wehub-resource-sync
227 lines
6.6 KiB
Plaintext
227 lines
6.6 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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"# Basic Loading of the Kernel\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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"### Setup\n",
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"\n",
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"Import Semantic Kernel SDK from pypi.org"
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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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"# Note: if using a virtual environment, do not run this cell\n",
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"%pip install -U semantic-kernel\n",
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"from semantic_kernel import __version__\n",
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"\n",
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"__version__"
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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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"Initial configuration for the notebook to run properly."
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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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"# Make sure paths are correct for the imports\n",
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"\n",
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"import os\n",
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"import sys\n",
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"\n",
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"notebook_dir = os.path.abspath(\"\")\n",
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"parent_dir = os.path.dirname(notebook_dir)\n",
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"grandparent_dir = os.path.dirname(parent_dir)\n",
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"\n",
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"\n",
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"sys.path.append(grandparent_dir)"
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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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"### Configuring the Kernel\n",
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"\n",
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"Let's get started with the necessary configuration to run Semantic Kernel. For Notebooks, we require a `.env` file with the proper settings for the model you use. Create a new file named `.env` and place it in this directory. Copy the contents of the `.env.example` file from this directory and paste it into the `.env` file that you just created.\n",
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"\n",
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"**NOTE: Please make sure to include `GLOBAL_LLM_SERVICE` set to either OpenAI, AzureOpenAI, or HuggingFace in your .env file. If this setting is not included, the Service will default to AzureOpenAI.**\n",
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"\n",
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"#### Option 1: using OpenAI\n",
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"\n",
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"Add your [OpenAI Key](https://openai.com/product/) key to your `.env` file (org Id only if you have multiple orgs):\n",
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"\n",
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"```\n",
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"GLOBAL_LLM_SERVICE=\"OpenAI\"\n",
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"OPENAI_API_KEY=\"sk-...\"\n",
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"OPENAI_ORG_ID=\"\"\n",
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"OPENAI_CHAT_MODEL_ID=\"\"\n",
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"OPENAI_TEXT_MODEL_ID=\"\"\n",
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"OPENAI_EMBEDDING_MODEL_ID=\"\"\n",
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"```\n",
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"The names should match the names used in the `.env` file, as shown above.\n",
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"\n",
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"#### Option 2: using Azure OpenAI\n",
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"\n",
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"Add your [Azure Open AI Service key](https://learn.microsoft.com/azure/cognitive-services/openai/quickstart?pivots=programming-language-studio) settings to the `.env` file in the same folder:\n",
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"\n",
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"```\n",
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"GLOBAL_LLM_SERVICE=\"AzureOpenAI\"\n",
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"AZURE_OPENAI_API_KEY=\"...\"\n",
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"AZURE_OPENAI_ENDPOINT=\"https://...\"\n",
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"AZURE_OPENAI_CHAT_DEPLOYMENT_NAME=\"...\"\n",
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"AZURE_OPENAI_TEXT_DEPLOYMENT_NAME=\"...\"\n",
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"AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME=\"...\"\n",
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"AZURE_OPENAI_API_VERSION=\"...\"\n",
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"```\n",
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"The names should match the names used in the `.env` file, as shown above.\n",
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"\n",
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"As alternative to `AZURE_OPENAI_API_KEY`, it's possible to authenticate using `credential` parameter, more information here: [Azure Identity](https://learn.microsoft.com/en-us/python/api/overview/azure/identity-readme).\n",
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"\n",
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"In the following example, `AzureCliCredential` is used. To authenticate using Azure CLI:\n",
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"\n",
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"1. Install [Azure CLI](https://learn.microsoft.com/en-us/cli/azure/install-azure-cli).\n",
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"2. Run `az login` command in terminal and follow the authentication steps.\n",
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"\n",
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"For more advanced configuration, please follow the steps outlined in the [setup guide](./CONFIGURING_THE_KERNEL.md)."
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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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"Let's define our kernel for this example."
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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 semantic_kernel import Kernel\n",
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"\n",
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"kernel = Kernel()"
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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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"We will load our settings and get the LLM service to use for the notebook."
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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 services import Service\n",
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"\n",
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"from samples.service_settings import ServiceSettings\n",
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"\n",
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"service_settings = ServiceSettings()\n",
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"\n",
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"# Select a service to use for this notebook (available services: OpenAI, AzureOpenAI, HuggingFace)\n",
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"selectedService = (\n",
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" Service.AzureOpenAI\n",
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" if service_settings.global_llm_service is None\n",
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" else Service(service_settings.global_llm_service.lower())\n",
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")\n",
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"print(f\"Using service type: {selectedService}\")"
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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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"# Remove all services so that this cell can be re-run without restarting the kernel\n",
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"kernel.remove_all_services()\n",
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"\n",
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"service_id = None\n",
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"if selectedService == Service.OpenAI:\n",
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" from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion\n",
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"\n",
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" service_id = \"default\"\n",
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" kernel.add_service(\n",
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" OpenAIChatCompletion(\n",
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" service_id=service_id,\n",
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" ),\n",
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" )\n",
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"elif selectedService == Service.AzureOpenAI:\n",
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" from azure.identity import AzureCliCredential\n",
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"\n",
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" from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion\n",
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"\n",
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" service_id = \"default\"\n",
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" kernel.add_service(\n",
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" AzureChatCompletion(service_id=service_id, credential=AzureCliCredential()),\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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"Great, now that you're familiar with setting up the Semantic Kernel, let's see [how we can use it to run prompts](02-running-prompts-from-file.ipynb).\n"
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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": "3.10.12"
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},
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"polyglot_notebook": {
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"kernelInfo": {
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"items": [
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{
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"aliases": [
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"frontend"
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],
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"name": "vscode"
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}
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]
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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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