From 589c6cdad00dc4a83fa5adf97cd2d8279739d44f Mon Sep 17 00:00:00 2001
From: Christopher Baer <30447746+christopherbaer@users.noreply.github.com>
Date: Fri, 20 Mar 2026 10:02:22 -0700
Subject: [PATCH] fix(gemini-embeddings): convert task_type to camelCase
 taskType for Gemini API (#24191)

The Gemini REST API documents the embedding task type parameter as
camelCase `taskType`. The existing transformation functions convert
`dimensions` to `outputDimensionality` but miss the parallel
`task_type` to `taskType` conversion. This adds that conversion to
both `transform_openai_input_gemini_content` (batchEmbedContents path)
and `transform_openai_input_gemini_embed_content` (embedContent path).

Fixes #24190
---
 .../batch_embed_content_transformation.py     |  4 ++
 .../vertex_ai/test_gemini_batch_embeddings.py | 39 +++++++++++++++++++
 2 files changed, 43 insertions(+)

diff --git a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py
index 0f6d85525d..08831a8215 100644
--- a/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py
+++ b/litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py
@@ -152,6 +152,8 @@ def transform_openai_input_gemini_content(
     gemini_params = optional_params.copy()
     if "dimensions" in gemini_params:
         gemini_params["outputDimensionality"] = gemini_params.pop("dimensions")
+    if "task_type" in gemini_params:
+        gemini_params["taskType"] = gemini_params.pop("task_type")
 
     requests: List[EmbedContentRequest] = []
     if isinstance(input, str):
@@ -196,6 +198,8 @@ def transform_openai_input_gemini_embed_content(
     gemini_params = optional_params.copy()
     if "dimensions" in gemini_params:
         gemini_params["outputDimensionality"] = gemini_params.pop("dimensions")
+    if "task_type" in gemini_params:
+        gemini_params["taskType"] = gemini_params.pop("task_type")
 
     input_list = [input] if isinstance(input, str) else input
     parts: List[PartType] = []
diff --git a/tests/litellm/llms/vertex_ai/test_gemini_batch_embeddings.py b/tests/litellm/llms/vertex_ai/test_gemini_batch_embeddings.py
index 1ed1de01b5..a8e427d3bc 100644
--- a/tests/litellm/llms/vertex_ai/test_gemini_batch_embeddings.py
+++ b/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,
+    transform_openai_input_gemini_content,
     transform_openai_input_gemini_embed_content,
 )
 from litellm.types.utils import EmbeddingResponse
@@ -396,6 +397,44 @@ def test_transform_with_optional_params():
     assert result["taskType"] == "SEMANTIC_SIMILARITY"
 
 
+def test_task_type_mapped_to_camel_case_batch():
+    """Test that snake_case task_type is converted to camelCase taskType for batchEmbedContents."""
+    result = transform_openai_input_gemini_content(
+        input="test text",
+        model="text-embedding-004",
+        optional_params={"task_type": "RETRIEVAL_DOCUMENT"},
+    )
+    for request in result["requests"]:
+        assert "taskType" in request
+        assert request["taskType"] == "RETRIEVAL_DOCUMENT"
+        assert "task_type" not in request
+
+
+def test_task_type_mapped_to_camel_case_embed_content():
+    """Test that snake_case task_type is converted to camelCase taskType for embedContent."""
+    result = transform_openai_input_gemini_embed_content(
+        input=["test text"],
+        model="gemini-embedding-2-preview",
+        optional_params={"task_type": "RETRIEVAL_DOCUMENT"},
+        resolved_files=None,
+    )
+    assert "taskType" in result
+    assert result["taskType"] == "RETRIEVAL_DOCUMENT"
+    assert "task_type" not in result
+
+
+def test_task_type_camel_case_passthrough():
+    """Test that camelCase taskType passed directly is preserved."""
+    result = transform_openai_input_gemini_embed_content(
+        input=["test text"],
+        model="gemini-embedding-2-preview",
+        optional_params={"taskType": "SEMANTIC_SIMILARITY"},
+        resolved_files=None,
+    )
+    assert result["taskType"] == "SEMANTIC_SIMILARITY"
+    assert "task_type" not in result
+
+
 def test_dimensions_mapped_to_output_dimensionality():
     """Test that OpenAI 'dimensions' param is mapped to Gemini 'outputDimensionality'."""
     input_data = ["test text"]