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Up until this point, we had not had full test coverage for all openai
classes, and mypy type checking was disabled.
These have now both been solved, lots of small fixes for the typing and
a set of new tests for the different parts of the openai suite of
classes have been added.
fixes#7131fixes#6930
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The unit tests for OpenAI all now have multiple ways of creating checked
and tested.
Also actual responses from the openai package are created and used to
test the parsing of those into our classes.
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Related to: https://github.com/microsoft/semantic-kernel/pull/6693
Azure Model-as-a-Service allows users to deploy certain models from the
Azure AI Studio model catalog as an API. This option also provides
pay-as-you-go access to the models hosted. Below are some of the models
that are supported:
- Microsoft Phi-3 family
- Meta Llama family (Llama 2 chat & Llama 3 instruct)
- Mistral-Small & Mistral-Large
- and more
We'd like to provide an AI connector for users of SK to use Azure
Model-as-a-Service.
### Description
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A new AI connector named `azure_ai_inference` is added to support Azure
Model-as-a-Service. This connector takes a new dependency on the Python
`azure.ai.inference` SDK.
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---------
Co-authored-by: Evan Mattson <[email protected]>
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extend mypy coverage for prompt_templates, template_engine and some
other smaller folders.
still todo: connectors, memory and planner folders.
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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
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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
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### Description
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Fixed a bunch of typos.
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Co-authored-by: Evan Mattson <[email protected]>
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Working through improving test coverage
Small changes introduced:
- ai_service_selector: select_ai_service method, now has extra optional
variable for type_, when not set the behavior is the same as before,
otherwise allows any type of AI service present to be selected,
including embeddings (which was not possible)
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---------
Co-authored-by: Evan Mattson <[email protected]>
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In order to keep better track of related pieces within the Kernel,
including easier unit testing, this PR splits the kernel into pieces
that are self-contained, but abstract.
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New:
- KernelServicesExtension
- KernelFunctionExtension
- KernelReliabilityExtension
Changed:
- Kernel, now imports the new ones.
- KernelFiltersExtension, moved to filters folder, in line with the
rest.
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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.
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### Motivation and Context
The `ai_service_selector` was not properly grabbing a registered service
if it didn't match the first registered `service_id`. It's possible that
there may be multiple prompt execution settings registered as part of a
kernel function; however, only one Chat/Text completion service could be
registered, and it may not be the first service id present in the
dictionary. If it isn't, we shouldn't be throwing a service not found
exception.
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### Description
This PR fixes the behavior, and allows us to try to find other
registered services based on the present service IDs.
- Closes#5977
- Adds a unit test to cover this behavior.
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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.
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I noticed that when running a piece without any execution settings, it
errored out, while it should use 'default' service with the default
settings for that service, that is now fixed.
Small updates to the rag example.
Added TODO: to replace the embedding_generator service in
SemanticTextMemory with Kernel and the same service_selector pattern as
for KernelFunctions.
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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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Closes#4633 and brings this part of the python sdk in sync with dotnet,
also makes for a much more readable set of classes.
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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]>
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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.
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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
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---------
Co-authored-by: Evan Mattson <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>
Co-authored-by: Evan Mattson <[email protected]>