fix(gemini): assign correct indices in batch embedding response (#25656)

### Background

The Gemini batchEmbedContents response handler hardcoded `index=0` for
every embedding in the response. Any consumer relying on the OpenAI-format
`index` field to match embeddings back to inputs would silently get wrong
associations.

### Changes

Use `enumerate` in `process_response` so each embedding gets its
positional index instead of 0.

### Test Plan

Added unit test asserting sequential indices and correct vector ordering
for a 3-element batch response.

litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py

@@ -292,10 +292,10 @@ def process_response(
     _predictions: VertexAIBatchEmbeddingsResponseObject,
 ) -> EmbeddingResponse:
     openai_embeddings: List[Embedding] = []
-    for embedding in _predictions["embeddings"]:
+    for idx, embedding in enumerate(_predictions["embeddings"]):
         openai_embedding = Embedding(
             embedding=embedding["values"],
-            index=0,
+            index=idx,
             object="embedding",
         )
         openai_embeddings.append(openai_embedding)

tests/litellm/llms/vertex_ai/test_gemini_batch_embeddings.py

@@ -22,6 +22,7 @@ from litellm.llms.vertex_ai.gemini_embeddings.batch_embed_content_transformation
     _is_multimodal_input,
     _parse_data_url,
     process_embed_content_response,
+    process_response,
     transform_openai_input_gemini_content,
     transform_openai_input_gemini_embed_content,
 )
@@ -563,3 +564,32 @@ def test_vertex_ai_text_only_embedding_uses_embed_content():
         assert data["content"]["parts"][0]["text"] == "Hello, world!"
         assert len(response.data) == 1
 
+
+def test_batch_embeddings_response_has_correct_indices_and_order():
+    """Test that process_response assigns sequential indices and preserves order."""
+    response_json = {
+        "embeddings": [
+            {"values": [0.1, 0.2, 0.3]},
+            {"values": [0.4, 0.5, 0.6]},
+            {"values": [0.7, 0.8, 0.9]},
+        ]
+    }
+    expected_values = [[0.1, 0.2, 0.3], [0.4, 0.5, 0.6], [0.7, 0.8, 0.9]]
+
+    model_response = EmbeddingResponse()
+    result = process_response(
+        input=["first", "second", "third"],
+        model_response=model_response,
+        model="text-embedding-004",
+        _predictions=response_json,
+    )
+
+    assert len(result.data) == 3
+    for i, embedding in enumerate(result.data):
+        assert (
+            embedding.index == i
+        ), f"embedding {i} has index={embedding.index}, expected {i}"
+        assert (
+            embedding.embedding == expected_values[i]
+        ), f"embedding {i} has wrong values: {embedding.embedding}"
+