python/samples/getting_started/00-getting-started.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup\n",
    "\n",
    "**Step 1**: Import Semantic Kernel SDK from pypi.org\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -m pip install semantic-kernel==1.0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from semantic_kernel import Kernel\n",
    "\n",
    "kernel = Kernel()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Configure the service you'd like to use via the `Service` Enum.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from services import Service\n",
    "\n",
    "from samples.service_settings import ServiceSettings\n",
    "\n",
    "service_settings = ServiceSettings()\n",
    "\n",
    "# Select a service to use for this notebook (available services: OpenAI, AzureOpenAI, HuggingFace)\n",
    "selectedService = (\n",
    "    Service.AzureOpenAI\n",
    "    if service_settings.global_llm_service is None\n",
    "    else Service(service_settings.global_llm_service.lower())\n",
    ")\n",
    "print(f\"Using service type: {selectedService}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Option 1: using OpenAI\n",
    "\n",
    "**Step 2**: Add your [OpenAI Key](https://openai.com/product/) key to either your environment variables or to the `.env` file in the same folder (org Id only if you have multiple orgs):\n",
    "\n",
    "```\n",
    "OPENAI_API_KEY=\"sk-...\"\n",
    "OPENAI_ORG_ID=\"\"\n",
    "```\n",
    "The environment variables names should match the names used in the `.env` file, as shown above.\n",
    "\n",
    "If using the `.env` file, please configure the `env_file_path` parameter with a valid path when creating the ChatCompletion class:\n",
    "\n",
    "```\n",
    "chat_completion = OpenAIChatCompletion(service_id=\"test\", env_file_path=<path_to_file>)\n",
    "```\n",
    "\n",
    "Use \"keyword arguments\" to instantiate an OpenAI Chat Completion service and add it to the kernel:\n",
    "\n",
    "## Option 2: using Azure OpenAI\n",
    "\n",
    "**Step 2**: Add your [Azure Open AI Service key](https://learn.microsoft.com/azure/cognitive-services/openai/quickstart?pivots=programming-language-studio) settings to either your system's environment variables or to the `.env` file in the same folder:\n",
    "\n",
    "```\n",
    "AZURE_OPENAI_API_KEY=\"...\"\n",
    "AZURE_OPENAI_ENDPOINT=\"https://...\"\n",
    "AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=\"...\"\n",
    "AZURE_OPENAI_TEXT_DEPLOYMENT_NAME=\"...\"\n",
    "AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME=\"...\"\n",
    "```\n",
    "The environment variables names should match the names used in the `.env` file, as shown above.\n",
    "\n",
    "If using the `.env` file, please configure the `env_file_path` parameter with a valid path when creating the ChatCompletion class:\n",
    "\n",
    "```\n",
    "chat_completion = AzureChatCompletion(service_id=\"test\", env_file_path=<path_to_file>)\n",
    "```\n",
    "\n",
    "Use \"keyword arguments\" to instantiate an Azure OpenAI Chat Completion service and add it to the kernel:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "service_id = None\n",
    "if selectedService == Service.OpenAI:\n",
    "    from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion\n",
    "\n",
    "    service_id = \"default\"\n",
    "    kernel.add_service(\n",
    "        OpenAIChatCompletion(service_id=service_id, ai_model_id=\"gpt-3.5-turbo-1106\"),\n",
    "    )\n",
    "elif selectedService == Service.AzureOpenAI:\n",
    "    from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion\n",
    "\n",
    "    service_id = \"default\"\n",
    "    kernel.add_service(\n",
    "        AzureChatCompletion(service_id=service_id),\n",
    "    )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run a Semantic Function\n",
    "\n",
    "**Step 3**: Load a Plugin and run a semantic function:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plugin = kernel.add_plugin(parent_directory=\"../../../prompt_template_samples/\", plugin_name=\"FunPlugin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from semantic_kernel.functions import KernelArguments\n",
    "\n",
    "joke_function = plugin[\"Joke\"]\n",
    "\n",
    "joke = await kernel.invoke(\n",
    "    joke_function,\n",
    "    KernelArguments(input=\"time travel to dinosaur age\", style=\"super silly\"),\n",
    ")\n",
    "print(joke)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}