fix: update SK model adapter constructor (#5150)

* update constructor

* fix typing error

* revert/fix doc changes

* add unsaved changes

---------

Co-authored-by: Leonardo Pinheiro <[email protected]>

python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/_sk_chat_completion_adapter.py

@@ -44,6 +44,15 @@ class SKChatCompletionAdapter(ChatCompletionClient):
     Args:
         sk_client (ChatCompletionClientBase):
             The Semantic Kernel client to wrap (e.g., AzureChatCompletion, GoogleAIChatCompletion, OllamaChatCompletion).
+        kernel (Optional[Kernel]):
+            The Semantic Kernel instance to use for executing requests. If not provided, one must be passed
+            in the extra_create_args for each request.
+        prompt_settings (Optional[PromptExecutionSettings]):
+            Default prompt execution settings to use. Can be overridden per request.
+        model_info (Optional[ModelInfo]):
+            Information about the model's capabilities.
+        service_id (Optional[str]):
+            Optional service identifier.
 
     Example usage:
 
@@ -100,8 +109,8 @@ async def main():
             api_key = "<AZURE_OPENAI_API_KEY>"
 
             azure_client = AzureChatCompletion(deployment_name=deployment_name, endpoint=endpoint, api_key=api_key)
-            azure_request_settings = AzureChatPromptExecutionSettings(temperature=0.8)
-            azure_adapter = SKChatCompletionAdapter(sk_client=azure_client, default_prompt_settings=azure_request_settings)
+            azure_settings = AzureChatPromptExecutionSettings(temperature=0.8)
+            azure_adapter = SKChatCompletionAdapter(sk_client=azure_client, kernel=kernel, prompt_settings=azure_settings)
 
             # ----------------------------------------------------------------
             # Example B: Google Gemini
@@ -127,7 +136,7 @@ async def main():
                     "temperature": 0.8,
                 },
             )
-            ollama_adapter = SKChatCompletionAdapter(sk_client=ollama_client, default_prompt_settings=request_settings)
+            ollama_adapter = SKChatCompletionAdapter(sk_client=ollama_client, prompt_settings=request_settings)
 
             # 3) Create a tool and register it with the kernel
             calc_tool = CalculatorTool()
@@ -143,23 +152,20 @@ async def main():
             azure_result = await azure_adapter.create(
                 messages=messages,
                 tools=[calc_tool],
-                extra_create_args={"kernel": kernel, "prompt_execution_settings": azure_request_settings},
             )
             print("Azure result:", azure_result.content)
 
             # Google example
             google_result = await google_adapter.create(
                 messages=messages,
                 tools=[calc_tool],
-                extra_create_args={"kernel": kernel},
             )
             print("Google result:", google_result.content)
 
             # Ollama example
             ollama_result = await ollama_adapter.create(
                 messages=messages,
                 tools=[calc_tool],
-                extra_create_args={"kernel": kernel, "prompt_execution_settings": request_settings},
             )
             print("Ollama result:", ollama_result.content)
 
@@ -171,12 +177,14 @@ async def main():
     def __init__(
         self,
         sk_client: ChatCompletionClientBase,
+        kernel: Optional[Kernel] = None,
+        prompt_settings: Optional[PromptExecutionSettings] = None,
         model_info: Optional[ModelInfo] = None,
         service_id: Optional[str] = None,
-        default_prompt_settings: Optional[PromptExecutionSettings] = None,
     ):
         self._service_id = service_id
-        self._default_prompt_settings = default_prompt_settings
+        self._kernel = kernel
+        self._prompt_settings = prompt_settings
         self._sk_client = sk_client
         self._model_info = model_info or ModelInfo(
             vision=False, function_calling=False, json_output=False, family=ModelFamily.UNKNOWN
@@ -287,6 +295,17 @@ def _process_tool_calls(self, result: ChatMessageContent) -> list[FunctionCall]:
                 function_calls.append(FunctionCall(id=item.id, name=full_name, arguments=arguments))
         return function_calls
 
+    def _get_kernel(self, extra_create_args: Mapping[str, Any]) -> Kernel:
+        kernel = extra_create_args.get("kernel", self._kernel)
+        if not kernel:
+            raise ValueError("kernel must be provided either in constructor or extra_create_args")
+        if not isinstance(kernel, Kernel):
+            raise ValueError("kernel must be an instance of semantic_kernel.kernel.Kernel")
+        return kernel
+
+    def _get_prompt_settings(self, extra_create_args: Mapping[str, Any]) -> Optional[PromptExecutionSettings]:
+        return extra_create_args.get("prompt_execution_settings", None) or self._prompt_settings
+
     async def create(
         self,
         messages: Sequence[LLMMessage],
@@ -300,9 +319,9 @@ async def create(
 
         The `extra_create_args` dictionary can include two special keys:
 
-        1) `"kernel"` (required):
+        1) `"kernel"` (optional):
             An instance of :class:`semantic_kernel.Kernel` used to execute the request.
-            If not provided, a ValueError is raised.
+            If not provided either in constructor or extra_create_args, a ValueError is raised.
 
         2) `"prompt_execution_settings"` (optional):
             An instance of a :class:`PromptExecutionSettings` subclass corresponding to the
@@ -320,19 +339,9 @@ async def create(
         Returns:
             CreateResult: The result of the chat completion.
         """
-        if "kernel" not in extra_create_args:
-            raise ValueError("kernel is required in extra_create_args")
-
-        kernel = extra_create_args["kernel"]
-        if not isinstance(kernel, Kernel):
-            raise ValueError("kernel must be an instance of semantic_kernel.kernel.Kernel")
-
+        kernel = self._get_kernel(extra_create_args)
         chat_history = self._convert_to_chat_history(messages)
-
-        # Build execution settings from extra args and tools
-        user_settings = extra_create_args.get("prompt_execution_settings", None)
-        if user_settings is None:
-            user_settings = self._default_prompt_settings
+        user_settings = self._get_prompt_settings(extra_create_args)
         settings = self._build_execution_settings(user_settings, tools)
 
         # Sync tools with kernel
@@ -380,9 +389,9 @@ async def create_stream(
 
         The `extra_create_args` dictionary can include two special keys:
 
-        1) `"kernel"` (required):
+        1) `"kernel"` (optional):
             An instance of :class:`semantic_kernel.Kernel` used to execute the request.
-            If not provided, a ValueError is raised.
+            If not provided either in constructor or extra_create_args, a ValueError is raised.
 
         2) `"prompt_execution_settings"` (optional):
             An instance of a :class:`PromptExecutionSettings` subclass corresponding to the
@@ -400,17 +409,9 @@ async def create_stream(
         Yields:
             Union[str, CreateResult]: Either a string chunk of the response or a CreateResult containing function calls.
         """
-        if "kernel" not in extra_create_args:
-            raise ValueError("kernel is required in extra_create_args")
-
-        kernel = extra_create_args["kernel"]
-        if not isinstance(kernel, Kernel):
-            raise ValueError("kernel must be an instance of semantic_kernel.kernel.Kernel")
-
+        kernel = self._get_kernel(extra_create_args)
         chat_history = self._convert_to_chat_history(messages)
-        user_settings = extra_create_args.get("prompt_execution_settings", None)
-        if user_settings is None:
-            user_settings = self._default_prompt_settings
+        user_settings = self._get_prompt_settings(extra_create_args)
         settings = self._build_execution_settings(user_settings, tools)
         self._sync_tools_with_kernel(kernel, tools)