SK assistant for agentchat

python/packages/autogen-core/docs/src/reference/index.md

@@ -46,6 +46,7 @@ python/autogen_ext.agents.web_surfer
 python/autogen_ext.agents.file_surfer
 python/autogen_ext.agents.video_surfer
 python/autogen_ext.agents.video_surfer.tools
+python/autogen_ext.agents.semantic_kernel
 python/autogen_ext.auth.azure
 python/autogen_ext.teams.magentic_one
 python/autogen_ext.models.cache

python/packages/autogen-core/docs/src/reference/python/autogen_ext.agents.semantic_kernel.rst

@@ -0,0 +1,8 @@
+autogen\_ext.agents.semantic_kernel
+====================================
+
+
+.. automodule:: autogen_ext.agents.semantic_kernel
+   :members:
+   :undoc-members:
+   :show-inheritance:

python/packages/autogen-ext/src/autogen_ext/agents/semantic_kernel/__init__.py

@@ -0,0 +1,3 @@
+from ._sk_assistant_agent import SKAssistantAgent
+
+__all__ = ["SKAssistantAgent"]

python/packages/autogen-ext/src/autogen_ext/agents/semantic_kernel/_sk_assistant_agent.py

@@ -0,0 +1,327 @@
+import base64
+from collections.abc import AsyncGenerator
+from dataclasses import asdict
+from typing import Any, Optional, Sequence
+
+from autogen_agentchat.agents._base_chat_agent import BaseChatAgent
+from autogen_agentchat.base import Response
+from autogen_agentchat.messages import ChatMessage, HandoffMessage, StopMessage, TextMessage, ToolCallSummaryMessage
+from autogen_core import CancellationToken
+
+from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
+from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings
+from semantic_kernel.contents import ImageContent, TextContent
+from semantic_kernel.contents.chat_history import ChatHistory
+from semantic_kernel.contents.chat_message_content import ChatMessageContent
+from semantic_kernel.contents.utils.author_role import AuthorRole
+from semantic_kernel.exceptions import KernelServiceNotFoundError
+from semantic_kernel.kernel import Kernel
+
+
+class SKAssistantAgent(BaseChatAgent):
+    """
+    SKAssistantAgent is a specialized agent that leverages Semantic Kernel for
+    conversation handling and response generation. It extends the autogen
+    ``BaseChatAgent`` class and uses a single Semantic Kernel ``ChatHistory``
+    to store and manage dialogue context.
+
+    Installation:
+
+    .. code-block:: bash
+
+        pip install "autogen-ext[semantic-kernel-core]"
+
+    For other model providers and semantic kernel features install the appropriate extra or install all providers with: semantic-kernel-all
+
+    This agent supports streaming responses (token by token) and final message
+    generation by calling the configured Semantic Kernel chat completion service.
+
+    Args:
+        name (str): The name of the agent.
+        description (str): A description of the agent's capabilities or purpose.
+        kernel (Kernel): The Semantic Kernel instance to use for chat completions.
+        service_id (str, optional): The ID of the chat completion service. Defaults to "default".
+        instructions (str, optional): Optional system-level instructions for the assistant.
+        execution_settings (PromptExecutionSettings, optional):
+            Optional prompt execution settings to override defaults.
+
+    Example usage:
+
+    The following example demonstrates how to create and use an ``SKAssistantAgent``
+    in conjunction with a Semantic Kernel. It sets up an Azure-based chat model,
+    adds a Bing search plugin, and then streams the agent's response to the console:
+
+    .. code-block:: python
+
+        import asyncio
+        import os
+
+        from dotenv import load_dotenv
+        from autogen_agentchat.ui._console import Console
+        from semantic_kernel import Kernel
+        from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior
+        from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, AzureChatPromptExecutionSettings
+        from semantic_kernel.connectors.search.bing import BingSearch
+        from semantic_kernel.functions import KernelArguments, KernelParameterMetadata, KernelPlugin
+        from autogen_core import CancellationToken
+        from autogen_ext.agents.semantic_kernel import SKAssistantAgent
+        from autogen_agentchat.messages import TextMessage
+
+        load_dotenv("../.env")
+
+
+        async def main():
+            # Initialize the kernel
+            kernel = Kernel()
+
+            # Configure OpenAI chat completion
+            ai_service = AzureChatCompletion(
+                api_key=os.getenv("AZURE_OPENAI_API_KEY"),
+                deployment_name="gpt-4o-mini",
+                endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
+                api_version=os.getenv("AZURE_OPENAI_VERSION"),
+            )
+            kernel.add_service(ai_service)
+
+            # Configure Bing search
+            bing_api_key = os.getenv("BING_API_KEY")
+            # Add the WebSearchEnginePlugin to the kernel
+            kernel.add_plugin(
+                KernelPlugin.from_text_search_with_search(
+                    BingSearch(bing_api_key),
+                    plugin_name="bing",
+                    description="Get details about Semantic Kernel concepts.",
+                    parameters=[
+                        KernelParameterMetadata(
+                            name="query",
+                            description="The search query.",
+                            type="str",
+                            is_required=True,
+                            type_object=str,
+                        ),
+                        KernelParameterMetadata(
+                            name="top",
+                            description="Number of results to return.",
+                            type="int",
+                            is_required=False,
+                            default_value=2,
+                            type_object=int,
+                        ),
+                        KernelParameterMetadata(
+                            name="skip",
+                            description="Number of results to skip.",
+                            type="int",
+                            is_required=False,
+                            default_value=0,
+                            type_object=int,
+                        ),
+                    ],
+                )
+            )
+
+            # Create the SKAssistantAgent
+            agent = SKAssistantAgent(
+                name="MyAssistant",
+                description="An AI assistant that can search the web and answer questions",
+                kernel=kernel,
+                execution_settings=AzureChatPromptExecutionSettings(
+                    function_choice_behavior=FunctionChoiceBehavior.Auto(auto_invoke=True)
+                ),
+            )
+
+            query = "What are the latest news on autogen?"
+            await Console(agent.run_stream(task=query))
+
+
+        if __name__ == "__main__":
+            asyncio.run(main())
+
+    """
+
+    def __init__(
+        self,
+        name: str,
+        description: str,
+        kernel: Kernel,
+        service_id: str = "default",
+        instructions: Optional[str] = None,
+        execution_settings: Optional[PromptExecutionSettings] = None,
+    ) -> None:
+        super().__init__(name, description)
+        self._kernel = kernel
+        self._service_id = service_id
+        self._instructions = instructions
+        self._execution_settings = execution_settings
+
+        # Maintain the entire conversation as a ChatHistory (SK concept).
+        self._chat_history: ChatHistory = ChatHistory()
+
+        # If instructions are provided, set them as the first system message
+        if instructions:
+            self._chat_history.add_system_message(instructions)
+
+    @property
+    def produced_message_types(self) -> Sequence[type[ChatMessage]]:
+        return [TextMessage]
+
+    async def on_messages(
+        self,
+        messages: Sequence[ChatMessage],
+        cancellation_token: CancellationToken,
+    ) -> Response:
+        """
+        Handle incoming messages, add them to our ChatHistory, call SK for a response,
+        and return a final single text response.
+        """
+        # 1) Convert & store new agent messages in ChatHistory
+        for msg in messages:
+            sk_msg = self._convert_chat_message_to_sk_chat_message_content(AuthorRole.USER, msg)
+            self._chat_history.add_message(sk_msg)
+
+        # 2) Retrieve the SK chat completion service
+        chat_completion_service = self._kernel.get_service(
+            service_id=self._service_id,
+            type=ChatCompletionClientBase,
+        )
+        if not chat_completion_service:
+            raise KernelServiceNotFoundError(f"Chat completion service not found with service_id: {self._service_id}")
+
+        assert isinstance(chat_completion_service, ChatCompletionClientBase)
+
+        # 3) Get or create the PromptExecutionSettings
+        settings = (
+            self._execution_settings
+            or self._kernel.get_prompt_execution_settings_from_service_id(self._service_id)
+            or chat_completion_service.instantiate_prompt_execution_settings(  # type: ignore
+                service_id=self._service_id,
+                extension_data={"ai_model_id": chat_completion_service.ai_model_id},
+            )
+        )
+
+        # 4) Invoke SK to get an assistant response
+        sk_responses = await chat_completion_service.get_chat_message_contents(
+            chat_history=self._chat_history,
+            settings=settings,
+            kernel=self._kernel,
+        )
+        # Convert SK's list of responses into a single final text
+        assistant_reply = "\n".join(r.content for r in sk_responses if r.content)
+        reply_message = TextMessage(content=assistant_reply, source=self.name)
+
+        # 5) Add the new assistant message into our chat history
+        if assistant_reply.strip():
+            self._chat_history.add_message(
+                self._convert_chat_message_to_sk_chat_message_content(AuthorRole.ASSISTANT, reply_message)
+            )
+
+        # 6) Return an autogen Response containing the text
+        return Response(chat_message=reply_message)
+
+    async def on_messages_stream(
+        self,
+        messages: Sequence[ChatMessage],
+        cancellation_token: CancellationToken,
+    ) -> AsyncGenerator[ChatMessage | Response, None]:
+        """
+        Handle new messages in streaming mode, yielding partial text messages
+        as we receive them, then yield a final single Response.
+        """
+        # 1) Convert & store new agent messages
+        for msg in messages:
+            sk_msg = self._convert_chat_message_to_sk_chat_message_content(AuthorRole.USER, msg)
+            self._chat_history.add_message(sk_msg)
+
+        # 2) Retrieve chat completion service
+        chat_completion_service = self._kernel.get_service(
+            service_id=self._service_id,
+            type=ChatCompletionClientBase,
+        )
+        if not chat_completion_service:
+            raise KernelServiceNotFoundError(f"Chat completion service not found with service_id: {self._service_id}")
+
+        assert isinstance(chat_completion_service, ChatCompletionClientBase)
+
+        settings = (
+            self._execution_settings
+            or self._kernel.get_prompt_execution_settings_from_service_id(self._service_id)
+            or chat_completion_service.instantiate_prompt_execution_settings(  # type: ignore
+                service_id=self._service_id,
+                extension_data={"ai_model_id": chat_completion_service.ai_model_id},
+            )
+        )
+
+        # 3) Stream the SK response
+        accumulated_reply: list[str] = []
+        async for sk_message_list in chat_completion_service.get_streaming_chat_message_contents(
+            chat_history=self._chat_history,
+            settings=settings,
+            kernel=self._kernel,
+        ):
+            for sk_message in sk_message_list:
+                # If it's streaming text, yield partial text as a new TextMessage
+                if sk_message.content:
+                    partial_text = sk_message.content
+                    accumulated_reply.append(partial_text)
+                    yield TextMessage(content=partial_text, source=self.name)
+
+        # 4) After streaming ends, save the entire assistant message
+        final_text = "".join(accumulated_reply).strip()
+        if final_text:
+            self._chat_history.add_assistant_message(final_text, name=self.name)
+
+        # 5) Finally, yield the single autogen Response
+        yield Response(chat_message=TextMessage(content=final_text, source=self.name))
+
+    async def on_reset(self, cancellation_token: CancellationToken) -> None:
+        """Clear the entire conversation history."""
+        self._chat_history.messages.clear()
+
+    @staticmethod
+    def _convert_chat_message_to_sk_chat_message_content(
+        role: AuthorRole, chat_message: ChatMessage
+    ) -> ChatMessageContent:
+        # Prepare a place to store metadata (e.g., usage)
+        metadata: dict[str, Any] = {}
+        if chat_message.models_usage is not None:
+            metadata["models_usage"] = asdict(chat_message.models_usage)
+
+        items: list[TextContent | ImageContent] = []
+        msg_type = chat_message.type
+
+        match msg_type:
+            case "TextMessage":
+                assert isinstance(chat_message, TextMessage)
+                items.append(TextContent(text=chat_message.content))
+
+            case "MultiModalMessage":
+                for entry in chat_message.content:
+                    if isinstance(entry, str):
+                        items.append(TextContent(text=entry))
+                    else:
+                        # entry is autogen_core.Image
+                        # Convert to base64 and then into bytes for ImageContent
+                        b64 = entry.to_base64()
+                        img_bytes = base64.b64decode(b64)
+                        items.append(
+                            ImageContent(
+                                data=img_bytes,  # type: ignore
+                                data_format="base64",  # type: ignore
+                                mime_type="image/png",  # type: ignore
+                            )
+                        )
+
+            case "StopMessage":
+                assert isinstance(chat_message, StopMessage)
+                items.append(TextContent(text=chat_message.content))
+
+            case "HandoffMessage":
+                assert isinstance(chat_message, HandoffMessage)
+                # Store handoff details as text
+                text = f"Handoff target: {chat_message.target}\n\n{chat_message.content}"
+                items.append(TextContent(text=text))
+
+            case "ToolCallSummaryMessage":
+                assert isinstance(chat_message, ToolCallSummaryMessage)
+                items.append(TextContent(text=chat_message.content))
+
+        return ChatMessageContent(role=role, items=items, metadata=metadata, name=chat_message.source)  # type: ignore