import json
from typing import Any, Literal, Mapping, Optional, Sequence
from autogen_core import FunctionCall
from autogen_core._cancellation_token import CancellationToken
from autogen_core.models import (
ChatCompletionClient,
CreateResult,
LLMMessage,
ModelFamily,
ModelInfo,
RequestUsage,
)
from autogen_core.tools import BaseTool, Tool, ToolSchema
from semantic_kernel.connectors.ai.chat_completion_client_base import ChatCompletionClientBase
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.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
[docs]
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.
- 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):
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.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.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
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 = "<AZURE_OPENAI_DEPLOYMENT_NAME>"
endpoint = "<AZURE_OPENAI_ENDPOINT>"
api_key = "<AZURE_OPENAI_API_KEY>"
azure_client = AzureChatCompletion(deployment_name=deployment_name, endpoint=endpoint, api_key=api_key)
azure_request_settings = AzureChatPromptExecutionSettings(temperature=0.8)
azure_adapter = SKChatCompletionAdapter(sk_client=azure_client, default_prompt_settings=azure_request_settings)
# ----------------------------------------------------------------
# Example B: Google Gemini
# ----------------------------------------------------------------
google_api_key = "<GCP_API_KEY>"
google_model = "gemini-1.5-flash"
google_client = GoogleAIChatCompletion(gemini_model_id=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",
)
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()
# 4) Prepare messages for a chat completion
messages: list[LLMMessage] = [
SystemMessage(content="You are a helpful assistant."),
UserMessage(content="What is 2 + 2?", source="user"),
]
# 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)
# Google example
google_result = await google_adapter.create(
messages=messages,
tools=[calc_tool],
extra_create_args={"kernel": kernel},
)
print("Google result:", google_result.content)
# Ollama example
ollama_result = await ollama_adapter.create(
messages=messages,
tools=[calc_tool],
extra_create_args={"kernel": kernel, "prompt_execution_settings": request_settings},
)
print("Ollama result:", ollama_result.content)
if __name__ == "__main__":
asyncio.run(main())
"""
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
)
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 _build_execution_settings(
self, default_prompt_settings: Optional[PromptExecutionSettings], tools: Sequence[Tool | ToolSchema]
) -> PromptExecutionSettings:
"""Build PromptExecutionSettings from extra_create_args"""
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=False
)
# Create settings with remaining args as extension_data
settings = PromptExecutionSettings(
service_id=self._service_id,
extension_data=prompt_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, BaseTool):
# Convert Tool to KernelFunction using KernelFunctionFromTool
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: 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
if plugin_name:
full_name = f"{plugin_name}-{function_name}"
else:
full_name = function_name
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
[docs]
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:
"""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")
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
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)
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
# 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: Literal["function_calls", "stop"] = "function_calls"
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,
)
[docs]
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]:
"""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")
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)
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
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,
)
[docs]
def actual_usage(self) -> RequestUsage:
return RequestUsage(prompt_tokens=self._total_prompt_tokens, completion_tokens=self._total_completion_tokens)
[docs]
def total_usage(self) -> RequestUsage:
return RequestUsage(prompt_tokens=self._total_prompt_tokens, completion_tokens=self._total_completion_tokens)
[docs]
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)
return total_tokens
[docs]
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)
return max_tokens - used_tokens
@property
def model_info(self) -> ModelInfo:
return self._model_info
@property
def capabilities(self) -> ModelInfo:
return self.model_info
