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/deployment/storage.mdx

@@ -4,6 +4,13 @@ sidebarTitle: "Storage"
 description: "Choosing and configuring where LanceDB stores its data -- how you deploy an embedded database."
 icon: database
 keywords: ["storage", "s3", "gcs", "azure", "object store", "configuration", "deploy"]
+availability:
+  oss: available
+  enterprise: unavailable
+  summary: >-
+    The embedded library chooses and configures its storage backend from the
+    connection URI and `storage_options`. On a deployment, storage is part of
+    the deployment's own configuration instead.
 ---
 
 LanceDB's storage layer is built on modular, disk-first components. That design makes it flexible enough to run across local NVMe, EBS, EFS, and any object store that exposes an S3-compatible API. It also supports region-specific backends such as [Tencent COS](/storage/configuration#tencent-cos) and cache-acceleration layers such as [GooseFS](/storage/configuration#goosefs).

docs/web/embedding/index.mdx

@@ -3,6 +3,13 @@ title: "Managing Embeddings"
 sidebarTitle: "Managing Embeddings"
 description: "Use the embedding API in LanceDB -- registry, functions, schemas, and multi-language SDK support."
 icon: "bars"
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Embedding functions run in your process, whether the table is local or on a
+    deployment, which stores their configuration but never runs them. Python
+    and TypeScript also embed query text; in Rust you embed queries yourself.
 ---
 
 import {
@@ -30,8 +37,9 @@ the data, so vectors that are close to each other are considered similar.
 
 LanceDB provides an embedding function registry in OSS as well as its Enterprise versions
 ([see below](#embeddings-in-lancedb-enterprise))
-that automatically generates vector embeddings during data ingestion. Automatic query-time embedding
-generation is available in LanceDB OSS, with SDK-specific query ergonomics. The API abstracts
+that automatically generates vector embeddings during data ingestion. Query text is embedded
+automatically too, in the client, for local and Enterprise tables alike, with SDK-specific query
+ergonomics. The API abstracts
 embedding generation, allowing you to focus on your application logic.
 
 ## Embedding Registry {#embedding-registry}

docs/web/integrations/ai/huggingface.mdx

@@ -2,6 +2,12 @@
 title: "Hugging Face Hub"
 sidebarTitle: "Hugging Face"
 description: "Use LanceDB directly on Lance datasets hosted on the Hugging Face Hub for multimodal search and retrieval."
+availability:
+  oss: available
+  enterprise: unavailable
+  summary: >-
+    The embedded library opens Lance datasets on the Hugging Face Hub in place
+    through `hf://` URIs. A deployment does not read `hf://` datasets.
 ---
 
 [Hugging Face Hub](https://huggingface.co/datasets?format=format:lance&sort=trending) is a popular platform for sharing machine learning datasets, models, and other resources.

docs/web/integrations/ai/langchain.mdx

@@ -80,6 +80,9 @@ It returns list of ids of the added texts.
   {PyFrameworksLangchainAddTexts}
 </CodeBlock>
 
+Against a deployment, `tbl.to_pandas()` raises in the synchronous Python client;
+read the rows with a query instead, such as `tbl.search().limit(100).to_pandas()`.
+
 ------
 
 

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.
 
 ---
 

docs/web/training/data-loading.mdx

@@ -3,6 +3,12 @@ title: "Loading Data for Model Training"
 sidebarTitle: "Data Loading & Shuffles"
 description: "Stream, shuffle, transform, and resume model training data with LanceDB."
 icon: boxes-stacked
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    Streaming, shuffling and resuming training data work with local,
+    object-storage and deployment tables in the Python SDK.
 ---
 
 LanceDB provides an excellent data backend for training machine learning models. For basic use cases, a `Table` can be fed directly into a data loader. For a more complete solution, a `StreamingDataset` adds prefetching, elastic determinism, resumability, multithreaded transformations, and more.

docs/web/training/torch.mdx

@@ -3,6 +3,12 @@ title: "PyTorch Integration"
 sidebarTitle: "PyTorch"
 description: Learn how to use LanceDB with PyTorch for training and inference.
 icon: fire
+availability:
+  oss: available
+  enterprise: available
+  summary: >-
+    The PyTorch integration works with local, object-storage and deployment
+    tables in the Python SDK.
 ---
 
 LanceDB provides a seamless integration with PyTorch for training and inference. This allows you to use LanceDB as a backend for your PyTorch models, and to use PyTorch for training and inference. You can use LanceDB to store your data, and PyTorch to train your models.
@@ -106,7 +112,7 @@ for batch in dataloader:
 
 ## Using multiple DataLoader workers {#using-multiple-dataloader-workers}
 
-Set `num_workers > 0` to read from LanceDB in multiple PyTorch worker processes. LanceDB tables and `Permutation` objects are picklable, so each worker reopens the table after it starts.
+Set `num_workers > 0` to read from LanceDB in multiple PyTorch worker processes. `Permutation` objects and tables on a deployment are picklable, so each worker reopens the table after it starts. A local table is not, so wrap it in a `Permutation`, as below, to read it from several workers.
 
 Prefer the `forkserver` start method when using multiple workers. LanceDB uses internal threads, so the default `fork` method is unsafe; `forkserver` avoids that while being cheaper to start than `spawn`, and it is set to become the Python default. See [the performance guide](/search#performance) for more multiprocessing guidance.