kaito_semantic_kernel_demo.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Semantic Kernel with Kaito ⚡\n",
    "\n",
    "This notebook is a simple demonstration of how to use the [Semantic Kernel](https://learn.microsoft.com/semantic-kernel/overview/?WT.mc_id=AZ-MVP-5003203) with [Kaito](https://github.com/Azure/kaito). There is currently no official Kaito connector available for the Semantic Kernel, so we have developed our own. You can find the source code for the connector in the `kaito` directory in this repository.\n",
    "\n",
    "## Install requirements\n",
    "We only need to install the Semantic Kernel SDK from pypi.org"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install semantic-kernel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Initialize the Kernel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from semantic_kernel import Kernel\n",
    "from kaito.services.kaito_text_completion import KaitoTextCompletion\n",
    "\n",
    "ai_model_id=\"kaito:falcon-7b-instruct\"\n",
    "url=\"http://4.207.32.145/chat\"\n",
    "\n",
    "kernel = Kernel()\n",
    "\n",
    "kernel.add_service(\n",
    "    KaitoTextCompletion(\n",
    "        ai_model_id=ai_model_id,\n",
    "        url='http://4.207.32.145/chat'\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add Plugins\n",
    "The following script will add all plugins from the `plugins_directory` to the kernel:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plugins_directory = \"Plugins\"\n",
    "kubernetesPlugin = kernel.add_plugin(parent_directory=plugins_directory, plugin_name=\"KubernetesPlugin\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the KubectlPrompt Plugin "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = await kernel.invoke(kubernetesPlugin[\"KubectlPrompt\"], input=\"list all namespaces\")\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the KubeMatch Plugin "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "goodFit=\"\"\"\n",
    "This project involves developing a high-traffic e-commerce platform that expects dynamic scaling and high availability to handle seasonal peaks in user traffic. \n",
    "The architecture is based on microservices, each responsible for different aspects such as user authentication, product catalog, and payment processing. \n",
    "The team is experienced in containerized environments and requires a solution that allows for efficient deployment, scaling, and management of services, with robust orchestration capabilities.\n",
    "\"\"\"\n",
    "\n",
    "result = await kernel.invoke(kubernetesPlugin[\"KubeMatch\"], input=goodFit)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "badFit=\"\"\"\n",
    "This project is a small-scale internal tool for automating office inventory management, expected to have a stable and predictable load with minimal scalability requirements. \n",
    "The application is built with a simple, monolithic architecture and will be deployed on a few servers. \n",
    "The team lacks experience with containerization and Kubernetes, and the simplicity of the project does not justify the overhead and complexity of implementing a Kubernetes environment.\n",
    "\"\"\"\n",
    "\n",
    "result = await kernel.invoke(kubernetesPlugin[\"KubeMatch\"], input=badFit)\n",
    "print(result)"
   ]
  }
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