mirror of
https://github.com/microsoft/autogen.git
synced 2026-04-20 03:02:16 -04:00
docs: Update user guide notebooks to enhance clarity and add structured output (#5224)
Resolves #5043
This commit is contained in:
Eric Zhu
@@ -35,9 +35,15 @@
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"from autogen_agentchat.messages import TextMessage\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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"from autogen_ext.models.openai import OpenAIChatCompletionClient"
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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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"source": [
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"# Define a tool that searches the web for information.\n",
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"async def web_search(query: str) -> str:\n",
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" \"\"\"Find information on the web\"\"\"\n",
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@@ -321,6 +327,82 @@
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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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"## Structured Output\n",
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"\n",
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"Structured output allows models to return structured JSON text with pre-defined schema\n",
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"provided by the application. Different from JSON-mode, the schema can be provided\n",
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"as a [Pydantic BaseModel](https://docs.pydantic.dev/latest/concepts/models/)\n",
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"class, which can also be used to validate the output. \n",
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"\n",
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"```{note}\n",
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"Structured output is only available for models that support it. It also\n",
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"requires the model client to support structured output as well.\n",
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"Currently, the {py:class}`~autogen_ext.models.openai.OpenAIChatCompletionClient`\n",
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"and {py:class}`~autogen_ext.models.openai.AzureOpenAIChatCompletionClient`\n",
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"support structured output.\n",
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"```\n",
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"\n",
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"Structured output is also useful for incorporating Chain-of-Thought\n",
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"reasoning in the agent's responses.\n",
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"See the example below for how to use structured output with the assistant agent."
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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": 2,
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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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"I am happy.\n",
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"---------- assistant ----------\n",
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"{\"thoughts\":\"The user explicitly states that they are happy.\",\"response\":\"happy\"}\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"TaskResult(messages=[TextMessage(source='user', models_usage=None, content='I am happy.', type='TextMessage'), TextMessage(source='assistant', models_usage=RequestUsage(prompt_tokens=89, completion_tokens=18), content='{\"thoughts\":\"The user explicitly states that they are happy.\",\"response\":\"happy\"}', type='TextMessage')], stop_reason=None)"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from typing import Literal\n",
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"\n",
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"from pydantic import BaseModel\n",
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"\n",
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"\n",
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"# The response format for the agent as a Pydantic base model.\n",
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"class AgentResponse(BaseModel):\n",
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" thoughts: str\n",
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" response: Literal[\"happy\", \"sad\", \"neutral\"]\n",
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"\n",
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"\n",
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"# Create an agent that uses the OpenAI GPT-4o model with the custom response format.\n",
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"model_client = OpenAIChatCompletionClient(\n",
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" model=\"gpt-4o\",\n",
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" response_format=AgentResponse, # type: ignore\n",
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")\n",
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"agent = AssistantAgent(\n",
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" \"assistant\",\n",
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" model_client=model_client,\n",
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" system_message=\"Categorize the input as happy, sad, or neutral following the JSON format.\",\n",
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")\n",
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"\n",
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"await Console(agent.run_stream(task=\"I am happy.\"))"
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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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@@ -6,7 +6,10 @@
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"source": [
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"# Models\n",
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"\n",
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"In many cases, agents need access to LLM model services such as OpenAI, Azure OpenAI, or local models. Since there are many different providers with different APIs, `autogen-core` implements a protocol for [model clients](../../core-user-guide/components/model-clients.ipynb) and `autogen-ext` implements a set of model clients for popular model services. AgentChat can use these model clients to interact with model services. \n",
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"In many cases, agents need access to LLM model services such as OpenAI, Azure OpenAI, or local models. Since there are many different providers with different APIs, `autogen-core` implements a protocol for model clients and `autogen-ext` implements a set of model clients for popular model services. AgentChat can use these model clients to interact with model services. \n",
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"\n",
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"This section provides a quick overview of available model clients.\n",
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"For more details on how to use them directly, please refer to [Model Clients](../../core-user-guide/components/model-clients.ipynb) in the Core API documentation.\n",
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"\n",
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"```{note}\n",
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"See {py:class}`~autogen_ext.models.cache.ChatCompletionCache` for a caching wrapper to use with the following clients.\n",
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@@ -14,17 +14,19 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Built-in Model Clients\n",
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"\n",
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"Currently there are three built-in model clients:\n",
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"* {py:class}`~autogen_ext.models.OpenAIChatCompletionClient`\n",
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"* {py:class}`~autogen_ext.models.AzureOpenAIChatCompletionClient`\n",
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"* {py:class}`~autogen_ext.models.AzureAIChatCompletionClient`\n",
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"* {py:class}`~autogen_ext.models.openai.OpenAIChatCompletionClient`\n",
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"* {py:class}`~autogen_ext.models.openai.AzureOpenAIChatCompletionClient`\n",
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"* {py:class}`~autogen_ext.models.azure.AzureAIChatCompletionClient`"
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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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"## OpenAI\n",
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"\n",
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"\n",
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"### OpenAI\n",
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"\n",
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"To use the {py:class}`~autogen_ext.models.OpenAIChatCompletionClient`, you need to install the `openai` extra."
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"To use the {py:class}`~autogen_ext.models.openai.OpenAIChatCompletionClient`, you need to install the `openai` extra."
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]
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},
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{
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@@ -68,7 +70,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"You can call the {py:meth}`~autogen_ext.models.OpenAIChatCompletionClient.create` method to create a\n",
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"You can call the {py:meth}`~autogen_ext.models.openai.BaseOpenAIChatCompletionClient.create` method to create a\n",
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"chat completion request, and await for an {py:class}`~autogen_core.models.CreateResult` object in return."
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]
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},
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@@ -118,12 +120,142 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### OpenAI-Compatible API\n",
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"## Azure OpenAI\n",
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"\n",
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"You can use the {py:class}`~autogen_ext.models.OpenAIChatCompletionClient` to interact with OpenAI-compatible APIs such as Ollama and Gemini (beta).\n",
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"To use the {py:class}`~autogen_ext.models.openai.AzureOpenAIChatCompletionClient`, you need to provide\n",
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"the deployment id, Azure Cognitive Services endpoint, api version, and model capabilities.\n",
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"For authentication, you can either provide an API key or an Azure Active Directory (AAD) token credential."
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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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"vscode": {
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"languageId": "shellscript"
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}
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},
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"outputs": [],
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"source": [
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"# pip install \"autogen-ext[openai,azure]\""
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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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"The following code snippet shows how to use AAD authentication.\n",
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"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."
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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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"source": [
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"from autogen_ext.models.openai import AzureOpenAIChatCompletionClient\n",
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"from azure.identity import DefaultAzureCredential, get_bearer_token_provider\n",
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"\n",
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"#### Ollama\n",
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"# Create the token provider\n",
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"token_provider = get_bearer_token_provider(DefaultAzureCredential(), \"https://cognitiveservices.azure.com/.default\")\n",
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"\n",
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"az_model_client = AzureOpenAIChatCompletionClient(\n",
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" azure_deployment=\"{your-azure-deployment}\",\n",
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" model=\"{model-name, such as gpt-4o}\",\n",
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" api_version=\"2024-06-01\",\n",
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" azure_endpoint=\"https://{your-custom-endpoint}.openai.azure.com/\",\n",
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" azure_ad_token_provider=token_provider, # Optional if you choose key-based authentication.\n",
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" # api_key=\"sk-...\", # For key-based authentication.\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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"```{note}\n",
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"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.\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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"## Azure AI Foundry\n",
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"\n",
|
||||
"[Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-studio/) (previously known as Azure AI Studio) offers models hosted on Azure.\n",
|
||||
"To use those models, you use the {py:class}`~autogen_ext.models.azure.AzureAIChatCompletionClient`.\n",
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"\n",
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"You need to install the `azure` extra to use this client."
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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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"vscode": {
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"languageId": "shellscript"
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}
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},
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"outputs": [],
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"source": [
|
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"# pip install \"autogen-ext[openai,azure]\""
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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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"Below is an example of using this client with the Phi-4 model from [GitHub Marketplace](https://github.com/marketplace/models)."
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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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"finish_reason='stop' content='The capital of France is Paris.' usage=RequestUsage(prompt_tokens=14, completion_tokens=8) cached=False logprobs=None\n"
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]
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}
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],
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"source": [
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"import os\n",
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"\n",
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"from autogen_core.models import UserMessage\n",
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"from autogen_ext.models.azure import AzureAIChatCompletionClient\n",
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"from azure.core.credentials import AzureKeyCredential\n",
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"\n",
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"client = AzureAIChatCompletionClient(\n",
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" model=\"Phi-4\",\n",
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" endpoint=\"https://models.inference.ai.azure.com\",\n",
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" # To authenticate with the model you will need to generate a personal access token (PAT) in your GitHub settings.\n",
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" # Create your PAT token by following instructions here: https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens\n",
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" credential=AzureKeyCredential(os.environ[\"GITHUB_TOKEN\"]),\n",
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" model_info={\n",
|
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" \"json_output\": False,\n",
|
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" \"function_calling\": False,\n",
|
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" \"vision\": False,\n",
|
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" \"family\": \"unknown\",\n",
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" },\n",
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")\n",
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"\n",
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"result = await client.create([UserMessage(content=\"What is the capital of France?\", source=\"user\")])\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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"## Ollama\n",
|
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"\n",
|
||||
"You can use the {py:class}`~autogen_ext.models.openai.OpenAIChatCompletionClient` to interact with OpenAI-compatible APIs such as Ollama and Gemini (beta).\n",
|
||||
"The below example shows how to use a local model running on [Ollama](https://ollama.com) server."
|
||||
]
|
||||
},
|
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@@ -164,9 +296,9 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### Gemini (beta)\n",
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"## Gemini (beta)\n",
|
||||
"\n",
|
||||
"The below example shows how to use the Gemini model."
|
||||
"The below example shows how to use the Gemini model via the {py:class}`~autogen_ext.models.openai.OpenAIChatCompletionClient`."
|
||||
]
|
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},
|
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{
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@@ -208,9 +340,9 @@
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"cell_type": "markdown",
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"metadata": {},
|
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"source": [
|
||||
"### Streaming Response\n",
|
||||
"## Streaming Response\n",
|
||||
"\n",
|
||||
"You can use the {py:meth}`~autogen_ext.models.OpenAIChatCompletionClient.create_streaming` method to create a\n",
|
||||
"You can use the {py:meth}`~autogen_ext.models.openai.BaseOpenAIChatCompletionClient.create_stream` method to create a\n",
|
||||
"chat completion request with streaming response."
|
||||
]
|
||||
},
|
||||
@@ -366,133 +498,70 @@
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||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Azure OpenAI\n",
|
||||
"## Structured Output\n",
|
||||
"\n",
|
||||
"To use the {py:class}`~autogen_ext.models.AzureOpenAIChatCompletionClient`, you need to provide\n",
|
||||
"the 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."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "shellscript"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# pip install \"autogen-ext[openai,azure]\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"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.openai import AzureOpenAIChatCompletionClient\n",
|
||||
"from azure.identity import DefaultAzureCredential, get_bearer_token_provider\n",
|
||||
"Structured output can be enabled by setting the `response_format` field in\n",
|
||||
"{py:class}`~autogen_ext.models.openai.OpenAIChatCompletionClient` and {py:class}`~autogen_ext.models.openai.AzureOpenAIChatCompletionClient` to\n",
|
||||
"as a [Pydantic BaseModel](https://docs.pydantic.dev/latest/concepts/models/) class.\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",
|
||||
" azure_deployment=\"{your-azure-deployment}\",\n",
|
||||
" model=\"{model-name, such as gpt-4o}\",\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",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"```{note}\n",
|
||||
"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.\n",
|
||||
"Structured output is only available for models that support it. It also\n",
|
||||
"requires the model client to support structured output as well.\n",
|
||||
"Currently, the {py:class}`~autogen_ext.models.openai.OpenAIChatCompletionClient`\n",
|
||||
"and {py:class}`~autogen_ext.models.openai.AzureOpenAIChatCompletionClient`\n",
|
||||
"support structured output.\n",
|
||||
"```"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Azure AI Foundry\n",
|
||||
"\n",
|
||||
"[Azure AI Foundry](https://learn.microsoft.com/en-us/azure/ai-studio/) (previously known as Azure AI Studio) offers models hosted on Azure.\n",
|
||||
"To use those models, you use the {py:class}`~autogen_ext.models.azure.AzureAIChatCompletionClient`.\n",
|
||||
"\n",
|
||||
"You need to install the `azure` extra to use this client."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"vscode": {
|
||||
"languageId": "shellscript"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# pip install \"autogen-ext[openai,azure]\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Below is an example of using this client with the Phi-4 model from [GitHub Marketplace](https://github.com/marketplace/models)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"finish_reason='stop' content='The capital of France is Paris.' usage=RequestUsage(prompt_tokens=14, completion_tokens=8) cached=False logprobs=None\n"
|
||||
"I'm glad to hear that you're feeling happy! It's such a great emotion that can brighten your whole day. Is there anything in particular that's bringing you joy today? 😊\n",
|
||||
"happy\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"from typing import Literal\n",
|
||||
"\n",
|
||||
"from autogen_core.models import UserMessage\n",
|
||||
"from autogen_ext.models.azure import AzureAIChatCompletionClient\n",
|
||||
"from azure.core.credentials import AzureKeyCredential\n",
|
||||
"from pydantic import BaseModel\n",
|
||||
"\n",
|
||||
"client = AzureAIChatCompletionClient(\n",
|
||||
" model=\"Phi-4\",\n",
|
||||
" endpoint=\"https://models.inference.ai.azure.com\",\n",
|
||||
" # To authenticate with the model you will need to generate a personal access token (PAT) in your GitHub settings.\n",
|
||||
" # Create your PAT token by following instructions here: https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/managing-your-personal-access-tokens\n",
|
||||
" credential=AzureKeyCredential(os.environ[\"GITHUB_TOKEN\"]),\n",
|
||||
" model_info={\n",
|
||||
" \"json_output\": False,\n",
|
||||
" \"function_calling\": False,\n",
|
||||
" \"vision\": False,\n",
|
||||
" \"family\": \"unknown\",\n",
|
||||
" },\n",
|
||||
"\n",
|
||||
"# The response format for the agent as a Pydantic base model.\n",
|
||||
"class AgentResponse(BaseModel):\n",
|
||||
" thoughts: str\n",
|
||||
" response: Literal[\"happy\", \"sad\", \"neutral\"]\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Create an agent that uses the OpenAI GPT-4o model with the custom response format.\n",
|
||||
"model_client = OpenAIChatCompletionClient(\n",
|
||||
" model=\"gpt-4o\",\n",
|
||||
" response_format=AgentResponse, # type: ignore\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"result = await client.create([UserMessage(content=\"What is the capital of France?\", source=\"user\")])\n",
|
||||
"print(result)"
|
||||
"# Send a message list to the model and await the response.\n",
|
||||
"messages = [\n",
|
||||
" UserMessage(content=\"I am happy.\", source=\"user\"),\n",
|
||||
"]\n",
|
||||
"response = await model_client.create(messages=messages)\n",
|
||||
"assert isinstance(response.content, str)\n",
|
||||
"parsed_response = AgentResponse.model_validate_json(response.content)\n",
|
||||
"print(parsed_response.thoughts)\n",
|
||||
"print(parsed_response.response)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You also use the `extra_create_args` parameter in the {py:meth}`~autogen_ext.models.openai.BaseOpenAIChatCompletionClient.create` method\n",
|
||||
"to set the `response_format` field so that the structured output can be configured for each request."
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
Reference in New Issue
Block a user