### Motivation and Context
Python PR merges were failing due to integration tests. Local
integration tests were hanging on running Azure text completions. It
appears text-davinci-003 was deprecated, and needed to be replaced by
gpt-35-turbo-instruct.
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### Description
Remove references to text-davinci-003 text models as it causes tests to
hang.to hang. Updated to gpt-35-turbo-instruct, which passes. Add EastUS
AOAI deployment name var.
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### Motivation and Context
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Introducing FunctionCallContent and FunctionResultContent
Changed ChatRole to AuthorRole
Adapting ChatMessageContent to have 1 or more other contents within
(currently TextContent or one of the above)
Closes#5890
### Description
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Changed OpenAI classes, to remove OpenAIChatMessageContent and
AzureChatMessageContent
Changed OpenAI classes to create and parse FunctionCallContent and other
new things.
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Even though the completion itself does not callout, loading the model is
a HTTP call, hence moving the Hugging Face tests to Integration tests.
Plus small fix to make them run.
### Description
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### Motivation and Context
This PR enriches the HTTP requests to the Astra DB Data API (occurring
in the AstraClient) with a User-Agent header specifying the identity of
the caller (specifically, "semantic_kernel"). While not mandatory, this
is good practice with Astra DB and helps collecting aggregate usage data
on the API side.
### Description
Using the `importlib.metadata` library the package version is extracted
(in a fail-safe way) and used, if present, to enrich the caller
information (variable `ASTRA_CALLER_IDENTITY`, later set as the
user-agent header for all HTTP requests to the Data API).
### Contribution Checklist
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---------
Co-authored-by: Eduard van Valkenburg <[email protected]>
### Motivation and Context
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extend mypy typing coverage to all classes in 'functions' folder.
### Description
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### Motivation and Context
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When the root init has to load a lot of stuff, loading the whole thing
becomes slower, only one concept there Kernel, all other things must now
be loaded from a sub package.
The following is not the guidance:
- imports within SK use the full path
- init files are created as much as possible on the root+1 level (like
semantic_kernel.functions) with the pieces that common developers need,
except in connectors and utils, there a developer needs to go deeper
- within the connectors folder this is further detailed first to ai,
memory, search_engine, within ai it is further spread into the different
connectors, for instance everything for openai and azure openai can be
imported using `from semantic_kernel.connectors.ai.open_ai import ...`,
the same within memory
- imports in samples use the abbreviated path as much as possible.
### 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
A recent feature was added to dotnet to allow for a chat_history object
to be passed into invoke. This only allows the user to affect the
`chatHistoryForSteps`, but doesn't account for generating a plan with
provided context (via the chat history).
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### Description
This PR allows the user to pass in optional kernel arguments or kwargs
(that get turned into kernel arguments). If the user then overrides the
prompt template/step prompt.txt, they can utilize any new arguments that
are supplied.
- Closes#5824
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Remove the ability to run multiple functions in go in kernel.invoke.
### Description
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Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
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Small change to allow a kernel function decorator without name and
description, to be defined without brackets.
### Description
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### Motivation and Context
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### Description
* Updated models to support the new preview API version
2024-02-15-preview, with some backward compatibility with previous API
shape through the use of aliases
* Updated samples, unit tests and integration tests
* Added experimental settings management using pydantic-settings -- not
strictly required here, but I thought it was useful for testing to be
able to set all values through the .env file, and validation comes for
free using Pydantic. Looking forward to feedback on this. :)
### Contribution Checklist
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---------
Co-authored-by: Abby Hartman <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
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Noticed a small issue when ChatHistory is created with different types
of messages, and then serialized.
This fixed that and adds a test for that case.
### Description
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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
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Introduces a polymorphic approach to creating and rendering Chat Message
Contents and it's subclasses, leverages Pydantic Rootmodel for that.
Fixes#5496
### Description
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Adds ChatMessageContentBase that can create all types of CMC's, based on
a type field (str)
Redoes some of the rendering and parsing
also retooled the hierarchy structure for Contents, including
introducing a StreamingContentMixin instead of the
StreamingKernelContent (which was never used), now all content classes
(TextContent, CMC, etc) determine the fields used, while the Mixin adds
one additional field and two additional methods for streaming messages,
especially __add__
everything else runs the same way.
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### Motivation and Context
The first iteration of the function calling stepwise planner only used
OpenAI. Now allow AzureChatService to use the planner.
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### Description
This PR:
- Allows the use of AzureOpenAI for the FC Stepwise Planner
- Fixes some formatting issues when generating the names/metadata of
Plugins. .NET gets the function metadata as JSON, and the Azure Model
performs much better with it formatted like this.
- Fix some missing spaces in the constant message used in the planner --
Azure is treating the new lines in that const message incorrectly and it
causes content filtering results (reaching out to the team to understand
why this is).
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### Motivation and Context
Jinja2 has a lot of built-in global functions, filters, and test
methods. There's no need to have the overlap between those built-ins and
the custom helpers we had defined.
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### Description
This PR:
- Removes the overlap custom methods we had added so that one can use
the built-in jinja2 filters, tests, and others.
- Add some unit tests to exercise those built-ins, mainly to show how
they can be used.
- Introduces a `messages` custom helper function for handlebars and
jinja2 which pretty prints the current chat_history.
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### Motivation and Context
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added handlebars tests
### Description
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### Motivation and Context
The inputs to some Ollama APIs were wrong as we were not always passing
"model"
The /embeddings API does not take in a list of texts, it just takes 1
"prompt".
This might resolve: #5124
### Description
Correct Ollama API params for /embeddings.
Ensure that the model set on the Ollama classes is passed in API calls
even if it's not in the settings.
### Contribution Checklist
- [x] The code builds clean without any errors or warnings
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raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [x] I didn't break anyone 😄 (I don't think so, also, things were
already not working for me).
---------
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
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We got a report stating that a empty chat_history between two other
params, didn't work, so had to change some things in the way
chat_history xml is done, and subsequent improved the parsing, now using
xml fully.
### Description
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- Added to_element methods to CMC classes, to_prompt uses that.
- Redid the chat_history from_prompt method to fully utilize xml
parsing.
- Added chat_history tags to detect an empty chatHistory in a prompt.
- Made sure when only 1 message is put into the ChatHistory after
rendering that that is a USER message, instead of SYSTEM, if multiple
the first one is still SYSTEM.
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- [x] I didn't break anyone 😄
### 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
Jinja2 is a heavily supported Python templating language and we're
introducing it to SK Python.
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### Description
This PR brings in the ability to specify Jinja2 templates for use within
SK Python:
- Closes#5343
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### Motivation and Context
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Based on #5474 went into the `using-the-planner` notebook and made sure
it works with the latest code.
### Description
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Moved all the planners to use '-' in line with the current work on new
templates and planners, once that is in several extension classes can be
changed.
Encountered several small bugs left over from changes over the last
months.
Got through the whole notebook with results.
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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
After introducing auto tool calls, they mostly work well for primitive
type arguments that are handled by the LLM; however, it may be that the
user would like to include other kernel arguments to be used as part of
their custom functions, that are of type like KernelArguments.
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### Description
This PR combines the kernel arguments plus the required arguments for
tool calls. It also includes the kernel function's metadata, which has
the kernel arguments while storing the chat message after performing
tool calling.
- This closes#5454
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### Motivation and Context
To further align with the SK dotnet SDK and its functionality, the
Python SDK needs a function calling stepwise planner. Now that we have
auto tool calling available, it makes sense to introduce this planner.
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### Description
This PR:
- Introduces the function calling stepwise planner, its planner options,
and planner result. Closes#4642
- Introduces a kernel example `function_calling_stepwise_planner.py` to
exercise the planner and show how it works.
- Adds unit tests and an integration test.
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Since Python 3.12 is now released we should test with it. In the fall
3.8 will be out of support so then we should remove support for that.
Since Numpy no longer support py 3.8 as of
[1.25](https://numpy.org/doc/stable/release/1.25.0-notes.html) we pin
that for py3.8, including other packages that depend on older version of
numpy for py3.8, and allow the new versions to work against newer
version of python.
Closes#5144
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Updates the usearch package to 2.9+, and fixes the tests for that.
Closes#2676
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### Motivation and Context
In a previous fix, the error -> exception key change was missed in a
line in the kernel. This fixes that.
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### Description
This PR:
- fixes the incorrect key (error) and now pulls the exception from the
metadata for streaming invoke for the key `exception`. Closes#5385
- Adds a unit test
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### Motivation and Context
Two things: 1) Add the exception to the on function invoked args if an
exception exists in the function result. 2) Set the stepwise planner's
goal to the plan's description, not the name.
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### Description
The PR addresses:
- Properly handling exceptions to raise them from the kernel if a
function result contains an exception in the metadata. Closes#5385
- In the stepwise planner, the goal was getting set as the plan's name,
instead of the description. Closes#5316
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I noticed that there was a slight bug in the way the chat_history is
parsed from a prompt when you have a message without content (like a
tool_call), fixed that and added a test for that case.
### Description
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### Motivation and Context
This bug prevent us from using `QdrantMemoryStore` as memory.
### Description
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Co-authored-by: Evan Mattson <[email protected]>
Co-authored-by: Eduard van Valkenburg <[email protected]>
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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.
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The Hugging Face tests sometimes failed, this combines the streaming and
non-streaming tests, loading the model once, instead of twice.
### Description
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### Motivation and Context
The kernel.invoke_prompt was missing the function name and plugin name
args and was failing.
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### Description
This PR:
- Adds the required arguments. Closes#5313
- Adds unit tests.
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### Motivation and Context
Auto tool calling hasn't been implemented in Python for OpenAI models.
This is a much needed feature, which helps open the possibilities and
extends being able to use the OpenAI models for just pure back and forth
chats.
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### Description
This PR:
- introduces auto tool calls for OpenAI models. If the prompt function
is of an OpenAI chat completion type, then we pass the kernel as an
argument to the OpenAI Chat Completion Base class. The kernel is used to
auto-invoke tools/functions seamlessly as part of the chat completion
request. Closes#4553
- splits the current chat completion/chat completion stream methods into
smaller, more readable/functional methods in the OpenAI Chat Completion
Base class.
- Adds an integration test to cover auto invoke tool calls
- Updates a kernel example to be able to work for a
streaming/non-streaming tool call. Other Kernel Examples that don't use
auto invoke tools still work as they did before.
- Removes old code in the utils file that was related to the manual form
of invoking function calls.
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---------
Co-authored-by: Eduard van Valkenburg <[email protected]>
### Motivation and Context
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Added support for complex types (classes) for KernelFunctionFromMethod.
Implements #4635
### Description
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This PR adds a field to the KernelParameterMetadata class for
type_object
When that field is filled and supplied by a KernelFunctionFromMethod
decorator, it is used during invoke to cast the type.
It checks if it is a pydantic type and calls `model_validate_json` in
that case, otherwise invokes it directly as a init (like with a str),
this then also casts strs to ints for example.
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### Motivation and Context
#4433
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### Description
`$match` operation queries need a different structure than provided. As
a result users receive `OperationFailure`s when trying to run queries.
This introduces a fix to provide to correct query for filtering results
by the min_relevance_score.
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### Motivation and Context
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The existing Exceptions structure was very much inspired by dotnet, this
is now replaced with a pythonic implementations.
This means all Exceptions derive from KernelException and then
specialise for different purposes.
Closes#2194
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Added folder for all exception types, all can be imported through `from
semantic_kernel.exceptions import ...` no need for a user to know which
file the relevant one is in, but keeps things tidy for developers.
Removed old KernelException, added back subtypes for the errorcodes.
Went through everything to make sure the `raise ... from exc` pattern is
used as much as possible as that returns a better stacktrace.
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adds a test to demonstrate multiple settings for a single prompt, should
be tested for yaml based functions as well, closes#4552
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Closes#4633 and brings this part of the python sdk in sync with dotnet,
also makes for a much more readable set of classes.
### Description
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Added:
- KernelFunctionFromPrompt class
- KernelFunctionFromMethod class
- tests for both.
Changed:
- removed parts of KernelFunction that are specific to one of the two
subtypes
- removed expose attribute of KernelParameterMetadata as that is no
longer needed
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---------
Co-authored-by: Evan Mattson <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
As we move toward v1, it is necessary to remove the single kernel memory
attribute so that memory connectors can be handled via plugins. This
change also refactors the `import_plugin` method name to be similar with
dotnet as `import_plugin_from_object`, and introduces a simple
`kernel.invoke_prompt` method.
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### Description
Updates are:
- The memory connector is now passed to the `SemanticTextMemory` class,
along with the embedding generator. This allows the user to add memory
to multiple plugins/services as needed. Closes#4632
- Kernel Examples and Notebooks were updated to reflect these changes.
Now using Service Enum for all notebooks. Closes#4557
- There were planners using memory related to the kernel, to store the
functions, and many of these planners are non-existent in dotnet's v1
SDK. Python has pending work to update the planner/remove deprecated
planners before getting to v1.
- A Kernel method called `invoke_prompt` was introduced to invoke a
simple prompt, and return the `FunctionResult` when complete.
- All unit/integration tests are passing.
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### Motivation and Context
After closing the large feature branch (#5077) there were a few leftover
names that need to be updated to remove things like "FunctionView"
(replaced with KernelFunctionMetadata).
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3. What scenario does it contribute to?
4. If it fixes an open issue, please link to the issue here.
-->
### Description
Updates are:
- Rename a few classes, methods and docstrings to remove references to
FunctionView. Closes#4628
<!-- 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: -->
- [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 😄
### Motivation and Context
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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. -->
Major work to replace the context with Kernel Arguments on the front-end
and Function Results on the back.
Updated the function decorator to a new approach, in line with dotnet.
Revamps the way function are called, allowing native functions to be
completely ignorant of SK, other then the decorator.
This also moves things into the new folder structure in sync with
dotnet.
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
Adds:
- KernelArguments, a dict-like class that replaces the variables in
KernelContext. Closes#4565
- FunctionResult, a class to hold the results of function executions,
includes the function, the value and metadata, as well as two convenient
function to get the value out of it (str and get_inner_content) the
first is generic, the second specifically for KernelContent responses
(from AI services).
- AI Service Selector class, has a default, which is based on the order
in the arguments followed by the order in the functions, can be
overridden to implement your own strategy. Closes#4631
- Introduces ChatHistory and refactors the PromptTemplateConfig. Closes
#4856, #4630
- Improves parsing of templates, will now all validate during creation
and throw an error then, instead of some that do not check for validaty
until used.
- Introduces named_args block and thereby the ability to have multiple
arguments for a function call in a template. Closes#5003
Updates:
- kernel_function decorators, the parameter decorator was removed and
instead we now use Annotated to add a description of a field and we get
the type and required from the function definition.
- core plugins, use the new approach to kernel_function decorators.
- planners, template engines have all been updated to use the kernel and
kernelarguments instead of Context.
- Events have been updated, now use kernelarguments and function_result
- Tokenizers support for named_args and improvements on parsing and
checking.
- Kernel examples and notebooks to use the latest code.
- All unit and integration tests. There is more code coverage now than
before.
Removed:
- kernelContext
- kernel_function_parameter_decorator
- delegate handling code for native functions
- file_io_plugin and tests
- SemanticFunctionConfig
- ChatPromptTemplate
### 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
- [ ] I didn't break anyone 😄
---------
Co-authored-by: Evan Mattson <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
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?
When we try to create an index in AI Search where the encryption is
enabled we have this issue :
Code: OperationNotAllowed
Message: The creation of non-encrypted Indexes is not alowed when
encryption policy is enforced.
Which make the operation create_collection unusable
2. What problem does it solve?
Use create_collection function
3. What scenario does it contribute to?
If the customer uses the CMK for the encryption
4. If it fixes an open issue, please link to the issue here.
### Description
The Azure Search Documents supports the encryption key with the class
SearchResourceEncryptionKey, adding it (Free to the customer to use it
or not) resolve the issue.
Maybe it may be use it in save_information function as well ?
### 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 😄
---------
Co-authored-by: Evan Mattson <[email protected]>
Evan Mattson