docs: declare where storage, training and integration pages apply

Storage configuration from the connection is embedded only, since a
deployment configures its own. Training data loading, PyTorch, embedding
functions, pandas, Polars and Pydantic work with local and deployment
tables, and the Hugging Face Hub integration is embedded only.

A local table cannot be pickled for DataLoader workers, so the PyTorch
page says to wrap it in a Permutation; a LazyFrame over a whole table,
to_pandas in the LangChain example and to_arrow in the DuckDB note are
local only; and query text is embedded in the client for both offerings.

docs/web/integrations/data/duckdb.mdx

@@ -6,7 +6,7 @@ description: "Learn how to use the DuckDB-Lance extension to query Lance tables
 
 LanceDB integrates with [DuckDB](https://duckdb.org/) through the [Lance extension](https://github.com/lance-format/lance-duckdb) for DuckDB. In this page, we'll show how LanceDB manages table lifecycle, and DuckDB provides SQL analytics (including joins) and search over those tables.
 
-Note that earlier versions of LanceDB used to recommend converting Lance tables to Arrow tables via `table.to_arrow()`. Although this method is still available (because DuckDB [natively scans Arrow tables](https://duckdb.org/2021/12/03/duck-arrow)), it is no longer the recommended workflow for working with Lance tables in DuckDB. This page shows how to use the Lance extension with namespace-attached LanceDB tables, allowing you to pushdown SQL queries directly to the Lance layer.
+Note that earlier versions of LanceDB used to recommend converting Lance tables to Arrow tables via `table.to_arrow()`. Although this method is still available for local tables (because DuckDB [natively scans Arrow tables](https://duckdb.org/2021/12/03/duck-arrow)), it is no longer the recommended workflow for working with Lance tables in DuckDB. This page shows how to use the Lance extension with namespace-attached LanceDB tables, allowing you to pushdown SQL queries directly to the Lance layer.
 
 
 ## Install {#install}

docs/web/integrations/data/pandas_and_pyarrow.mdx

@@ -1,6 +1,12 @@
 ---
 title: "Pandas and PyArrow"
 sidebarTitle: "Pandas & PyArrow"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    DataFrames go into `create_table` and come back from query results the same
+    way for local and deployment tables.
 
 ---
 

docs/web/integrations/data/polars_arrow.mdx

@@ -1,6 +1,13 @@
 ---
 title: "Polars"
 sidebarTitle: "Polars & Arrow"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Polars data goes into `create_table`, and query results come back as Polars
+    DataFrames, for local and deployment tables alike; a LazyFrame over a whole
+    table is local only.
 
 ---
 
@@ -36,7 +43,7 @@ Run vector search and keep the results as a Polars `DataFrame` for further proce
 
 ## Work with LazyFrames {#work-with-lazyframes}
 
-When you want to operate on the entire table (potentially larger than RAM), convert to a Polars `LazyFrame` so you can chain transformations without loading everything at once.
+When you want to operate on the entire table (potentially larger than RAM), convert to a Polars `LazyFrame` so you can chain transformations without loading everything at once. This works for local tables only; against a deployment, `search(...).to_polars()` returns an eager DataFrame of query results.
 
 <CodeBlock filename="Python" language="Python" icon="python">
   {PyPlatformsPolarsLazyframe}

docs/web/integrations/data/pydantic.mdx

@@ -1,6 +1,13 @@
 ---
 title: "Pydantic"
 sidebarTitle: "Pydantic"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    The SDK converts `LanceModel` schemas to Arrow, and query rows back to
+    models, in your process, so they work the same with local and deployment
+    tables.
 
 ---