semantic-kernel/python/samples/getting_started/10-multiple-results-per-prompt.ipynb

Multiple Results

In this notebook we show how you can in a single request, have the LLM model return multiple results per prompt. This is useful for running experiments where you want to evaluate the robustness of your prompt and the parameters of your config against a particular large language model.

Import Semantic Kernel SDK from pypi.org

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# Note: if using a Poetry virtual environment, do not run this cell
%pip install semantic-kernel==1.2.0

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()

# 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}")

First, we will set up the text and chat services we will be submitting prompts to.

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from semantic_kernel import Kernel
from semantic_kernel.connectors.ai.open_ai import (
    AzureChatCompletion,
    AzureChatPromptExecutionSettings,  # noqa: F401
    AzureTextCompletion,
    OpenAIChatCompletion,
    OpenAIChatPromptExecutionSettings,  # noqa: F401
    OpenAITextCompletion,
    OpenAITextPromptExecutionSettings,  # noqa: F401
)

kernel = Kernel()

# Configure Azure LLM service
service_id = None
if selectedService == Service.OpenAI:
    from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

    service_id = "default"
    oai_chat_service = OpenAIChatCompletion(
        service_id="oai_chat",
    )
    oai_text_service = OpenAITextCompletion(
        service_id="oai_text",
    )
elif selectedService == Service.AzureOpenAI:
    from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion

    service_id = "default"
    aoai_chat_service = AzureChatCompletion(
        service_id="aoai_chat",
    )
    aoai_text_service = AzureTextCompletion(
        service_id="aoai_text",
    )

# Configure Hugging Face service
if selectedService == Service.HuggingFace:
    from semantic_kernel.connectors.ai.hugging_face import (  # noqa: F401
        HuggingFacePromptExecutionSettings,
        HuggingFaceTextCompletion,
    )

    hf_text_service = HuggingFaceTextCompletion(service_id="hf_text", ai_model_id="distilgpt2", task="text-generation")

Next, we'll set up the completion request settings for text completion services.

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oai_text_prompt_execution_settings = OpenAITextPromptExecutionSettings(
    service="oai_text",
    extension_data={
        "max_tokens": 80,
        "temperature": 0.7,
        "top_p": 1,
        "frequency_penalty": 0.5,
        "presence_penalty": 0.5,
        "number_of_responses": 3,
    },
)

Multiple Open AI Text Completions

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if selectedService == Service.OpenAI:
    prompt = "What is the purpose of a rubber duck?"

    results = await oai_text_service.get_text_contents(prompt=prompt, settings=oai_text_prompt_execution_settings)

    for i, result in enumerate(results):
        print(f"Result {i + 1}: {result}")

Multiple Azure Open AI Text Completions

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if selectedService == Service.AzureOpenAI:
    prompt = "provide me a list of possible meanings for the acronym 'ORLD'"

    results = await aoai_text_service.get_text_contents(prompt=prompt, settings=oai_text_prompt_execution_settings)

    for i, result in enumerate(results):
        print(f"Result {i + 1}: {result}")

Multiple Hugging Face Text Completions

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if selectedService == Service.HuggingFace:
    hf_prompt_execution_settings = HuggingFacePromptExecutionSettings(
        service_id="hf_text",
        extension_data={"max_new_tokens": 80, "temperature": 0.7, "top_p": 1, "num_return_sequences": 3},
    )

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if selectedService == Service.HuggingFace:
    prompt = "The purpose of a rubber duck is"

    results = await hf_text_service.get_text_contents(prompt=prompt, settings=hf_prompt_execution_settings)

    for i, result in enumerate(results):
        print(f"Result {i + 1}: {result}")

Here, we're setting up the settings for Chat completions.

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oai_chat_prompt_execution_settings = OpenAIChatPromptExecutionSettings(
    service_id="oai_chat",
    max_tokens=80,
    temperature=0.7,
    top_p=1,
    frequency_penalty=0.5,
    presence_penalty=0.5,
    number_of_responses=3,
)

Multiple OpenAI Chat Completions

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from semantic_kernel.contents import ChatHistory

if selectedService == Service.OpenAI:
    chat = ChatHistory()
    chat.add_user_message(
        "It's a beautiful day outside, birds are singing, flowers are blooming. On days like these, kids like you..."
    )
    results = await oai_chat_service.get_chat_message_contents(
        chat_history=chat, settings=oai_chat_prompt_execution_settings
    )

    for i, result in enumerate(results):
        print(f"Result {i + 1}: {result!s}")

Multiple Azure OpenAI Chat Completions

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az_oai_prompt_execution_settings = AzureChatPromptExecutionSettings(
    service_id="aoai_chat",
    max_tokens=80,
    temperature=0.7,
    top_p=1,
    frequency_penalty=0.5,
    presence_penalty=0.5,
    number_of_responses=3,
)

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if selectedService == Service.AzureOpenAI:
    content = (
        "Tomorrow is going to be a great day, I can feel it. I'm going to wake up early, go for a run, and then..."
    )
    chat = ChatHistory()
    chat.add_user_message(content)
    results = await aoai_chat_service.get_chat_message_contents(
        chat_history=chat, settings=az_oai_prompt_execution_settings
    )

    for i, result in enumerate(results):
        print(f"Result {i + 1}: {result!s}")

Streaming Multiple Results

Here is an example pattern if you want to stream your multiple results. Note that this is not supported for Hugging Face text completions at this time.

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if selectedService == Service.OpenAI:
    import os
    import time

    from IPython.display import clear_output

    # Determine the clear command based on OS
    clear_command = "cls" if os.name == "nt" else "clear"

    chat = ChatHistory()
    chat.add_user_message("what is the purpose of a rubber duck?")

    stream = oai_chat_service.get_streaming_chat_message_contents(
        chat_history=chat, settings=oai_chat_prompt_execution_settings
    )
    number_of_responses = oai_chat_prompt_execution_settings.number_of_responses
    texts = [""] * number_of_responses

    last_clear_time = time.time()
    clear_interval = 0.5  # seconds

    # Note: there are some quirks with displaying the output, which sometimes flashes and disappears.
    # This could be influenced by a few factors specific to Jupyter notebooks and asynchronous processing.
    # The following code attempts to buffer the results to avoid the output flashing on/off the screen.

    async for results in stream:
        current_time = time.time()

        # Update texts with new results
        for result in results:
            texts[result.choice_index] += str(result)

        # Clear and display output at intervals
        if current_time - last_clear_time > clear_interval:
            clear_output(wait=True)
            for idx, text in enumerate(texts):
                print(f"Result {idx + 1}: {text}")
            last_clear_time = current_time

    print("----------------------------------------")