fix: fix inconsistent top_k validation in SentenceTransformersDiversityRanker (#9698)

* Fix inconsistent top_k validation in SentenceTransformersDiversityRanker
- change elif to if in run() method to ensure top_k validation always
  runs regardless of whatever top_k comes from init or runtime
- Both scenarios now consistently raise ValueError with descriptive
  message format: 'top_k must be between 1 and X, but got Y'
- Fixes inconsistency where init top_k gave confusing MMR error while
  runtime top_k gave clear validation error

* improvements

---------

Co-authored-by: Stefano Fiorucci <stefanofiorucci@gmail.com>

Author: SaurabhLingam

haystack/components/rankers/sentence_transformers_diversity.py

@@ -353,7 +353,7 @@ def _maximum_margin_relevance(
 
         texts_to_embed = self._prepare_texts_to_embed(documents)
         doc_embeddings, query_embedding = self._embed_and_normalize(query, texts_to_embed)
-        top_k = top_k if top_k else len(documents)
+        top_k = min(top_k, len(documents))
 
         selected: list[int] = []
         query_similarities_as_tensor = query_embedding @ doc_embeddings.T
@@ -375,9 +375,8 @@ def _maximum_margin_relevance(
                 if mmr_score > best_score:
                     best_score = mmr_score
                     best_idx = idx
-            if best_idx is None:
-                raise ValueError("No best document found, check if the documents list contains any documents.")
-            selected.append(best_idx)
+            # loop condition ensures unselected docs exist with valid scores
+            selected.append(best_idx)  # type: ignore[arg-type]
 
         return [documents[i] for i in selected]
 
@@ -421,8 +420,8 @@ def run(
 
         if top_k is None:
             top_k = self.top_k
-        elif not 0 < top_k <= len(documents):
-            raise ValueError(f"top_k must be between 1 and {len(documents)}, but got {top_k}")
+        if top_k <= 0:
+            raise ValueError(f"top_k must be > 0, but got {top_k}")
 
         if self.strategy == DiversityRankingStrategy.MAXIMUM_MARGIN_RELEVANCE:
             if lambda_threshold is None:

releasenotes/notes/st-diversity-ranker-top-k-consistency-29535a76a07a8ff3.yaml

@@ -0,0 +1,5 @@
+---
+fixes:
+  - |
+    Ensure consistent behavior in `SentenceTransformersDiversityRanker`. Like other rankers, it now returns
+    all documents instead of raising an error when `top_k` exceeds the number of available documents.

test/components/rankers/test_sentence_transformers_diversity.py

@@ -381,7 +381,7 @@ def test_run_negative_top_k(self, similarity):
         query = "test"
         documents = [Document(content="doc1"), Document(content="doc2"), Document(content="doc3")]
 
-        with pytest.raises(ValueError, match="top_k must be between"):
+        with pytest.raises(ValueError, match="top_k must be > 0"):
             ranker.run(query=query, documents=documents, top_k=-5)
 
     @pytest.mark.parametrize("similarity", ["dot_product", "cosine"])