feat(python): declare indexed dedup sources for materialized views

docs/src/python/python.md

@@ -198,6 +198,33 @@ listing a storage directory.
 
 ::: lancedb.materialized_view.MaterializedViewDefinition
 
+::: lancedb.vector_dedup
+
+::: lancedb.materialized_view.VectorDedupSource
+
+For an existing indexed source, declare and materialize a dedup result without
+writing SQL. Creation captures the source version once and waits for the complete
+result; remote connections use the service's MV jobs.
+
+```python
+from lancedb import vector_dedup
+
+view = db.create_materialized_view(
+    "images_clean",
+    vector_dedup("images", column="phash", distance_threshold=4),
+)
+cleaned = view.table
+```
+
+To submit without waiting, call `db.create_materialized_view_async(...)` with the
+same source expression and wait on the returned job. To declare only, pass
+`with_no_data=True`, then use `view.refresh_async().wait()` when ready. Pass
+`dataset_version=...` to `vector_dedup` for an explicit snapshot. The default
+policy retains direct representatives (A-B-C without A-C keeps A and C), preserves
+the source, and materializes its original columns. Only within-segment/partition
+pairs are covered, using the index's distance representation.
+
+
 ## Views
 
 ::: lancedb.view.ViewDescription