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v0.9.3

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docs(agentic-ci): document prefer_dict_key_access in UserTemplateSand…

…boxEnvironment (#952)

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: Andre Manoel <165937436+andreatnvidia@users.noreply.github.com>

v0.9.2

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docs: add install dependency notice (#912)

v0.9.1

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fix: unify Parquet schemas when reading (#858)

* fix: normalize structured JSON numbers (#857)

Signed-off-by: Steve Han <sthan@nvidia.com>

* fix: handle boolean JSON subschemas (#857)

Signed-off-by: Steve Han <sthan@nvidia.com>

* fix: unify parquet schemas when reading (#857)

Signed-off-by: Steve Han <sthan@nvidia.com>

---------

Signed-off-by: Steve Han <sthan@nvidia.com>

v0.9.0

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fix: bump vulnerable dependencies (#855)

Raise the minimum and locked versions for aiohttp, MCP, setuptools,
and Soup Sieve to releases recommended by the security scan.

Keep the documented Bleach exception because nbconvert still requires it
and no patched release is available.

Signed-off-by: Nabin Mulepati <nmulepati@nvidia.com>

v0.9.0rc2

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fix: bump vulnerable dependencies (#855)

Raise the minimum and locked versions for aiohttp, MCP, setuptools,
and Soup Sieve to releases recommended by the security scan.

Keep the documented Bleach exception because nbconvert still requires it
and no patched release is available.

Signed-off-by: Nabin Mulepati <nmulepati@nvidia.com>

v0.9.0rc1

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chore: regenerate only changed colab notebooks (#838)

* chore: regenerate only changed colab notebooks

Pass changed notebook sources through the existing generator filter to avoid unrelated cell ID churn. Remove stale generated notebooks when sources are deleted or renamed.

Closes #413

Signed-off-by: Andre Manoel <amanoel@nvidia.com>

* fix: detect new colab notebooks

Signed-off-by: Andre Manoel <amanoel@nvidia.com>

* fix: preserve notebook filename boundaries

Signed-off-by: Andre Manoel <amanoel@nvidia.com>

---------

Signed-off-by: Andre Manoel <amanoel@nvidia.com>
Co-authored-by: Nabin Mulepati <nmulepati@nvidia.com>

v0.8.0

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chore: bump cryptography dependency to address <48 CVE (#821)

Signed-off-by: Matt Kornfield <mkornfield@nvidia.com>

v0.8.0rc1

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chore: bump cryptography dependency to address <48 CVE (#821)

Signed-off-by: Matt Kornfield <mkornfield@nvidia.com>

v0.7.0

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fix: drop invalid expression rows (#757)

* fix: drop invalid expression rows

Handle per-row expression render and cast failures by dropping only affected rows, preserving row identity in sync and async full-column paths, and failing when no valid rows remain.

Fixes #749

* fix: fail async all-dropped expressions

* fix: extend expression row drops to root from_scratch tasks

Address PR #757 review feedback from @andreatgretel:

- Root expression columns are scheduled as ``from_scratch`` rather than
  ``batch``, so the prior ``_task_supports_row_drops`` check (which gated
  on ``task_type == "batch"``) silently dropped the whole row group on a
  user template error instead of raising. Accept both task types and route
  expression ``from_scratch`` tasks through a shared row-drop helper that
  preserves indices and honors the fatal-when-all-dropped contract.
- ``DataFrame.to_dict(orient='records')`` returns ``[]`` for any 0-column
  DataFrame regardless of row count, so a root expression dispatched
  against a fresh row group buffer would otherwise skip rendering
  entirely. Synthesize empty per-row dicts in ``ExpressionColumnGenerator``
  so the loop fires once per row.
- Add async coverage for the root-expression fatal path and for the
  skip-then-drop interaction (async equivalent of the existing sync
  skip-aware regression).

v0.7.0rc1

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fix: drop invalid expression rows (#757)

* fix: drop invalid expression rows

Handle per-row expression render and cast failures by dropping only affected rows, preserving row identity in sync and async full-column paths, and failing when no valid rows remain.

Fixes #749

* fix: fail async all-dropped expressions

* fix: extend expression row drops to root from_scratch tasks

Address PR #757 review feedback from @andreatgretel:

- Root expression columns are scheduled as ``from_scratch`` rather than
  ``batch``, so the prior ``_task_supports_row_drops`` check (which gated
  on ``task_type == "batch"``) silently dropped the whole row group on a
  user template error instead of raising. Accept both task types and route
  expression ``from_scratch`` tasks through a shared row-drop helper that
  preserves indices and honors the fatal-when-all-dropped contract.
- ``DataFrame.to_dict(orient='records')`` returns ``[]`` for any 0-column
  DataFrame regardless of row count, so a root expression dispatched
  against a fresh row group buffer would otherwise skip rendering
  entirely. Synthesize empty per-row dicts in ``ExpressionColumnGenerator``
  so the loop fires once per row.
- Add async coverage for the root-expression fatal path and for the
  skip-then-drop interaction (async equivalent of the existing sync
  skip-aware regression).