diff --git a/backend/open_webui/retrieval/vector/dbs/pgvector.py b/backend/open_webui/retrieval/vector/dbs/pgvector.py
index 75daeccd12..18963bdf9a 100644
--- a/backend/open_webui/retrieval/vector/dbs/pgvector.py
+++ b/backend/open_webui/retrieval/vector/dbs/pgvector.py
@@ -223,6 +223,9 @@ class PgvectorClient(VectorDBBase):
             )
 
         if not existing_index_def:
+            if index_method == 'ivfflat' and not self._has_enough_ivfflat_training_rows():
+                return
+
             index_sql = (
                 f'CREATE INDEX IF NOT EXISTS {index_name} '
                 f'ON document_chunk USING {index_method} (vector {VECTOR_OPCLASS})'
@@ -237,6 +240,24 @@ class PgvectorClient(VectorDBBase):
                 f' {index_options}' if index_options else '',
             )
 
+    def _has_enough_ivfflat_training_rows(self) -> bool:
+        # ivfflat samples 50 rows per list to place its centroids, so recall stays poor until the table holds that many
+        min_training_rows = 50 * PGVECTOR_IVFFLAT_LISTS
+        row_count = self.session.execute(
+            text('SELECT count(*) FROM (SELECT 1 FROM document_chunk LIMIT :min_training_rows) AS sample'),
+            {'min_training_rows': min_training_rows},
+        ).scalar()
+
+        if row_count < min_training_rows:
+            log.info(
+                "Deferring vector index 'idx_document_chunk_vector' until document_chunk holds %s rows to cluster on, "
+                'it has %s. Searches run as an exact scan until then.',
+                min_training_rows,
+                row_count,
+            )
+            return False
+        return True
+
     def _ensure_text_search_index(self) -> None:
         if PGVECTOR_PGCRYPTO:
             return