From 73e5bab1f8335b796314d8cfa692a7de474b7176 Mon Sep 17 00:00:00 2001 From: Leonardo Pinheiro Date: Mon, 30 Dec 2024 12:25:06 +1000 Subject: [PATCH 01/18] 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 | 291 +++++++++++++++++- 6 files changed, 645 insertions(+), 3 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 156d3ff79ddd..83088c7cb449 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 067520cb3e8f..bd7d002e7c27 100644 --- a/python/uv.lock +++ b/python/uv.lock @@ -345,6 +345,9 @@ dependencies 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.../semantic_kernel/_kernel_function_from_tool.py | 0 2 files changed, 6 insertions(+) create mode 100644 python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/__init__.py rename python/packages/autogen-ext/src/autogen_ext/{models => tools}/semantic_kernel/_kernel_function_from_tool.py (100%) diff --git a/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/__init__.py b/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/__init__.py new file mode 100644 index 000000000000..e48e6d1bfd71 --- /dev/null +++ b/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/__init__.py @@ -0,0 +1,6 @@ +from ._kernel_function_from_tool import KernelFunctionFromTool + +__all__ = [ + "KernelFunctionFromTool", +] + 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/tools/semantic_kernel/_kernel_function_from_tool.py similarity index 100% rename from python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/_kernel_function_from_tool.py rename to python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/_kernel_function_from_tool.py From 2e2a2a518b0121438a7ac88c8542f6813474679e Mon Sep 17 00:00:00 2001 From: Leonardo Pinheiro Date: Sat, 4 Jan 2025 08:32:59 +1000 Subject: [PATCH 03/18] implement streaming and update tests --- .../_sk_chat_completion_adapter.py | 110 +++++++++++++++-- .../models/test_sk_chat_completion_adapter.py | 113 ++++++++++++++++-- 2 files changed, 202 insertions(+), 21 deletions(-) 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 index fe21c134f621..278e448e9885 100644 --- 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 @@ -12,7 +12,10 @@ 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 +from autogen_ext.tools.semantic_kernel import KernelFunctionFromTool +from semantic_kernel.contents.function_call_content import FunctionCallContent +from autogen_core import FunctionCall +from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent class SKChatCompletionAdapter(ChatCompletionClient): @@ -76,7 +79,7 @@ def _build_execution_settings(self, extra_create_args: Mapping[str, Any], tools: # If tools are available, configure function choice behavior with auto_invoke disabled function_choice_behavior = None if tools: - function_choice_behavior = FunctionChoiceBehavior.NoneInvoke() + function_choice_behavior = FunctionChoiceBehavior.Auto(auto_invoke=extra_create_args.get("auto_invoke", False)) # Create settings with remaining args as extension_data settings = PromptExecutionSettings( @@ -111,6 +114,28 @@ def _sync_tools_with_kernel(self, kernel: Kernel, tools: Sequence[Tool | ToolSch kernel_function = KernelFunctionFromTool(tool, plugin_name="autogen_tools") self._tools_plugin.functions[tool.name] = kernel_function + def _process_tool_calls(self, result: ChatMessageContent) -> list[FunctionCall]: + """Process tool calls from SK ChatMessageContent""" + function_calls = [] + for item in result.items: + if isinstance(item, FunctionCallContent): + # Extract plugin name and function name + plugin_name = item.plugin_name or "" + function_name = item.function_name or item.name + if plugin_name: + full_name = f"{plugin_name}-{function_name}" + else: + full_name = function_name + + function_calls.append( + FunctionCall( + id=item.id, + name=full_name, + arguments=item.arguments or "{}" + ) + ) + return function_calls + async def create( self, messages: Sequence[LLMMessage], @@ -150,10 +175,19 @@ async def create( self._total_prompt_tokens += prompt_tokens self._total_completion_tokens += completion_tokens + + # Process content based on whether there are tool calls + content: Union[str, list[FunctionCall]] + if any(isinstance(item, FunctionCallContent) for item in result[0].items): + content = self._process_tool_calls(result[0]) + finish_reason = "function_calls" + else: + content = result[0].content + finish_reason = "stop" return CreateResult( - content=result[0].content, - finish_reason="stop", + content=content, + finish_reason=finish_reason, usage=RequestUsage( prompt_tokens=prompt_tokens, completion_tokens=completion_tokens @@ -169,12 +203,68 @@ async def create_stream( 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") + 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) + settings = self._build_execution_settings(extra_create_args, tools) + self._sync_tools_with_kernel(kernel, tools) + + prompt_tokens = 0 + completion_tokens = 0 + accumulated_content = "" + + async for streaming_messages in self._sk_client.get_streaming_chat_message_contents( + chat_history, + settings=settings, + kernel=kernel + ): + for msg in streaming_messages: + if not isinstance(msg, StreamingChatMessageContent): + continue + + # Track token usage + if msg.metadata and 'usage' in msg.metadata: + usage = msg.metadata['usage'] + prompt_tokens = getattr(usage, 'prompt_tokens', 0) + completion_tokens = getattr(usage, 'completion_tokens', 0) + + # Check for function calls + if any(isinstance(item, FunctionCallContent) for item in msg.items): + function_calls = self._process_tool_calls(msg) + yield CreateResult( + content=function_calls, + finish_reason="function_calls", + usage=RequestUsage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens + ), + cached=False + ) + return + + # Handle text content + if msg.content: + accumulated_content += msg.content + yield msg.content + + # Final yield if there was text content + if accumulated_content: + self._total_prompt_tokens += prompt_tokens + self._total_completion_tokens += completion_tokens + yield CreateResult( + content=accumulated_content, + finish_reason="stop", + usage=RequestUsage( + prompt_tokens=prompt_tokens, + completion_tokens=completion_tokens + ), + cached=False + ) def actual_usage(self) -> RequestUsage: return RequestUsage( 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 index 80944df6b778..c79568f4bc12 100644 --- 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 @@ -28,20 +28,20 @@ def __init__(self): 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( +@pytest.fixture +def sk_client(): + deployment_name = os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME") + endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") + api_key = os.getenv("AZURE_OPENAI_API_KEY") + + return AzureChatCompletion( deployment_name=deployment_name, endpoint=endpoint, - api_key=os.getenv("AZURE_OPENAI_API_KEY"), + api_key=api_key, ) - + +@pytest.mark.asyncio +async def test_sk_chat_completion_with_tools(sk_client): # Create adapter adapter = SKChatCompletionAdapter(sk_client) @@ -63,7 +63,31 @@ async def test_sk_chat_completion_with_tools(): tools=[tool], extra_create_args={"kernel": kernel} ) + + # Verify response + assert isinstance(result.content, list) + assert result.finish_reason == "function_calls" + assert result.usage.prompt_tokens >= 0 + assert result.usage.completion_tokens >= 0 + assert not result.cached +@pytest.mark.asyncio +async def test_sk_chat_completion_without_tools(sk_client): + # Create adapter and kernel + adapter = SKChatCompletionAdapter(sk_client) + kernel = Kernel(memory=NullMemory()) + + # Test messages + messages = [ + SystemMessage(content="You are a helpful assistant."), + UserMessage(content="Say hello!", source="user"), + ] + + # Call create without tools + result = await adapter.create( + messages=messages, + extra_create_args={"kernel": kernel} + ) # Verify response assert isinstance(result.content, str) @@ -71,3 +95,70 @@ async def test_sk_chat_completion_with_tools(): assert result.usage.prompt_tokens >= 0 assert result.usage.completion_tokens >= 0 assert not result.cached + +@pytest.mark.asyncio +async def test_sk_chat_completion_stream_with_tools(sk_client): + # Create adapter and kernel + adapter = SKChatCompletionAdapter(sk_client) + kernel = Kernel(memory=NullMemory()) + + # Create calculator tool + tool = CalculatorTool() + + # Test messages + messages = [ + SystemMessage(content="You are a helpful assistant."), + UserMessage(content="What is 2 + 2?", source="user"), + ] + + # Call create_stream with tool + response_chunks = [] + async for chunk in adapter.create_stream( + messages=messages, + tools=[tool], + extra_create_args={"kernel": kernel} + ): + response_chunks.append(chunk) + + # Verify response + assert len(response_chunks) > 0 + final_chunk = response_chunks[-1] + assert isinstance(final_chunk.content, list) # Function calls + assert final_chunk.finish_reason == "function_calls" + assert final_chunk.usage.prompt_tokens >= 0 + assert final_chunk.usage.completion_tokens >= 0 + assert not final_chunk.cached + +@pytest.mark.asyncio +async def test_sk_chat_completion_stream_without_tools(sk_client): + # Create adapter and kernel + adapter = SKChatCompletionAdapter(sk_client) + kernel = Kernel(memory=NullMemory()) + + # Test messages + messages = [ + SystemMessage(content="You are a helpful assistant."), + UserMessage(content="Say hello!", source="user"), + ] + + # Call create_stream without tools + response_chunks = [] + async for chunk in adapter.create_stream( + messages=messages, + extra_create_args={"kernel": kernel} + ): + response_chunks.append(chunk) + + # Verify response + assert len(response_chunks) > 0 + # All chunks except last should be strings + for chunk in response_chunks[:-1]: + assert isinstance(chunk, str) + + # Final chunk should be CreateResult + final_chunk = response_chunks[-1] + assert isinstance(final_chunk.content, str) + assert final_chunk.finish_reason == "stop" + assert final_chunk.usage.prompt_tokens >= 0 + assert final_chunk.usage.completion_tokens >= 0 + assert not final_chunk.cached From 3fe641e915f945f53db1b0f6fcf552253657892e Mon Sep 17 00:00:00 2001 From: Leonardo Pinheiro Date: Sat, 4 Jan 2025 08:38:53 +1000 Subject: [PATCH 04/18] update lock --- python/uv.lock | 1 - 1 file changed, 1 deletion(-) diff --git a/python/uv.lock b/python/uv.lock index bd7d002e7c27..23528c42fe21 100644 --- a/python/uv.lock +++ b/python/uv.lock @@ -3225,7 +3225,6 @@ dependencies = [ { name = "more-itertools" }, { name = "openapi-schema-validator" }, { name = "openapi-spec-validator" }, - { name = "parse" }, { name = "werkzeug" }, ] sdist = { url = "https://files.pythonhosted.org/packages/34/b9/a769ae516c7f016465b2d9abc6e8dc4d5a1b54c57ab99b3cc95e9587955f/openapi_core-0.19.4.tar.gz", hash = "sha256:1150d9daa5e7b4cacfd7d7e097333dc89382d7d72703934128dcf8a1a4d0df49", size = 109095 } From a5adcf0042fe6caec8b98d3306357d19a075f761 Mon Sep 17 00:00:00 2001 From: Leonardo Pinheiro Date: Mon, 6 Jan 2025 16:37:18 +1000 Subject: [PATCH 05/18] linting --- .../models/semantic_kernel/__init__.py | 4 +- .../_sk_chat_completion_adapter.py | 198 +++++++++--------- .../tools/semantic_kernel/__init__.py | 1 - .../_kernel_function_from_tool.py | 30 ++- .../models/test_sk_chat_completion_adapter.py | 93 ++++---- 5 files changed, 167 insertions(+), 159 deletions(-) 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 index f5ed940a91a9..da66737d9734 100644 --- 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 @@ -1,5 +1,3 @@ from ._sk_chat_completion_adapter import SKChatCompletionAdapter -__all__ = [ - "SKChatCompletionAdapter" -] +__all__ = ["SKChatCompletionAdapter"] 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 index 278e448e9885..56b915d85e80 100644 --- 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 @@ -1,21 +1,35 @@ +import json from typing import Any, Mapping, Optional, Sequence + +from autogen_core import FunctionCall 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 autogen_core.models import ( + AssistantMessage, + ChatCompletionClient, + CreateResult, + FunctionExecutionResult, + FunctionExecutionResultMessage, + LLMMessage, + ModelFamily, + ModelInfo, + RequestUsage, + SystemMessage, + UserMessage, +) +from autogen_core.tools import BaseTool, 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.connectors.ai.prompt_execution_settings import PromptExecutionSettings from semantic_kernel.contents.chat_history import ChatHistory from semantic_kernel.contents.chat_message_content import ChatMessageContent +from semantic_kernel.contents.function_call_content import FunctionCallContent +from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent from semantic_kernel.contents.utils.author_role import AuthorRole -from semantic_kernel.kernel import Kernel from semantic_kernel.functions.kernel_plugin import KernelPlugin +from semantic_kernel.kernel import Kernel from typing_extensions import AsyncGenerator, Union + from autogen_ext.tools.semantic_kernel import KernelFunctionFromTool -from semantic_kernel.contents.function_call_content import FunctionCallContent -from autogen_core import FunctionCall -from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent class SKChatCompletionAdapter(ChatCompletionClient): @@ -28,25 +42,25 @@ def __init__(self, sk_client: ChatCompletionClientBase): 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) @@ -57,35 +71,39 @@ def _convert_from_chat_message(self, message: ChatMessageContent, source: str = """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: + 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.Auto(auto_invoke=extra_create_args.get("auto_invoke", False)) - + function_choice_behavior = FunctionChoiceBehavior.Auto( # type: ignore + auto_invoke=extra_create_args.get("auto_invoke", False) + ) + # 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 + function_choice_behavior=function_choice_behavior, ) return settings @@ -101,7 +119,7 @@ def _sync_tools_with_kernel(self, kernel: Kernel, tools: Sequence[Tool | ToolSch 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} + 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: @@ -109,36 +127,39 @@ def _sync_tools_with_kernel(self, kernel: Kernel, tools: Sequence[Tool | ToolSch # Add or update tools for tool in tools: - if isinstance(tool, Tool): + if isinstance(tool, BaseTool): # Convert Tool to KernelFunction using KernelFunctionFromTool - kernel_function = KernelFunctionFromTool(tool, plugin_name="autogen_tools") - self._tools_plugin.functions[tool.name] = kernel_function + kernel_function = KernelFunctionFromTool(tool, plugin_name="autogen_tools") # type: ignore + self._tools_plugin.functions[tool.schema["name"]] = kernel_function def _process_tool_calls(self, result: ChatMessageContent) -> list[FunctionCall]: """Process tool calls from SK ChatMessageContent""" - function_calls = [] + function_calls: list[FunctionCall] = [] for item in result.items: if isinstance(item, FunctionCallContent): # Extract plugin name and function name plugin_name = item.plugin_name or "" - function_name = item.function_name or item.name + function_name = item.function_name if plugin_name: full_name = f"{plugin_name}-{function_name}" else: full_name = function_name - - function_calls.append( - FunctionCall( - id=item.id, - name=full_name, - arguments=item.arguments or "{}" - ) - ) + + if item.id is None: + raise ValueError("Function call ID is required") + + if isinstance(item.arguments, Mapping): + arguments = json.dumps(item.arguments) + else: + arguments = item.arguments or "{}" + + function_calls.append(FunctionCall(id=item.id, name=full_name, arguments=arguments)) return function_calls async def create( self, messages: Sequence[LLMMessage], + *, tools: Sequence[Tool | ToolSchema] = [], json_output: Optional[bool] = None, extra_create_args: Mapping[str, Any] = {}, @@ -146,33 +167,29 @@ async def create( ) -> 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 - ) + + 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) - + + 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 @@ -184,20 +201,18 @@ async def create( else: content = result[0].content finish_reason = "stop" - + return CreateResult( content=content, finish_reason=finish_reason, - usage=RequestUsage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens - ), - cached=False + 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] = {}, @@ -205,7 +220,7 @@ async def create_stream( ) -> AsyncGenerator[Union[str, CreateResult], None]: 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") @@ -217,21 +232,19 @@ async def create_stream( prompt_tokens = 0 completion_tokens = 0 accumulated_content = "" - + async for streaming_messages in self._sk_client.get_streaming_chat_message_contents( - chat_history, - settings=settings, - kernel=kernel + chat_history, settings=settings, kernel=kernel ): for msg in streaming_messages: if not isinstance(msg, StreamingChatMessageContent): continue # Track token usage - if msg.metadata and 'usage' in msg.metadata: - usage = msg.metadata['usage'] - prompt_tokens = getattr(usage, 'prompt_tokens', 0) - completion_tokens = getattr(usage, 'completion_tokens', 0) + if msg.metadata and "usage" in msg.metadata: + usage = msg.metadata["usage"] + prompt_tokens = getattr(usage, "prompt_tokens", 0) + completion_tokens = getattr(usage, "completion_tokens", 0) # Check for function calls if any(isinstance(item, FunctionCallContent) for item in msg.items): @@ -239,11 +252,8 @@ async def create_stream( yield CreateResult( content=function_calls, finish_reason="function_calls", - usage=RequestUsage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens - ), - cached=False + usage=RequestUsage(prompt_tokens=prompt_tokens, completion_tokens=completion_tokens), + cached=False, ) return @@ -259,46 +269,42 @@ async def create_stream( yield CreateResult( content=accumulated_content, finish_reason="stop", - usage=RequestUsage( - prompt_tokens=prompt_tokens, - completion_tokens=completion_tokens - ), - cached=False + usage=RequestUsage(prompt_tokens=prompt_tokens, completion_tokens=completion_tokens), + cached=False, ) def actual_usage(self) -> RequestUsage: - return RequestUsage( - prompt_tokens=self._total_prompt_tokens, - completion_tokens=self._total_completion_tokens - ) + 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 - ) + return RequestUsage(prompt_tokens=self._total_prompt_tokens, completion_tokens=self._total_completion_tokens) - def count_tokens(self, messages: Sequence[LLMMessage]) -> int: + def count_tokens(self, messages: Sequence[LLMMessage], *, tools: Sequence[Tool | ToolSchema] = []) -> 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) + 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: + def remaining_tokens(self, messages: Sequence[LLMMessage], *, tools: Sequence[Tool | ToolSchema] = []) -> 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) + max_tokens = getattr(self._sk_client, "max_tokens", 4096) return max_tokens - used_tokens @property - def capabilities(self) -> ModelCapabilities: + def model_info(self) -> ModelInfo: # Return something consistent with the underlying SK client - return { - "vision": False, - "function_calling": self._sk_client.SUPPORTS_FUNCTION_CALLING, - "json_output": False, - } + return ModelInfo( # type: ignore + vision=False, + function_calling=self._sk_client.SUPPORTS_FUNCTION_CALLING, + json_output=False, + model_family=ModelFamily.UNKNOWN, + ) + + @property + def capabilities(self) -> ModelInfo: + return self.model_info diff --git a/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/__init__.py b/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/__init__.py index e48e6d1bfd71..eed4547717da 100644 --- a/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/__init__.py +++ b/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/__init__.py @@ -3,4 +3,3 @@ __all__ = [ "KernelFunctionFromTool", ] - diff --git a/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/_kernel_function_from_tool.py b/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/_kernel_function_from_tool.py index b1b6659efdc3..ce4089aae689 100644 --- a/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/_kernel_function_from_tool.py +++ b/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/_kernel_function_from_tool.py @@ -1,20 +1,28 @@ +from typing import TypeVar + +from autogen_core import CancellationToken +from autogen_core.tools import BaseTool +from pydantic import BaseModel +from semantic_kernel.exceptions import FunctionExecutionException +from semantic_kernel.filters.functions.function_invocation_context import FunctionInvocationContext +from semantic_kernel.functions.function_result import FunctionResult 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 + +InputT = TypeVar("InputT", bound=BaseModel) +OutputT = TypeVar("OutputT", bound=BaseModel) + 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. + def __init__(self, tool: BaseTool[InputT, OutputT], plugin_name: str | None = None): + # Build up KernelFunctionMetadata. You can also parse the tool's schema for parameters. parameters = [ KernelParameterMetadata( - name="args", + name="args", description="JSON arguments for the tool", default_value=None, - type_="dict", + type="dict", type_object=dict, is_required=True, ) @@ -23,7 +31,7 @@ def __init__(self, tool: BaseTool, plugin_name: str | None = None): name="return", description="Result from the tool", default_value=None, - type_="str", + type="str", type_object=str, is_required=False, ) @@ -44,10 +52,10 @@ 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"] + tool_args = context.arguments # Call your tool’s run_json - result = await self._tool.run_json(tool_args, cancellation_token=None) + result = await self._tool.run_json(tool_args, cancellation_token=CancellationToken()) # Wrap in a FunctionResult context.result = FunctionResult( 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 index c79568f4bc12..528357487d36 100644 --- 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 @@ -1,69 +1,71 @@ 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 import CancellationToken +from autogen_core.models import CreateResult, 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 +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 + 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" + description="Add two numbers together", ) async def run(self, args: CalculatorArgs, cancellation_token: CancellationToken) -> CalculatorResult: return CalculatorResult(result=args.a + args.b) + @pytest.fixture -def sk_client(): +def sk_client() -> AzureChatCompletion: deployment_name = os.getenv("AZURE_OPENAI_DEPLOYMENT_NAME") endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") api_key = os.getenv("AZURE_OPENAI_API_KEY") - + return AzureChatCompletion( deployment_name=deployment_name, endpoint=endpoint, api_key=api_key, ) + @pytest.mark.asyncio -async def test_sk_chat_completion_with_tools(sk_client): +async def test_sk_chat_completion_with_tools(sk_client: AzureChatCompletion) -> None: # 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} - ) - + result = await adapter.create(messages=messages, tools=[tool], extra_create_args={"kernel": kernel}) + # Verify response assert isinstance(result.content, list) assert result.finish_reason == "function_calls" @@ -71,24 +73,22 @@ async def test_sk_chat_completion_with_tools(sk_client): assert result.usage.completion_tokens >= 0 assert not result.cached + @pytest.mark.asyncio -async def test_sk_chat_completion_without_tools(sk_client): +async def test_sk_chat_completion_without_tools(sk_client: AzureChatCompletion) -> None: # Create adapter and kernel adapter = SKChatCompletionAdapter(sk_client) kernel = Kernel(memory=NullMemory()) - + # Test messages messages = [ SystemMessage(content="You are a helpful assistant."), UserMessage(content="Say hello!", source="user"), ] - + # Call create without tools - result = await adapter.create( - messages=messages, - extra_create_args={"kernel": kernel} - ) - + result = await adapter.create(messages=messages, extra_create_args={"kernel": kernel}) + # Verify response assert isinstance(result.content, str) assert result.finish_reason == "stop" @@ -96,67 +96,64 @@ async def test_sk_chat_completion_without_tools(sk_client): assert result.usage.completion_tokens >= 0 assert not result.cached + @pytest.mark.asyncio -async def test_sk_chat_completion_stream_with_tools(sk_client): +async def test_sk_chat_completion_stream_with_tools(sk_client: AzureChatCompletion) -> None: # Create adapter and kernel adapter = SKChatCompletionAdapter(sk_client) kernel = Kernel(memory=NullMemory()) - + # Create calculator tool tool = CalculatorTool() - + # Test messages messages = [ SystemMessage(content="You are a helpful assistant."), UserMessage(content="What is 2 + 2?", source="user"), ] - + # Call create_stream with tool - response_chunks = [] - async for chunk in adapter.create_stream( - messages=messages, - tools=[tool], - extra_create_args={"kernel": kernel} - ): + response_chunks: list[CreateResult | str] = [] + async for chunk in adapter.create_stream(messages=messages, tools=[tool], extra_create_args={"kernel": kernel}): response_chunks.append(chunk) - + # Verify response assert len(response_chunks) > 0 final_chunk = response_chunks[-1] + assert isinstance(final_chunk, CreateResult) assert isinstance(final_chunk.content, list) # Function calls assert final_chunk.finish_reason == "function_calls" assert final_chunk.usage.prompt_tokens >= 0 assert final_chunk.usage.completion_tokens >= 0 assert not final_chunk.cached + @pytest.mark.asyncio -async def test_sk_chat_completion_stream_without_tools(sk_client): +async def test_sk_chat_completion_stream_without_tools(sk_client: AzureChatCompletion) -> None: # Create adapter and kernel adapter = SKChatCompletionAdapter(sk_client) kernel = Kernel(memory=NullMemory()) - + # Test messages messages = [ SystemMessage(content="You are a helpful assistant."), UserMessage(content="Say hello!", source="user"), ] - + # Call create_stream without tools - response_chunks = [] - async for chunk in adapter.create_stream( - messages=messages, - extra_create_args={"kernel": kernel} - ): + response_chunks: list[CreateResult | str] = [] + async for chunk in adapter.create_stream(messages=messages, extra_create_args={"kernel": kernel}): response_chunks.append(chunk) - + # Verify response assert len(response_chunks) > 0 # All chunks except last should be strings for chunk in response_chunks[:-1]: assert isinstance(chunk, str) - + # Final chunk should be CreateResult final_chunk = response_chunks[-1] + assert isinstance(final_chunk, CreateResult) assert isinstance(final_chunk.content, str) assert final_chunk.finish_reason == "stop" assert final_chunk.usage.prompt_tokens >= 0 From 107d743c6e9412e1ea2a61bcecab19ad688d1668 Mon Sep 17 00:00:00 2001 From: Leonardo Pinheiro Date: Sun, 12 Jan 2025 17:01:50 +1000 Subject: [PATCH 06/18] add semantic kernel extras --- python/packages/autogen-ext/pyproject.toml | 87 +- python/uv.lock | 3837 ++++++++++++++++---- 2 files changed, 3135 insertions(+), 789 deletions(-) diff --git a/python/packages/autogen-ext/pyproject.toml b/python/packages/autogen-ext/pyproject.toml index 5357f1c66dbe..0c4e7fa9092d 100644 --- a/python/packages/autogen-ext/pyproject.toml +++ b/python/packages/autogen-ext/pyproject.toml @@ -49,10 +49,95 @@ video-surfer = [ grpc = [ "grpcio~=1.62.0", # TODO: update this once we have a stable version. ] -semantic-kernel = [ + +semantic-kernel-core = [ "semantic-kernel>=1.17.1", ] +semantic-kernel-google = [ + "semantic-kernel[google]>=1.17.1", +] + +semantic-kernel-azure = [ + "semantic-kernel[azure]>=1.17.1", +] + +semantic-kernel-mongo = [ + "semantic-kernel[mongo]>=1.17.1", +] + +semantic-kernel-chroma = [ + "semantic-kernel[chroma]>=1.17.1", +] + +semantic-kernel-hugging-face = [ + "semantic-kernel[hugging_face]>=1.17.1", +] + +semantic-kernel-notebooks = [ + "semantic-kernel[notebooks]>=1.17.1", +] + +semantic-kernel-milvus = [ + "semantic-kernel[milvus]>=1.17.1", +] + +semantic-kernel-mistralai = [ + "semantic-kernel[mistralai]>=1.17.1", +] + +semantic-kernel-ollama = [ + "semantic-kernel[ollama]>=1.17.1", +] + +semantic-kernel-onnx = [ + "semantic-kernel[onnx]>=1.17.1", +] + +semantic-kernel-anthropic = [ + "semantic-kernel[anthropic]>=1.17.1", +] + +semantic-kernel-pinecone = [ + "semantic-kernel[pinecone]>=1.17.1", +] + +semantic-kernel-postgres = [ + "semantic-kernel[postgres]>=1.17.1", +] + +semantic-kernel-qdrant = [ + "semantic-kernel[qdrant]>=1.17.1", +] + +semantic-kernel-redis = [ + "semantic-kernel[redis]>=1.17.1", +] + +semantic-kernel-usearch = [ + "semantic-kernel[usearch]>=1.17.1", +] + +semantic-kernel-weaviate = [ + "semantic-kernel[weaviate]>=1.17.1", +] + +semantic-kernel-pandas = [ + "semantic-kernel[pandas]>=1.17.1", +] + +semantic-kernel-aws = [ + "semantic-kernel[aws]>=1.17.1", +] + +semantic-kernel-dapr = [ + "semantic-kernel[dapr]>=1.17.1", +] + +semantic-kernel-all = [ + "semantic-kernel[azure,chroma,google,hugging_face,mongo,notebooks,milvus,mistralai,ollama,onnx,anthropic,pinecone,postgres,qdrant,redis,usearch,weaviate,pandas,aws,dapr]>=1.17.1", +] + [tool.hatch.build.targets.wheel] packages = ["src/autogen_ext"] diff --git a/python/uv.lock b/python/uv.lock index 241ab30cdb4c..676d7e57affa 100644 --- a/python/uv.lock +++ b/python/uv.lock @@ -1,21 +1,23 @@ version = 1 requires-python = ">=3.10, <3.13" resolution-markers = [ - "python_full_version < '3.11' and sys_platform == 'darwin'", - "python_version < '0'", - "python_full_version < '3.11' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version == '3.11.*' and sys_platform == 'darwin'", - "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 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python_full_version < '3.12.4' and platform_machine == 'aarch64' and sys_platform == 'linux'", "(python_full_version >= '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')", + "(python_full_version >= '3.12' and python_full_version < '3.12.4' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and python_full_version < '3.12.4' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version == '3.11.*' and sys_platform == 'darwin'", + "python_full_version == '3.11.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "(python_full_version == '3.11.*' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version == '3.11.*' and sys_platform != 'darwin' and sys_platform != 'linux')", + "python_full_version < '3.11' and sys_platform == 'darwin'", + "python_full_version < 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b/python/packages/autogen-ext/src/autogen_ext/models/semantic_kernel/_sk_chat_completion_adapter.py index 56b915d85e80..b5019f8cbde4 100644 --- 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 @@ -1,5 +1,5 @@ import json -from typing import Any, Mapping, Optional, Sequence +from typing import Any, Literal, Mapping, Optional, Sequence from autogen_core import FunctionCall from autogen_core._cancellation_token import CancellationToken @@ -33,6 +33,122 @@ class SKChatCompletionAdapter(ChatCompletionClient): + """ + SKChatCompletionAdapter is an adapter that allows using Semantic Kernel model clients + as Autogen ChatCompletion clients. This makes it possible to seamlessly integrate + Semantic Kernel connectors (e.g., Azure OpenAI, Google Gemini, Ollama, etc.) into + Autogen agents that rely on a ChatCompletionClient interface. + + By leveraging this adapter, you can: + + - 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. + + Args: + sk_client (ChatCompletionClientBase): + The Semantic Kernel client to wrap (e.g., AzureChatCompletion, GoogleAIChatCompletion, OllamaChatCompletion). + + Example usage: + + .. code-block:: python + + 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 autogen_core.models import SystemMessage, UserMessage, LLMMessage + from autogen_ext.models.semantic_kernel import SKChatCompletionAdapter + from autogen_core import CancellationToken + from autogen_core.tools import BaseTool + from pydantic import BaseModel + + + # 1) Basic tool definition (for demonstration) + class CalculatorArgs(BaseModel): + a: float + b: float + + + class CalculatorResult(BaseModel): + result: float + + + class CalculatorTool(BaseTool[CalculatorArgs, CalculatorResult]): + def __init__(self) -> None: + 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) + + + async def main(): + # 2) Create a Semantic Kernel instance (with null memory for simplicity) + kernel = Kernel(memory=NullMemory()) + + # ---------------------------------------------------------------- + # Example A: Azure OpenAI + # ---------------------------------------------------------------- + deployment_name = "" + endpoint = "" + api_key = "" + + azure_client = AzureChatCompletion(deployment_name=deployment_name, endpoint=endpoint, api_key=api_key) + azure_adapter = SKChatCompletionAdapter(sk_client=azure_client) + + # ---------------------------------------------------------------- + # Example B: Google Gemini + # ---------------------------------------------------------------- + google_api_key = "" + google_model = "gemini-1.5-flash" + google_client = GoogleAIChatCompletion(model=google_model, api_key=google_api_key) + google_adapter = SKChatCompletionAdapter(sk_client=google_client) + + # ---------------------------------------------------------------- + # Example C: Ollama (local Llama-based model) + # ---------------------------------------------------------------- + ollama_client = OllamaChatCompletion( + service_id="ollama", # custom ID + host="http://localhost:11434", + ai_model_id="llama3.1", + ) + ollama_adapter = SKChatCompletionAdapter(sk_client=ollama_client) + + # 3) Create a tool and register it with the kernel + calc_tool = CalculatorTool() + + # 4) Prepare messages for a chat completion + messages: list[LLMMessage] = [ + SystemMessage(content="You are a helpful assistant."), + UserMessage(content="What is 2 + 2?"), + ] + + # 5) Invoke chat completion with the Azure adapter (as an example) + # Provide the kernel in extra_create_args, and pass the tool. + # The same pattern applies to Google or Ollama adapters. + result = await azure_adapter.create(messages=messages, tools=[calc_tool], extra_create_args={"kernel": kernel}) + + # Print or use the result + print("Result content:", result.content) + print("Finish reason:", result.finish_reason) + + # Note: Tools are invoked if the model calls them (function calls). + # If the model simply returns text, you get text. + # You can also stream with `create_stream(...)` using the same approach. + + + if __name__ == "__main__": + asyncio.run(main()) + """ + def __init__(self, sk_client: ChatCompletionClientBase): self._sk_client = sk_client self._total_prompt_tokens = 0 @@ -197,7 +313,7 @@ async def create( content: Union[str, list[FunctionCall]] if any(isinstance(item, FunctionCallContent) for item in result[0].items): content = self._process_tool_calls(result[0]) - finish_reason = "function_calls" + finish_reason: Literal["function_calls", "stop"] = "function_calls" else: content = result[0].content finish_reason = "stop" 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 index 528357487d36..454b6eac7c85 100644 --- 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 @@ -2,7 +2,7 @@ import pytest from autogen_core import CancellationToken -from autogen_core.models import CreateResult, SystemMessage, UserMessage +from autogen_core.models import CreateResult, LLMMessage, SystemMessage, UserMessage from autogen_core.tools import BaseTool from autogen_ext.models.semantic_kernel import SKChatCompletionAdapter from pydantic import BaseModel @@ -21,7 +21,7 @@ class CalculatorResult(BaseModel): class CalculatorTool(BaseTool[CalculatorArgs, CalculatorResult]): - def __init__(self): + def __init__(self) -> None: super().__init__( args_type=CalculatorArgs, return_type=CalculatorResult, @@ -58,7 +58,7 @@ async def test_sk_chat_completion_with_tools(sk_client: AzureChatCompletion) -> tool = CalculatorTool() # Test messages - messages = [ + messages: list[LLMMessage] = [ SystemMessage(content="You are a helpful assistant."), UserMessage(content="What is 2 + 2?", source="user"), ] @@ -81,7 +81,7 @@ async def test_sk_chat_completion_without_tools(sk_client: AzureChatCompletion) kernel = Kernel(memory=NullMemory()) # Test messages - messages = [ + messages: list[LLMMessage] = [ SystemMessage(content="You are a helpful assistant."), UserMessage(content="Say hello!", source="user"), ] @@ -107,7 +107,7 @@ async def test_sk_chat_completion_stream_with_tools(sk_client: AzureChatCompleti tool = CalculatorTool() # Test messages - messages = [ + messages: list[LLMMessage] = [ SystemMessage(content="You are a helpful assistant."), UserMessage(content="What is 2 + 2?", source="user"), ] @@ -135,7 +135,7 @@ async def test_sk_chat_completion_stream_without_tools(sk_client: AzureChatCompl kernel = Kernel(memory=NullMemory()) # Test messages - messages = [ + messages: list[LLMMessage] = [ SystemMessage(content="You are a helpful assistant."), UserMessage(content="Say hello!", source="user"), ] From d96e04e2739b9eb0a478bc3179e7aec6e610527b Mon Sep 17 00:00:00 2001 From: Leonardo Pinheiro Date: Mon, 13 Jan 2025 11:35:47 +1000 Subject: [PATCH 08/18] update dependencies and format/lint --- python/packages/autogen-ext/pyproject.toml | 46 +- .../models/test_sk_chat_completion_adapter.py | 185 +++ python/uv.lock | 1186 +---------------- 3 files changed, 233 insertions(+), 1184 deletions(-) diff --git a/python/packages/autogen-ext/pyproject.toml b/python/packages/autogen-ext/pyproject.toml index 0c4e7fa9092d..f4a3fa85b299 100644 --- a/python/packages/autogen-ext/pyproject.toml +++ b/python/packages/autogen-ext/pyproject.toml @@ -58,30 +58,10 @@ semantic-kernel-google = [ "semantic-kernel[google]>=1.17.1", ] -semantic-kernel-azure = [ - "semantic-kernel[azure]>=1.17.1", -] - -semantic-kernel-mongo = [ - "semantic-kernel[mongo]>=1.17.1", -] - -semantic-kernel-chroma = [ - "semantic-kernel[chroma]>=1.17.1", -] - semantic-kernel-hugging-face = [ "semantic-kernel[hugging_face]>=1.17.1", ] -semantic-kernel-notebooks = [ - "semantic-kernel[notebooks]>=1.17.1", -] - -semantic-kernel-milvus = [ - "semantic-kernel[milvus]>=1.17.1", -] - semantic-kernel-mistralai = [ "semantic-kernel[mistralai]>=1.17.1", ] @@ -98,30 +78,6 @@ semantic-kernel-anthropic = [ "semantic-kernel[anthropic]>=1.17.1", ] -semantic-kernel-pinecone = [ - "semantic-kernel[pinecone]>=1.17.1", -] - -semantic-kernel-postgres = [ - "semantic-kernel[postgres]>=1.17.1", -] - -semantic-kernel-qdrant = [ - "semantic-kernel[qdrant]>=1.17.1", -] - -semantic-kernel-redis = [ - "semantic-kernel[redis]>=1.17.1", -] - -semantic-kernel-usearch = [ - "semantic-kernel[usearch]>=1.17.1", -] - -semantic-kernel-weaviate = [ - "semantic-kernel[weaviate]>=1.17.1", -] - semantic-kernel-pandas = [ "semantic-kernel[pandas]>=1.17.1", ] @@ -135,7 +91,7 @@ semantic-kernel-dapr = [ ] semantic-kernel-all = [ - "semantic-kernel[azure,chroma,google,hugging_face,mongo,notebooks,milvus,mistralai,ollama,onnx,anthropic,pinecone,postgres,qdrant,redis,usearch,weaviate,pandas,aws,dapr]>=1.17.1", + "semantic-kernel[google,hugging_face,mistralai,ollama,onnx,anthropic,usearch,pandas,aws,dapr]>=1.17.1", ] [tool.hatch.build.targets.wheel] 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 index 454b6eac7c85..db3b3cbd3c47 100644 --- 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 @@ -1,12 +1,25 @@ import os +from typing import Any, AsyncGenerator +from unittest.mock import AsyncMock import pytest from autogen_core import CancellationToken from autogen_core.models import CreateResult, LLMMessage, SystemMessage, UserMessage from autogen_core.tools import BaseTool from autogen_ext.models.semantic_kernel import SKChatCompletionAdapter +from openai.types.chat.chat_completion_chunk import ChatCompletionChunk, Choice, ChoiceDelta +from openai.types.completion_usage import CompletionUsage from pydantic import BaseModel from semantic_kernel.connectors.ai.open_ai.services.azure_chat_completion import AzureChatCompletion +from semantic_kernel.connectors.ai.prompt_execution_settings import PromptExecutionSettings +from semantic_kernel.contents.chat_history import ChatHistory +from semantic_kernel.contents.chat_message_content import ChatMessageContent +from semantic_kernel.contents.function_call_content import FunctionCallContent +from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent +from semantic_kernel.contents.streaming_text_content import StreamingTextContent +from semantic_kernel.contents.text_content import TextContent +from semantic_kernel.contents.utils.author_role import AuthorRole +from semantic_kernel.contents.utils.finish_reason import FinishReason from semantic_kernel.kernel import Kernel from semantic_kernel.memory.null_memory import NullMemory @@ -39,6 +52,178 @@ def sk_client() -> AzureChatCompletion: endpoint = os.getenv("AZURE_OPENAI_ENDPOINT") api_key = os.getenv("AZURE_OPENAI_API_KEY") + if not all([deployment_name, endpoint, api_key]): + mock_client = AsyncMock(spec=AzureChatCompletion) + + async def mock_get_chat_message_contents( + chat_history: ChatHistory, + settings: PromptExecutionSettings, + **kwargs: Any, + ) -> list[ChatMessageContent]: + if "What is 2 + 2?" in str(chat_history): + # Mock response for calculator tool test + return [ + ChatMessageContent( + ai_model_id="gpt-4o-mini", + role=AuthorRole.ASSISTANT, + metadata={"usage": {"prompt_tokens": 53, "completion_tokens": 13}}, + items=[ + FunctionCallContent( + id="call_UwVVI0iGEmcPwmKUigJcuuuF", + function_name="calculator", + plugin_name="autogen_tools", + arguments="{}", + ) + ], + finish_reason=FinishReason.TOOL_CALLS, + ) + ] + else: + # Mock response for hello test + return [ + ChatMessageContent( + ai_model_id="gpt-4o-mini", + role=AuthorRole.ASSISTANT, + metadata={"usage": {"prompt_tokens": 20, "completion_tokens": 9}}, + items=[TextContent(text="Hello! How can I assist you today?")], + finish_reason=FinishReason.STOP, + ) + ] + + async def mock_get_streaming_chat_message_contents( + chat_history: ChatHistory, + settings: PromptExecutionSettings, + **kwargs: Any, + ) -> AsyncGenerator[list["StreamingChatMessageContent"], Any]: + if "What is 2 + 2?" in str(chat_history): + # Mock response for calculator tool test - single message with function call + yield [ + StreamingChatMessageContent( + choice_index=0, + inner_content=None, + ai_model_id="gpt-4o-mini", + metadata={ + "logprobs": None, + "id": "chatcmpl-AooRjGxKtdTke46keWkBQBKg033XW", + "created": 1736673679, + "usage": {"prompt_tokens": 53, "completion_tokens": 13}, + }, + role=AuthorRole.ASSISTANT, + items=[ # type: ignore + FunctionCallContent( + id="call_n8135GXc2kbiaaDdpImsB1VW", + function_name="calculator", + plugin_name="autogen_tools", + arguments="", + content_type="function_call", # type: ignore + ) + ], + finish_reason=None, + function_invoke_attempt=0, + ) + ] + else: + # First chunk with empty content and role + yield [ + StreamingChatMessageContent( + choice_index=0, + inner_content=ChatCompletionChunk( + id="chatcmpl-AooXcRvW2Dhvr6VL6tqatzvRMXTx9", + choices=[ + Choice(delta=ChoiceDelta(content="", role="assistant"), finish_reason=None, index=0) + ], + created=1736674044, + model="gpt-4o-mini-2024-07-18", + object="chat.completion.chunk", + service_tier="scale", + system_fingerprint="fingerprint", + usage=CompletionUsage(prompt_tokens=20, completion_tokens=9, total_tokens=29), + ), + ai_model_id="gpt-4o-mini", + metadata={"id": "chatcmpl-AooXcRvW2Dhvr6VL6tqatzvRMXTx9", "created": 1736674044}, + role=AuthorRole.ASSISTANT, + items=[StreamingTextContent(choice_index=0, text="")], # type: ignore + ) + ] + + # Second chunk with "Hello" + yield [ + StreamingChatMessageContent( + choice_index=0, + inner_content=ChatCompletionChunk( + id="chatcmpl-AooXcRvW2Dhvr6VL6tqatzvRMXTx9", + choices=[Choice(delta=ChoiceDelta(content="Hello"), finish_reason=None, index=0)], + created=1736674044, + model="gpt-4o-mini-2024-07-18", + object="chat.completion.chunk", + service_tier="scale", + system_fingerprint="fingerprint", + usage=CompletionUsage(prompt_tokens=20, completion_tokens=9, total_tokens=29), + ), + ai_model_id="gpt-4o-mini", + metadata={"id": "chatcmpl-AooXcRvW2Dhvr6VL6tqatzvRMXTx9", "created": 1736674044}, + role=AuthorRole.ASSISTANT, + items=[StreamingTextContent(choice_index=0, text="Hello")], # type: ignore + ) + ] + + # Third chunk with "!" + yield [ + StreamingChatMessageContent( + choice_index=0, + inner_content=ChatCompletionChunk( + id="chatcmpl-AooXcRvW2Dhvr6VL6tqatzvRMXTx9", + choices=[Choice(delta=ChoiceDelta(content="!"), finish_reason=None, index=0)], + created=1736674044, + model="gpt-4o-mini-2024-07-18", + object="chat.completion.chunk", + service_tier="scale", + system_fingerprint="fingerprint", + usage=CompletionUsage(prompt_tokens=20, completion_tokens=9, total_tokens=29), + ), + ai_model_id="gpt-4o-mini", + metadata={"id": "chatcmpl-AooXcRvW2Dhvr6VL6tqatzvRMXTx9", "created": 1736674044}, + role=AuthorRole.ASSISTANT, + items=[StreamingTextContent(choice_index=0, text="!")], # type: ignore + ) + ] + + # Fourth chunk with " How can I assist you today?" + yield [ + StreamingChatMessageContent( + choice_index=0, + inner_content=ChatCompletionChunk( + id="chatcmpl-AooXcRvW2Dhvr6VL6tqatzvRMXTx9", + choices=[ + Choice( + delta=ChoiceDelta(content=" How can I assist you today?"), + finish_reason=None, + index=0, + ) + ], + created=1736674044, + model="gpt-4o-mini-2024-07-18", + object="chat.completion.chunk", + service_tier="scale", + system_fingerprint="fingerprint", + usage=CompletionUsage(prompt_tokens=20, completion_tokens=9, total_tokens=29), + ), + ai_model_id="gpt-4o-mini", + metadata={ + "id": "chatcmpl-AooXcRvW2Dhvr6VL6tqatzvRMXTx9", + "created": 1736674044, + "usage": {"prompt_tokens": 20, "completion_tokens": 9, "total_tokens": 29}, + }, + role=AuthorRole.ASSISTANT, + items=[StreamingTextContent(choice_index=0, text=" How can I assist you today?")], + finish_reason=FinishReason.STOP, + ) + ] + + mock_client.get_chat_message_contents = mock_get_chat_message_contents + mock_client.get_streaming_chat_message_contents = mock_get_streaming_chat_message_contents + return mock_client + return AzureChatCompletion( deployment_name=deployment_name, endpoint=endpoint, diff --git a/python/uv.lock b/python/uv.lock index 676d7e57affa..191bd30e2a9c 100644 --- a/python/uv.lock +++ b/python/uv.lock @@ -15,9 +15,6 @@ resolution-markers = [ "(python_full_version < '3.11' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform != 'darwin' and sys_platform != 'linux')", ] -[options] -prerelease-mode = "allow" - [manifest] members = [ "agbench", @@ -155,7 +152,7 @@ source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "aiohappyeyeballs" }, { name = "aiosignal" }, - { name = "async-timeout", 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"ollama", "onnx", "pandas", "usearch"] }, ] semantic-kernel-anthropic = [ { name = "semantic-kernel", extra = ["anthropic"] }, @@ -603,12 +557,6 @@ semantic-kernel-anthropic = [ semantic-kernel-aws = [ { name = "semantic-kernel", extra = ["aws"] }, ] -semantic-kernel-azure = [ - { name = "semantic-kernel", extra = ["azure"] }, -] -semantic-kernel-chroma = [ - { name = "semantic-kernel", extra = ["chroma"] }, -] semantic-kernel-core = [ { name = "semantic-kernel" }, ] @@ -621,18 +569,9 @@ semantic-kernel-google = [ semantic-kernel-hugging-face = [ { name = "semantic-kernel", extra = ["hugging-face"] }, ] -semantic-kernel-milvus = [ - { name = "semantic-kernel", extra = ["milvus"] }, -] semantic-kernel-mistralai = [ { name = "semantic-kernel", extra = ["mistralai"] }, ] -semantic-kernel-mongo = [ - { name = "semantic-kernel", extra = ["mongo"] }, -] -semantic-kernel-notebooks = [ - { name = "semantic-kernel", extra = ["notebooks"] }, -] semantic-kernel-ollama = [ { name = 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09/18] add model info to sk constructor --- .../_sk_chat_completion_adapter.py | 37 +++---------------- .../models/test_sk_chat_completion_adapter.py | 35 +++++++++++++++++- 2 files changed, 39 insertions(+), 33 deletions(-) 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 index b5019f8cbde4..b391d4d4d8d1 100644 --- 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 @@ -4,17 +4,12 @@ from autogen_core import FunctionCall from autogen_core._cancellation_token import CancellationToken from autogen_core.models import ( - AssistantMessage, ChatCompletionClient, CreateResult, - FunctionExecutionResult, - FunctionExecutionResultMessage, LLMMessage, ModelFamily, ModelInfo, RequestUsage, - SystemMessage, - UserMessage, ) from autogen_core.tools import BaseTool, Tool, ToolSchema from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase @@ -24,7 +19,6 @@ from semantic_kernel.contents.chat_message_content import ChatMessageContent from semantic_kernel.contents.function_call_content import FunctionCallContent from semantic_kernel.contents.streaming_chat_message_content import StreamingChatMessageContent -from semantic_kernel.contents.utils.author_role import AuthorRole from semantic_kernel.functions.kernel_plugin import KernelPlugin from semantic_kernel.kernel import Kernel from typing_extensions import AsyncGenerator, Union @@ -149,8 +143,11 @@ async def main(): asyncio.run(main()) """ - def __init__(self, sk_client: ChatCompletionClientBase): + def __init__(self, sk_client: ChatCompletionClientBase, model_info: Optional[ModelInfo] = None): self._sk_client = sk_client + self._model_info = model_info or ModelInfo( + vision=False, function_calling=False, json_output=False, family=ModelFamily.UNKNOWN + ) self._total_prompt_tokens = 0 self._total_completion_tokens = 0 self._tools_plugin: Optional[KernelPlugin] = None @@ -183,24 +180,6 @@ def _convert_to_chat_history(self, messages: Sequence[LLMMessage]) -> ChatHistor 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: @@ -413,13 +392,7 @@ def remaining_tokens(self, messages: Sequence[LLMMessage], *, tools: Sequence[To @property def model_info(self) -> ModelInfo: - # Return something consistent with the underlying SK client - return ModelInfo( # type: ignore - vision=False, - function_calling=self._sk_client.SUPPORTS_FUNCTION_CALLING, - json_output=False, - model_family=ModelFamily.UNKNOWN, - ) + return self._model_info @property def capabilities(self) -> ModelInfo: 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 index db3b3cbd3c47..69be349c38c9 100644 --- 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 @@ -4,7 +4,7 @@ import pytest from autogen_core import CancellationToken -from autogen_core.models import CreateResult, LLMMessage, SystemMessage, UserMessage +from autogen_core.models import CreateResult, LLMMessage, ModelFamily, ModelInfo, SystemMessage, UserMessage from autogen_core.tools import BaseTool from autogen_ext.models.semantic_kernel import SKChatCompletionAdapter from openai.types.chat.chat_completion_chunk import ChatCompletionChunk, Choice, ChoiceDelta @@ -344,3 +344,36 @@ async def test_sk_chat_completion_stream_without_tools(sk_client: AzureChatCompl assert final_chunk.usage.prompt_tokens >= 0 assert final_chunk.usage.completion_tokens >= 0 assert not final_chunk.cached + + +@pytest.mark.asyncio +async def test_sk_chat_completion_default_model_info(sk_client: AzureChatCompletion) -> None: + # Create adapter with default model_info + adapter = SKChatCompletionAdapter(sk_client) + + # Verify default model_info values + assert adapter.model_info["vision"] is False + assert adapter.model_info["function_calling"] is False + assert adapter.model_info["json_output"] is False + assert adapter.model_info["family"] == ModelFamily.UNKNOWN + + # Verify capabilities returns the same ModelInfo + assert adapter.capabilities == adapter.model_info + + +@pytest.mark.asyncio +async def test_sk_chat_completion_custom_model_info(sk_client: AzureChatCompletion) -> None: + # Create custom model info + custom_model_info = ModelInfo(vision=True, function_calling=True, json_output=True, family=ModelFamily.GPT_4) + + # Create adapter with custom model_info + adapter = SKChatCompletionAdapter(sk_client, model_info=custom_model_info) + + # Verify custom model_info values + assert adapter.model_info["vision"] is True + assert adapter.model_info["function_calling"] is True + assert adapter.model_info["json_output"] is True + assert adapter.model_info["family"] == ModelFamily.GPT_4 + + # Verify capabilities returns the same ModelInfo + assert adapter.capabilities == 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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 @@ -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_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 From 764e283014fb19865736b9a40224837bea9961b6 Mon Sep 17 00:00:00 2001 From: Leonardo Pinheiro Date: Sat, 18 Jan 2025 11:59:03 +1000 Subject: [PATCH 12/18] update uv.lock --- python/uv.lock | 82 ++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 63 insertions(+), 19 deletions(-) diff --git a/python/uv.lock b/python/uv.lock index 405c9469f1b5..dcb958f9b9b7 100644 --- a/python/uv.lock +++ b/python/uv.lock @@ -59,7 +59,7 @@ dev = [ [[package]] name = "accelerate" -version = "1.2.1" +version = "1.3.0" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "huggingface-hub" }, @@ -70,9 +70,9 @@ dependencies = [ { name = "safetensors" }, { name = "torch" }, ] -sdist = { url = "https://files.pythonhosted.org/packages/42/09/7947691b7d44bfc739da4a44cc47d6a6d75e6fe9adf047c5234d7cb6be64/accelerate-1.2.1.tar.gz", hash = "sha256:03e161fc69d495daf2b9b5c8d5b43d06e2145520c04727b5bda56d49f1a43ab5", size = 341652 } +sdist = { url = "https://files.pythonhosted.org/packages/85/15/0fab0260ab4069e5224e637d2e400538bb27b0dfc36f17daf68db9770d78/accelerate-1.3.0.tar.gz", hash = "sha256:518631c0adb80bd3d42fb29e7e2dc2256bcd7c786b0ba9119bbaa08611b36d9c", size = 342758 } wheels = [ - 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from semantic_kernel.connectors.ai.open_ai.services.azure_chat_completion import ( - AzureChatCompletion, + from semantic_kernel.connectors.ai.open_ai.services.azure_chat_completion import AzureChatCompletion + from semantic_kernel.connectors.ai.open_ai.prompt_execution_settings.azure_chat_prompt_execution_settings import ( AzureChatPromptExecutionSettings, ) - from semantic_kernel.connectors.ai.google.google_ai import GoogleAIChatCompletion, + 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 @@ -100,9 +100,7 @@ async def main(): api_key = "" azure_client = AzureChatCompletion(deployment_name=deployment_name, endpoint=endpoint, api_key=api_key) - azure_request_settings = AzureChatPromptExecutionSettings( - options={"temperature": 0.8}, - ) + azure_request_settings = AzureChatPromptExecutionSettings(temperature=0.8) azure_adapter = SKChatCompletionAdapter(sk_client=azure_client, default_prompt_settings=azure_request_settings) # ---------------------------------------------------------------- @@ -110,7 +108,7 @@ async def main(): # ---------------------------------------------------------------- google_api_key = "" google_model = "gemini-1.5-flash" - google_client = GoogleAIChatCompletion(model=google_model, api_key=google_api_key) + google_client = GoogleAIChatCompletion(gemini_model_id=google_model, api_key=google_api_key) google_adapter = SKChatCompletionAdapter(sk_client=google_client) # ---------------------------------------------------------------- @@ -137,25 +135,33 @@ async def main(): # 4) Prepare messages for a chat completion messages: list[LLMMessage] = [ SystemMessage(content="You are a helpful assistant."), - UserMessage(content="What is 2 + 2?"), + UserMessage(content="What is 2 + 2?", source="user"), ] - # 5) Invoke chat completion with the Azure adapter (as an example) - # 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( + # 5) Invoke chat completion with different adapters + # Azure example + 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) - # Print or use the result - print("Result content:", result.content) - print("Finish reason:", result.finish_reason) + # Google example + google_result = await google_adapter.create( + messages=messages, + tools=[calc_tool], + extra_create_args={"kernel": kernel}, + ) + print("Google result:", google_result.content) - # Note: Tools are invoked if the model calls them (function calls). - # If the model simply returns text, you get text. - # You can also stream with `create_stream(...)` using the same approach. + # 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) if __name__ == "__main__": From 154bcb9105912c1da0bb30a8e6aba758a5262a56 Mon Sep 17 00:00:00 2001 From: Leonardo Pinheiro Date: Sat, 18 Jan 2025 15:15:18 +1000 Subject: [PATCH 15/18] update create docstrings --- .../python/autogen_ext.tools.semantic_kernel.rst | 2 +- .../models/semantic_kernel/_sk_chat_completion_adapter.py | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/python/packages/autogen-core/docs/src/reference/python/autogen_ext.tools.semantic_kernel.rst b/python/packages/autogen-core/docs/src/reference/python/autogen_ext.tools.semantic_kernel.rst index 1b735032dc1a..8b51d0960e89 100644 --- a/python/packages/autogen-core/docs/src/reference/python/autogen_ext.tools.semantic_kernel.rst +++ b/python/packages/autogen-core/docs/src/reference/python/autogen_ext.tools.semantic_kernel.rst @@ -1,5 +1,5 @@ autogen\_ext.tools.semantic_kernel -==================================== +=================================== .. automodule:: autogen_ext.tools.semantic_kernel 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 index baaf3f1d2f4d..588c4985ac2b 100644 --- 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 @@ -296,10 +296,10 @@ async def create( extra_create_args: Mapping[str, Any] = {}, cancellation_token: Optional[CancellationToken] = None, ) -> CreateResult: - """ - Create a chat completion using the Semantic Kernel client. + """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. @@ -376,10 +376,10 @@ 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. + """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. From e9c43b7783718b6b9b8b8fc6578acb8f92b3aafc Mon Sep 17 00:00:00 2001 From: Leonardo Pinheiro Date: Sat, 18 Jan 2025 15:31:37 +1000 Subject: [PATCH 16/18] fmt and improve tool docstring --- .../_sk_chat_completion_adapter.py | 2 +- .../_kernel_function_from_tool.py | 47 ++++++++++++++++++- 2 files changed, 46 insertions(+), 3 deletions(-) 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 index 588c4985ac2b..0ee28e78cfe6 100644 --- 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 @@ -379,7 +379,7 @@ async def create_stream( """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. diff --git a/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/_kernel_function_from_tool.py b/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/_kernel_function_from_tool.py index ce4089aae689..b703f7e0965b 100644 --- a/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/_kernel_function_from_tool.py +++ b/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/_kernel_function_from_tool.py @@ -15,6 +15,49 @@ class KernelFunctionFromTool(KernelFunction): + """ + KernelFunctionFromTool is an adapter that allows using Autogen tools as Semantic Kernel functions. + This makes it possible to integrate Autogen tools into Semantic Kernel when using the Semantic Kernel's + chat completion adapter or agent. + + By leveraging this adapter, you can: + - Convert any Autogen BaseTool into a Semantic Kernel KernelFunction + - Register the converted tool with a Semantic Kernel plugin + - Execute the tool through Semantic Kernel's function invocation mechanism + - Access tool metadata (name, description, parameters) through Semantic Kernel's metadata system + + Args: + tool (BaseTool[InputT, OutputT]): + The Autogen tool to wrap. Must be a subclass of BaseTool with Pydantic models for input/output. + plugin_name (str | None): + Optional name of the plugin this function belongs to. Defaults to None. + + Example usage: + .. code-block:: python + + # 1) Create an Autogen tool + class CalculatorTool(BaseTool[CalculatorArgs, CalculatorResult]): + def __init__(self) -> None: + super().__init__(name="calculator", description="Add two numbers together") + + + # 2) Convert to Semantic Kernel function + calc_tool = CalculatorTool() + kernel_function = KernelFunctionFromTool(calc_tool, plugin_name="math") + + # 3) Add to Semantic Kernel plugin/kernel + plugin = KernelPlugin(name="math") + plugin.functions[calc_tool.name] = kernel_function + kernel.add_plugin(plugin) + """ + + class Config: + # Hide fields that are not JSON-serializable from the generated schema + fields = { + "invocation_duration_histogram": {"exclude": True}, + "streaming_duration_histogram": {"exclude": True}, + } + def __init__(self, tool: BaseTool[InputT, OutputT], plugin_name: str | None = None): # Build up KernelFunctionMetadata. You can also parse the tool's schema for parameters. parameters = [ @@ -54,7 +97,7 @@ async def _invoke_internal(self, context: FunctionInvocationContext) -> None: raise FunctionExecutionException("Missing 'args' in FunctionInvocationContext.arguments") tool_args = context.arguments - # Call your tool’s run_json + # Call your tool's run_json result = await self._tool.run_json(tool_args, cancellation_token=CancellationToken()) # Wrap in a FunctionResult @@ -65,6 +108,6 @@ async def _invoke_internal(self, context: FunctionInvocationContext) -> None: ) 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 + # 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) From 33bba7e3a330ff7e9202047f1af5aad8d7c4db5b Mon Sep 17 00:00:00 2001 From: Leonardo Pinheiro Date: Sat, 18 Jan 2025 17:56:34 +1000 Subject: [PATCH 17/18] update sk tool docs --- .../autogen_ext.tools.semantic_kernel.rst | 6 +-- .../_kernel_function_from_tool.py | 42 ++++++++++++++----- 2 files changed, 33 insertions(+), 15 deletions(-) diff --git a/python/packages/autogen-core/docs/src/reference/python/autogen_ext.tools.semantic_kernel.rst b/python/packages/autogen-core/docs/src/reference/python/autogen_ext.tools.semantic_kernel.rst index 8b51d0960e89..c94fafd2900b 100644 --- a/python/packages/autogen-core/docs/src/reference/python/autogen_ext.tools.semantic_kernel.rst +++ b/python/packages/autogen-core/docs/src/reference/python/autogen_ext.tools.semantic_kernel.rst @@ -2,8 +2,6 @@ autogen\_ext.tools.semantic_kernel =================================== -.. automodule:: autogen_ext.tools.semantic_kernel - :members: - :undoc-members: - :show-inheritance: +.. autopydantic_model:: autogen_ext.tools.semantic_kernel.KernelFunctionFromTool + :model-show-json-error-strategy: coerce diff --git a/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/_kernel_function_from_tool.py b/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/_kernel_function_from_tool.py index b703f7e0965b..a11646c876ad 100644 --- a/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/_kernel_function_from_tool.py +++ b/python/packages/autogen-ext/src/autogen_ext/tools/semantic_kernel/_kernel_function_from_tool.py @@ -35,29 +35,49 @@ class KernelFunctionFromTool(KernelFunction): Example usage: .. code-block:: python - # 1) Create an Autogen tool + from pydantic import BaseModel + from autogen_core.tools import BaseTool + from autogen_core import CancellationToken + from autogen_ext.tools.semantic_kernel import KernelFunctionFromTool + from semantic_kernel.functions.kernel_plugin import KernelPlugin + from semantic_kernel.kernel import Kernel + + + # 1) Define input/output models + class CalculatorArgs(BaseModel): + a: float + b: float + + + class CalculatorResult(BaseModel): + result: float + + + # 2) Create an Autogen tool class CalculatorTool(BaseTool[CalculatorArgs, CalculatorResult]): def __init__(self) -> None: - super().__init__(name="calculator", description="Add two numbers together") + 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) - # 2) Convert to Semantic Kernel function + + # 3) Convert to Semantic Kernel function calc_tool = CalculatorTool() kernel_function = KernelFunctionFromTool(calc_tool, plugin_name="math") - # 3) Add to Semantic Kernel plugin/kernel + # 4) Add to Semantic Kernel plugin/kernel plugin = KernelPlugin(name="math") plugin.functions[calc_tool.name] = kernel_function + kernel = Kernel() kernel.add_plugin(plugin) """ - class Config: - # Hide fields that are not JSON-serializable from the generated schema - fields = { - "invocation_duration_histogram": {"exclude": True}, - "streaming_duration_histogram": {"exclude": True}, - } - def __init__(self, tool: BaseTool[InputT, OutputT], plugin_name: str | None = None): # Build up KernelFunctionMetadata. You can also parse the tool's schema for parameters. parameters = [ From 0402136997c2a876d97c538e3bac895a040506b0 Mon Sep 17 00:00:00 2001 From: Leonardo Pinheiro Date: Sat, 18 Jan 2025 18:31:09 +1000 Subject: [PATCH 18/18] coerce doc json serialization failure --- python/packages/autogen-core/docs/src/conf.py | 1 + .../reference/python/autogen_ext.tools.semantic_kernel.rst | 7 +++++-- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/python/packages/autogen-core/docs/src/conf.py b/python/packages/autogen-core/docs/src/conf.py index fc46900dc004..dcca810071a5 100644 --- a/python/packages/autogen-core/docs/src/conf.py +++ b/python/packages/autogen-core/docs/src/conf.py @@ -158,6 +158,7 @@ } autodoc_pydantic_model_show_config_summary = False +autodoc_pydantic_model_show_json_error_strategy = "coerce" python_use_unqualified_type_names = True autodoc_preserve_defaults = True diff --git a/python/packages/autogen-core/docs/src/reference/python/autogen_ext.tools.semantic_kernel.rst b/python/packages/autogen-core/docs/src/reference/python/autogen_ext.tools.semantic_kernel.rst index c94fafd2900b..b3675fc1e1bb 100644 --- a/python/packages/autogen-core/docs/src/reference/python/autogen_ext.tools.semantic_kernel.rst +++ b/python/packages/autogen-core/docs/src/reference/python/autogen_ext.tools.semantic_kernel.rst @@ -2,6 +2,9 @@ autogen\_ext.tools.semantic_kernel =================================== -.. autopydantic_model:: autogen_ext.tools.semantic_kernel.KernelFunctionFromTool - :model-show-json-error-strategy: coerce +.. automodule:: autogen_ext.tools.semantic_kernel + :members: + :undoc-members: + :show-inheritance: +