improve Llama Stack -> OpenAI message conversion

llama_stack/providers/remote/inference/nvidia/openai_utils.py

@@ -17,13 +17,23 @@ from llama_models.llama3.api.datatypes import (
     ToolDefinition,
 )
 from openai import AsyncStream
-from openai.types.chat import ChatCompletionChunk as OpenAIChatCompletionChunk
+
+from openai.types.chat import (
+    ChatCompletionAssistantMessageParam as OpenAIChatCompletionAssistantMessage,
+    ChatCompletionChunk as OpenAIChatCompletionChunk,
+    ChatCompletionMessageParam as OpenAIChatCompletionMessage,
+    ChatCompletionMessageToolCallParam as OpenAIChatCompletionMessageToolCall,
+    ChatCompletionSystemMessageParam as OpenAIChatCompletionSystemMessage,
+    ChatCompletionToolMessageParam as OpenAIChatCompletionToolMessage,
+    ChatCompletionUserMessageParam as OpenAIChatCompletionUserMessage,
+)
 from openai.types.chat.chat_completion import (
     Choice as OpenAIChoice,
     ChoiceLogprobs as OpenAIChoiceLogprobs,  # same as chat_completion_chunk ChoiceLogprobs
 )
-from openai.types.chat.chat_completion_message_tool_call import (
+
-    ChatCompletionMessageToolCall as OpenAIChatCompletionMessageToolCall,
+from openai.types.chat.chat_completion_message_tool_call_param import (
+    Function as OpenAIFunction,
 )
 
 from llama_stack.apis.inference import (
@@ -34,8 +44,11 @@ from llama_stack.apis.inference import (
     ChatCompletionResponseStreamChunk,
     JsonSchemaResponseFormat,
     Message,
+    SystemMessage,
     ToolCallDelta,
     ToolCallParseStatus,
+    ToolResponseMessage,
+    UserMessage,
 )
 
 
@@ -115,21 +128,63 @@ def _convert_tooldef_to_openai_tool(tool: ToolDefinition) -> dict:
     return out
 
 
-def _convert_message(message: Message) -> Dict:
+def _convert_message(message: Message | Dict) -> OpenAIChatCompletionMessage:
     """
     Convert a Message to an OpenAI API-compatible dictionary.
     """
-    out_dict = message.model_dump()
+    # users can supply a dict instead of a Message object, we'll
-    # Llama Stack uses role="ipython" for tool call messages, OpenAI uses "tool"
+    # convert it to a Message object and proceed with some type safety.
-    if out_dict["role"] == "ipython":
+    if isinstance(message, dict):
-        out_dict.update(role="tool")
+        if "role" not in message:
+            raise ValueError("role is required in message")
+        if message["role"] == "user":
+            message = UserMessage(**message)
+        elif message["role"] == "assistant":
+            message = CompletionMessage(**message)
+        elif message["role"] == "ipython":
+            message = ToolResponseMessage(**message)
+        elif message["role"] == "system":
+            message = SystemMessage(**message)
+        else:
+            raise ValueError(f"Unsupported message role: {message['role']}")
 
-    if "stop_reason" in out_dict:
+    out: OpenAIChatCompletionMessage = None
-        out_dict.update(stop_reason=out_dict["stop_reason"].value)
+    if isinstance(message, UserMessage):
+        out = OpenAIChatCompletionUserMessage(
+            role="user",
+            content=message.content,  # TODO(mf): handle image content
+        )
+    elif isinstance(message, CompletionMessage):
+        out = OpenAIChatCompletionAssistantMessage(
+            role="assistant",
+            content=message.content,
+            tool_calls=[
+                OpenAIChatCompletionMessageToolCall(
+                    id=tool.call_id,
+                    function=OpenAIFunction(
+                        name=tool.tool_name,
+                        arguments=json.dumps(tool.arguments),
+                    ),
+                    type="function",
+                )
+                for tool in message.tool_calls
+            ],
+        )
+    elif isinstance(message, ToolResponseMessage):
+        out = OpenAIChatCompletionToolMessage(
+            role="tool",
+            tool_call_id=message.call_id,
+            content=message.content,
+        )
+    elif isinstance(message, SystemMessage):
+        out = OpenAIChatCompletionSystemMessage(
+            role="system",
+            content=message.content,
+        )
+    else:
+        raise ValueError(f"Unsupported message type: {type(message)}")
 
-    # TODO(mf): tool_calls
+    return out
-
-    return out_dict
 
 
 def convert_chat_completion_request(