docs: adopt Embedded/Distributed terminology across docs and SDKs (#4713)

## Summary

Adopts the product terminology guide across the docs site pages, SDK
docstrings and error messages. "LanceDB OSS" and local mode become
**LanceDB Embedded**. "Remote" as the execution model becomes **LanceDB
Distributed**, which LanceDB Enterprise provides. Where free vs paid is
the point, pages contrast "LanceDB Embedded (open source)" with "LanceDB
Enterprise". This is a first trial of the Embedded name, so reviewers
can judge it in context.

- **Embedded vs Distributed** for where execution runs (`optimize()`,
consistency, branching, maintenance). Main pages define it once:
"LanceDB Enterprise runs LanceDB Distributed: …"
- **LanceDB Cloud** is kept only for legacy-Cloud specifics (`db://`
region, the brute-force KNN limit) and marked legacy. "Cloud and
Enterprise" becomes Enterprise or Distributed.
- **Plan executor** becomes "executor" or the fleet name.
- **Error messages** change, e.g. `optimize is not supported for remote
tables.` → `... in LanceDB Distributed.` Tests that match on them are
updated.
- **Vale rules** (`EmbeddedName`, `OSSProduct`, `RemoteExecution`,
`DistributedEnterprise`, `LegacyCloud`, `InternalNames`) enforce the
terms in prose.

Unchanged: identifiers (`availability.oss`, the `remote` module and
feature, `RemoteTable`, `db://`), heading anchors, internal code
comments, and Geneva.

## Evidence

- Rust lib tests for the changed messages and the Python tests in
`test_remote_db.py`, `test_materialized_views.py` and `test_table.py`
pass locally.
- Not run locally:
- The Node tests: `remote.test.ts` and `table.test.ts` have updated
assertions.
  - Vale.
  - The `experimental_functions`-marked materialized-view tests.
- `docs/src` API reference was hand-edited to match the docstrings
rather than regenerated.

## Merge Risk

**Door:** two-way; prose and message text only.
**Blast Radius:** `docs`, `error-messages`: callers matching on old
error strings would break.

Companion PRs: lancedb/docs (assembler labels, STYLE.md) and sophon
(Enterprise pages).

🤖 Generated with [Claude Code](https://claude.com/claude-code)

---------

Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>

docs/web/tutorials/agents/nvidia-rag-blueprint/index.mdx

@@ -6,7 +6,7 @@ description: "Use LanceDB as the retrieval layer for NVIDIA RAG Blueprint with a
 
 ## What this tutorial shows {#what-this-tutorial-shows}
 
-If you are using [NVIDIA RAG Blueprints](https://build.nvidia.com/blueprints) and want to evaluate LanceDB in that stack, this tutorial gives you a concrete starting point. It shows how to use LanceDB as the retrieval layer for a Docker-based NVIDIA RAG deployment with a small, script-driven reference integration where LanceDB OSS is embedded directly in the NVIDIA containers, the collection is prepared ahead of time, and the RAG server retrieves from it for search and generation. The example is intentionally retrieval-only, but it also includes hybrid search and reranker selection so you can see how LanceDB fits into a realistic NVIDIA retrieval workflow.
+If you are using [NVIDIA RAG Blueprints](https://build.nvidia.com/blueprints) and want to evaluate LanceDB in that stack, this tutorial gives you a concrete starting point. It shows how to use LanceDB as the retrieval layer for a Docker-based NVIDIA RAG deployment with a small, script-driven reference integration where LanceDB Embedded runs directly in the NVIDIA containers, the collection is prepared ahead of time, and the RAG server retrieves from it for search and generation. The example is intentionally retrieval-only, but it also includes hybrid search and reranker selection so you can see how LanceDB fits into a realistic NVIDIA retrieval workflow.
 
 <Tip>
 The runnable example for this tutorial lives in the
@@ -31,7 +31,7 @@ yet teach NVIDIA's ingestor how to write new documents into LanceDB automaticall
 
 ## Deployment model {#deployment-model}
 
-This reference integration uses **LanceDB OSS as an embedded retrieval library**, not as a separate
+This reference integration uses **LanceDB Embedded as an embedded retrieval library**, not as a separate
 database service. In practice, `APP_VECTORSTORE_NAME` is set to `lancedb`, `APP_VECTORSTORE_URL`
 points to a local filesystem path inside the NVIDIA containers, the LanceDB collection is prepared
 ahead of time, and the NVIDIA RAG server loads the LanceDB adapter to retrieve directly from that