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Update custom agent doc with arithmetic agent example (#4720)
* Update custom agent doc with arithmetic agent example * fix mypy * fix model capabilities usage
This commit is contained in:
Eric Zhu
GitHub
parent
7eaffa83a7
commit
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@ -34,9 +34,20 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"3...\n",
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"2...\n",
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"1...\n",
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"Done!\n"
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]
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}
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],
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"source": [
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"from typing import AsyncGenerator, List, Sequence\n",
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"\n",
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@ -100,12 +111,17 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## UserProxyAgent \n",
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"## ArithmeticAgent\n",
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"\n",
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"A common use case for building a custom agent is to create an agent that acts as a proxy for the user.\n",
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"In this example, we create an agent class that can perform simple arithmetic operations\n",
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"on a given integer. Then, we will use different instances of this agent class\n",
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"in a {py:class}`~autogen_agentchat.teams.SelectorGroupChat`\n",
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"to transform a given integer into another integer by applying a sequence of arithmetic operations.\n",
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"\n",
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"In the example below we show how to implement a `UserProxyAgent` - an agent that asks the user to enter\n",
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"some text through console and then returns that message as a response."
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"The `ArithmeticAgent` class takes an `operator_func` that takes an integer and returns an integer,\n",
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"after applying an arithmetic operation to the integer.\n",
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"In its `on_messages` method, it applies the `operator_func` to the integer in the input message,\n",
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"and returns a response with the result."
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]
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},
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{
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@ -114,39 +130,162 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"import asyncio\n",
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"from typing import List, Sequence\n",
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"from typing import Callable, List, Sequence\n",
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"\n",
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"from autogen_agentchat.agents import BaseChatAgent\n",
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"from autogen_agentchat.base import Response\n",
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"from autogen_agentchat.conditions import MaxMessageTermination\n",
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"from autogen_agentchat.messages import ChatMessage\n",
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"from autogen_agentchat.teams import SelectorGroupChat\n",
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"from autogen_agentchat.ui import Console\n",
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"from autogen_core import CancellationToken\n",
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"from autogen_ext.models.openai import OpenAIChatCompletionClient\n",
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"\n",
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"\n",
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"class UserProxyAgent(BaseChatAgent):\n",
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" def __init__(self, name: str) -> None:\n",
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" super().__init__(name, \"A human user.\")\n",
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"class ArithmeticAgent(BaseChatAgent):\n",
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" def __init__(self, name: str, description: str, operator_func: Callable[[int], int]) -> None:\n",
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" super().__init__(name, description=description)\n",
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" self._operator_func = operator_func\n",
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" self._message_history: List[ChatMessage] = []\n",
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"\n",
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" @property\n",
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" def produced_message_types(self) -> List[type[ChatMessage]]:\n",
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" return [TextMessage]\n",
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"\n",
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" async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> Response:\n",
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" user_input = await asyncio.get_event_loop().run_in_executor(None, input, \"Enter your response: \")\n",
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" return Response(chat_message=TextMessage(content=user_input, source=self.name))\n",
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" # Update the message history.\n",
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" # NOTE: it is possible the messages is an empty list, which means the agent was selected previously.\n",
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" self._message_history.extend(messages)\n",
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" # Parse the number in the last message.\n",
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" assert isinstance(self._message_history[-1], TextMessage)\n",
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" number = int(self._message_history[-1].content)\n",
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" # Apply the operator function to the number.\n",
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" result = self._operator_func(number)\n",
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" # Create a new message with the result.\n",
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" response_message = TextMessage(content=str(result), source=self.name)\n",
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" # Update the message history.\n",
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" self._message_history.append(response_message)\n",
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" # Return the response.\n",
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" return Response(chat_message=response_message)\n",
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"\n",
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" async def on_reset(self, cancellation_token: CancellationToken) -> None:\n",
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" pass\n",
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" pass"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"```{note}\n",
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"The `on_messages` method may be called with an empty list of messages, in which\n",
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"case it means the agent was called previously and is now being called again,\n",
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"without any new messages from the caller. So it is important to keep a history\n",
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"of the previous messages received by the agent, and use that history to generate\n",
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"the response.\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Now we can create a {py:class}`~autogen_agentchat.teams.SelectorGroupChat` with 5 instances of `ArithmeticAgent`:\n",
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"\n",
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"- one that adds 1 to the input integer,\n",
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"- one that subtracts 1 from the input integer,\n",
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"- one that multiplies the input integer by 2,\n",
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"- one that divides the input integer by 2 and rounds down to the nearest integer, and\n",
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"- one that returns the input integer unchanged.\n",
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"\n",
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"We then create a {py:class}`~autogen_agentchat.teams.SelectorGroupChat` with these agents,\n",
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"and set the appropriate selector settings:\n",
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"\n",
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"- allow the same agent to be selected consecutively to allow for repeated operations, and\n",
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"- customize the selector prompt to tailor the model's response to the specific task."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"---------- user ----------\n",
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"Apply the operations to turn the given number into 25.\n",
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"---------- user ----------\n",
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"10\n",
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"---------- multiply_agent ----------\n",
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"20\n",
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"---------- add_agent ----------\n",
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"21\n",
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"---------- multiply_agent ----------\n",
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"42\n",
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"---------- divide_agent ----------\n",
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"21\n",
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"---------- add_agent ----------\n",
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"22\n",
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"---------- add_agent ----------\n",
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"23\n",
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"---------- add_agent ----------\n",
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"24\n",
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"---------- add_agent ----------\n",
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"25\n",
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"---------- Summary ----------\n",
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"Number of messages: 10\n",
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"Finish reason: Maximum number of messages 10 reached, current message count: 10\n",
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"Total prompt tokens: 0\n",
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"Total completion tokens: 0\n",
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"Duration: 2.40 seconds\n"
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]
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}
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],
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"source": [
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"async def run_number_agents() -> None:\n",
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" # Create agents for number operations.\n",
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" add_agent = ArithmeticAgent(\"add_agent\", \"Adds 1 to the number.\", lambda x: x + 1)\n",
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" multiply_agent = ArithmeticAgent(\"multiply_agent\", \"Multiplies the number by 2.\", lambda x: x * 2)\n",
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" subtract_agent = ArithmeticAgent(\"subtract_agent\", \"Subtracts 1 from the number.\", lambda x: x - 1)\n",
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" divide_agent = ArithmeticAgent(\"divide_agent\", \"Divides the number by 2 and rounds down.\", lambda x: x // 2)\n",
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" identity_agent = ArithmeticAgent(\"identity_agent\", \"Returns the number as is.\", lambda x: x)\n",
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"\n",
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" # The termination condition is to stop after 10 messages.\n",
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" termination_condition = MaxMessageTermination(10)\n",
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"\n",
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" # Create a selector group chat.\n",
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" selector_group_chat = SelectorGroupChat(\n",
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" [add_agent, multiply_agent, subtract_agent, divide_agent, identity_agent],\n",
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" model_client=OpenAIChatCompletionClient(model=\"gpt-4o\"),\n",
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" termination_condition=termination_condition,\n",
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" allow_repeated_speaker=True, # Allow the same agent to speak multiple times, necessary for this task.\n",
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" selector_prompt=(\n",
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" \"Available roles:\\n{roles}\\nTheir job descriptions:\\n{participants}\\n\"\n",
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" \"Current conversation history:\\n{history}\\n\"\n",
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" \"Please select the most appropriate role for the next message, and only return the role name.\"\n",
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" ),\n",
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" )\n",
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"\n",
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" # Run the selector group chat with a given task and stream the response.\n",
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" task: List[ChatMessage] = [\n",
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" TextMessage(content=\"Apply the operations to turn the given number into 25.\", source=\"user\"),\n",
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" TextMessage(content=\"10\", source=\"user\"),\n",
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" ]\n",
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" stream = selector_group_chat.run_stream(task=task)\n",
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" await Console(stream)\n",
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"\n",
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"\n",
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"async def run_user_proxy_agent() -> None:\n",
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" user_proxy_agent = UserProxyAgent(name=\"user_proxy_agent\")\n",
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" response = await user_proxy_agent.on_messages([], CancellationToken())\n",
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" print(response.chat_message.content)\n",
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"\n",
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"\n",
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"# Use asyncio.run(run_user_proxy_agent()) when running in a script.\n",
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"await run_user_proxy_agent()"
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"# Use asyncio.run(run_number_agents()) when running in a script.\n",
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"await run_number_agents()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"From the output, we can see that the agents have successfully transformed the input integer\n",
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"from 10 to 25 by choosing appropriate agents that apply the arithmetic operations in sequence."
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]
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}
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],
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@ -157,8 +296,16 @@
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"version": "3.11.5"
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.7"
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}
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},
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"nbformat": 4,
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@ -15,7 +15,7 @@
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"source": [
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"## OpenAI\n",
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"\n",
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"To access OpenAI models, install the `openai` extension, which allows you to use the {py:class}`~autogen_ext.models.OpenAIChatCompletionClient`."
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"To access OpenAI models, install the `openai` extension, which allows you to use the {py:class}`~autogen_ext.models.openai.OpenAIChatCompletionClient`."
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]
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},
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{
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@ -85,7 +85,7 @@
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"source": [
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"```{note}\n",
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"You can use this client with models hosted on OpenAI-compatible endpoints, however, we have not tested this functionality.\n",
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"See {py:class}`~autogen_ext.models.OpenAIChatCompletionClient` for more information.\n",
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"See {py:class}`~autogen_ext.models.openai.OpenAIChatCompletionClient` for more information.\n",
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"```"
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]
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},
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"source": [
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"## Azure OpenAI\n",
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"\n",
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"Similarly, install the `azure` and `openai` extensions to use the {py:class}`~autogen_ext.models.AzureOpenAIChatCompletionClient`."
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"Similarly, install the `azure` and `openai` extensions to use the {py:class}`~autogen_ext.models.openai.AzureOpenAIChatCompletionClient`."
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]
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},
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{
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@ -179,7 +179,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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"version": "3.12.7"
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}
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},
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"nbformat": 4,
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@ -15,10 +15,10 @@
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"```\n",
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"curl -fsSL https://ollama.com/install.sh | sh\n",
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"\n",
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"ollama pull llama3:instruct\n",
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"ollama pull llama3.2:1b\n",
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"\n",
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"pip install 'litellm[proxy]'\n",
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"litellm --model ollama/llama3:instruct\n",
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"litellm --model ollama/llama3.2:1b\n",
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"``` \n",
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"\n",
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"This will run the proxy server and it will be available at 'http://0.0.0.0:4000/'."
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@ -48,7 +48,7 @@
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" default_subscription,\n",
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" message_handler,\n",
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")\n",
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"from autogen_core.components.model_context import BufferedChatCompletionContext\n",
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"from autogen_core.model_context import BufferedChatCompletionContext\n",
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"from autogen_core.models import (\n",
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" AssistantMessage,\n",
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" ChatCompletionClient,\n",
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"def get_model_client() -> OpenAIChatCompletionClient: # type: ignore\n",
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" \"Mimic OpenAI API using Local LLM Server.\"\n",
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" return OpenAIChatCompletionClient(\n",
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" model=\"gpt-4o\", # Need to use one of the OpenAI models as a placeholder for now.\n",
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" model=\"llama3.2:1b\",\n",
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" api_key=\"NotRequiredSinceWeAreLocal\",\n",
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" base_url=\"http://0.0.0.0:4000\",\n",
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" model_capabilities={\n",
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" \"json_output\": False,\n",
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" \"vision\": False,\n",
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" \"function_calling\": True,\n",
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" },\n",
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" )"
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]
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},
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@ -225,7 +230,7 @@
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],
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"metadata": {
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"kernelspec": {
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"display_name": "pyautogen",
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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