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308
python/packages/autogen-core/docs/src/user-guide/agentchat-user-guide/quickstart.ipynb
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,160 +1,152 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Quickstart" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "```{include} warning.md\n", | ||
| "\n", | ||
| "```\n", | ||
| "\n", | ||
| ":::{note}\n", | ||
| "For installation instructions, please refer to the [installation guide](./installation).\n", | ||
| ":::\n", | ||
| "\n", | ||
| "In AutoGen AgentChat, you can build applications quickly using preset agents.\n", | ||
| "To illustrate this, we will begin with creating a team of a single agent\n", | ||
| "that can use tools and respond to messages.\n", | ||
| "\n", | ||
| "The following code uses the OpenAI model. If you haven't already, you need to\n", | ||
| "install the following package and extension:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "vscode": { | ||
| "languageId": "shellscript" | ||
| } | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "pip install 'autogen-agentchat==0.4.0.dev8' 'autogen-ext[openai]==0.4.0.dev8'" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "To use Azure OpenAI models and AAD authentication,\n", | ||
| "you can follow the instructions [here](./tutorial/models.ipynb#azure-openai)." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "---------- user ----------\n", | ||
| "What is the weather in New York?\n", | ||
| "---------- weather_agent ----------\n", | ||
| "[FunctionCall(id='call_AhTZ2q3TNL8x0qs00e3wIZ7y', arguments='{\"city\":\"New York\"}', name='get_weather')]\n", | ||
| "[Prompt tokens: 79, Completion tokens: 15]\n", | ||
| "---------- weather_agent ----------\n", | ||
| "[FunctionExecutionResult(content='The weather in New York is 73 degrees and Sunny.', call_id='call_AhTZ2q3TNL8x0qs00e3wIZ7y')]\n", | ||
| "---------- weather_agent ----------\n", | ||
| "The weather in New York is currently 73 degrees and sunny.\n", | ||
| "[Prompt tokens: 90, Completion tokens: 14]\n", | ||
| "---------- weather_agent ----------\n", | ||
| "TERMINATE\n", | ||
| "[Prompt tokens: 137, Completion tokens: 4]\n", | ||
| "---------- Summary ----------\n", | ||
| "Number of messages: 5\n", | ||
| "Finish reason: Text 'TERMINATE' mentioned\n", | ||
| "Total prompt tokens: 306\n", | ||
| "Total completion tokens: 33\n", | ||
| "Duration: 1.43 seconds\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "from autogen_agentchat.agents import AssistantAgent\n", | ||
| "from autogen_agentchat.task import Console, TextMentionTermination\n", | ||
| "from autogen_agentchat.teams import RoundRobinGroupChat\n", | ||
| "from autogen_ext.models import OpenAIChatCompletionClient\n", | ||
| "\n", | ||
| "\n", | ||
| "# Define a tool\n", | ||
| "async def get_weather(city: str) -> str:\n", | ||
| " return f\"The weather in {city} is 73 degrees and Sunny.\"\n", | ||
| "\n", | ||
| "\n", | ||
| "async def main() -> None:\n", | ||
| " # Define an agent\n", | ||
| " weather_agent = AssistantAgent(\n", | ||
| " name=\"weather_agent\",\n", | ||
| " model_client=OpenAIChatCompletionClient(\n", | ||
| " model=\"gpt-4o-2024-08-06\",\n", | ||
| " # api_key=\"YOUR_API_KEY\",\n", | ||
| " ),\n", | ||
| " tools=[get_weather],\n", | ||
| " )\n", | ||
| "\n", | ||
| " # Define termination condition\n", | ||
| " termination = TextMentionTermination(\"TERMINATE\")\n", | ||
| "\n", | ||
| " # Define a team\n", | ||
| " agent_team = RoundRobinGroupChat([weather_agent], termination_condition=termination)\n", | ||
| "\n", | ||
| " # Run the team and stream messages to the console\n", | ||
| " stream = agent_team.run_stream(task=\"What is the weather in New York?\")\n", | ||
| " await Console(stream)\n", | ||
| "\n", | ||
| "\n", | ||
| "# NOTE: if running this inside a Python script you'll need to use asyncio.run(main()).\n", | ||
| "await main()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The code snippet above introduces two high level concepts in AgentChat: *Agent* and *Team*. An Agent helps us define what actions are taken when a message is received. Specifically, we use the {py:class}`~autogen_agentchat.agents.AssistantAgent` preset - an agent that can be given access to a model (e.g., LLM) and tools (functions) that it can then use to address tasks. A Team helps us define the rules for how agents interact with each other. In the {py:class}`~autogen_agentchat.teams.RoundRobinGroupChat` team, agents respond in a sequential round-robin fashion.\n", | ||
| "In this case, we have a single agent, so the same agent is used for each round." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## What's Next?\n", | ||
| "\n", | ||
| "Now that you have a basic understanding of how to define an agent and a team, consider following the [tutorial](./tutorial/index) for a walkthrough on other features of AgentChat.\n", | ||
| "\n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": ".venv", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.11.5" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Quickstart" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Via AgentChat, you can build applications quickly using preset agents.\n", | ||
| "To illustrate this, we will begin with creating a team of a single agent\n", | ||
| "that can use tools and respond to messages.\n", | ||
| "\n", | ||
| "The following code uses the OpenAI model. If you haven't already, you need to\n", | ||
| "install the following package and extension:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": { | ||
| "vscode": { | ||
| "languageId": "shellscript" | ||
| } | ||
| }, | ||
| "outputs": [], | ||
| "source": [ | ||
| "pip install 'autogen-agentchat==0.4.0.dev8' 'autogen-ext[openai]==0.4.0.dev8'" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "To use Azure OpenAI models and AAD authentication,\n", | ||
| "you can follow the instructions [here](./tutorial/models.ipynb#azure-openai)." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "---------- user ----------\n", | ||
| "What is the weather in New York?\n", | ||
| "---------- weather_agent ----------\n", | ||
| "[FunctionCall(id='call_AhTZ2q3TNL8x0qs00e3wIZ7y', arguments='{\"city\":\"New York\"}', name='get_weather')]\n", | ||
| "[Prompt tokens: 79, Completion tokens: 15]\n", | ||
| "---------- weather_agent ----------\n", | ||
| "[FunctionExecutionResult(content='The weather in New York is 73 degrees and Sunny.', call_id='call_AhTZ2q3TNL8x0qs00e3wIZ7y')]\n", | ||
| "---------- weather_agent ----------\n", | ||
| "The weather in New York is currently 73 degrees and sunny.\n", | ||
| "[Prompt tokens: 90, Completion tokens: 14]\n", | ||
| "---------- weather_agent ----------\n", | ||
| "TERMINATE\n", | ||
| "[Prompt tokens: 137, Completion tokens: 4]\n", | ||
| "---------- Summary ----------\n", | ||
| "Number of messages: 5\n", | ||
| "Finish reason: Text 'TERMINATE' mentioned\n", | ||
| "Total prompt tokens: 306\n", | ||
| "Total completion tokens: 33\n", | ||
| "Duration: 1.43 seconds\n" | ||
| ] | ||
| } | ||
| ], | ||
| "source": [ | ||
| "from autogen_agentchat.agents import AssistantAgent\n", | ||
| "from autogen_agentchat.task import Console, TextMentionTermination\n", | ||
| "from autogen_agentchat.teams import RoundRobinGroupChat\n", | ||
| "from autogen_ext.models import OpenAIChatCompletionClient\n", | ||
| "\n", | ||
| "\n", | ||
| "# Define a tool\n", | ||
| "async def get_weather(city: str) -> str:\n", | ||
| " return f\"The weather in {city} is 73 degrees and Sunny.\"\n", | ||
| "\n", | ||
| "\n", | ||
| "async def main() -> None:\n", | ||
| " # Define an agent\n", | ||
| " weather_agent = AssistantAgent(\n", | ||
| " name=\"weather_agent\",\n", | ||
| " model_client=OpenAIChatCompletionClient(\n", | ||
| " model=\"gpt-4o-2024-08-06\",\n", | ||
| " # api_key=\"YOUR_API_KEY\",\n", | ||
| " ),\n", | ||
| " tools=[get_weather],\n", | ||
| " )\n", | ||
| "\n", | ||
| " # Define termination condition\n", | ||
| " termination = TextMentionTermination(\"TERMINATE\")\n", | ||
| "\n", | ||
| " # Define a team\n", | ||
| " agent_team = RoundRobinGroupChat([weather_agent], termination_condition=termination)\n", | ||
| "\n", | ||
| " # Run the team and stream messages to the console\n", | ||
| " stream = agent_team.run_stream(task=\"What is the weather in New York?\")\n", | ||
| " await Console(stream)\n", | ||
| "\n", | ||
| "\n", | ||
| "# NOTE: if running this inside a Python script you'll need to use asyncio.run(main()).\n", | ||
| "await main()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "The code snippet above introduces two high level concepts in AgentChat: *Agent* and *Team*. An Agent helps us define what actions are taken when a message is received. Specifically, we use the {py:class}`~autogen_agentchat.agents.AssistantAgent` preset - an agent that can be given access to a model (e.g., LLM) and tools (functions) that it can then use to address tasks. A Team helps us define the rules for how agents interact with each other. In the {py:class}`~autogen_agentchat.teams.RoundRobinGroupChat` team, agents respond in a sequential round-robin fashion.\n", | ||
| "In this case, we have a single agent, so the same agent is used for each round." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## What's Next?\n", | ||
| "\n", | ||
| "Now that you have a basic understanding of how to define an agent and a team, consider following the [tutorial](./tutorial/index) for a walkthrough on other features of AgentChat.\n", | ||
| "\n" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": ".venv", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.11.5" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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