Add parallel tool call support for litellm (#172)

Co-authored-by: Hangfei Lin <hangfei@google.com>

src/google/adk/models/lite_llm.py

@@ -136,49 +136,61 @@ def _safe_json_serialize(obj) -> str:
 
 def _content_to_message_param(
     content: types.Content,
-) -> Message:
-  """Converts a types.Content to a litellm Message.
+) -> Union[Message, list[Message]]:
+  """Converts a types.Content to a litellm Message or list of Messages.
+
+  Handles multipart function responses by returning a list of
+  ChatCompletionToolMessage objects if multiple function_response parts exist.
 
   Args:
     content: The content to convert.
 
   Returns:
-    The litellm Message.
+    A litellm Message, a list of litellm Messages.
   """
 
-  if content.parts and content.parts[0].function_response:
-    return ChatCompletionToolMessage(
-        role="tool",
-        tool_call_id=content.parts[0].function_response.id,
-        content=_safe_json_serialize(
-            content.parts[0].function_response.response
-        ),
-    )
+  tool_messages = []
+  for part in content.parts:
+    if part.function_response:
+      tool_messages.append(
+          ChatCompletionToolMessage(
+              role="tool",
+              tool_call_id=part.function_response.id,
+              content=_safe_json_serialize(part.function_response.response),
+          )
+      )
+  if tool_messages:
+    return tool_messages if len(tool_messages) > 1 else tool_messages[0]
 
+  # Handle user or assistant messages
   role = _to_litellm_role(content.role)
+  message_content = _get_content(content.parts) or None
 
   if role == "user":
-    return ChatCompletionUserMessage(
-        role="user", content=_get_content(content.parts)
-    )
-  else:
+    return ChatCompletionUserMessage(role="user", content=message_content)
+  else:  # assistant/model
+    tool_calls = []
+    content_present = False
+    for part in content.parts:
+        if part.function_call:
+            tool_calls.append(
+                ChatCompletionMessageToolCall(
+                    type="function",
+                    id=part.function_call.id,
+                    function=Function(
+                        name=part.function_call.name,
+                        arguments=part.function_call.args,
+                    ),
+                )
+            )
+        elif part.text or part.inline_data:
+            content_present = True
 
-    tool_calls = [
-        ChatCompletionMessageToolCall(
-            type="function",
-            id=part.function_call.id,
-            function=Function(
-                name=part.function_call.name,
-                arguments=part.function_call.args,
-            ),
-        )
-        for part in content.parts
-        if part.function_call
-    ]
+    final_content = message_content if content_present else None
 
     return ChatCompletionAssistantMessage(
         role=role,
-        content=_get_content(content.parts) or None,
+        content=final_content,
         tool_calls=tool_calls or None,
     )
 
@@ -437,10 +449,13 @@ def _get_completion_inputs(
   Returns:
     The litellm inputs (message list and tool dictionary).
   """
-  messages = [
-      _content_to_message_param(content)
-      for content in llm_request.contents or []
-  ]
+  messages = []
+  for content in llm_request.contents or []:
+    message_param_or_list = _content_to_message_param(content)
+    if isinstance(message_param_or_list, list):
+        messages.extend(message_param_or_list)
+    elif message_param_or_list: # Ensure it's not None before appending
+        messages.append(message_param_or_list)
 
   if llm_request.config.system_instruction:
     messages.insert(