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Evan Mattson f5564c08e7 Python: Bump python project version for release. Update notebooks. (#4951 )

### Motivation and Context

Bump python project version for release. Update notebooks.

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### Description

Bump python project version for release. Update notebooks.

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### Contribution Checklist

<!-- Before submitting this PR, please make sure: -->

- [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 😄

2024-02-09 15:48:26 +00:00

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Setup

Step 1: Import Semantic Kernel SDK from pypi.org

In [ ]:

!python -m pip install semantic-kernel==0.5.1.dev0

In [ ]:

import semantic_kernel as sk

kernel = sk.Kernel()

Option 1: using OpenAI

Step 2: Add your OpenAI Key key to a .env file in the same folder (org Id only if you have multiple orgs):

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

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

In [ ]:

from semantic_kernel.connectors.ai.open_ai import OpenAIChatCompletion

api_key, org_id = sk.openai_settings_from_dot_env()

kernel.add_chat_service(
    "chat-gpt",
    OpenAIChatCompletion(ai_model_id="gpt-3.5-turbo-1106", api_key=api_key, org_id=org_id),
)

Option 2: using Azure OpenAI

Step 2: Add your Azure Open AI Service key settings to a .env file in the same folder:

AZURE_OPENAI_API_KEY="..."
AZURE_OPENAI_ENDPOINT="https://..."
AZURE_OPENAI_DEPLOYMENT_NAME="..."

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

In [ ]:

from semantic_kernel.connectors.ai.open_ai import AzureChatCompletion

deployment, api_key, endpoint = sk.azure_openai_settings_from_dot_env()

kernel.add_chat_service(
    "chat_completion",
    AzureChatCompletion(deployment_name=deployment, endpoint=endpoint, api_key=api_key),
)

Run a Semantic Function

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

In [ ]:

plugin = kernel.import_semantic_plugin_from_directory("../../samples/plugins", "FunPlugin")
joke_function = plugin["Joke"]

print(await joke_function("time travel to dinosaur age"))

3.6 KiB Raw Blame History

Setup

Option 1: using OpenAI

Option 2: using Azure OpenAI

Run a Semantic Function

3.6 KiB

Raw Blame History