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Julio Carlos MenendezandEduard van Valkenburg d3dd5c8e7e Python: Uses Python standard logging (#4115 )

### Motivation and Context

This replaces passing Logger instances as arguments around with the
preferred Python way of having each module define a module-wide `logger`
instance at the top of the module. Fixes #3711

### Description

Removes any `log` argument and argument documentation. Replaces usage of
those `log` arguments with the module-wide `logger` instance.

### Contribution Checklist

- [x] The code builds clean without any errors or warnings
- [x] The PR follows the [SK Contribution
Guidelines](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md)
and the [pre-submission formatting
script](https://github.com/microsoft/semantic-kernel/blob/main/CONTRIBUTING.md#development-scripts)
raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄

---------

Co-authored-by: Eduard van Valkenburg <[email protected]>

2023-12-16 18:41:41 +00:00

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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==0.4.3.dev0

In [ ]:

import semantic_kernel as sk
from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion, OpenAIChatCompletion

You can instantiate the kernel in a few ways, depending on your use case.

In [ ]:

# Simple instance
kernel_1 = sk.Kernel()

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, other services will be added over time.

If you need an Azure OpenAI key, go here.

In [ ]:

kernel = sk.Kernel()

kernel.add_chat_service(                                # We are adding a text service
    "Azure_curie",                                      # The alias we can use in prompt templates' config.json
    AzureChatCompletion(
        deployment_name="my-finetuned-Curie",           # Azure OpenAI *Deployment name*
        endpoint="https://contoso.openai.azure.com/",   # Azure OpenAI *Endpoint*
        api_key="...your Azure OpenAI Key..."           # Azure OpenAI *Key*
    )
)

kernel.add_chat_service(                                # We are adding a text service
    "OpenAI_chat_gpt",                                  # The alias we can use in prompt templates' config.json
    OpenAIChatCompletion(
        ai_model_id="gpt-3.5-turbo",                    # OpenAI Model Name
        api_key="...your OpenAI API Key...",            # OpenAI API key
        org_id="...your OpenAI Org ID..."               # *optional* OpenAI Organization ID
    )
)

When working with multiple services and multiple models, the first service defined is also the "default" used in these scenarios:

a prompt configuration doesn't specify which AI service to use
a prompt configuration requires a service unknown to the kernel

The default can be set and changed programmatically:

In [ ]:

kernel.set_default_text_completion_service("Azure_curie")

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

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Basic Loading of the Kernel

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