wehub-resource-sync
396 lines
12 KiB
Plaintext
396 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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"id": "692e361b",
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"metadata": {},
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"source": [
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"# How to run a prompt plugins from file\n",
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"\n",
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"Now that you're familiar with Kernel basics, let's see how the kernel allows you to run Prompt Plugins and Prompt Functions stored on disk.\n",
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"\n",
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"A Prompt Plugin is a collection of Semantic Functions, where each function is defined with natural language that can be provided with a text file.\n",
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"\n",
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"Refer to our [glossary](https://github.com/microsoft/semantic-kernel/blob/main/docs/GLOSSARY.md) for an in-depth guide to the terms.\n",
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"\n",
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"The repository includes some examples under the [samples](https://github.com/microsoft/semantic-kernel/tree/main/samples) folder.\n",
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"\n",
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"For instance, [this](../../../prompt_template_samples/FunPlugin/Joke/skprompt.txt) is the **Joke function** part of the **FunPlugin plugin**:\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "3feecb6e",
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"metadata": {},
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"source": [
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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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"id": "32187534",
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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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"id": "cc58d362",
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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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"id": "bc1bc941",
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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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"id": "b5074884",
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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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"id": "93d7361e",
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"metadata": {},
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"source": [
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"Let's move on to learning what prompts are and how to write them."
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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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"id": "f3ce1efe",
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"metadata": {},
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"source": [
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"```\n",
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"WRITE EXACTLY ONE JOKE or HUMOROUS STORY ABOUT THE TOPIC BELOW.\n",
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"JOKE MUST BE:\n",
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"- G RATED\n",
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"- WORKPLACE/FAMILY SAFE\n",
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"NO SEXISM, RACISM OR OTHER BIAS/BIGOTRY.\n",
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"BE CREATIVE AND FUNNY. I WANT TO LAUGH.\n",
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"+++++\n",
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"{{$input}}\n",
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"+++++\n",
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"```\n"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "afdb96d6",
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"metadata": {},
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"source": [
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"Note the special **`{{$input}}`** token, which is a variable that is automatically passed when invoking the function, commonly referred to as a \"function parameter\".\n",
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"\n",
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"We'll explore later how functions can accept multiple variables, as well as invoke other functions.\n"
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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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"id": "c3bd5134",
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"metadata": {},
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"source": [
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"In the same folder you'll notice a second [config.json](../../../prompt_template_samples/FunPlugin/Joke/config.json) file. The file is optional, and is used to set some parameters for large language models like Temperature, TopP, Stop Sequences, etc.\n",
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"\n",
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"```\n",
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"{\n",
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" \"schema\": 1,\n",
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" \"description\": \"Generate a funny joke\",\n",
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" \"execution_settings\": {\n",
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" \"default\": {\n",
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" \"max_tokens\": 1000,\n",
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" \"temperature\": 0.9,\n",
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" \"top_p\": 0.0,\n",
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" \"presence_penalty\": 0.0,\n",
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" \"frequency_penalty\": 0.0\n",
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" }\n",
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" },\n",
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" \"input_variables\": [\n",
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" {\n",
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" \"name\": \"input\",\n",
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" \"description\": \"Joke subject\",\n",
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" \"default\": \"\"\n",
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" },\n",
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" {\n",
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" \"name\": \"style\",\n",
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" \"description\": \"Give a hint about the desired joke style\",\n",
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" \"default\": \"\"\n",
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" }\n",
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" ]\n",
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"}\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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"attachments": {},
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"cell_type": "markdown",
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"id": "384ff07f",
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"metadata": {},
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"source": [
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"Given a prompt function defined by these files, this is how to load and use a file based prompt function.\n",
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"\n",
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"Load and configure the kernel, as usual, loading also the AI service settings defined in the [Setup notebook](00-getting-started.ipynb):\n"
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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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"id": "9c0688c5",
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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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"id": "63f0788e",
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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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"id": "82d16ce6",
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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": "markdown",
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"id": "04ad7f35",
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"metadata": {},
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"source": [
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"Let's load our settings and validate that the required ones exist."
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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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"id": "fdb865a7",
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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": "markdown",
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"id": "c50b4d7a",
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"metadata": {},
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"source": [
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"We now configure our Chat Completion service on the kernel."
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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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"id": "b0062a24",
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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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"id": "fd5ff1f4",
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"metadata": {},
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"source": [
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"Import the plugin and all its functions:\n"
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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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"id": "56ee184d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# note: using plugins from the samples folder\n",
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"plugins_directory = \"../../../prompt_template_samples/\"\n",
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"\n",
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"funFunctions = kernel.add_plugin(parent_directory=plugins_directory, plugin_name=\"FunPlugin\")\n",
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"\n",
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"jokeFunction = funFunctions[\"Joke\"]"
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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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"id": "edd99fa0",
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"metadata": {},
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"source": [
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"How to use the plugin functions, e.g. generate a joke about \"_time travel to dinosaur age_\":\n"
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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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"id": "6effe63b",
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"metadata": {},
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"outputs": [],
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"source": [
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"result = await kernel.invoke(jokeFunction, input=\"travel to dinosaur age\", style=\"silly\")\n",
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"print(result)"
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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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"id": "2281a1fc",
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"metadata": {},
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"source": [
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"Great, now that you know how to load a plugin from disk, let's show how you can [create and run a prompt function inline.](./03-prompt-function-inline.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": "3.12.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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