytest.raises(NotImplementedError):
+            table.to_pandas()
+        with pytest.raises(NotImplementedError):
+            table.to_pandas(blob_mode="bytes", split_blocks=True)
+
+
 def test_table_add_in_threadpool():
     def handler(request):
         if request.path == "/v1/table/test/insert/":
@@ -342,6 +512,22 @@ def test_table_create_indices():
                     schema=dict(
                         fields=[
                             dict(name="id", type={"type": "int64"}, nullable=False),
+                            dict(name="text", type={"type": "string"}, nullable=False),
+                            dict(
+                                name="vector",
+                                type={
+                                    "type": "fixed_size_list",
+                                    "fields": [
+                                        dict(
+                                            name="item",
+                                            type={"type": "float"},
+                                            nullable=True,
+                                        )
+                                    ],
+                                    "length": 2,
+                                },
+                                nullable=False,
+                            ),
                         ]
                     ),
                 )
@@ -400,22 +586,25 @@ def test_table_create_indices():
         # This is a smoke-test.
         table = db.create_table("test", [{"id": 1}])
 
-        # Test create_scalar_index with custom name
-        table.create_scalar_index(
-            "id", wait_timeout=timedelta(seconds=2), name="custom_scalar_idx"
-        )
+        # Test create_scalar_index with custom name (legacy method)
+        with pytest.warns(DeprecationWarning, match="create_scalar_index"):
+            table.create_scalar_index(
+                "id", wait_timeout=timedelta(seconds=2), name="custom_scalar_idx"
+            )
 
-        # Test create_fts_index with custom name
-        table.create_fts_index(
-            "text", wait_timeout=timedelta(seconds=2), name="custom_fts_idx"
-        )
+        # Test create_fts_index with custom name (legacy method)
+        with pytest.warns(DeprecationWarning, match="create_fts_index"):
+            table.create_fts_index(
+                "text", wait_timeout=timedelta(seconds=2), name="custom_fts_idx"
+            )
 
-        # Test create_index with custom name
-        table.create_index(
-            vector_column_name="vector",
-            wait_timeout=timedelta(seconds=10),
-            name="custom_vector_idx",
-        )
+        # Test create_index with custom name (legacy form: vector_column_name kwarg)
+        with pytest.warns(DeprecationWarning, match="create_index"):
+            table.create_index(
+                vector_column_name="vector",
+                wait_timeout=timedelta(seconds=10),
+                name="custom_vector_idx",
+            )
 
         # Validate that the name parameter was passed correctly in requests
         assert len(received_requests) == 3
@@ -444,6 +633,98 @@ def test_table_create_indices():
         table.drop_index("custom_fts_idx")
 
 
+def test_remote_create_index_new_api():
+    received_requests = []
+
+    def handler(request):
+        if request.path == "/v1/table/test/create_index/":
+            content_len = int(request.headers.get("Content-Length", 0))
+            body = request.rfile.read(content_len) if content_len > 0 else b""
+            received_requests.append(json.loads(body) if body else {})
+            request.send_response(200)
+            request.end_headers()
+        elif request.path == "/v1/table/test/create/?mode=create":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(b"{}")
+        elif request.path == "/v1/table/test/describe/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(
+                json.dumps(
+                    dict(
+                        version=1,
+                        schema=dict(
+                            fields=[
+                                dict(name="id", type={"type": "int64"}, nullable=False),
+                                dict(
+                                    name="category",
+                                    type={"type": "string"},
+                                    nullable=False,
+                                ),
+                                dict(
+                                    name="text", type={"type": "string"}, nullable=False
+                                ),
+                                dict(
+                                    name="vector",
+                                    type={
+                                        "type": "fixed_size_list",
+                                        "fields": [
+                                            dict(
+                                                name="item",
+                                                type={"type": "float"},
+                                                nullable=True,
+                                            )
+                                        ],
+                                        "length": 2,
+                                    },
+                                    nullable=False,
+                                ),
+                            ]
+                        ),
+                    )
+                ).encode()
+            )
+        else:
+            request.send_response(404)
+            request.end_headers()
+
+    from lancedb.index import BTree, FTS, IvfPq, IvfRq
+
+    with mock_lancedb_connection(handler) as db:
+        table = db.create_table("test", [{"id": 1}])
+
+        # New API: column-first, config= kwarg. Should NOT emit DeprecationWarning.
+        import warnings as _warnings
+
+        with _warnings.catch_warnings():
+            _warnings.simplefilter("error", DeprecationWarning)
+            table.create_index("vector", config=IvfPq(distance_type="l2"))
+            table.create_index("category", config=BTree())
+            table.create_index("text", config=FTS())
+            # IvfRq via new API
+            table.create_index("vector", config=IvfRq(distance_type="l2"))
+
+        # Legacy index_type="IVF_RQ" routes to IvfRq config under the hood.
+        with pytest.warns(DeprecationWarning, match="create_index"):
+            table.create_index(
+                vector_column_name="vector",
+                index_type="IVF_RQ",
+                num_partitions=8,
+            )
+
+        assert len(received_requests) == 5
+        assert [req["column"] for req in received_requests] == [
+            "vector",
+            "category",
+            "text",
+            "vector",
+            "vector",
+        ]
+
+
 def test_table_wait_for_index_timeout():
     def handler(request):
         index_stats = dict(
@@ -660,6 +941,18 @@ def test_query_sync_maximal():
             "ef": None,
             "filter": "id > 0",
             "columns": ["id", "name"],
+            "order_by": [
+                {
+                    "column_name": "score",
+                    "ascending": False,
+                    "nulls_first": True,
+                },
+                {
+                    "column_name": "id",
+                    "ascending": True,
+                    "nulls_first": False,
+                },
+            ],
             "vector_column": "vector2",
             "fast_search": True,
             "with_row_id": True,
@@ -677,6 +970,14 @@ def test_query_sync_maximal():
             .refine_factor(10)
             .nprobes(5)
             .where("id > 0", prefilter=True)
+            .order_by(
+                [
+                    ColumnOrdering(
+                        column_name="score", ascending=False, nulls_first=True
+                    ),
+                    ColumnOrdering(column_name="id", ascending=True, nulls_first=False),
+                ]
+            )
             .with_row_id(True)
             .select(["id", "name"])
             .to_list()
@@ -1249,6 +1550,10 @@ def _remote_fork_child(port: int, queue) -> None:
     queue.put(db.table_names())
 
 
+def _remote_table_fork_child(table, queue) -> None:
+    queue.put(table.count_rows())
+
+
 @pytest.mark.skipif(
     sys.platform != "linux",
     reason=(
@@ -1311,3 +1616,65 @@ def test_remote_connection_after_fork():
     finally:
         server.shutdown()
         server_thread.join()
+
+
+@pytest.mark.skipif(
+    sys.platform != "linux",
+    reason=(
+        "fork() is unavailable on Windows and unsafe on macOS "
+        "(Apple frameworks/TLS are not fork-safe)"
+    ),
+)
+def test_inherited_remote_table_reopens_after_fork():
+    def handler(request):
+        if request.path == "/v1/table/test/describe/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(b'{"version": 1, "schema": {"fields": []}}')
+        elif request.path == "/v1/table/test/count_rows/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(b"7")
+        else:
+            request.send_response(404)
+            request.end_headers()
+
+    server = http.server.HTTPServer(("localhost", 0), make_mock_http_handler(handler))
+    port = server.server_address[1]
+    server_thread = threading.Thread(target=server.serve_forever)
+    server_thread.start()
+    try:
+        db = lancedb.connect(
+            "db://dev",
+            api_key="fake",
+            host_override=f"http://localhost:{port}",
+            client_config={
+                "retry_config": {"retries": 0},
+                "timeout_config": {"connect_timeout": 2, "read_timeout": 2},
+            },
+        )
+        table = db.open_table("test")
+        assert table.count_rows() == 7
+
+        ctx = mp.get_context("fork")
+        queue = ctx.Queue()
+        proc = ctx.Process(target=_remote_table_fork_child, args=(table, queue))
+        proc.start()
+        proc.join(timeout=15)
+
+        if proc.is_alive():
+            proc.terminate()
+            proc.join(timeout=5)
+            if proc.is_alive():
+                proc.kill()
+                proc.join()
+            pytest.fail("Remote table hung after fork")
+
+        assert proc.exitcode == 0, f"child exited with code {proc.exitcode}"
+        assert not queue.empty(), "child produced no result"
+        assert queue.get() == 7
+    finally:
+        server.shutdown()
+        server_thread.join()

python/python/tests/test_rerankers.py

@@ -603,3 +603,89 @@ def test_cross_encoder_reranker_return_all(tmp_path):
     assert "_relevance_score" in result.column_names
     assert "_score" in result.column_names
     assert "_distance" in result.column_names
+
+
+# ---------------------------------------------------------------------------
+# Regression tests for LinearCombinationReranker scoring bugs (issue #3154)
+# ---------------------------------------------------------------------------
+
+
+def test_linear_combination_best_match_ranks_first():
+    """
+    The document that is BOTH the closest vector match AND the only FTS match
+    must rank first.  Previously _combine_score subtracted from 1, inverting
+    the ranking so the worst document ranked highest.
+    """
+    reranker = LinearCombinationReranker(weight=0.7, return_score="all")
+
+    # rowid 0: perfect vector match, sole FTS match  → should rank 1st
+    # rowid 1: mediocre vector, no FTS match
+    # rowid 2: bad vector, no FTS match
+    vector_results = pa.Table.from_pydict(
+        {
+            "_rowid": [0, 1, 2],
+            "_distance": [0.0, 0.5, 0.9],
+        }
+    )
+    fts_results = pa.Table.from_pydict(
+        {
+            "_rowid": [0],
+            "_score": [1.0],
+        }
+    )
+
+    combined = reranker.merge_results(vector_results, fts_results, fill=1.0)
+    scores = dict(
+        zip(
+            combined["_rowid"].to_pylist(),
+            combined["_relevance_score"].to_pylist(),
+        )
+    )
+
+    # rowid 0 must have the highest relevance score
+    assert scores[0] > scores[1], (
+        f"Best match (rowid 0, score={scores[0]:.4f}) should beat "
+        f"mid match (rowid 1, score={scores[1]:.4f})"
+    )
+    assert scores[1] > scores[2], (
+        f"Mid match (rowid 1, score={scores[1]:.4f}) should beat "
+        f"bad match (rowid 2, score={scores[2]:.4f})"
+    )
+
+
+def test_linear_combination_missing_fts_is_penalised():
+    """
+    A document with no FTS match must score *lower* than a document that
+    has a mediocre FTS match, everything else being equal.  Previously
+    missing-FTS entries used fill=1.0 directly, which gave them a reward
+    (via the 1-(...) inversion) instead of a penalty.
+    """
+    reranker = LinearCombinationReranker(weight=0.5, return_score="all")
+
+    vector_results = pa.Table.from_pydict(
+        {
+            "_rowid": [0, 1],
+            "_distance": [0.2, 0.2],  # identical vector scores
+        }
+    )
+    fts_results = pa.Table.from_pydict(
+        {
+            "_rowid": [0],  # rowid 1 has no FTS match
+            "_score": [0.3],  # small FTS score
+        }
+    )
+
+    combined = reranker.merge_results(vector_results, fts_results, fill=1.0)
+    scores = dict(
+        zip(
+            combined["_rowid"].to_pylist(),
+            combined["_relevance_score"].to_pylist(),
+        )
+    )
+
+    # rowid 0 has a small FTS score; rowid 1 has none.
+    # Even a small FTS contribution should beat having none at all.
+    assert scores[0] > scores[1], (
+        f"Document with FTS score (rowid 0, {scores[0]:.4f}) should beat "
+        f"document with no FTS match (rowid 1, {scores[1]:.4f})"
+    )

python/python/tests/test_table.py

@@ -4,6 +4,7 @@
 
 import os
 import sys
+import warnings
 from datetime import date, datetime, timedelta
 from time import sleep
 from typing import List
@@ -11,7 +12,7 @@ from unittest.mock import patch
 
 import lancedb
 from lancedb.dependencies import _PANDAS_AVAILABLE
-from lancedb.index import HnswFlat, HnswPq, HnswSq, IvfPq
+from lancedb.index import BTree, FTS, HnswFlat, HnswPq, HnswSq, IvfPq
 import numpy as np
 import polars as pl
 import pyarrow as pa
@@ -25,6 +26,28 @@ from lancedb.table import LanceTable
 from pydantic import BaseModel
 
 
+def _blob_test_data():
+    return pa.table(
+        {
+            "id": pa.array([1, 2], pa.int64()),
+            "blob": pa.array([b"hello", b"world"], pa.large_binary()),
+        },
+        schema=pa.schema(
+            [
+                pa.field("id", pa.int64()),
+                pa.field(
+                    "blob", pa.large_binary(), metadata={"lance-encoding:blob": "true"}
+                ),
+            ]
+        ),
+    )
+
+
+def _assert_lazy_blob(value, expected: bytes):
+    assert hasattr(value, "readall")
+    assert value.readall() == expected
+
+
 def test_basic(mem_db: DBConnection):
     data = [
         {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
@@ -33,7 +56,7 @@ def test_basic(mem_db: DBConnection):
     table = mem_db.create_table("test", data=data)
 
     assert table.name == "test"
-    assert "LanceTable(name='test', version=1, _conn=LanceDBConnection(" in repr(table)
+    assert "LanceTable(name='test', _conn=LanceDBConnection(" in repr(table)
     expected_schema = pa.schema(
         {
             "vector": pa.list_(pa.float32(), 2),
@@ -47,6 +70,66 @@ def test_basic(mem_db: DBConnection):
     assert table.to_arrow() == expected_data
 
 
+def test_table_to_pandas_default_matches_arrow(tmp_db: DBConnection):
+    pd = pytest.importorskip("pandas")
+    data = pa.table({"id": [1, 2], "text": ["one", "two"]})
+    table = tmp_db.create_table("test_to_pandas_old_call", data=data)
+
+    expected = data.to_pandas()
+    pd.testing.assert_frame_equal(table.to_pandas(), expected)
+
+
+@pytest.mark.parametrize("blob_mode", ["lazy", "bytes", "descriptions"])
+def test_table_to_pandas_blob_modes(tmp_db: DBConnection, blob_mode):
+    pytest.importorskip("lance")
+    table = tmp_db.create_table(f"test_to_pandas_blob_{blob_mode}", _blob_test_data())
+
+    df = table.to_pandas(blob_mode=blob_mode)
+
+    if blob_mode == "lazy":
+        _assert_lazy_blob(df["blob"].iloc[0], b"hello")
+        _assert_lazy_blob(df["blob"].iloc[1], b"world")
+    elif blob_mode == "bytes":
+        assert df["blob"].tolist() == [b"hello", b"world"]
+    else:
+        first = df["blob"].iloc[0]
+        assert first != b"hello"
+        assert not hasattr(first, "readall")
+
+
+def test_table_to_pandas_kwargs(tmp_db: DBConnection):
+    pd = pytest.importorskip("pandas")
+    data = pa.table({"id": pa.array([1, 2], pa.int64())})
+    table = tmp_db.create_table("test_to_pandas_kwargs", data=data)
+
+    df = table.to_pandas(types_mapper=pd.ArrowDtype)
+
+    assert str(df["id"].dtype) == "int64[pyarrow]"
+
+
+@pytest.mark.asyncio
+async def test_async_table_to_pandas_blob_bytes(tmp_db_async: AsyncConnection):
+    pytest.importorskip("lance")
+    table = await tmp_db_async.create_table(
+        "test_async_to_pandas_blob_bytes", data=_blob_test_data()
+    )
+
+    df = await table.to_pandas(blob_mode="bytes")
+
+    assert df["blob"].tolist() == [b"hello", b"world"]
+
+
+@pytest.mark.asyncio
+async def test_async_table_to_pandas_kwargs(tmp_db_async: AsyncConnection):
+    pd = pytest.importorskip("pandas")
+    data = pa.table({"id": pa.array([1, 2], pa.int64())})
+    table = await tmp_db_async.create_table("test_async_to_pandas_kwargs", data=data)
+
+    df = await table.to_pandas(types_mapper=pd.ArrowDtype)
+
+    assert str(df["id"].dtype) == "int64[pyarrow]"
+
+
 def test_create_table_infers_large_int_vectors(mem_db: DBConnection):
     data = [{"vector": [0, 300]}]
 
@@ -849,7 +932,12 @@ def test_create_index_method(mock_create_index, mem_db: DBConnection):
         num_bits=4,
     )
     mock_create_index.assert_called_with(
-        "vector", replace=True, config=expected_config, name=None, train=True
+        "vector",
+        replace=True,
+        config=expected_config,
+        wait_timeout=None,
+        name=None,
+        train=True,
     )
 
     # Test with target_partition_size
@@ -869,7 +957,12 @@ def test_create_index_method(mock_create_index, mem_db: DBConnection):
         target_partition_size=8192,
     )
     mock_create_index.assert_called_with(
-        "vector", replace=True, config=expected_config, name=None, train=True
+        "vector",
+        replace=True,
+        config=expected_config,
+        wait_timeout=None,
+        name=None,
+        train=True,
     )
 
     # target_partition_size has a default value,
@@ -888,7 +981,12 @@ def test_create_index_method(mock_create_index, mem_db: DBConnection):
         num_bits=4,
     )
     mock_create_index.assert_called_with(
-        "vector", replace=True, config=expected_config, name=None, train=True
+        "vector",
+        replace=True,
+        config=expected_config,
+        wait_timeout=None,
+        name=None,
+        train=True,
     )
 
     table.create_index(
@@ -899,7 +997,12 @@ def test_create_index_method(mock_create_index, mem_db: DBConnection):
     )
     expected_config = HnswPq(distance_type="dot")
     mock_create_index.assert_called_with(
-        "my_vector", replace=False, config=expected_config, name=None, train=True
+        "my_vector",
+        replace=False,
+        config=expected_config,
+        wait_timeout=None,
+        name=None,
+        train=True,
     )
 
     table.create_index(
@@ -914,7 +1017,12 @@ def test_create_index_method(mock_create_index, mem_db: DBConnection):
         distance_type="cosine", sample_rate=0.1, m=29, ef_construction=10
     )
     mock_create_index.assert_called_with(
-        "my_vector", replace=True, config=expected_config, name=None, train=True
+        "my_vector",
+        replace=True,
+        config=expected_config,
+        wait_timeout=None,
+        name=None,
+        train=True,
     )
 
     table.create_index(
@@ -929,7 +1037,12 @@ def test_create_index_method(mock_create_index, mem_db: DBConnection):
         distance_type="cosine", sample_rate=0.1, m=29, ef_construction=10
     )
     mock_create_index.assert_called_with(
-        "my_vector", replace=True, config=expected_config, name=None, train=True
+        "my_vector",
+        replace=True,
+        config=expected_config,
+        wait_timeout=None,
+        name=None,
+        train=True,
     )
 
 
@@ -953,6 +1066,7 @@ def test_create_index_name_and_train_parameters(
         "vector",
         replace=True,
         config=expected_config,
+        wait_timeout=None,
         name="my_custom_index",
         train=True,
     )
@@ -960,13 +1074,82 @@ def test_create_index_name_and_train_parameters(
     # Test with train=False
     table.create_index(vector_column_name="vector", train=False)
     mock_create_index.assert_called_with(
-        "vector", replace=True, config=expected_config, name=None, train=False
+        "vector",
+        replace=True,
+        config=expected_config,
+        wait_timeout=None,
+        name=None,
+        train=False,
     )
 
     # Test with both name and train
     table.create_index(vector_column_name="vector", name="my_index_name", train=True)
     mock_create_index.assert_called_with(
-        "vector", replace=True, config=expected_config, name="my_index_name", train=True
+        "vector",
+        replace=True,
+        config=expected_config,
+        wait_timeout=None,
+        name="my_index_name",
+        train=True,
+    )
+
+
+@patch("lancedb.table.AsyncTable.create_index")
+def test_create_index_legacy_emits_deprecation_warning(
+    mock_create_index, mem_db: DBConnection
+):
+    table = mem_db.create_table(
+        "test",
+        data=[{"vector": [3.1, 4.1]}, {"vector": [5.9, 26.5]}],
+    )
+
+    with pytest.warns(DeprecationWarning, match="create_index"):
+        table.create_index(metric="l2", num_partitions=8, vector_column_name="vector")
+
+
+@patch("lancedb.table.AsyncTable.create_index")
+def test_create_index_new_api(mock_create_index, mem_db: DBConnection):
+    table = mem_db.create_table(
+        "test",
+        data=[
+            {"vector": [3.1, 4.1], "category": "a", "text": "hello world"},
+            {"vector": [5.9, 26.5], "category": "b", "text": "goodbye"},
+        ],
+    )
+
+    # Vector index via new API should not warn
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", DeprecationWarning)
+        table.create_index("vector", config=IvfPq(distance_type="l2"))
+    mock_create_index.assert_called_with(
+        "vector",
+        replace=True,
+        config=IvfPq(distance_type="l2"),
+        wait_timeout=None,
+        name=None,
+        train=True,
+    )
+
+    # Scalar index via new API
+    table.create_index("category", config=BTree())
+    mock_create_index.assert_called_with(
+        "category",
+        replace=True,
+        config=BTree(),
+        wait_timeout=None,
+        name=None,
+        train=True,
+    )
+
+    # FTS index via new API
+    table.create_index("text", config=FTS(with_position=True))
+    mock_create_index.assert_called_with(
+        "text",
+        replace=True,
+        config=FTS(with_position=True),
+        wait_timeout=None,
+        name=None,
+        train=True,
     )
 
 
@@ -1782,8 +1965,9 @@ def test_create_scalar_index(mem_db: DBConnection):
         "my_table",
         data=test_data,
     )
-    # Test with default name
-    table.create_scalar_index("x")
+    # Test with default name; confirm DeprecationWarning fires
+    with pytest.warns(DeprecationWarning, match="create_scalar_index"):
+        table.create_scalar_index("x")
     indices = table.list_indices()
     assert len(indices) == 1
     scalar_index = indices[0]
@@ -1811,6 +1995,59 @@ def test_create_scalar_index(mem_db: DBConnection):
     assert scalar_index.name == "custom_y_index"
 
 
+def test_create_index_nested_field_paths(mem_db: DBConnection):
+    schema = pa.schema(
+        [
+            pa.field("metadata", pa.struct([pa.field("user_id", pa.int32())])),
+            pa.field(
+                "image",
+                pa.struct([pa.field("embedding", pa.list_(pa.float32(), 2))]),
+            ),
+        ]
+    )
+    data = pa.Table.from_pylist(
+        [
+            {
+                "metadata": {"user_id": i},
+                "image": {"embedding": [float(i), float(i + 1)]},
+            }
+            for i in range(256)
+        ],
+        schema=schema,
+    )
+    table = mem_db.create_table("nested_index_paths", data=data)
+
+    table.create_scalar_index("metadata.user_id", name="metadata_user_id_idx")
+    table.create_index(
+        vector_column_name="image.embedding",
+        num_partitions=1,
+        num_sub_vectors=1,
+        name="image_embedding_idx",
+    )
+
+    indices = sorted(table.list_indices(), key=lambda idx: idx.name)
+    assert [(idx.name, idx.index_type, idx.columns) for idx in indices] == [
+        ("image_embedding_idx", "IvfPq", ["image.embedding"]),
+        ("metadata_user_id_idx", "BTree", ["metadata.user_id"]),
+    ]
+
+    vector_results = (
+        table.search([0.0, 1.0], vector_column_name="image.embedding")
+        .limit(1)
+        .to_list()
+    )
+    assert len(vector_results) == 1
+    assert vector_results[0]["metadata"]["user_id"] == 0
+
+    default_vector_results = table.search([0.0, 1.0]).limit(1).to_list()
+    assert len(default_vector_results) == 1
+    assert default_vector_results[0]["metadata"]["user_id"] == 0
+
+    filtered_results = table.search().where("metadata.user_id = 42").limit(1).to_list()
+    assert len(filtered_results) == 1
+    assert filtered_results[0]["metadata"]["user_id"] == 42
+
+
 def test_empty_query(mem_db: DBConnection):
     table = mem_db.create_table(
         "my_table",
@@ -1885,6 +2122,74 @@ def test_search_with_schema_inf_multiple_vector(mem_db: DBConnection):
         table.search(q).limit(1).to_arrow()
 
 
+def test_search_infers_single_nested_vector(mem_db: DBConnection):
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int32()),
+            pa.field(
+                "image",
+                pa.struct([pa.field("embedding", pa.list_(pa.float32(), 2))]),
+            ),
+        ]
+    )
+    data = pa.Table.from_pylist(
+        [
+            {"id": 0, "image": {"embedding": [0.0, 1.0]}},
+            {"id": 1, "image": {"embedding": [10.0, 11.0]}},
+        ],
+        schema=schema,
+    )
+    table = mem_db.create_table("nested_vector_default_search", data=data)
+
+    result = table.search([0.0, 1.0]).limit(1).to_list()
+    assert result[0]["id"] == 0
+
+
+def test_search_nested_vector_multiple_candidates(mem_db: DBConnection):
+    schema = pa.schema(
+        [
+            pa.field(
+                "image",
+                pa.struct([pa.field("embedding", pa.list_(pa.float32(), 2))]),
+            ),
+            pa.field(
+                "text",
+                pa.struct([pa.field("embedding", pa.list_(pa.float32(), 2))]),
+            ),
+        ]
+    )
+    data = pa.Table.from_pylist(
+        [
+            {
+                "image": {"embedding": [0.0, 1.0]},
+                "text": {"embedding": [2.0, 3.0]},
+            }
+        ],
+        schema=schema,
+    )
+    table = mem_db.create_table("nested_vector_multiple_candidates", data=data)
+
+    with pytest.raises(ValueError, match="image.embedding.*text.embedding"):
+        table.search([0.0, 1.0]).limit(1).to_arrow()
+
+
+def test_search_nested_vector_no_candidates(mem_db: DBConnection):
+    schema = pa.schema(
+        [
+            pa.field("id", pa.int32()),
+            pa.field("metadata", pa.struct([pa.field("label", pa.string())])),
+        ]
+    )
+    data = pa.Table.from_pylist(
+        [{"id": 0, "metadata": {"label": "cat"}}],
+        schema=schema,
+    )
+    table = mem_db.create_table("nested_vector_no_candidates", data=data)
+
+    with pytest.raises(ValueError, match="no vector column"):
+        table.search([0.0, 1.0]).limit(1).to_arrow()
+
+
 def test_compact_cleanup(tmp_db: DBConnection):
     pytest.importorskip("lance")
     table = tmp_db.create_table(

python/python/tests/test_torch.py

@@ -1,10 +1,15 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
+import contextlib
 import functools
+import http.server
+import json
 import multiprocessing as mp
 import pickle
+import re
 import sys
+import threading
 
 import lancedb
 import pyarrow as pa
@@ -15,6 +20,107 @@ from lancedb.util import tbl_to_tensor
 torch = pytest.importorskip("torch")
 
 
+REMOTE_ROWS = list(range(100))
+
+
+def _make_mock_http_handler(handler):
+    class MockLanceDBHandler(http.server.BaseHTTPRequestHandler):
+        def do_GET(self):
+            handler(self)
+
+        def do_POST(self):
+            handler(self)
+
+    return MockLanceDBHandler
+
+
+def _remote_schema_payload():
+    return {
+        "version": 1,
+        "schema": {
+            "fields": [
+                {"name": "a", "type": {"type": "int64"}, "nullable": False},
+            ]
+        },
+    }
+
+
+def _offsets_from_filter(filter_sql: str | None) -> list[int]:
+    if filter_sql is None:
+        return REMOTE_ROWS
+    match = re.search(r"_rowoffset in \((.*?)\)", filter_sql)
+    if match is None:
+        return REMOTE_ROWS
+    raw_offsets = match.group(1).strip()
+    if raw_offsets == "":
+        return []
+    return [int(offset.strip()) for offset in raw_offsets.split(",")]
+
+
+def _remote_dataset_handler(request):
+    request.close_connection = True
+    if request.path == "/v1/table/test/describe/":
+        request.send_response(200)
+        request.send_header("Content-Type", "application/json")
+        request.end_headers()
+        request.wfile.write(json.dumps(_remote_schema_payload()).encode())
+    elif request.path == "/v1/table/test/count_rows/":
+        request.send_response(200)
+        request.send_header("Content-Type", "application/json")
+        request.end_headers()
+        request.wfile.write(str(len(REMOTE_ROWS)).encode())
+    elif request.path == "/v1/table/test/query/":
+        content_len = int(request.headers.get("Content-Length"))
+        body = json.loads(request.rfile.read(content_len))
+        offsets = _offsets_from_filter(body.get("filter"))
+        requested_columns = body.get("columns") or ["a"]
+        if isinstance(requested_columns, dict):
+            requested_columns = list(requested_columns)
+
+        data = {}
+        for column in requested_columns:
+            if column == "a":
+                data[column] = [REMOTE_ROWS[offset] for offset in offsets]
+            elif column == "_rowoffset":
+                data[column] = offsets
+            elif column == "_rowid":
+                data[column] = offsets
+
+        table = pa.table(data)
+        request.send_response(200)
+        request.send_header("Content-Type", "application/vnd.apache.arrow.file")
+        request.end_headers()
+        with pa.ipc.new_file(request.wfile, schema=table.schema) as writer:
+            writer.write_table(table)
+    else:
+        request.send_response(404)
+        request.end_headers()
+
+
+@contextlib.contextmanager
+def _remote_dataset_table():
+    with http.server.ThreadingHTTPServer(
+        ("localhost", 0), _make_mock_http_handler(_remote_dataset_handler)
+    ) as server:
+        port = server.server_address[1]
+        handle = threading.Thread(target=server.serve_forever)
+        handle.start()
+        try:
+            db = lancedb.connect(
+                "db://dev",
+                api_key="fake",
+                host_override=f"http://localhost:{port}",
+                client_config={
+                    "retry_config": {"retries": 0},
+                    "timeout_config": {"connect_timeout": 2, "read_timeout": 2},
+                },
+            )
+            yield db.open_table("test")
+        finally:
+            server.shutdown()
+            handle.join()
+
+
 def _open_native_table(uri: str, table_name: str):
     """Top-level connection factory used by the explicit-factory pickle test.
 
@@ -107,6 +213,39 @@ def test_permutation_dataloader_multiprocessing(tmp_db):
     assert seen == 1000
 
 
+def test_remote_table_dataloader_multiprocessing():
+    with _remote_dataset_table() as table:
+        dataloader = torch.utils.data.DataLoader(
+            table,
+            collate_fn=tbl_to_tensor,
+            batch_size=10,
+            num_workers=2,
+            multiprocessing_context="spawn",
+        )
+        seen = 0
+        for batch in dataloader:
+            assert batch.size(0) == 1
+            assert batch.size(1) == 10
+            seen += batch.size(1)
+        assert seen == len(REMOTE_ROWS)
+
+
+def test_remote_permutation_dataloader_multiprocessing():
+    with _remote_dataset_table() as table:
+        permutation = Permutation.identity(table)
+        dataloader = torch.utils.data.DataLoader(
+            permutation,
+            batch_size=10,
+            num_workers=2,
+            multiprocessing_context="spawn",
+        )
+        seen = 0
+        for batch in dataloader:
+            assert batch["a"].size(0) == 10
+            seen += batch["a"].size(0)
+        assert seen == len(REMOTE_ROWS)
+
+
 def test_permutation_pickle_with_connection_factory(tmp_path):
     """When the user provides a connection_factory, pickling should round-trip
     through that factory rather than introspecting the connection URI. Useful
@@ -171,6 +310,35 @@ def _multiworker_dataloader_target(db_uri: str, result_queue):
     result_queue.put(count)
 
 
+def _remote_multiworker_dataloader_target(port: int, result_queue):
+    import lancedb
+    from lancedb.permutation import Permutation
+
+    db = lancedb.connect(
+        "db://dev",
+        api_key="fake",
+        host_override=f"http://localhost:{port}",
+        client_config={
+            "retry_config": {"retries": 0},
+            "timeout_config": {"connect_timeout": 2, "read_timeout": 2},
+        },
+    )
+    table = db.open_table("test")
+    permutation = Permutation.identity(table)
+
+    dataloader = torch.utils.data.DataLoader(
+        permutation,
+        batch_size=10,
+        num_workers=2,
+        multiprocessing_context="fork",
+    )
+    count = 0
+    for batch in dataloader:
+        assert batch["a"].size(0) == 10
+        count += 1
+    result_queue.put(count)
+
+
 @pytest.mark.skipif(
     sys.platform != "linux",
     reason=(
@@ -208,3 +376,46 @@ def test_permutation_dataloader_fork_workers(tmp_path):
     assert proc.exitcode == 0, f"child exited with code {proc.exitcode}"
     assert not queue.empty(), "child produced no batches"
     assert queue.get() == 100
+
+
+@pytest.mark.skipif(
+    sys.platform != "linux",
+    reason=(
+        "fork() is unavailable on Windows and unsafe on macOS "
+        "(Apple frameworks/TLS are not fork-safe)"
+    ),
+)
+def test_remote_permutation_dataloader_fork_workers():
+    with http.server.ThreadingHTTPServer(
+        ("localhost", 0), _make_mock_http_handler(_remote_dataset_handler)
+    ) as server:
+        port = server.server_address[1]
+        handle = threading.Thread(target=server.serve_forever)
+        handle.start()
+        try:
+            ctx = mp.get_context("spawn")
+            queue = ctx.Queue()
+            proc = ctx.Process(
+                target=_remote_multiworker_dataloader_target,
+                args=(port, queue),
+            )
+            proc.start()
+            proc.join(timeout=30)
+
+            if proc.is_alive():
+                proc.terminate()
+                proc.join(timeout=5)
+                if proc.is_alive():
+                    proc.kill()
+                    proc.join()
+                pytest.fail(
+                    "Remote permutation hung when iterated in a fork-based "
+                    "DataLoader worker"
+                )
+
+            assert proc.exitcode == 0, f"child exited with code {proc.exitcode}"
+            assert not queue.empty(), "child produced no batches"
+            assert queue.get() == 10
+        finally:
+            server.shutdown()
+            handle.join()

python/src/connection.rs

@@ -395,12 +395,17 @@ impl Connection {
         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_str = mode
+                .map(|m| match m.to_lowercase().as_str() {
+                    "create" => Ok("Create".to_string()),
+                    "exist_ok" => Ok("ExistOk".to_string()),
+                    "overwrite" => Ok("Overwrite".to_string()),
+                    _ => Err(PyValueError::new_err(format!(
+                        "Invalid mode {:?}: expected one of 'create', 'exist_ok', 'overwrite'",
+                        m
+                    ))),
+                })
+                .transpose()?;
             let request = CreateNamespaceRequest {
                 id: Some(namespace_path),
                 mode: mode_str,
@@ -428,16 +433,26 @@ impl Connection {
         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 behavior_str = behavior.and_then(|b| match b.to_uppercase().as_str() {
-                "RESTRICT" => Some("Restrict".to_string()),
-                "CASCADE" => Some("Cascade".to_string()),
-                _ => None,
-            });
+            let mode_str = mode
+                .map(|m| match m.to_uppercase().as_str() {
+                    "SKIP" => Ok("Skip".to_string()),
+                    "FAIL" => Ok("Fail".to_string()),
+                    _ => Err(PyValueError::new_err(format!(
+                        "Invalid mode {:?}: expected one of 'skip', 'fail'",
+                        m
+                    ))),
+                })
+                .transpose()?;
+            let behavior_str = behavior
+                .map(|b| match b.to_uppercase().as_str() {
+                    "RESTRICT" => Ok("Restrict".to_string()),
+                    "CASCADE" => Ok("Cascade".to_string()),
+                    _ => Err(PyValueError::new_err(format!(
+                        "Invalid behavior {:?}: expected one of 'restrict', 'cascade'",
+                        b
+                    ))),
+                })
+                .transpose()?;
             let request = DropNamespaceRequest {
                 id: Some(namespace_path),
                 mode: mode_str,

python/src/expr.rs

@@ -8,7 +8,9 @@
 //! DataFusion [`Expr`] nodes, bypassing SQL string parsing.
 
 use arrow::{datatypes::DataType, pyarrow::PyArrowType};
+use datafusion_common::ScalarValue;
 use lancedb::expr::{DfExpr, col as ldb_col, contains, expr_cast, lit as df_lit, lower, upper};
+use pyo3::types::PyBytes;
 use pyo3::{Bound, PyAny, PyResult, exceptions::PyValueError, prelude::*, pyfunction};
 
 /// A type-safe DataFusion expression.
@@ -141,7 +143,7 @@ pub fn expr_col(name: &str) -> PyExpr {
 
 /// Create a literal value expression.
 ///
-/// Supported Python types: `bool`, `int`, `float`, `str`.
+/// Supported Python types: `bool`, `int`, `float`, `str`, `bytes`.
 #[pyfunction]
 pub fn expr_lit(value: Bound<'_, PyAny>) -> PyResult<PyExpr> {
     // bool must be checked before int because bool is a subclass of int in Python
@@ -157,8 +159,12 @@ pub fn expr_lit(value: Bound<'_, PyAny>) -> PyResult<PyExpr> {
     if let Ok(s) = value.extract::<String>() {
         return Ok(PyExpr(df_lit(s)));
     }
+    if value.is_instance_of::<PyBytes>() {
+        let bytes = value.extract::<Vec<u8>>()?;
+        return Ok(PyExpr(df_lit(ScalarValue::Binary(Some(bytes)))));
+    }
     Err(PyValueError::new_err(format!(
-        "unsupported literal type: {}. Supported: bool, int, float, str",
+        "unsupported literal type: {}. Supported: bool, int, float, str, bytes",
         value.get_type().name()?
     )))
 }