semantic-kernel/python/samples/getting_started/01-basic-loading-the-kernel.ipynb

Basic Loading of the Kernel

To run the notebooks we recommend using Poetry and starting a shell with a virtual environment prepared to use SK.

See DEV_SETUP.md for more information.

In [ ]:

!python -m pip install semantic-kernel==1.0.3

In [1]:

from semantic_kernel import Kernel

kernel = Kernel()

Configuring API Keys and Endpoints

Option 1: using OpenAI

Add your OpenAI Key key to either your environment variables or to the .env file in the same folder (org Id only if you have multiple orgs):

OPENAI_API_KEY="sk-..."
OPENAI_ORG_ID=""

The environment variables names should match the names used in the .env file, as shown above.

If using the .env file, please configure the env_file_path parameter with a valid path when creating the ChatCompletion class:

chat_completion = OpenAIChatCompletion(service_id="test", env_file_path=<path_to_file>)

Use "keyword arguments" to instantiate an OpenAI Chat Completion service and add it to the kernel:

Option 2: using Azure OpenAI

Add your Azure Open AI Service key settings to either your system's environment variables or to the .env file in the same folder:

AZURE_OPENAI_API_KEY="..."
AZURE_OPENAI_ENDPOINT="https://..."
AZURE_OPENAI_CHAT_DEPLOYMENT_NAME="..."
AZURE_OPENAI_TEXT_DEPLOYMENT_NAME="..."
AZURE_OPENAI_EMBEDDING_DEPLOYMENT_NAME="..."

The environment variables names should match the names used in the .env file, as shown above.

If using the .env file, please configure the env_file_path parameter with a valid path when creating the ChatCompletion class:

chat_completion = AzureChatCompletion(service_id="test", env_file_path=<path_to_file>)

When using the kernel for AI requests, the kernel needs some settings like URL and credentials to the AI models.

The SDK currently supports OpenAI and Azure OpenAI, among other connectors.

If you need an Azure OpenAI key, go here.

In [3]:

from services import Service

from samples.service_settings import ServiceSettings

service_settings = ServiceSettings()

# 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}")

In [4]:

service_id = None
if selectedService == Service.OpenAI:
    from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

    service_id = "oai_chat_gpt"
    kernel.add_service(
        OpenAIChatCompletion(service_id=service_id, ai_model_id="gpt-3.5-turbo-1106"),
    )
elif selectedService == Service.AzureOpenAI:
    from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion

    service_id = "aoai_chat_completion"
    kernel.add_service(
        AzureChatCompletion(service_id=service_id),
    )

Great, now that you're familiar with setting up the Semantic Kernel, let's see how we can use it to run prompts.