fix: Make TransformersSimilarityRanker run with single document list (

#6503)

* Make `TransformersSimilarityRanker` run with single document list

* Add release note

* Remove unused import in test

haystack/components/rankers/transformers_similarity.py

@@ -123,7 +123,7 @@ def run(self, query: str, documents: List[Document], top_k: Optional[int] = None
             self.device
         )
         with torch.inference_mode():
-            similarity_scores = self.model(**features).logits.squeeze()  # type: ignore
+            similarity_scores = self.model(**features).logits.squeeze(dim=1)  # type: ignore
 
         _, sorted_indices = torch.sort(similarity_scores, descending=True)
         ranked_docs = []

releasenotes/notes/fix-transformersranker-single-doc-12448c3d7bc8dc6c.yaml

@@ -0,0 +1,3 @@
+---
+fixes:
+  - Make `TransformersSimilarityRanker` run with a list containing a single document as input.

test/components/rankers/test_transformers_similarity.py

@@ -98,3 +98,16 @@ def test_run_top_k(self, query, docs_before_texts, expected_first_text):
 
         sorted_scores = sorted([doc.score for doc in docs_after], reverse=True)
         assert [doc.score for doc in docs_after] == sorted_scores
+
+    @pytest.mark.integration
+    def test_run_single_document(self):
+        """
+        Test if the component runs with a single document.
+        """
+        ranker = TransformersSimilarityRanker(model_name_or_path="cross-encoder/ms-marco-MiniLM-L-6-v2")
+        ranker.warm_up()
+        docs_before = [Document(content="Berlin")]
+        output = ranker.run(query="City in Germany", documents=docs_before)
+        docs_after = output["documents"]
+
+        assert len(docs_after) == 1