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* Support structured output * use ruff format * add type checking for cookbook * add the notebook to index.md * fix the type error * pass response_format explicitly * remove casting * ensure type are correct * seperate response_format arg * fix type and resolve pyright errors --------- Co-authored-by: Eric Zhu <[email protected]>
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...s/autogen-core/docs/src/user-guide/core-user-guide/cookbook/structured-output-agent.ipynb
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|---|---|---|
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| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Strcutured output using GPT-4o models\n", | ||
| "\n", | ||
| "This cookbook demonstrates how to obtain structured output using GPT-4o models. The OpenAI beta client SDK provides a parse helper that allows you to use your own Pydantic model, eliminating the need to define a JSON schema. This approach is recommended for supported models.\n", | ||
| "\n", | ||
| "Currently, this feature is supported for:\n", | ||
| "\n", | ||
| "- gpt-4o-mini on OpenAI\n", | ||
| "- gpt-4o-2024-08-06 on OpenAI\n", | ||
| "- gpt-4o-2024-08-06 on Azure" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Let's define a simple message type that carries explanation and output for a Math problem" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 5, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from pydantic import BaseModel\n", | ||
| "\n", | ||
| "\n", | ||
| "class MathReasoning(BaseModel):\n", | ||
| " class Step(BaseModel):\n", | ||
| " explanation: str\n", | ||
| " output: str\n", | ||
| "\n", | ||
| " steps: list[Step]\n", | ||
| " final_answer: str" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 6, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import os\n", | ||
| "\n", | ||
| "# Set the environment variable\n", | ||
| "os.environ[\"AZURE_OPENAI_ENDPOINT\"] = \"https://YOUR_ENDPOINT_DETAILS.openai.azure.com/\"\n", | ||
| "os.environ[\"AZURE_OPENAI_API_KEY\"] = \"YOUR_API_KEY\"\n", | ||
| "os.environ[\"AZURE_OPENAI_DEPLOYMENT_NAME\"] = \"gpt-4o-2024-08-06\"\n", | ||
| "os.environ[\"AZURE_OPENAI_API_VERSION\"] = \"2024-08-01-preview\"" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 7, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import json\n", | ||
| "import os\n", | ||
| "from typing import Optional\n", | ||
| "\n", | ||
| "from autogen_core.components.models import AzureOpenAIChatCompletionClient, UserMessage\n", | ||
| "\n", | ||
| "\n", | ||
| "# Function to get environment variable and ensure it is not None\n", | ||
| "def get_env_variable(name: str) -> str:\n", | ||
| " value = os.getenv(name)\n", | ||
| " if value is None:\n", | ||
| " raise ValueError(f\"Environment variable {name} is not set\")\n", | ||
| " return value\n", | ||
| "\n", | ||
| "\n", | ||
| "# Create the client with type-checked environment variables\n", | ||
| "client = AzureOpenAIChatCompletionClient(\n", | ||
| " model=get_env_variable(\"AZURE_OPENAI_DEPLOYMENT_NAME\"),\n", | ||
| " api_version=get_env_variable(\"AZURE_OPENAI_API_VERSION\"),\n", | ||
| " azure_endpoint=get_env_variable(\"AZURE_OPENAI_ENDPOINT\"),\n", | ||
| " api_key=get_env_variable(\"AZURE_OPENAI_API_KEY\"),\n", | ||
| " model_capabilities={\n", | ||
| " \"vision\": False,\n", | ||
| " \"function_calling\": True,\n", | ||
| " \"json_output\": True,\n", | ||
| " },\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 8, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "name": "stdout", | ||
| "output_type": "stream", | ||
| "text": [ | ||
| "{'steps': [{'explanation': 'Start by aligning the numbers vertically.', 'output': '\\n 16\\n+ 32'}, {'explanation': 'Add the units digits: 6 + 2 = 8.', 'output': '\\n 16\\n+ 32\\n 8'}, {'explanation': 'Add the tens digits: 1 + 3 = 4.', 'output': '\\n 16\\n+ 32\\n 48'}], 'final_answer': '48'}\n" | ||
| ] | ||
| }, | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "MathReasoning(steps=[Step(explanation='Start by aligning the numbers vertically.', output='\\n 16\\n+ 32'), Step(explanation='Add the units digits: 6 + 2 = 8.', output='\\n 16\\n+ 32\\n 8'), Step(explanation='Add the tens digits: 1 + 3 = 4.', output='\\n 16\\n+ 32\\n 48')], final_answer='48')" | ||
| ] | ||
| }, | ||
| "execution_count": 8, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "# Define the user message\n", | ||
| "messages = [\n", | ||
| " UserMessage(content=\"What is 16 + 32?\", source=\"user\"),\n", | ||
| "]\n", | ||
| "\n", | ||
| "# Call the create method on the client, passing the messages and additional arguments\n", | ||
| "# The extra_create_args dictionary includes the response format as MathReasoning model we defined above\n", | ||
| "# Providing the response format and pydantic model will use the new parse method from beta SDK\n", | ||
| "response = await client.create(messages=messages, extra_create_args={\"response_format\": MathReasoning})\n", | ||
| "\n", | ||
| "# Ensure the response content is a valid JSON string before loading it\n", | ||
| "response_content: Optional[str] = response.content if isinstance(response.content, str) else None\n", | ||
| "if response_content is None:\n", | ||
| " raise ValueError(\"Response content is not a valid JSON string\")\n", | ||
| "\n", | ||
| "# Print the response content after loading it as JSON\n", | ||
| "print(json.loads(response_content))\n", | ||
| "\n", | ||
| "# Validate the response content with the MathReasoning model\n", | ||
| "MathReasoning.model_validate(json.loads(response_content))" | ||
| ] | ||
| } | ||
| ], | ||
| "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": "3.11.7" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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