mirror of https://github.com/microsoft/semantic-kernel.git synced 2026-10-12 02:19:06 +00:00

Files

T

0a477b198c Python: Update examples and notebooks to stop using text-davinci-003 (#2359 )

### Motivation and Context

text-davinci-003 is being retired by Azure OpenAI and is no longer
supported for new deployments

Resolves - https://github.com/microsoft/semantic-kernel/issues/2309

<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
information:
  1. Why is this change required?
  2. What problem does it solve?
  3. What scenario does it contribute to?
  4. If it fixes an open issue, please link to the issue here.
-->

### Description

Update examples and notebooks to use gpt-35-turbo / gpt-3.5-turbo
instead

<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->

### Contribution Checklist

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

- [ ] The code builds clean without any errors or warnings
- [ ] 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
- [ ] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄

Signed-off-by: karthikmurali60 <[email protected]>
Co-authored-by: Lee Miller <[email protected]>
Co-authored-by: Gil LaHaye <[email protected]>

2023-08-23 17:24:56 +00:00

4.6 KiB

Raw Blame History

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==0.3.1.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()

In [ ]:

# Instance with a custom logger
my_logger = sk.NullLogger()
kernel_2 = sk.Kernel(log=my_logger)

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(
        "my-finetuned-Curie",                 # Azure OpenAI *Deployment name*
        "https://contoso.openai.azure.com/",  # Azure OpenAI *Endpoint*
        "...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(
        "gpt-3.5-turbo",                      # OpenAI Model Name
        "...your OpenAI API Key...",          # OpenAI API key
        "...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.

4.6 KiB Raw Blame History

Basic Loading of the Kernel

4.6 KiB

Raw Blame History