### Motivation and Context
This is the first installment of a series of PRs related to adding
single agent declarative spec support.
SK .NET has had support for single agent declarative specs for a bit
now, and Python needs the functionality as well. This PR adds the core
abstractions and functionality to be able to define agents via a
declarative yaml spec.
Declarative specs require the use of a kernel, and if in the case of
function calling, require that any plugins defined in the spec already
be present in the kernel.
For naming, there are placeholder names that are camel case. This needs
to be the case so it can match .NET.
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### Description
Provide declarative spec support for ChatCompletionAgent and
AzureAIAgent
- Adds many samples to show the use of declarative specs to handle
various AzureAIAgent + ChatCompletionAgent tools/plugins.
- Closes#11978
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### Motivation and Context
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Adds MCP server configs and a function that turns the server into a
plugin, with each tools of the server represented as a function.
Adds a sample showing how to use that with a Github MCP server.
Closes: #10785 and #11190
With special thanks to @nmoeller
### Description
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---------
Co-authored-by: Nico Möller <[email protected]>
### Motivation and Context
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Addresses: https://github.com/microsoft/semantic-kernel/issues/7434
### Description
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Add audio to text to SK Python.
### Contribution Checklist
1. Add audio to text to SK Python.
2. Add text to image integration tests.
3. Refactor Azure AI connector AD auth.
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### Motivation and Context
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Introduces a ImageContent class
Allows that type to be part of ChatMessageContent
Adds a sample for both local and online images
Adds many tests with data uri edge cases, and binary parsing edge cases.
Closes#6536
### Description
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### Motivation and Context
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Added a section on documentation to the DEV_SETUP
Added a check for the copyright row to pre-commit
Closes#5629
### Description
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- Added static code checking to pre-commit (check-ast and
nbqa-check-ast)
- Added Bandit security checking to pre-commit
- Added pre-commit step to python-lint workflow, if it works, can delete
seperate mypy, ruff and black tests
### Description
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### Motivation and Context
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Now that we only support 310 and up, we could unify all files, to use
the new style typing and annotations.
### Description
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### Motivation and Context
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The loading of plugins and functions into Kernel was not fully
consistent and clear, this is now simplified.
Adding a plugin can now be done in two ways:
- multiple: `kernel.add_plugins[plugin1, plugin2]`
- single plugin: `kernel.add_plugin(plugin)`
- if the plugin here is a class, with methods that have the
kernel_function decorator then that is parsed into a plugin
Adding a function can now be done in three ways:
- multiple: `kernel.add_functions([func1, func2]` or
`kernel.add_function({'func1': func1})`
- single: `kernel.add_function('plugin_name', func1)`
- from a prompt: `kernel.add_function(function_name='func1',
prompt='test prompt', ...)`
In other words, all the different methods that were available have been
simplified, these methods also return the created plugin (or updated
plugin for the `add_functions` method. The `add_function` (singular)
method has a parameter `return_plugin` to control whether you get the
created or updated plugin, instead of the created function.
**An important callout:**
One big internal change that this introduces is that a function that
gets added to a plugin (new or existing) is automatically copied, the
metadata is deep-copied and the plugin_name in the metadata is updated,
so this sequence is valid now:
```python
@kernel_function(name='test')
def f(...):
etc
func = KernelFunctionFromMethod(method=f)
func2 = kernel.add_function('plugin', func)
assert func != func2
```
Also closes: #5855
### Description
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Removed KernelPluginCollection
Updated KernelPlugin
added from_... method to load from different sources
Updated KernelFunctionFromPrompt
added from_yaml and from_directory methods.
Added and updated tests to 99% coverage of KernelPlugin and
KernelFunctionFromPrompt
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### Motivation and Context
Python had the ability to handle operations for OpenAPI plugins, but at
a basic level. It did not have the ability to handle OpenAI plugins, nor
did it allow the user to configure an authentication callback. This PR
brings in that functionality.
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### Description
This PR:
- Allows a user to handle OpenAI plugins with/without auth. Closes#5355
- Improves the functionality around handling OpenAPI specs.
- Introduces an auth callback mechanism to allow for REST API calls to
use auth
- Introduces OpenAI/OpenAPI function execution settings.
- Provides two new kernel examples that show how to handle a simple
OpenAI plugin (Klarna) as well as a more complex plugin (Azure Key
Vault) that requires auth.
- Updates unit tests.
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### Motivation and Context
SK Python doesn't have a way to create a prompt template config from a
yaml file. This is neeed to further achieve parity with dotnet and java.
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### Description
This PR introduces:
- some kernel methods to load yaml prompts from a directory as well as
create a kernel function from a string that contains yaml. Closes#4640
- Unit tests and a syntax example showing how to create a yaml prompt
and load it
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### Motivation and Context
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Introducing Handlebars templating to the Python Semantic Kernel!
Closes: #5446
Prework for: #4641
### Description
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Adds HandlebarsPromptTemplate as a subclass of PromptTemplateBase
Adds second literal to the PromptTemplateConfig definition
Adds helpers for Handlebars (system_helpers) and
`create_helper_from_function` function
Moves fully_qualified_name into KernelFunctionMetadata instead of in
different extensions.
Defaults are still 'semantic-kernel' templates, and without making
explicit changes that still is the norm.
Adds a azure_chat_gpt_handlebars.py sample
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### Motivation and Context
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Adding a whole bunch of tests to better cover Kernel.py.
Also reviewed and updated the conftest and tweaked the scope, now runs
faster!
Skipped efforts for handlers since work is being done there.
### Description
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Evan Mattson