25070,provide the databricks tool prompt cache

Signed-off-by: frank <frankzye@qq.com>

litellm/llms/databricks/chat/transformation.py

@@ -227,11 +227,18 @@ class DatabricksConfig(DatabricksBase, OpenAILikeChatConfig, AnthropicConfig):
         if description is not None:
             function_params["description"] = cast(Union[dict, str], description)
 
-        return DatabricksTool(
+        databricks_tool = DatabricksTool(
             type="function",
             function=function_params,
         )
 
+        if tool.get("cache_control"):
+            databricks_tool["cache_control"] = cast(dict, tool.get("cache_control"))
+
+        return databricks_tool
+
+
+
     def _map_openai_to_dbrx_tool(self, model: str, tools: List) -> List[DatabricksTool]:
         # if not claude, send as is
         if "claude" not in model:

litellm/types/llms/databricks.py

@@ -57,9 +57,10 @@ class DatabricksFunction(TypedDict, total=False):
     strict: bool
 
 
-class DatabricksTool(TypedDict):
+class DatabricksTool(TypedDict, total=False):
     function: DatabricksFunction
     type: Literal["function"]
+    cache_control: Optional[dict]
 
 
 class DatabricksMessage(TypedDict, total=False):

tests/test_litellm/llms/databricks/chat/test_databricks_chat_transformation.py

@@ -123,6 +123,12 @@ def test_convert_anthropic_tool_to_databricks_tool_without_description():
     assert databricks_tool is not None
     assert databricks_tool["type"] == "function"
     assert databricks_tool["function"].get("description") is None
+    assert "cache_control" not in databricks_tool
+
+    anthropic_tool["cache_control"] = {"type": "ephemeral"}
+    databricks_tool = config.convert_anthropic_tool_to_databricks_tool(anthropic_tool)
+    assert databricks_tool is not None
+    assert databricks_tool.get("cache_control") == {"type": "ephemeral"}
 
 
 def test_transform_choices_with_citations():