From f77cd39fdb52b4d825f9cc261c2d1bfa95da4c52 Mon Sep 17 00:00:00 2001 From: Leonardo Pinheiro Date: Mon, 30 Dec 2024 12:25:06 +1000 Subject: [PATCH] initial sk model adapter implementation --- python/packages/autogen-ext/pyproject.toml | 3 + .../models/semantic_kernel/__init__.py | 5 + .../_kernel_function_from_tool.py | 62 ++ .../_sk_chat_completion_adapter.py | 214 ++++++ .../models/test_sk_chat_completion_adapter.py | 73 ++ python/uv.lock | 689 ++++++++---------- 6 files changed, 672 insertions(+), 374 deletions(-) create mode 100644 python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/__init__.py create mode 100644 python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/_kernel_function_from_tool.py create mode 100644 python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/_sk_chat_completion_adapter.py create mode 100644 python/packages/autogen-ext/tests/models/test_sk_chat_completion_adapter.py diff --git a/python/packages/autogen-ext/pyproject.toml b/python/packages/autogen-ext/pyproject.toml index 0d04e747ea27..9a90833ee442 100644 --- a/python/packages/autogen-ext/pyproject.toml +++ b/python/packages/autogen-ext/pyproject.toml @@ -52,6 +52,9 @@ video-surfer = [ grpc = [ "grpcio~=1.62.0", # TODO: update this once we have a stable version. ] +semantic-kernel = [ + "semantic-kernel>=1.17.1", +] [tool.hatch.build.targets.wheel] packages = ["src/autogen_ext"] diff --git a/python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/__init__.py b/python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/__init__.py new file mode 100644 index 000000000000..f5ed940a91a9 --- /dev/null +++ b/python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/__init__.py @@ -0,0 +1,5 @@ +from ._sk_chat_completion_adapter import SKChatCompletionAdapter + +__all__ = [ + "SKChatCompletionAdapter" +] diff --git a/python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/_kernel_function_from_tool.py b/python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/_kernel_function_from_tool.py new file mode 100644 index 000000000000..b1b6659efdc3 --- /dev/null +++ b/python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/_kernel_function_from_tool.py @@ -0,0 +1,62 @@ +from semantic_kernel.functions.kernel_function import KernelFunction +from semantic_kernel.functions.kernel_function_metadata import KernelFunctionMetadata +from semantic_kernel.functions.kernel_parameter_metadata import KernelParameterMetadata +from semantic_kernel.functions.function_result import FunctionResult +from semantic_kernel.filters.functions.function_invocation_context import FunctionInvocationContext +from semantic_kernel.exceptions import FunctionExecutionException +from autogen_core.tools import BaseTool + +class KernelFunctionFromTool(KernelFunction): + def __init__(self, tool: BaseTool, plugin_name: str | None = None): + # Build up KernelFunctionMetadata. You can also parse the tool’s schema for parameters. + parameters = [ + KernelParameterMetadata( + name="args", + description="JSON arguments for the tool", + default_value=None, + type_="dict", + type_object=dict, + is_required=True, + ) + ] + return_param = KernelParameterMetadata( + name="return", + description="Result from the tool", + default_value=None, + type_="str", + type_object=str, + is_required=False, + ) + + metadata = KernelFunctionMetadata( + name=tool.name, + description=tool.description, + parameters=parameters, + return_parameter=return_param, + is_prompt=False, + is_asynchronous=True, + plugin_name=plugin_name or "", + ) + super().__init__(metadata=metadata) + self._tool = tool + + async def _invoke_internal(self, context: FunctionInvocationContext) -> None: + # Extract the "args" parameter from the context + if "args" not in context.arguments: + raise FunctionExecutionException("Missing 'args' in FunctionInvocationContext.arguments") + tool_args = context.arguments["args"] + + # Call your tool’s run_json + result = await self._tool.run_json(tool_args, cancellation_token=None) + + # Wrap in a FunctionResult + context.result = FunctionResult( + function=self.metadata, + value=result, + metadata={"used_arguments": tool_args}, + ) + + async def _invoke_internal_stream(self, context: FunctionInvocationContext) -> None: + # If you don’t have a streaming mechanism in your tool, you can simply reuse _invoke_internal + # or raise NotImplementedError. For example: + await self._invoke_internal(context) diff --git a/python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/_sk_chat_completion_adapter.py b/python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/_sk_chat_completion_adapter.py new file mode 100644 index 000000000000..fe21c134f621 --- /dev/null +++ b/python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/_sk_chat_completion_adapter.py @@ -0,0 +1,214 @@ +from typing import Any, Mapping, Optional, Sequence +from autogen_core._cancellation_token import CancellationToken +from autogen_core.models import RequestUsage, FunctionExecutionResultMessage, ModelCapabilities, AssistantMessage, SystemMessage, UserMessage, FunctionExecutionResult +from autogen_core.models import ChatCompletionClient, CreateResult, LLMMessage +from autogen_core.tools import Tool, ToolSchema +from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase +from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings +from semantic_kernel.connectors.ai.function_choice_behavior import FunctionChoiceBehavior +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.kernel import Kernel +from semantic_kernel.functions.kernel_plugin import KernelPlugin +from typing_extensions import AsyncGenerator, Union +from ._kernel_function_from_tool import KernelFunctionFromTool + + +class SKChatCompletionAdapter(ChatCompletionClient): + def __init__(self, sk_client: ChatCompletionClientBase): + self._sk_client = sk_client + self._total_prompt_tokens = 0 + self._total_completion_tokens = 0 + self._tools_plugin: Optional[KernelPlugin] = None + + def _convert_to_chat_history(self, messages: Sequence[LLMMessage]) -> ChatHistory: + """Convert Autogen LLMMessages to SK ChatHistory""" + chat_history = ChatHistory() + + for msg in messages: + if msg.type == "SystemMessage": + chat_history.add_system_message(msg.content) + + elif msg.type == "UserMessage": + if isinstance(msg.content, str): + chat_history.add_user_message(msg.content) + else: + # Handle list of str/Image - would need to convert to SK content types + chat_history.add_user_message(str(msg.content)) + + elif msg.type == "AssistantMessage": + if isinstance(msg.content, str): + chat_history.add_assistant_message(msg.content) + else: + # Handle function calls - would need to convert to SK function call format + chat_history.add_assistant_message(str(msg.content)) + + elif msg.type == "FunctionExecutionResultMessage": + for result in msg.content: + chat_history.add_tool_message(result.content) + + return chat_history + + def _convert_from_chat_message(self, message: ChatMessageContent, source: str = "assistant") -> LLMMessage: + """Convert SK ChatMessageContent to Autogen LLMMessage""" + if message.role == AuthorRole.SYSTEM: + return SystemMessage(content=message.content) + + elif message.role == AuthorRole.USER: + return UserMessage(content=message.content, source=source) + + elif message.role == AuthorRole.ASSISTANT: + return AssistantMessage(content=message.content, source=source) + + elif message.role == AuthorRole.TOOL: + return FunctionExecutionResultMessage( + content=[FunctionExecutionResult(content=message.content, call_id="")] + ) + + raise ValueError(f"Unknown role: {message.role}") + + def _build_execution_settings(self, extra_create_args: Mapping[str, Any], 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 tools are available, configure function choice behavior with auto_invoke disabled + function_choice_behavior = None + if tools: + function_choice_behavior = FunctionChoiceBehavior.NoneInvoke() + + # Create settings with remaining args as extension_data + settings = PromptExecutionSettings( + service_id=service_id, + extension_data=dict(extra_create_args), + function_choice_behavior=function_choice_behavior + ) + + return settings + + def _sync_tools_with_kernel(self, kernel: Kernel, tools: Sequence[Tool | ToolSchema]) -> None: + """Sync tools with kernel by updating the plugin""" + # Create new plugin if none exists + if not self._tools_plugin: + self._tools_plugin = KernelPlugin(name="autogen_tools") + kernel.add_plugin(self._tools_plugin) + + # Get current tool names in plugin + current_tool_names = set(self._tools_plugin.functions.keys()) + + # Get new tool names + new_tool_names = {tool.schema["name"] if isinstance(tool, Tool) else tool.name for tool in tools} + + # Remove tools that are no longer needed + for tool_name in current_tool_names - new_tool_names: + del self._tools_plugin.functions[tool_name] + + # Add or update tools + for tool in tools: + if isinstance(tool, Tool): + # Convert Tool to KernelFunction using KernelFunctionFromTool + kernel_function = KernelFunctionFromTool(tool, plugin_name="autogen_tools") + self._tools_plugin.functions[tool.name] = kernel_function + + async def create( + self, + messages: Sequence[LLMMessage], + tools: Sequence[Tool | ToolSchema] = [], + json_output: Optional[bool] = None, + extra_create_args: Mapping[str, Any] = {}, + cancellation_token: Optional[CancellationToken] = None, + ) -> CreateResult: + 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") + + 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) + + # Sync tools with kernel + self._sync_tools_with_kernel(kernel, tools) + + result = await self._sk_client.get_chat_message_contents( + chat_history, + settings=settings, + kernel=kernel + ) + # Track token usage from result metadata + prompt_tokens = 0 + completion_tokens = 0 + + if result[0].metadata and 'usage' in result[0].metadata: + usage = result[0].metadata['usage'] + prompt_tokens = getattr(usage, 'prompt_tokens', 0) + completion_tokens = getattr(usage, 'completion_tokens', 0) + + self._total_prompt_tokens += prompt_tokens + self._total_completion_tokens += completion_tokens + + return CreateResult( + content=result[0].content, + finish_reason="stop", + usage=RequestUsage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens + ), + cached=False + ) + + async def create_stream( + self, + messages: Sequence[LLMMessage], + tools: Sequence[Tool | ToolSchema] = [], + json_output: Optional[bool] = None, + extra_create_args: Mapping[str, Any] = {}, + cancellation_token: Optional[CancellationToken] = None, + ) -> AsyncGenerator[Union[str, CreateResult], None]: + # Very similar to create(), but orchestrates streaming. + # 1. Convert messages -> ChatHistory + # 2. Possibly set function-calling if needed + # 3. Build generator that yields str segments or a final CreateResult + # from SK's get_streaming_chat_message_contents(...) + raise NotImplementedError("create_stream is not implemented") + + def actual_usage(self) -> RequestUsage: + return RequestUsage( + prompt_tokens=self._total_prompt_tokens, + completion_tokens=self._total_completion_tokens + ) + + def total_usage(self) -> RequestUsage: + return RequestUsage( + prompt_tokens=self._total_prompt_tokens, + completion_tokens=self._total_completion_tokens + ) + + def count_tokens(self, messages: Sequence[LLMMessage]) -> int: + chat_history = self._convert_to_chat_history(messages) + total_tokens = 0 + for message in chat_history.messages: + if message.metadata and 'usage' in message.metadata: + usage = message.metadata['usage'] + total_tokens += getattr(usage, 'total_tokens', 0) + return total_tokens + + def remaining_tokens(self, messages: Sequence[LLMMessage]) -> int: + # Get total token count + used_tokens = self.count_tokens(messages) + # Assume max tokens from SK client if available, otherwise use default + max_tokens = getattr(self._sk_client, 'max_tokens', 4096) + return max_tokens - used_tokens + + @property + def capabilities(self) -> ModelCapabilities: + # Return something consistent with the underlying SK client + return { + "vision": False, + "function_calling": self._sk_client.SUPPORTS_FUNCTION_CALLING, + "json_output": False, + } diff --git a/python/packages/autogen-ext/tests/models/test_sk_chat_completion_adapter.py b/python/packages/autogen-ext/tests/models/test_sk_chat_completion_adapter.py new file mode 100644 index 000000000000..80944df6b778 --- /dev/null +++ b/python/packages/autogen-ext/tests/models/test_sk_chat_completion_adapter.py @@ -0,0 +1,73 @@ +import os +import pytest +from semantic_kernel.connectors.ai.open_ai.services.azure_chat_completion import AzureChatCompletion +from semantic_kernel.kernel import Kernel +from semantic_kernel.memory.null_memory import NullMemory +from autogen_core.models import SystemMessage, UserMessage +from autogen_core.tools import BaseTool +from autogen_ext.models.semantic_kernel import SKChatCompletionAdapter +from pydantic import BaseModel +from autogen_core import CancellationToken + +class CalculatorArgs(BaseModel): + a: float + b: float + +class CalculatorResult(BaseModel): + result: float + +class CalculatorTool(BaseTool[CalculatorArgs, CalculatorResult]): + def __init__(self): + super().__init__( + args_type=CalculatorArgs, + return_type=CalculatorResult, + name="calculator", + description="Add two numbers together" + ) + + async def run(self, args: CalculatorArgs, cancellation_token: CancellationToken) -> CalculatorResult: + return CalculatorResult(result=args.a + args.b) + +@pytest.mark.asyncio +async def test_sk_chat_completion_with_tools(): + # Set up Azure OpenAI client with token auth + deployment_name = "gpt-4o-mini" + endpoint = "https://.openai.azure.com/" + api_version = "2024-07-18" + + # Create SK client + sk_client = AzureChatCompletion( + deployment_name=deployment_name, + endpoint=endpoint, + api_key=os.getenv("AZURE_OPENAI_API_KEY"), + ) + + # Create adapter + adapter = SKChatCompletionAdapter(sk_client) + + # Create kernel + kernel = Kernel(memory=NullMemory()) + + # Create calculator tool instance + tool = CalculatorTool() + + # Test messages + messages = [ + SystemMessage(content="You are a helpful assistant."), + UserMessage(content="What is 2 + 2?", source="user"), + ] + + # Call create with tool + result = await adapter.create( + messages=messages, + tools=[tool], + extra_create_args={"kernel": kernel} + ) + + + # Verify response + assert isinstance(result.content, str) + assert result.finish_reason == "stop" + assert result.usage.prompt_tokens >= 0 + assert result.usage.completion_tokens >= 0 + assert not result.cached diff --git a/python/uv.lock b/python/uv.lock index 94aa848bf97b..2a89201da34c 100644 --- a/python/uv.lock +++ b/python/uv.lock @@ -29,20 +29,26 @@ 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