fix(gemini): don't emit empty choices on metadata-only stream chunks

web_search + reasoning makes Gemini stream mid-chunks that carry only
grounding/thought metadata — no content part, no finishReason.
_process_candidates skips content-less candidates and the existing
fallback only ran when finishReason was set, so choices stayed empty
and the downstream streaming handler raised IndexError on choices[0].
Emit an empty-delta choice for content-less chunks regardless of
finishReason.

Fixes #28884

litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py

@@ -3351,14 +3351,18 @@ class ModelResponseIterator:
                     self.has_seen_tool_calls = True
                     break
 
-        # Handle final chunk with finishReason but no content.
-        # _process_candidates skips candidates without "content",
-        # so the finish_reason from the final chunk is lost.
+        # _process_candidates skips candidates without a "content" part, so a
+        # content-less chunk leaves choices empty and the downstream streaming
+        # handler hits IndexError on choices[0]. This covers the final chunk
+        # (finishReason, no content) and mid-stream metadata-only chunks
+        # (grounding/web-search/thought, no content and no finishReason — seen
+        # with web_search + reasoning) by emitting an empty-delta choice.
         if not model_response.choices and _candidates:
             from litellm.types.utils import Delta, StreamingChoices
 
             for candidate in _candidates:
                 finish_reason_str = candidate.get("finishReason")
+                mapped_finish_reason = None
                 if finish_reason_str is not None:
                     if self.has_seen_tool_calls:
                         mapped_finish_reason = "tool_calls"
@@ -3366,14 +3370,14 @@ class ModelResponseIterator:
                         mapped_finish_reason = VertexGeminiConfig._check_finish_reason(
                             None, finish_reason_str
                         )
-                    choice = StreamingChoices(
-                        finish_reason=mapped_finish_reason,
-                        index=candidate.get("index", 0),
-                        delta=Delta(content=None, role=None),
-                        logprobs=None,
-                        enhancements=None,
-                    )
-                    model_response.choices.append(choice)
+                choice = StreamingChoices(
+                    finish_reason=mapped_finish_reason,
+                    index=candidate.get("index", 0),
+                    delta=Delta(content=None, role=None),
+                    logprobs=None,
+                    enhancements=None,
+                )
+                model_response.choices.append(choice)
 
         # Also handle the case where the final chunk has empty
         # content (e.g. text:"") WITH finishReason. In this case

tests/test_litellm/llms/vertex_ai/gemini/test_gemini_streaming_tool_call_finish_reason.py

@@ -302,3 +302,39 @@ def test_streaming_tool_call_finish_reason_with_empty_content_in_final_chunk():
     assert len(response2.choices) == 1
     # Must be "tool_calls", NOT "stop"
     assert response2.choices[0].finish_reason == "tool_calls"
+
+
+def test_streaming_metadata_only_chunk_does_not_yield_empty_choices():
+    """
+    web_search + reasoning makes Gemini emit mid-stream chunks that carry only
+    grounding/thought metadata — no content part and no finishReason.
+    _process_candidates skips content-less candidates, so without a fallback
+    `choices` is empty and the downstream streaming handler hits
+    `IndexError: list index out of range` on choices[0].
+
+    Ref: https://github.com/BerriAI/litellm/issues/28884
+    """
+    logging_obj = _make_logging_obj()
+    iterator = ModelResponseIterator(
+        streaming_response=iter([]),
+        sync_stream=True,
+        logging_obj=logging_obj,
+    )
+
+    # Grounding-only chunk: a candidate with groundingMetadata but no content
+    # part and no finishReason (what web_search + reasoning produces mid-stream).
+    metadata_only_chunk = {
+        "candidates": [
+            {
+                "index": 0,
+                "groundingMetadata": {"webSearchQueries": ["weather boston"]},
+            }
+        ]
+    }
+
+    response = iterator.chunk_parser(metadata_only_chunk)
+    assert response is not None
+    # Must expose at least one choice so downstream choices[0] is safe.
+    assert len(response.choices) == 1
+    assert response.choices[0].finish_reason is None
+    assert response.choices[0].delta.content is None