mirror of
https://github.com/microsoft/autogen.git
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Improve agentchat tutorial (#4233)
This commit is contained in:
Eric Zhu
@@ -158,6 +158,28 @@
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"For more information on tool calling, refer to the documentation from [OpenAI](https://platform.openai.com/docs/guides/function-calling) and [Anthropic](https://docs.anthropic.com/en/docs/build-with-claude/tool-use)."
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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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"## Other Preset Agents\n",
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"\n",
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"The following preset agents are available:\n",
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"\n",
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"- {py:class}`~autogen_agentchat.agents.CodeExecutorAgent`: An agent that can execute code.\n",
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"- {py:class}`~autogen_ext.agents.MultimodalWebSurfer`: A multi-modal agent that can search the web and visit web pages for information."
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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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"## Next Step\n",
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"\n",
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"Now we have discussed how to use the {py:class}`~autogen_agentchat.agents.AssistantAgent`,\n",
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"we can move on to the next section to learn how to use the teams feature of AgentChat."
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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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@@ -186,246 +208,6 @@
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")\n",
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"print(result) -->"
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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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"## CodeExecutorAgent\n",
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"\n",
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"The {py:class}`~autogen_agentchat.agents.CodeExecutorAgent`\n",
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"preset extracts and executes code snippets found in received messages and returns the output. It is typically used within a team with another agent that generates code snippets to be executed.\n",
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"\n",
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"```{note}\n",
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"It is recommended that the {py:class}`~autogen_agentchat.agents.CodeExecutorAgent` agent\n",
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"uses a Docker container to execute code. This ensures that model-generated code is executed in an isolated environment. To use Docker, your environment must have Docker installed and running. \n",
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"Follow the installation instructions for [Docker](https://docs.docker.com/get-docker/).\n",
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"```\n",
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"\n",
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"In this example, we show how to set up a {py:class}`~autogen_agentchat.agents.CodeExecutorAgent` agent that uses the \n",
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"{py:class}`~autogen_ext.code_executors.DockerCommandLineCodeExecutor` \n",
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"to execute code snippets in a Docker container. The `work_dir` parameter indicates where all executed files are first saved locally before being executed in the Docker container."
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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": null,
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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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"source='code_executor' models_usage=None content='Hello world\\n'\n"
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]
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}
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],
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"source": [
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"from autogen_agentchat.agents import CodeExecutorAgent\n",
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"from autogen_ext.code_executors import DockerCommandLineCodeExecutor\n",
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"\n",
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"\n",
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"async def run_code_executor_agent() -> None:\n",
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" # Create a code executor agent that uses a Docker container to execute code.\n",
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" code_executor = DockerCommandLineCodeExecutor(work_dir=\"coding\")\n",
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" await code_executor.start()\n",
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" code_executor_agent = CodeExecutorAgent(\"code_executor\", code_executor=code_executor)\n",
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"\n",
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" # Run the agent with a given code snippet.\n",
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" task = TextMessage(\n",
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" content=\"\"\"Here is some code\n",
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"```python\n",
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"print('Hello world')\n",
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"```\n",
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"\"\"\",\n",
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" source=\"user\",\n",
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" )\n",
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" response = await code_executor_agent.on_messages([task], CancellationToken())\n",
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" print(response.chat_message)\n",
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"\n",
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" # Stop the code executor.\n",
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" await code_executor.stop()\n",
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"\n",
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"\n",
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"# Use asyncio.run(run_code_executor_agent()) when running in a script.\n",
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"await run_code_executor_agent()"
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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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"This example shows the agent executing a code snippet that prints \"Hello world\".\n",
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"The agent then returns the output of the code execution."
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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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"## Build Your Own Agents\n",
|
||||
"\n",
|
||||
"You may have agents with behaviors that do not fall into a preset. \n",
|
||||
"In such cases, you can build custom agents.\n",
|
||||
"\n",
|
||||
"All agents in AgentChat inherit from {py:class}`~autogen_agentchat.agents.BaseChatAgent` \n",
|
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"class and implement the following abstract methods and attributes:\n",
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"\n",
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"- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages`: The abstract method that defines the behavior of the agent in response to messages. This method is called when the agent is asked to provide a response in {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run`. It returns a {py:class}`~autogen_agentchat.base.Response` object.\n",
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"- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_reset`: The abstract method that resets the agent to its initial state. This method is called when the agent is asked to reset itself.\n",
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"- {py:attr}`~autogen_agentchat.agents.BaseChatAgent.produced_message_types`: The list of possible {py:class}`~autogen_agentchat.messages.ChatMessage` message types the agent can produce in its response.\n",
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"\n",
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"Optionally, you can implement the the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream` method to stream messages as they are generated by the agent. If this method is not implemented, the agent\n",
|
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"uses the default implementation of {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream`\n",
|
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"that calls the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages` method and\n",
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"yields all messages in the response."
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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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"### CounterDownAgent\n",
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"\n",
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||||
"In this example, we create a simple agent that counts down from a given number to zero,\n",
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"and produces a stream of messages with the current count."
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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": null,
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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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"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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"from autogen_core.base import CancellationToken\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.messages import AgentMessage, ChatMessage, TextMessage\n",
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"\n",
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"\n",
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"class CountDownAgent(BaseChatAgent):\n",
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" def __init__(self, name: str, count: int = 3):\n",
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" super().__init__(name, \"A simple agent that counts down.\")\n",
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" self._count = count\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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" # Calls the on_messages_stream.\n",
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" response: Response | None = None\n",
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" async for message in self.on_messages_stream(messages, cancellation_token):\n",
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" if isinstance(message, Response):\n",
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" response = message\n",
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" assert response is not None\n",
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" return response\n",
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"\n",
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" async def on_messages_stream(\n",
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" self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken\n",
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" ) -> AsyncGenerator[AgentMessage | Response, None]:\n",
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" inner_messages: List[AgentMessage] = []\n",
|
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" for i in range(self._count, 0, -1):\n",
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" msg = TextMessage(content=f\"{i}...\", source=self.name)\n",
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" inner_messages.append(msg)\n",
|
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" yield msg\n",
|
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" # The response is returned at the end of the stream.\n",
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" # It contains the final message and all the inner messages.\n",
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" yield Response(chat_message=TextMessage(content=\"Done!\", source=self.name), inner_messages=inner_messages)\n",
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"\n",
|
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" async def on_reset(self, cancellation_token: CancellationToken) -> None:\n",
|
||||
" pass\n",
|
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"\n",
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"\n",
|
||||
"async def run_countdown_agent() -> None:\n",
|
||||
" # Create a countdown agent.\n",
|
||||
" countdown_agent = CountDownAgent(\"countdown\")\n",
|
||||
"\n",
|
||||
" # Run the agent with a given task and stream the response.\n",
|
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" async for message in countdown_agent.on_messages_stream([], CancellationToken()):\n",
|
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" if isinstance(message, Response):\n",
|
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" print(message.chat_message.content)\n",
|
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" else:\n",
|
||||
" print(message.content)\n",
|
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"\n",
|
||||
"\n",
|
||||
"# Use asyncio.run(run_countdown_agent()) when running in a script.\n",
|
||||
"await run_countdown_agent()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
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||||
"metadata": {},
|
||||
"source": [
|
||||
"### UserProxyAgent \n",
|
||||
"\n",
|
||||
"A common use case for building a custom agent is to create an agent that acts as a proxy for the user.\n",
|
||||
"\n",
|
||||
"In the example below we show how to implement a `UserProxyAgent` - an agent that asks the user to enter\n",
|
||||
"some text through console and then returns that message as a response."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
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"execution_count": null,
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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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"I am glad to be here.\n"
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]
|
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}
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],
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"source": [
|
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"import asyncio\n",
|
||||
"from typing import List, Sequence\n",
|
||||
"\n",
|
||||
"from autogen_core.base import CancellationToken\n",
|
||||
"from autogen_agentchat.base import Response\n",
|
||||
"from autogen_agentchat.agents import BaseChatAgent\n",
|
||||
"from autogen_agentchat.messages import ChatMessage, TextMessage\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class UserProxyAgent(BaseChatAgent):\n",
|
||||
" def __init__(self, name: str) -> None:\n",
|
||||
" super().__init__(name, \"A human user.\")\n",
|
||||
"\n",
|
||||
" @property\n",
|
||||
" def produced_message_types(self) -> List[type[ChatMessage]]:\n",
|
||||
" return [TextMessage]\n",
|
||||
"\n",
|
||||
" async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> Response:\n",
|
||||
" user_input = await asyncio.get_event_loop().run_in_executor(None, input, \"Enter your response: \")\n",
|
||||
" return Response(chat_message=TextMessage(content=user_input, source=self.name))\n",
|
||||
"\n",
|
||||
" async def on_reset(self, cancellation_token: CancellationToken) -> None:\n",
|
||||
" pass\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"async def run_user_proxy_agent() -> None:\n",
|
||||
" user_proxy_agent = UserProxyAgent(name=\"user_proxy_agent\")\n",
|
||||
" response = await user_proxy_agent.on_messages([], CancellationToken())\n",
|
||||
" print(response.chat_message.content)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Use asyncio.run(run_user_proxy_agent()) when running in a script.\n",
|
||||
"await run_user_proxy_agent()"
|
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]
|
||||
}
|
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],
|
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"metadata": {
|
||||
@@ -444,7 +226,7 @@
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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": "3.12.5"
|
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"version": "3.11.5"
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}
|
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},
|
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"nbformat": 4,
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|
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@@ -0,0 +1,166 @@
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{
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||||
"cells": [
|
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{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Custom Agents\n",
|
||||
"\n",
|
||||
"You may have agents with behaviors that do not fall into a preset. \n",
|
||||
"In such cases, you can build custom agents.\n",
|
||||
"\n",
|
||||
"All agents in AgentChat inherit from {py:class}`~autogen_agentchat.agents.BaseChatAgent` \n",
|
||||
"class and implement the following abstract methods and attributes:\n",
|
||||
"\n",
|
||||
"- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages`: The abstract method that defines the behavior of the agent in response to messages. This method is called when the agent is asked to provide a response in {py:meth}`~autogen_agentchat.agents.BaseChatAgent.run`. It returns a {py:class}`~autogen_agentchat.base.Response` object.\n",
|
||||
"- {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_reset`: The abstract method that resets the agent to its initial state. This method is called when the agent is asked to reset itself.\n",
|
||||
"- {py:attr}`~autogen_agentchat.agents.BaseChatAgent.produced_message_types`: The list of possible {py:class}`~autogen_agentchat.messages.ChatMessage` message types the agent can produce in its response.\n",
|
||||
"\n",
|
||||
"Optionally, you can implement the the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream` method to stream messages as they are generated by the agent. If this method is not implemented, the agent\n",
|
||||
"uses the default implementation of {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages_stream`\n",
|
||||
"that calls the {py:meth}`~autogen_agentchat.agents.BaseChatAgent.on_messages` method and\n",
|
||||
"yields all messages in the response."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## CounterDownAgent\n",
|
||||
"\n",
|
||||
"In this example, we create a simple agent that counts down from a given number to zero,\n",
|
||||
"and produces a stream of messages with the current count."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from typing import AsyncGenerator, List, Sequence\n",
|
||||
"\n",
|
||||
"from autogen_agentchat.agents import BaseChatAgent\n",
|
||||
"from autogen_agentchat.base import Response\n",
|
||||
"from autogen_agentchat.messages import AgentMessage, ChatMessage, TextMessage\n",
|
||||
"from autogen_core.base import CancellationToken\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class CountDownAgent(BaseChatAgent):\n",
|
||||
" def __init__(self, name: str, count: int = 3):\n",
|
||||
" super().__init__(name, \"A simple agent that counts down.\")\n",
|
||||
" self._count = count\n",
|
||||
"\n",
|
||||
" @property\n",
|
||||
" def produced_message_types(self) -> List[type[ChatMessage]]:\n",
|
||||
" return [TextMessage]\n",
|
||||
"\n",
|
||||
" async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> Response:\n",
|
||||
" # Calls the on_messages_stream.\n",
|
||||
" response: Response | None = None\n",
|
||||
" async for message in self.on_messages_stream(messages, cancellation_token):\n",
|
||||
" if isinstance(message, Response):\n",
|
||||
" response = message\n",
|
||||
" assert response is not None\n",
|
||||
" return response\n",
|
||||
"\n",
|
||||
" async def on_messages_stream(\n",
|
||||
" self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken\n",
|
||||
" ) -> AsyncGenerator[AgentMessage | Response, None]:\n",
|
||||
" inner_messages: List[AgentMessage] = []\n",
|
||||
" for i in range(self._count, 0, -1):\n",
|
||||
" msg = TextMessage(content=f\"{i}...\", source=self.name)\n",
|
||||
" inner_messages.append(msg)\n",
|
||||
" yield msg\n",
|
||||
" # The response is returned at the end of the stream.\n",
|
||||
" # It contains the final message and all the inner messages.\n",
|
||||
" yield Response(chat_message=TextMessage(content=\"Done!\", source=self.name), inner_messages=inner_messages)\n",
|
||||
"\n",
|
||||
" async def on_reset(self, cancellation_token: CancellationToken) -> None:\n",
|
||||
" pass\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"async def run_countdown_agent() -> None:\n",
|
||||
" # Create a countdown agent.\n",
|
||||
" countdown_agent = CountDownAgent(\"countdown\")\n",
|
||||
"\n",
|
||||
" # Run the agent with a given task and stream the response.\n",
|
||||
" async for message in countdown_agent.on_messages_stream([], CancellationToken()):\n",
|
||||
" if isinstance(message, Response):\n",
|
||||
" print(message.chat_message.content)\n",
|
||||
" else:\n",
|
||||
" print(message.content)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Use asyncio.run(run_countdown_agent()) when running in a script.\n",
|
||||
"await run_countdown_agent()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## UserProxyAgent \n",
|
||||
"\n",
|
||||
"A common use case for building a custom agent is to create an agent that acts as a proxy for the user.\n",
|
||||
"\n",
|
||||
"In the example below we show how to implement a `UserProxyAgent` - an agent that asks the user to enter\n",
|
||||
"some text through console and then returns that message as a response."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import asyncio\n",
|
||||
"from typing import List, Sequence\n",
|
||||
"\n",
|
||||
"from autogen_agentchat.agents import BaseChatAgent\n",
|
||||
"from autogen_agentchat.base import Response\n",
|
||||
"from autogen_agentchat.messages import ChatMessage\n",
|
||||
"from autogen_core.base import CancellationToken\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"class UserProxyAgent(BaseChatAgent):\n",
|
||||
" def __init__(self, name: str) -> None:\n",
|
||||
" super().__init__(name, \"A human user.\")\n",
|
||||
"\n",
|
||||
" @property\n",
|
||||
" def produced_message_types(self) -> List[type[ChatMessage]]:\n",
|
||||
" return [TextMessage]\n",
|
||||
"\n",
|
||||
" async def on_messages(self, messages: Sequence[ChatMessage], cancellation_token: CancellationToken) -> Response:\n",
|
||||
" user_input = await asyncio.get_event_loop().run_in_executor(None, input, \"Enter your response: \")\n",
|
||||
" return Response(chat_message=TextMessage(content=user_input, source=self.name))\n",
|
||||
"\n",
|
||||
" async def on_reset(self, cancellation_token: CancellationToken) -> None:\n",
|
||||
" pass\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"async def run_user_proxy_agent() -> None:\n",
|
||||
" user_proxy_agent = UserProxyAgent(name=\"user_proxy_agent\")\n",
|
||||
" response = await user_proxy_agent.on_messages([], CancellationToken())\n",
|
||||
" print(response.chat_message.content)\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Use asyncio.run(run_user_proxy_agent()) when running in a script.\n",
|
||||
"await run_user_proxy_agent()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python",
|
||||
"version": "3.11.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -46,6 +46,12 @@ A smart team that uses a model-based strategy and custom selector.
|
||||
A dynamic team that uses handoffs to pass tasks between agents.
|
||||
:::
|
||||
|
||||
:::{grid-item-card} {fas}`users;pst-color-primary` Custom Agents
|
||||
:link: ./custom-agents.html
|
||||
|
||||
How to build custom agents.
|
||||
:::
|
||||
|
||||
::::
|
||||
|
||||
```{toctree}
|
||||
@@ -58,4 +64,5 @@ teams
|
||||
selector-group-chat
|
||||
swarm
|
||||
termination
|
||||
custom-agents
|
||||
```
|
||||
|
||||
@@ -1,181 +1,191 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Models\n",
|
||||
"\n",
|
||||
"In many cases, agents need access to model services such as OpenAI, Azure OpenAI, and local models.\n",
|
||||
"AgentChat utilizes model clients provided by the\n",
|
||||
"[`autogen-ext`](../../core-user-guide/framework/model-clients.ipynb) package."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## OpenAI\n",
|
||||
"\n",
|
||||
"To access OpenAI models, you need to install the `openai` extension to use the {py:class}`~autogen_ext.models.OpenAIChatCompletionClient`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "shellscript"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pip install 'autogen-ext[openai]==0.4.0.dev6'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You will also need to obtain an [API key](https://platform.openai.com/account/api-keys) from OpenAI."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from autogen_ext.models import OpenAIChatCompletionClient\n",
|
||||
"\n",
|
||||
"opneai_model_client = OpenAIChatCompletionClient(\n",
|
||||
" model=\"gpt-4o-2024-08-06\",\n",
|
||||
" # api_key=\"sk-...\", # Optional if you have an OPENAI_API_KEY environment variable set.\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To test the model client, you can use the following code:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CreateResult(finish_reason='stop', content='The capital of France is Paris.', usage=RequestUsage(prompt_tokens=15, completion_tokens=7), cached=False, logprobs=None)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from autogen_core.components.models import UserMessage\n",
|
||||
"\n",
|
||||
"result = await opneai_model_client.create([UserMessage(content=\"What is the capital of France?\", source=\"user\")])\n",
|
||||
"print(result)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Azure OpenAI\n",
|
||||
"\n",
|
||||
"Install the `azure` and `openai` extensions to use the {py:class}`~autogen_ext.models.AzureOpenAIChatCompletionClient`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "shellscript"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pip install 'autogen-ext[openai,azure]==0.4.0.dev6'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To use the client, you need to provide your deployment id, Azure Cognitive Services endpoint, api version, and model capabilities.\n",
|
||||
"For authentication, you can either provide an API key or an Azure Active Directory (AAD) token credential.\n",
|
||||
"\n",
|
||||
"The following code snippet shows how to use AAD authentication.\n",
|
||||
"The identity used must be assigned the [Cognitive Services OpenAI User](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/role-based-access-control#cognitive-services-openai-user) role."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from autogen_ext.models import AzureOpenAIChatCompletionClient\n",
|
||||
"from azure.identity import DefaultAzureCredential, get_bearer_token_provider\n",
|
||||
"\n",
|
||||
"# Create the token provider\n",
|
||||
"token_provider = get_bearer_token_provider(DefaultAzureCredential(), \"https://cognitiveservices.azure.com/.default\")\n",
|
||||
"\n",
|
||||
"az_model_client = AzureOpenAIChatCompletionClient(\n",
|
||||
" model=\"{your-azure-deployment}\",\n",
|
||||
" api_version=\"2024-06-01\",\n",
|
||||
" azure_endpoint=\"https://{your-custom-endpoint}.openai.azure.com/\",\n",
|
||||
" azure_ad_token_provider=token_provider, # Optional if you choose key-based authentication.\n",
|
||||
" # api_key=\"sk-...\", # For key-based authentication.\n",
|
||||
" model_capabilities={\n",
|
||||
" \"vision\": True,\n",
|
||||
" \"function_calling\": True,\n",
|
||||
" \"json_output\": True,\n",
|
||||
" },\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"See [here](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/managed-identity#chat-completions) for how to use the Azure client directly or for more info."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Local Models\n",
|
||||
"\n",
|
||||
"We are working on it. Stay tuned!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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": [
|
||||
"# Models\n",
|
||||
"\n",
|
||||
"In many cases, agents need access to model services such as OpenAI, Azure OpenAI, and local models.\n",
|
||||
"AgentChat utilizes model clients provided by the\n",
|
||||
"[`autogen-ext`](../../core-user-guide/framework/model-clients.ipynb) package."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## OpenAI\n",
|
||||
"\n",
|
||||
"To access OpenAI models, you need to install the `openai` extension to use the {py:class}`~autogen_ext.models.OpenAIChatCompletionClient`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "shellscript"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pip install 'autogen-ext[openai]==0.4.0.dev6'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You will also need to obtain an [API key](https://platform.openai.com/account/api-keys) from OpenAI."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from autogen_ext.models import OpenAIChatCompletionClient\n",
|
||||
"\n",
|
||||
"opneai_model_client = OpenAIChatCompletionClient(\n",
|
||||
" model=\"gpt-4o-2024-08-06\",\n",
|
||||
" # api_key=\"sk-...\", # Optional if you have an OPENAI_API_KEY environment variable set.\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To test the model client, you can use the following code:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"CreateResult(finish_reason='stop', content='The capital of France is Paris.', usage=RequestUsage(prompt_tokens=15, completion_tokens=7), cached=False, logprobs=None)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from autogen_core.components.models import UserMessage\n",
|
||||
"\n",
|
||||
"result = await opneai_model_client.create([UserMessage(content=\"What is the capital of France?\", source=\"user\")])\n",
|
||||
"print(result)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"```{note}\n",
|
||||
"You can use this client with models hosted on OpenAI-compatible endpoints, however, we have not tested this functionality.\n",
|
||||
"See {py:class}`~autogen_ext.models.OpenAIChatCompletionClient` for more information.\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Azure OpenAI\n",
|
||||
"\n",
|
||||
"Install the `azure` and `openai` extensions to use the {py:class}`~autogen_ext.models.AzureOpenAIChatCompletionClient`."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "shellscript"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"pip install 'autogen-ext[openai,azure]==0.4.0.dev6'"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"To use the client, you need to provide your deployment id, Azure Cognitive Services endpoint, api version, and model capabilities.\n",
|
||||
"For authentication, you can either provide an API key or an Azure Active Directory (AAD) token credential.\n",
|
||||
"\n",
|
||||
"The following code snippet shows how to use AAD authentication.\n",
|
||||
"The identity used must be assigned the [Cognitive Services OpenAI User](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/role-based-access-control#cognitive-services-openai-user) role."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from autogen_ext.models import AzureOpenAIChatCompletionClient\n",
|
||||
"from azure.identity import DefaultAzureCredential, get_bearer_token_provider\n",
|
||||
"\n",
|
||||
"# Create the token provider\n",
|
||||
"token_provider = get_bearer_token_provider(DefaultAzureCredential(), \"https://cognitiveservices.azure.com/.default\")\n",
|
||||
"\n",
|
||||
"az_model_client = AzureOpenAIChatCompletionClient(\n",
|
||||
" model=\"{your-azure-deployment}\",\n",
|
||||
" api_version=\"2024-06-01\",\n",
|
||||
" azure_endpoint=\"https://{your-custom-endpoint}.openai.azure.com/\",\n",
|
||||
" azure_ad_token_provider=token_provider, # Optional if you choose key-based authentication.\n",
|
||||
" # api_key=\"sk-...\", # For key-based authentication.\n",
|
||||
" model_capabilities={\n",
|
||||
" \"vision\": True,\n",
|
||||
" \"function_calling\": True,\n",
|
||||
" \"json_output\": True,\n",
|
||||
" },\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"See [here](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/managed-identity#chat-completions) for how to use the Azure client directly or for more info."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Local Models\n",
|
||||
"\n",
|
||||
"We are working on it. Stay tuned!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"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
|
||||
}
|
||||
|
||||
@@ -281,7 +281,7 @@
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from autogen_agentchat.agents import AssistantAgent\n",
|
||||
"from autogen_agentchat.task import MaxMessageTermination, TextMentionTermination, Console\n",
|
||||
"from autogen_agentchat.task import Console, MaxMessageTermination, TextMentionTermination\n",
|
||||
"from autogen_agentchat.teams import RoundRobinGroupChat\n",
|
||||
"from autogen_ext.models import OpenAIChatCompletionClient\n",
|
||||
"\n",
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
import json
|
||||
from abc import ABC, abstractmethod
|
||||
from collections.abc import Sequence
|
||||
from typing import Any, Dict, Generic, Mapping, Protocol, Type, TypedDict, TypeVar, runtime_checkable, cast
|
||||
from typing import Any, Dict, Generic, Mapping, Protocol, Type, TypedDict, TypeVar, cast, runtime_checkable
|
||||
|
||||
import jsonref
|
||||
from pydantic import BaseModel
|
||||
|
||||
@@ -1,12 +1,12 @@
|
||||
import inspect
|
||||
from typing import Annotated, List
|
||||
|
||||
from autogen_core.components.tools._base import ToolSchema
|
||||
import pytest
|
||||
from autogen_core.base import CancellationToken
|
||||
from autogen_core.components._function_utils import get_typed_signature
|
||||
from autogen_core.components.models._openai_client import convert_tools
|
||||
from autogen_core.components.tools import BaseTool, FunctionTool
|
||||
from autogen_core.components.tools._base import ToolSchema
|
||||
from pydantic import BaseModel, Field, model_serializer
|
||||
from pydantic_core import PydanticUndefined
|
||||
|
||||
|
||||
Reference in New Issue
Block a user