customize prompt settings

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

@@ -38,6 +38,8 @@ class SKChatCompletionAdapter(ChatCompletionClient):
         - Pass in a `Kernel` and any supported Semantic Kernel `ChatCompletionClientBase` connector.
         - Provide tools (via Autogen `Tool` or `ToolSchema`) for function calls during chat completion.
         - Stream responses or retrieve them in a single request.
+        - Provide prompt settings to control the chat completion behavior either globally through the constructor
+          or on a per-request basis through the `extra_create_args` dictionary.
 
     Args:
         sk_client (ChatCompletionClientBase):
@@ -50,10 +52,12 @@ class SKChatCompletionAdapter(ChatCompletionClient):
         import asyncio
         from semantic_kernel import Kernel
         from semantic_kernel.memory.null_memory import NullMemory
-        from semantic_kernel.connectors.ai.open_ai.services.azure_chat_completion import AzureChatCompletion
-        from semantic_kernel.connectors.ai.google.google_ai import GoogleAIChatCompletion
-        from semantic_kernel.connectors.ai.ollama import OllamaChatCompletion
-        from semantic_kernel.connectors.ai.ollama.ollama_prompt_execution_settings import OllamaChatPromptExecutionSettings
+        from semantic_kernel.connectors.ai.open_ai.services.azure_chat_completion import (
+            AzureChatCompletion,
+            AzureChatPromptExecutionSettings,
+        )
+        from semantic_kernel.connectors.ai.google.google_ai import GoogleAIChatCompletion,
+        from semantic_kernel.connectors.ai.ollama import OllamaChatCompletion, OllamaChatPromptExecutionSettings
         from autogen_core.models import SystemMessage, UserMessage, LLMMessage
         from autogen_ext.models.semantic_kernel import SKChatCompletionAdapter
         from autogen_core import CancellationToken
@@ -96,7 +100,10 @@ async def main():
             api_key = "<AZURE_OPENAI_API_KEY>"
 
             azure_client = AzureChatCompletion(deployment_name=deployment_name, endpoint=endpoint, api_key=api_key)
-            azure_adapter = SKChatCompletionAdapter(sk_client=azure_client)
+            azure_request_settings = AzureChatPromptExecutionSettings(
+                options={"temperature": 0.8},
+            )
+            azure_adapter = SKChatCompletionAdapter(sk_client=azure_client, default_prompt_settings=azure_request_settings)
 
             # ----------------------------------------------------------------
             # Example B: Google Gemini
@@ -114,7 +121,15 @@ async def main():
                 host="http://localhost:11434",
                 ai_model_id="llama3.1",
             )
-            ollama_adapter = SKChatCompletionAdapter(sk_client=ollama_client)
+            request_settings = OllamaChatPromptExecutionSettings(
+                # For model specific settings, specify them in the options dictionary.
+                # For more information on the available options, refer to the Ollama API documentation:
+                # https://github.com/ollama/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values
+                options={
+                    "temperature": 0.8,
+                },
+            )
+            ollama_adapter = SKChatCompletionAdapter(sk_client=ollama_client, default_prompt_settings=request_settings)
 
             # 3) Create a tool and register it with the kernel
             calc_tool = CalculatorTool()
@@ -126,9 +141,13 @@ async def main():
             ]
 
             # 5) Invoke chat completion with the Azure adapter (as an example)
-            #    Provide the kernel in extra_create_args, and pass the tool.
+            #    Provide the kernel in extra_create_args, and pass the tool and optional prompt settings.
             #    The same pattern applies to Google or Ollama adapters.
-            result = await azure_adapter.create(messages=messages, tools=[calc_tool], extra_create_args={"kernel": kernel})
+            result = await azure_adapter.create(
+                messages=messages,
+                tools=[calc_tool],
+                extra_create_args={"kernel": kernel, "prompt_execution_settings": azure_request_settings},
+            )
 
             # Print or use the result
             print("Result content:", result.content)
@@ -143,7 +162,15 @@ async def main():
             asyncio.run(main())
     """
 
-    def __init__(self, sk_client: ChatCompletionClientBase, model_info: Optional[ModelInfo] = None):
+    def __init__(
+        self,
+        sk_client: ChatCompletionClientBase,
+        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._sk_client = sk_client
         self._model_info = model_info or ModelInfo(
             vision=False, function_calling=False, json_output=False, family=ModelFamily.UNKNOWN
@@ -181,23 +208,26 @@ def _convert_to_chat_history(self, messages: Sequence[LLMMessage]) -> ChatHistor
         return chat_history
 
     def _build_execution_settings(
-        self, extra_create_args: Mapping[str, Any], tools: Sequence[Tool | ToolSchema]
+        self, default_prompt_settings: Optional[PromptExecutionSettings], tools: Sequence[Tool | ToolSchema]
     ) -> PromptExecutionSettings:
         """Build PromptExecutionSettings from extra_create_args"""
-        # Extract service_id if provided, otherwise use None
-        service_id = extra_create_args.get("service_id")
+
+        if default_prompt_settings is not None:
+            prompt_args: dict[str, Any] = default_prompt_settings.prepare_settings_dict()  # type: ignore
+        else:
+            prompt_args = {}
 
         # If tools are available, configure function choice behavior with auto_invoke disabled
         function_choice_behavior = None
         if tools:
             function_choice_behavior = FunctionChoiceBehavior.Auto(  # type: ignore
-                auto_invoke=extra_create_args.get("auto_invoke", False)
+                auto_invoke=False
             )
 
         # Create settings with remaining args as extension_data
         settings = PromptExecutionSettings(
-            service_id=service_id,
-            extension_data=dict(extra_create_args),
+            service_id=self._service_id,
+            extension_data=prompt_args,
             function_choice_behavior=function_choice_behavior,
         )
 
@@ -260,6 +290,30 @@ async def create(
         extra_create_args: Mapping[str, Any] = {},
         cancellation_token: Optional[CancellationToken] = None,
     ) -> CreateResult:
+        """
+        Create a chat completion using the Semantic Kernel client.
+
+        The `extra_create_args` dictionary can include two special keys:
+        1) `"kernel"` (required):
+            An instance of :class:`semantic_kernel.Kernel` used to execute the request.
+            If not provided, a ValueError is raised.
+
+        2) `"prompt_execution_settings"` (optional):
+            An instance of a :class:`PromptExecutionSettings` subclass corresponding to the
+            underlying Semantic Kernel client (e.g., `AzureChatPromptExecutionSettings`,
+            `GoogleAIChatPromptExecutionSettings`). If not provided, the adapter's default
+            prompt settings will be used.
+
+        Args:
+            messages: The list of LLM messages to send.
+            tools: The tools that may be invoked during the chat.
+            json_output: Whether the model is expected to return JSON.
+            extra_create_args: Additional arguments to control the chat completion behavior.
+            cancellation_token: Token allowing cancellation of the request.
+
+        Returns:
+            CreateResult: The result of the chat completion.
+        """
         if "kernel" not in extra_create_args:
             raise ValueError("kernel is required in extra_create_args")
 
@@ -270,7 +324,10 @@ async def create(
         chat_history = self._convert_to_chat_history(messages)
 
         # Build execution settings from extra args and tools
-        settings = self._build_execution_settings(extra_create_args, tools)
+        user_settings = extra_create_args.get("prompt_execution_settings", None)
+        if user_settings is None:
+            user_settings = self._default_prompt_settings
+        settings = self._build_execution_settings(user_settings, tools)
 
         # Sync tools with kernel
         self._sync_tools_with_kernel(kernel, tools)
@@ -313,6 +370,30 @@ async def create_stream(
         extra_create_args: Mapping[str, Any] = {},
         cancellation_token: Optional[CancellationToken] = None,
     ) -> AsyncGenerator[Union[str, CreateResult], None]:
+        """
+        Create a streaming chat completion using the Semantic Kernel client.
+
+        The `extra_create_args` dictionary can include two special keys:
+        1) `"kernel"` (required):
+            An instance of :class:`semantic_kernel.Kernel` used to execute the request.
+            If not provided, a ValueError is raised.
+
+        2) `"prompt_execution_settings"` (optional):
+            An instance of a :class:`PromptExecutionSettings` subclass corresponding to the
+            underlying Semantic Kernel client (e.g., `AzureChatPromptExecutionSettings`,
+            `GoogleAIChatPromptExecutionSettings`). If not provided, the adapter's default
+            prompt settings will be used.
+
+        Args:
+            messages: The list of LLM messages to send.
+            tools: The tools that may be invoked during the chat.
+            json_output: Whether the model is expected to return JSON.
+            extra_create_args: Additional arguments to control the chat completion behavior.
+            cancellation_token: Token allowing cancellation of the request.
+
+        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")
 
@@ -321,7 +402,10 @@ async def create_stream(
             raise ValueError("kernel must be an instance of semantic_kernel.kernel.Kernel")
 
         chat_history = self._convert_to_chat_history(messages)
-        settings = self._build_execution_settings(extra_create_args, tools)
+        user_settings = extra_create_args.get("prompt_execution_settings", None)
+        if user_settings is None:
+            user_settings = self._default_prompt_settings
+        settings = self._build_execution_settings(user_settings, tools)
         self._sync_tools_with_kernel(kernel, tools)
 
         prompt_tokens = 0