semantic-kernel/python/samples/getting_started/04-kernel-arguments-chat.ipynb

Creating a basic chat experience with kernel arguments

In this example, we show how you can build a simple chat bot by sending and updating the kernel arguments with your requests.

We introduce the Kernel Arguments object which in this demo functions similarly as a key-value store that you can use when running the kernel.

The chat history is local (i.e. in your computer's RAM) and not persisted anywhere beyond the life of this Jupyter session.

In future examples, we will show how to persist the chat history on disk so that you can bring it into your applications.

In this chat scenario, as the user talks back and forth with the bot, the chat context gets populated with the history of the conversation. During each new run of the kernel, the kernel arguments and chat history can provide the AI with its variables' content.

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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.

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}")

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from semantic_kernel import Kernel

kernel = Kernel()

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,
        ),
    )

Let's define a prompt outlining a dialogue chat bot.

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prompt = """
ChatBot can have a conversation with you about any topic.
It can give explicit instructions or say 'I don't know' if it does not have an answer.

{{$history}}
User: {{$user_input}}
ChatBot: """

Register your semantic function

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from semantic_kernel.connectors.ai.open_ai import AzureChatPromptExecutionSettings, OpenAIChatPromptExecutionSettings
from semantic_kernel.prompt_template import PromptTemplateConfig
from semantic_kernel.prompt_template.input_variable import InputVariable

if selectedService == Service.OpenAI:
    execution_settings = OpenAIChatPromptExecutionSettings(
        service_id=service_id,
        ai_model_id="gpt-3.5-turbo",
        max_tokens=2000,
        temperature=0.7,
    )
elif selectedService == Service.AzureOpenAI:
    execution_settings = AzureChatPromptExecutionSettings(
        service_id=service_id,
        ai_model_id="gpt-35-turbo",
        max_tokens=2000,
        temperature=0.7,
    )

prompt_template_config = PromptTemplateConfig(
    template=prompt,
    name="chat",
    template_format="semantic-kernel",
    input_variables=[
        InputVariable(name="user_input", description="The user input", is_required=True),
        InputVariable(name="history", description="The conversation history", is_required=True),
    ],
    execution_settings=execution_settings,
)

chat_function = kernel.add_function(
    function_name="chat",
    plugin_name="chatPlugin",
    prompt_template_config=prompt_template_config,
)

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from semantic_kernel.contents import ChatHistory

chat_history = ChatHistory()
chat_history.add_system_message("You are a helpful chatbot who is good about giving book recommendations.")

Initialize the Kernel Arguments

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from semantic_kernel.functions import KernelArguments

arguments = KernelArguments(user_input="Hi, I'm looking for book suggestions", history=chat_history)

Chat with the Bot

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response = await kernel.invoke(chat_function, arguments)
print(response)

Update the history with the output

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chat_history.add_assistant_message(str(response))

Keep Chatting!

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async def chat(input_text: str) -> None:
    # Save new message in the context variables
    print(f"User: {input_text}")

    # Process the user message and get an answer
    answer = await kernel.invoke(chat_function, KernelArguments(user_input=input_text, history=chat_history))

    # Show the response
    print(f"ChatBot: {answer}")

    chat_history.add_user_message(input_text)
    chat_history.add_assistant_message(str(answer))

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await chat("I love history and philosophy, I'd like to learn something new about Greece, any suggestion?")

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await chat("that sounds interesting, what is it about?")

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await chat("if I read that book, what exactly will I learn about Greek history?")

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await chat("could you list some more books I could read about this topic?")

After chatting for a while, we have built a growing history, which we are attaching to each prompt and which contains the full conversation. Let's take a look!

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print(chat_history)