docs: correct data workflows, SQL and operations (#4534)

This PR is part of the documentation revamp. Once the full set of PRs is
merged, we’ll publish the combined site to staging for review before
updating docs.lancedb.com.

The curation, SQL and operations pages contain outdated claims and
examples that do not reliably work as written.

This corrects those pages against the reviewed SDK and local server
evidence, including prepared statements, background jobs, cleanup and
monitoring. Integration guides are grouped with their examples in #4533.

Function and Secret-object documentation stays unpublished until its
release. The two Function pages are listed in `.mintignore` and left out
of the navigation, and the sections about them on other pages are MDX
comments.

Stacked on #4533. Each MDX file has one PR owner. Rebase onto main after
#4533 is squash-merged.

Local assembly, Mint validation and link checks pass, and the assembled
site shows no Function or Secret-object content. A local release
rehearsal that uncomments the drafts and restores the navigation also
passes. Hold publication until this PR and lancedb/sophon#8688 are on
`main`.

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}
@@ -41,6 +49,9 @@ Once configured, the embedding function will automatically generate embeddings w
 into the table. Query-time behavior depends on SDK: Python/TypeScript can query with text directly,
 while Rust examples typically compute query embeddings explicitly before vector search.
 
+In the Rust examples, replace `your-openai-api-key` with your OpenAI API key. In
+application code, load credentials from environment variables or a secrets manager.
+
 <CodeGroup>
     <CodeBlock filename="Python" language="python" icon="python">
     {PyOpenaiEmbeddings}