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448 lines
14 KiB
Plaintext
448 lines
14 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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"attachments": {},
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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."
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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\n",
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"# (Optional) If you want to use Microsoft Active Directory\n",
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"! pip install azure-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": 3,
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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"
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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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"\n",
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"Additionally, to properly access the Azure OpenAI Service, we need to create the proper resources at the [Azure Portal](https://portal.azure.com) (you can check a detailed guide on how to do this in the [Microsoft Docs](https://learn.microsoft.com/en-us/azure/cognitive-services/openai/how-to/create-resource?pivots=web-portal))\n",
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"\n",
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"Once the resource is created, the first thing we need to use is its endpoint. You can get the endpoint by looking at the *\"Keys and Endpoints\"* section under the *\"Resource Management\"* section. Having this, we will set up the SDK using this information:"
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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": 27,
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"metadata": {},
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"outputs": [],
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"source": [
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"openai.api_base = \"\" # Add your endpoint here\n",
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"\n",
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"# functions is only supported by the 2023-07-01-preview API version\n",
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"openai.api_version = \"2023-07-01-preview\""
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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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"### 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 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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"use_azure_active_directory = False"
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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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"\n",
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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 up the `api_type` to `azure` and 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))"
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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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"if not use_azure_active_directory:\n",
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" openai.api_type = \"azure\"\n",
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" openai.api_key = os.environ[\"OPENAI_API_KEY\"]"
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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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"> Note: In this example, we configured the library to use the Azure API by setting the variables in code. For development, consider setting the environment variables instead:\n",
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"\n",
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"```\n",
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"OPENAI_API_BASE\n",
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"OPENAI_API_KEY\n",
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"OPENAI_API_TYPE\n",
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"OPENAI_API_VERSION\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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"#### Authentication using Microsoft Active Directory\n",
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"Let's now see how we can get a key via Microsoft Active Directory Authentication."
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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 azure.identity import DefaultAzureCredential\n",
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"\n",
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"if use_azure_active_directory:\n",
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" default_credential = DefaultAzureCredential()\n",
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" token = default_credential.get_token(\"https://cognitiveservices.azure.com/.default\")\n",
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"\n",
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" openai.api_type = \"azure_ad\"\n",
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" openai.api_key = token.token"
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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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"A token is valid for a period of time, after which it will expire. To ensure a valid token is sent with every request, you can refresh an expiring token by hooking into requests.auth:"
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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 typing\n",
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"import time\n",
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"import requests\n",
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"\n",
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"if typing.TYPE_CHECKING:\n",
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" from azure.core.credentials import TokenCredential\n",
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"\n",
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"class TokenRefresh(requests.auth.AuthBase):\n",
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"\n",
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" def __init__(self, credential: \"TokenCredential\", scopes: typing.List[str]) -> None:\n",
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" self.credential = credential\n",
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" self.scopes = scopes\n",
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" self.cached_token: typing.Optional[str] = None\n",
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"\n",
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" def __call__(self, req):\n",
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" if not self.cached_token or self.cached_token.expires_on - time.time() < 300:\n",
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" self.cached_token = self.credential.get_token(*self.scopes)\n",
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" req.headers[\"Authorization\"] = f\"Bearer {self.cached_token.token}\"\n",
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" return req\n",
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"\n",
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"if use_azure_active_directory:\n",
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" session = requests.Session()\n",
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" session.auth = TokenRefresh(default_credential, [\"https://cognitiveservices.azure.com/.default\"])\n",
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"\n",
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" openai.requestssession = session"
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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": 21,
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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 \"function_call\" 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 `function_call` keyword argument to force the model to call a particular function (e.g. `function_call={\"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": 22,
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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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"{\n",
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" \"choices\": [\n",
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" {\n",
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" \"content_filter_results\": {},\n",
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" \"finish_reason\": \"function_call\",\n",
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" \"index\": 0,\n",
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" \"message\": {\n",
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" \"function_call\": {\n",
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" \"arguments\": \"{\\n \\\"location\\\": \\\"Seattle, WA\\\"\\n}\",\n",
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" \"name\": \"get_current_weather\"\n",
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" },\n",
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" \"role\": \"assistant\"\n",
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" }\n",
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" }\n",
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" ],\n",
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" \"created\": 1689702512,\n",
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" \"id\": \"chatcmpl-7dj6GkYdM7Vw9eGn02bc2qqjN70Ps\",\n",
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" \"model\": \"gpt-4\",\n",
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" \"object\": \"chat.completion\",\n",
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" \"prompt_annotations\": [\n",
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" {\n",
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" \"content_filter_results\": {\n",
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" \"hate\": {\n",
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" \"filtered\": false,\n",
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" \"severity\": \"safe\"\n",
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" },\n",
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" \"self_harm\": {\n",
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" \"filtered\": false,\n",
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" \"severity\": \"safe\"\n",
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" },\n",
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" \"sexual\": {\n",
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" \"filtered\": false,\n",
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" \"severity\": \"safe\"\n",
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" },\n",
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" \"violence\": {\n",
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" \"filtered\": false,\n",
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" \"severity\": \"safe\"\n",
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" }\n",
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" },\n",
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" \"prompt_index\": 0\n",
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" }\n",
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" ],\n",
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" \"usage\": {\n",
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" \"completion_tokens\": 18,\n",
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" \"prompt_tokens\": 115,\n",
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" \"total_tokens\": 133\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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"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 = openai.ChatCompletion.create(\n",
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" deployment_id=\"gpt-35-turbo-0613\",\n",
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" messages=messages,\n",
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" functions=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": 23,
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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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"get_current_weather\n",
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"{\n",
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" \"location\": \"Seattle, WA\"\n",
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"}\n"
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]
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}
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],
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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.function_call\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": 24,
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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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"Today in Seattle, the weather is sunny with a temperature of 22 degrees celsius.\n"
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]
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}
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],
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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 = openai.ChatCompletion.create(\n",
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" deployment_id=\"gpt-35-turbo-0613\",\n",
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" messages=messages,\n",
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" functions=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": {
|
|
"name": "ipython",
|
|
"version": 3
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},
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|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
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"version": "3.10.0"
|
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},
|
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"vscode": {
|
|
"interpreter": {
|
|
"hash": "3a5103089ab7e7c666b279eeded403fcec76de49a40685dbdfe9f9c78ad97c17"
|
|
}
|
|
}
|
|
},
|
|
"nbformat": 4,
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|
"nbformat_minor": 2
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|
}
|