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5 changes: 3 additions & 2 deletions
5
python/packages/autogen-agentchat/src/autogen_agentchat/agents/__init__.py
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140 changes: 140 additions & 0 deletions
140
python/packages/autogen-agentchat/src/autogen_agentchat/agents/_assistant_agent.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,140 @@ | ||
| import asyncio | ||
| import json | ||
| import logging | ||
| from typing import Any, Awaitable, Callable, List, Sequence | ||
|
|
||
| from autogen_core.base import CancellationToken | ||
| from autogen_core.components import FunctionCall | ||
| from autogen_core.components.models import ( | ||
| AssistantMessage, | ||
| ChatCompletionClient, | ||
| FunctionExecutionResult, | ||
| FunctionExecutionResultMessage, | ||
| LLMMessage, | ||
| SystemMessage, | ||
| UserMessage, | ||
| ) | ||
| from autogen_core.components.tools import FunctionTool, Tool | ||
| from pydantic import BaseModel, ConfigDict | ||
|
|
||
| from .. import EVENT_LOGGER_NAME | ||
| from ..messages import ( | ||
| ChatMessage, | ||
| StopMessage, | ||
| TextMessage, | ||
| ) | ||
| from ._base_chat_agent import BaseChatAgent | ||
|
|
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| event_logger = logging.getLogger(EVENT_LOGGER_NAME) | ||
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| class ToolCallEvent(BaseModel): | ||
| """A tool call event.""" | ||
|
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| tool_calls: List[FunctionCall] | ||
| """The tool call message.""" | ||
|
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| model_config = ConfigDict(arbitrary_types_allowed=True) | ||
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| class ToolCallResultEvent(BaseModel): | ||
| """A tool call result event.""" | ||
|
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| tool_call_results: List[FunctionExecutionResult] | ||
| """The tool call result message.""" | ||
|
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| model_config = ConfigDict(arbitrary_types_allowed=True) | ||
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| class AssistantAgent(BaseChatAgent): | ||
| """An agent that provides assistance with tool use. | ||
| It responds with a StopMessage when 'terminate' is detected in the response. | ||
| Args: | ||
| name (str): The name of the agent. | ||
| model_client (ChatCompletionClient): The model client to use for inference. | ||
| tools (List[Tool | Callable[..., Any] | Callable[..., Awaitable[Any]]] | None, optional): The tools to register with the agent. | ||
| description (str, optional): The description of the agent. | ||
| system_message (str, optional): The system message for the model. | ||
| """ | ||
|
|
||
| def __init__( | ||
| self, | ||
| name: str, | ||
| model_client: ChatCompletionClient, | ||
| *, | ||
| tools: List[Tool | Callable[..., Any] | Callable[..., Awaitable[Any]]] | None = None, | ||
| description: str = "An agent that provides assistance with ability to use tools.", | ||
| system_message: str = "You are a helpful AI assistant. Solve tasks using your tools. Reply with 'TERMINATE' when the task has been completed.", | ||
| ): | ||
| super().__init__(name=name, description=description) | ||
| self._model_client = model_client | ||
| self._system_messages = [SystemMessage(content=system_message)] | ||
| self._tools: List[Tool] = [] | ||
| if tools is not None: | ||
| for tool in tools: | ||
| if isinstance(tool, Tool): | ||
| self._tools.append(tool) | ||
| elif callable(tool): | ||
| if hasattr(tool, "__doc__") and tool.__doc__ is not None: | ||
| description = tool.__doc__ | ||
| else: | ||
| description = "" | ||
| self._tools.append(FunctionTool(tool, description=description)) | ||
| else: | ||
| raise ValueError(f"Unsupported tool type: {type(tool)}") | ||
| self._model_context: List[LLMMessage] = [] | ||
|
|
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| async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> ChatMessage: | ||
| # Add messages to the model context. | ||
| for msg in messages: | ||
| # TODO: add special handling for handoff messages | ||
| self._model_context.append(UserMessage(content=msg.content, source=msg.source)) | ||
|
|
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| # Generate an inference result based on the current model context. | ||
| llm_messages = self._system_messages + self._model_context | ||
| result = await self._model_client.create(llm_messages, tools=self._tools, cancellation_token=cancellation_token) | ||
|
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| # Add the response to the model context. | ||
| self._model_context.append(AssistantMessage(content=result.content, source=self.name)) | ||
|
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| # Run tool calls until the model produces a string response. | ||
| while isinstance(result.content, list) and all(isinstance(item, FunctionCall) for item in result.content): | ||
| event_logger.debug(ToolCallEvent(tool_calls=result.content)) | ||
| # Execute the tool calls. | ||
| results = await asyncio.gather( | ||
| *[self._execute_tool_call(call, cancellation_token) for call in result.content] | ||
| ) | ||
| event_logger.debug(ToolCallResultEvent(tool_call_results=results)) | ||
| self._model_context.append(FunctionExecutionResultMessage(content=results)) | ||
| # Generate an inference result based on the current model context. | ||
| result = await self._model_client.create( | ||
| self._model_context, tools=self._tools, cancellation_token=cancellation_token | ||
| ) | ||
| self._model_context.append(AssistantMessage(content=result.content, source=self.name)) | ||
|
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| assert isinstance(result.content, str) | ||
| # Detect stop request. | ||
| request_stop = "terminate" in result.content.strip().lower() | ||
| if request_stop: | ||
| return StopMessage(content=result.content, source=self.name) | ||
|
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| return TextMessage(content=result.content, source=self.name) | ||
|
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| async def _execute_tool_call( | ||
| self, tool_call: FunctionCall, cancellation_token: CancellationToken | ||
| ) -> FunctionExecutionResult: | ||
| """Execute a tool call and return the result.""" | ||
| try: | ||
| if not self._tools: | ||
| raise ValueError("No tools are available.") | ||
| tool = next((t for t in self._tools if t.name == tool_call.name), None) | ||
| if tool is None: | ||
| raise ValueError(f"The tool '{tool_call.name}' is not available.") | ||
| arguments = json.loads(tool_call.arguments) | ||
| result = await tool.run_json(arguments, cancellation_token) | ||
| result_as_str = tool.return_value_as_string(result) | ||
| return FunctionExecutionResult(content=result_as_str, call_id=tool_call.id) | ||
| except Exception as e: | ||
| return FunctionExecutionResult(content=f"Error: {e}", call_id=tool_call.id) |
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