Notebook

Setup¶

Import Semantic Kernel SDK from pypi.org

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# 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.

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# 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)

Configuring the Kernel¶

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.

Option 1: using OpenAI¶

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.

Option 2: using Azure OpenAI¶

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 define our kernel for this example.

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from semantic_kernel import Kernel

kernel = Kernel()

We will load our settings and get the LLM service to use for the notebook.

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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.

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# 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,
        ),
    )

Run a Semantic Function¶

Step 3: Load a Plugin and run a semantic function:

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plugin = kernel.add_plugin(parent_directory="../../../prompt_template_samples/", plugin_name="FunPlugin")

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from semantic_kernel.functions import KernelArguments

joke_function = plugin["Joke"]

joke = await kernel.invoke(
    joke_function,
    KernelArguments(input="time travel to dinosaur age", style="super silly"),
)
print(joke)