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350 lines
12 KiB
Plaintext
350 lines
12 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Azure functions example\n",
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"\n",
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"This notebook shows how to use the function calling capability with the Azure OpenAI service. Functions allow a caller of chat completions to define capabilities that the model can use to extend its\n",
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"functionality into external tools and data sources.\n",
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"\n",
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"You can read more about chat functions on OpenAI's blog: https://openai.com/blog/function-calling-and-other-api-updates\n",
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"\n",
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"**NOTE**: Chat functions require model versions beginning with gpt-4 and gpt-35-turbo's `-0613` labels. They are not supported by older versions of the models."
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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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"## Setup\n",
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"\n",
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"First, we install the necessary dependencies and import the libraries we will be using."
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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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"! pip install \"openai>=1.0.0,<2.0.0\"\n",
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"! pip install python-dotenv"
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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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"import os\n",
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"import openai\n",
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"import dotenv\n",
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"\n",
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"dotenv.load_dotenv()"
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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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"### Authentication\n",
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"\n",
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"The Azure OpenAI service supports multiple authentication mechanisms that include API keys and Azure Active Directory token credentials."
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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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"source": [
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"use_azure_active_directory = False # Set this flag to True if you are using Azure Active Directory"
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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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"#### Authentication using API key\n",
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"\n",
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"To set up the OpenAI SDK to use an *Azure API Key*, we need to set `api_key` to a key associated with your endpoint (you can find this key in *\"Keys and Endpoints\"* under *\"Resource Management\"* in the [Azure Portal](https://portal.azure.com)). You'll also find the endpoint for your resource here."
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"if not use_azure_active_directory:\n",
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" endpoint = os.environ[\"AZURE_OPENAI_ENDPOINT\"]\n",
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" api_key = os.environ[\"AZURE_OPENAI_API_KEY\"]\n",
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"\n",
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" client = openai.AzureOpenAI(\n",
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" azure_endpoint=endpoint,\n",
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" api_key=api_key,\n",
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" api_version=\"2023-09-01-preview\"\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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"#### Authentication using Azure Active Directory\n",
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"Let's now see how we can autheticate via Azure Active Directory. We'll start by installing the `azure-identity` library. This library will provide the token credentials we need to authenticate and help us build a token credential provider through the `get_bearer_token_provider` helper function. It's recommended to use `get_bearer_token_provider` over providing a static token to `AzureOpenAI` because this API will automatically cache and refresh tokens for you. \n",
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"\n",
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"For more information on how to set up Azure Active Directory authentication with Azure OpenAI, see the [documentation](https://learn.microsoft.com/azure/ai-services/openai/how-to/managed-identity)."
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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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"! pip install \"azure-identity>=1.15.0\""
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"from azure.identity import DefaultAzureCredential, get_bearer_token_provider\n",
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"\n",
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"if use_azure_active_directory:\n",
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" endpoint = os.environ[\"AZURE_OPENAI_ENDPOINT\"]\n",
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" api_key = os.environ[\"AZURE_OPENAI_API_KEY\"]\n",
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"\n",
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" client = openai.AzureOpenAI(\n",
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" azure_endpoint=endpoint,\n",
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" azure_ad_token_provider=get_bearer_token_provider(DefaultAzureCredential(), \"https://cognitiveservices.azure.com/.default\"),\n",
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" api_version=\"2023-09-01-preview\"\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: the AzureOpenAI infers the following arguments from their corresponding environment variables if they are not provided:\n",
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"\n",
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"- `api_key` from `AZURE_OPENAI_API_KEY`\n",
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"- `azure_ad_token` from `AZURE_OPENAI_AD_TOKEN`\n",
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"- `api_version` from `OPENAI_API_VERSION`\n",
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"- `azure_endpoint` from `AZURE_OPENAI_ENDPOINT`\n"
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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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"## Deployments\n",
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"\n",
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"In this section we are going to create a deployment of a GPT model that we can use to call functions."
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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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"### Deployments: Create in the Azure OpenAI Studio\n",
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"Let's deploy a model to use with chat completions. Go to https://portal.azure.com, find your Azure OpenAI resource, and then navigate to the Azure OpenAI Studio. Click on the \"Deployments\" tab and then create a deployment for the model you want to use for chat completions. The deployment name that you give the model will be used in the code below."
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"deployment = \"\" # Fill in the deployment name from the portal here"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Functions\n",
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"\n",
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"With setup and authentication complete, you can now use functions with the Azure OpenAI service. This will be split into a few steps:\n",
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"\n",
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"1. Define the function(s)\n",
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"2. Pass function definition(s) into chat completions API\n",
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"3. Call function with arguments from the response\n",
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"4. Feed function response back into chat completions API"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 1. Define the function(s)\n",
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"\n",
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"A list of functions can be defined, each containing the name of the function, an optional description, and the parameters the function accepts (described as a JSON schema)."
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"functions = [\n",
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" {\n",
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" \"name\": \"get_current_weather\",\n",
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" \"description\": \"Get the current weather\",\n",
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" \"parameters\": {\n",
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" \"type\": \"object\",\n",
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" \"properties\": {\n",
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" \"location\": {\n",
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" \"type\": \"string\",\n",
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" \"description\": \"The city and state, e.g. San Francisco, CA\",\n",
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" },\n",
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" \"format\": {\n",
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" \"type\": \"string\",\n",
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" \"enum\": [\"celsius\", \"fahrenheit\"],\n",
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" \"description\": \"The temperature unit to use. Infer this from the users location.\",\n",
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" },\n",
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" },\n",
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" \"required\": [\"location\"],\n",
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" },\n",
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" }\n",
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"]"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 2. Pass function definition(s) into chat completions API\n",
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"\n",
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"Now we can pass the function into the chat completions API. If the model determines it should call the function, a `finish_reason` of \"tool_calls\" will be populated on the choice and the details of which function to call and its arguments will be present in the `message`. Optionally, you can set the `tool_choice` keyword argument to force the model to call a particular function (e.g. `{\"type\": \"function\", \"function\": {\"name\": get_current_weather}}`). By default, this is set to `auto`, allowing the model to choose whether to call the function or not. "
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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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"messages = [\n",
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" {\"role\": \"system\", \"content\": \"Don't make assumptions about what values to plug into functions. Ask for clarification if a user request is ambiguous.\"},\n",
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" {\"role\": \"user\", \"content\": \"What's the weather like today in Seattle?\"}\n",
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"]\n",
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"\n",
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"chat_completion = client.chat.completions.create(\n",
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" model=deployment,\n",
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" messages=messages,\n",
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" tools=functions,\n",
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")\n",
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"print(chat_completion)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 3. Call function with arguments from the response\n",
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"\n",
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"The name of the function call will be one that was provided initially and the arguments will include JSON matching the schema included in the function definition."
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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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"import json\n",
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"\n",
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"def get_current_weather(request):\n",
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" \"\"\"\n",
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" This function is for illustrative purposes.\n",
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" The location and unit should be used to determine weather\n",
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" instead of returning a hardcoded response.\n",
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" \"\"\"\n",
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" location = request.get(\"location\")\n",
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" unit = request.get(\"unit\")\n",
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" return {\"temperature\": \"22\", \"unit\": \"celsius\", \"description\": \"Sunny\"}\n",
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"\n",
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"function_call = chat_completion.choices[0].message.tool_calls[0].function\n",
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"print(function_call.name)\n",
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"print(function_call.arguments)\n",
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"\n",
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"if function_call.name == \"get_current_weather\":\n",
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" response = get_current_weather(json.loads(function_call.arguments))"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### 4. Feed function response back into chat completions API\n",
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"\n",
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"The response from the function should be serialized into a new message with the role set to \"function\". Now the model will use the response data to formulate its answer."
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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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"messages.append(\n",
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" {\n",
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" \"role\": \"function\",\n",
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" \"name\": \"get_current_weather\",\n",
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" \"content\": json.dumps(response)\n",
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" }\n",
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")\n",
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"\n",
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"function_completion = client.chat.completions.create(\n",
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" model=deployment,\n",
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" messages=messages,\n",
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" tools=functions,\n",
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")\n",
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"\n",
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"print(function_completion.choices[0].message.content.strip())"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.0"
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},
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"vscode": {
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"interpreter": {
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"hash": "3a5103089ab7e7c666b279eeded403fcec76de49a40685dbdfe9f9c78ad97c17"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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