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.
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.
Refer to our glossary for an in-depth guide to the terms.
The repository includes some examples under the samples folder.
For instance, this is the Joke function part of the FunPlugin plugin:
Import Semantic Kernel SDK from pypi.org
# Note: if using a virtual environment, do not run this cell
%pip install -U semantic-kernel
from semantic_kernel import __version__
__version__
Initial configuration for the notebook to run properly.
# Make sure paths are correct for the imports
import os
import sys
notebook_dir = os.path.abspath("")
parent_dir = os.path.dirname(notebook_dir)
grandparent_dir = os.path.dirname(parent_dir)
sys.path.append(grandparent_dir)
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.
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.
Add your OpenAI Key key to your .env file (org Id only if you have multiple orgs):
GLOBAL_LLM_SERVICE="OpenAI"
OPENAI_API_KEY="sk-..."
OPENAI_ORG_ID=""
OPENAI_CHAT_MODEL_ID=""
OPENAI_TEXT_MODEL_ID=""
OPENAI_EMBEDDING_MODEL_ID=""
The names should match the names used in the .env file, as shown above.
Add your Azure Open AI Service key settings to the .env file in the same folder:
GLOBAL_LLM_SERVICE="AzureOpenAI"
AZURE_OPENAI_API_KEY="..."
AZURE_OPENAI_ENDPOINT="https://..."
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="..."
AZURE_OPENAI_TEXT_DEPLOYMENT_NAME="..."
AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME="..."
AZURE_OPENAI_API_VERSION="..."
The names should match the names used in the .env file, as shown above.
For more advanced configuration, please follow the steps outlined in the setup guide.
Let's move on to learning what prompts are and how to write them.
WRITE EXACTLY ONE JOKE or HUMOROUS STORY ABOUT THE TOPIC BELOW.
JOKE MUST BE:
- G RATED
- WORKPLACE/FAMILY SAFE
NO SEXISM, RACISM OR OTHER BIAS/BIGOTRY.
BE CREATIVE AND FUNNY. I WANT TO LAUGH.
+++++
{{$input}}
+++++
Note the special {{$input}} token, which is a variable that is automatically passed when invoking the function, commonly referred to as a "function parameter".
We'll explore later how functions can accept multiple variables, as well as invoke other functions.
In the same folder you'll notice a second config.json file. The file is optional, and is used to set some parameters for large language models like Temperature, TopP, Stop Sequences, etc.
{
"schema": 1,
"description": "Generate a funny joke",
"execution_settings": {
"default": {
"max_tokens": 1000,
"temperature": 0.9,
"top_p": 0.0,
"presence_penalty": 0.0,
"frequency_penalty": 0.0
}
},
"input_variables": [
{
"name": "input",
"description": "Joke subject",
"default": ""
},
{
"name": "style",
"description": "Give a hint about the desired joke style",
"default": ""
}
]
}
Given a prompt function defined by these files, this is how to load and use a file based prompt function.
Load and configure the kernel, as usual, loading also the AI service settings defined in the Setup notebook:
from semantic_kernel import Kernel
kernel = Kernel()
We will load our settings and get the LLM service to use for the notebook.
from services import Service
from samples.service_settings import ServiceSettings
service_settings = ServiceSettings.create()
# Select a service to use for this notebook (available services: OpenAI, AzureOpenAI, HuggingFace)
selectedService = (
Service.AzureOpenAI
if service_settings.global_llm_service is None
else Service(service_settings.global_llm_service.lower())
)
print(f"Using service type: {selectedService}")
Let's load our settings and validate that the required ones exist.
from services import Service
from samples.service_settings import ServiceSettings
service_settings = ServiceSettings.create()
# Select a service to use for this notebook (available services: OpenAI, AzureOpenAI, HuggingFace)
selectedService = (
Service.AzureOpenAI
if service_settings.global_llm_service is None
else Service(service_settings.global_llm_service.lower())
)
print(f"Using service type: {selectedService}")
We now configure our Chat Completion service on the kernel.
# Remove all services so that this cell can be re-run without restarting the kernel
kernel.remove_all_services()
service_id = None
if selectedService == Service.OpenAI:
from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion
service_id = "default"
kernel.add_service(
OpenAIChatCompletion(
service_id=service_id,
),
)
elif selectedService == Service.AzureOpenAI:
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion
service_id = "default"
kernel.add_service(
AzureChatCompletion(
service_id=service_id,
),
)
Import the plugin and all its functions:
# note: using plugins from the samples folder
plugins_directory = "../../../prompt_template_samples/"
funFunctions = kernel.add_plugin(parent_directory=plugins_directory, plugin_name="FunPlugin")
jokeFunction = funFunctions["Joke"]
How to use the plugin functions, e.g. generate a joke about "time travel to dinosaur age":
result = await kernel.invoke(jokeFunction, input="travel to dinosaur age", style="silly")
print(result)
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.