fix(similarity): address code review - column-only queries, configurable batch size, WCAG touch targets

- Use column-only query in embeddings overview to reduce memory for 100K+ files
- Add embedding_backfill_batch_size config setting (default 50)
- Fix WCAG touch target on backfill button (min-height/min-width 44px)
- Add inline comment explaining 3 chars/token truncation estimate
- Import settings in compute_embedding task for configurable batch size

Co-authored-by: christianlouis <361235+christianlouis@users.noreply.github.com>

app/config.py

@@ -339,6 +339,13 @@ class Settings(BaseSettings):
             "Set this below the model's context window (e.g. 8000 for an 8192-token model)."
         ),
     )
+    embedding_backfill_batch_size: int = Field(
+        default=50,
+        description=(
+            "Maximum number of files to queue for embedding computation per "
+            "backfill run.  Keeps the worker and embedding API load bounded."
+        ),
+    )
 
     # Text quality check - AI-based assessment of embedded PDF text
     enable_text_quality_check: bool = Field(