Update xarray version in docs build

PiperOrigin-RevId: 813456305

Author: shoyer

docs/high-level.ipynb

@@ -166,11 +166,15 @@
       },
       "cell_type": "markdown",
       "source": [
-        "Weather/climate datasets are typically generated and stored in pancake chunks, but pencil chunks are more useful for most analytics queries, which requires large histories of weather at a single location. Intermediate \"compromise\" chunks can sometimes be a good idea, although if performance and flexibility are critical it may be worth storing multiple copies of your data in different formats. Using Zarr v3's sharding feature to group smaller chunks into shards can also help mitigate the challenges of picking an optimal chunk size.\n",
+        "Weather/climate datasets are typically generated and stored in \"pancake\" chunks, but \"pencil\" chunks are more suitable for most analytics queries, which requires large histories of weather at small numbers of locations.\n",
         "\n",
         "Using the right chunks is *absolutely essentially* for efficient operations with Xarray-Beam and Zarr. For example, reading data from a single location across all times (a \"pencil\" query) is extremely inefficient for a dataset stored in \"pancake\" chunks -- it would require loading the entire dataset from disk!\n",
         "\n",
-        "Rechunking is a fundamentally an expensive operation (it requires multiple complete reads/writes of a dataset from disk), but in Xarray-Beam it's straightforward, via {py:meth}`~xarray_beam.Dataset.rechunk`."
+        "Rechunking is a fundamentally an expensive operation (it requires multiple complete reads/writes of a dataset from disk), but in Xarray-Beam it's straightforward, via {py:meth}`~xarray_beam.Dataset.rechunk`.\n",
+        "\n",
+        "```{tip}\n",
+        "Intermediate \"compromise\" chunks can sometimes be a good idea, although if performance and flexibility are critical it may be worth storing multiple copies of your data in different formats. Using Zarr v3's sharding feature in {py:meth}`~xarray_beam.Dataset.to_zarr` to group smaller chunks into shards can also help mitigate the challenges of picking an optimal chunk size.\n",
+        "```"
       ]
     },
     {

docs/requirements.txt

@@ -14,5 +14,5 @@ immutabledict==4.2.1
 numpy==2.2.6
 pandas==2.3.2
 pooch==1.8.2
-xarray==2025.9.0
+xarray==2025.9.1
 zarr==3.1.3

xarray_beam/__init__.py

@@ -52,4 +52,4 @@
     DatasetToZarr,
 )
 
-__version__ = '0.10.0'  # automatically synchronized to pyproject.toml
+__version__ = '0.10.1'  # automatically synchronized to pyproject.toml