fix: chunk-budget blowups, FastEmbed built-ins, and re-embed gaps

Follow-up review pass over the embeddings branch.

- Fold an oversized header back into the body, and drop header duplication
  when it would leave under a quarter of the chunk budget. A header at or
  over max_tokens collapsed the body budget to one token, so a document
  became one chunk per body token, each still over the cap: a 95 KB file
  produced 20k chunks of 2563 tokens against a 1250 cap. Also clamp
  max_tokens to at least 1, as the strategy chunkers already do.
- Emit a header-only document as its own chunk. With no body piece to
  attach it to, splitting returned nothing and the document was dropped
  from the index with no error and no log line.
- Skip add_custom_model for a repository FastEmbed already ships. It
  rejects a name it knows, so configuring any of its ~30 built-ins
  (MiniLM, bge, e5, gte, ...) failed every embed call and every query.
- Decide "the user chose this model" by comparing against the field
  default rather than model_fields_set, which is true for anything read
  from .env. Every setup script has always written EMBEDDINGS_NAME, so an
  upgraded remote-embeddings install inherited mpnet's 384-token window
  and silently clipped ~80% off every chunk.
- Cut tiktoken splits at character offsets instead of decoding each token
  window. A multi-byte character straddling a boundary decoded to U+FFFD
  on both sides, destroying one character at roughly one boundary in five
  on CJK text -- including at the default max_tokens of 2000.
- Let the re-embed script open a FAISS index whose width does not match
  the configured model. That mismatch is the main reason to run it, and
  the error recommending the script was raised by the script itself, so
  the advice failed on every source.
- Re-embed graph_nodes.name_embedding when GraphRAG is enabled. Those
  vectors seed every traversal and share the chunk vectors' width, so a
  same-width model swap left the graph retrieving from the old space with
  nothing to report it.
- Prefetch the models before copying the application source, so editing
  any file no longer re-downloads ~780 MB of artifacts on every build.
- Mirror the setup.sh embedding menu into setup.ps1: granite default,
  legacy mpnet as an explicit option, and both engine flows updated.
  Windows users were otherwise stranded on mpnet with no granite path.
- Drop the unused EmbeddingsWrapper.tokenizer property.

docs/content/Models/embeddings.md

@@ -111,7 +111,11 @@ Switching between same-width models therefore still requires re-embedding:
 python -m application.scripts.reembed
 ```
 
-Run it after changing `EMBEDDINGS_NAME` and before serving queries. See [Upgrading](/upgrading) for the granite migration specifically. Changing to a model of a *different* width is not supported by the script — re-ingest those sources instead.
+Run it after changing `EMBEDDINGS_NAME` and before serving queries. See [Upgrading](/upgrading) for the granite migration specifically.
+
+Changing to a model of a *different* width is supported for FAISS: the script rebuilds the index at the new width and keeps the existing chunk ids. For `pgvector` the vector column is sized at creation time, so a width change there still means re-ingesting those sources.
+
+With `GRAPHRAG_ENABLED`, the script also rewrites `graph_nodes.name_embedding` on `pgvector`. Those vectors seed every graph traversal, and they share the chunk vectors' width, so leaving them behind degrades graph retrieval just as silently.
 
 ## Adding Support for Other Embedding Models