### Motivation and Context
Consolidated agent imports initially caused issues by eagerly loading
all submodules, which forced users to install every optional dependency
-- even if only one agent was needed. This PR introduces a lazy-loading
mechanism via `__getattr__` in the top-level `__init__.py` to defer
imports until actually accessed. Additionally, a corresponding`
__init__.pyi` type stub file is provided to restore full IDE
autocompletion and type hints without compromising runtime behavior.
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### Description
Lazy load agent dependencies.
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### Motivation and Context
Bump Python version to 1.25.0 for a release.
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Bump Python version to 1.25.0 for a release.
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### Motivation and Context
Bump Python version to 1.24.1 and rc3 for a release
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### Description
Bump Python version to 1.24.1 and rc3 for a release
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Co-authored-by: Devis Lucato <[email protected]>
### Motivation and Context
Bump Python version to 1.24.0 for a release.
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Bump Python version to 1.24.0 for a release.
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Bump version to 1.23.1
fixes ENV names in call automation sample
fixes pipeline
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Version bump to 1.23.0
And rc3
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### Motivation and Context
Bump Python version to 1.22.1 for a release
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Bump Python version to 1.22.1 for a release
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### Motivation and Context
Bump Python version to 1.22.0 for a release.
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Bump Python version to 1.22.0 for a release.
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This change is required to improve the flexibility and maintainability
of our feature annotation system. Previously, separate decorators (e.g.,
`experimental_function` and `experimental_class`) were used to mark
experimental features, resulting in code duplication and limiting our
ability to handle additional feature stages. As our SDK evolves, we need
a unified approach that can support multiple stages—such as
experimental, release candidate, and future states—while also allowing
version information for release candidate features to be centrally
managed.
### Description
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This PR refactors our feature decorators by introducing a unified
`stage` decorator that updates the docstring and attaches metadata for
both functions and classes. Two convenience decorators, `experimental`
and `release_candidate`, are built on top of `stage`:
- The `experimental` decorator marks features as experimental and sets
an `is_experimental` attribute.
- The `release_candidate` decorator supports multiple usage patterns
(with or without parentheses and with an optional version parameter) to
mark features as release candidate and sets an `is_release_candidate`
attribute.
This unified approach reduces duplication, simplifies the codebase, and
lays the groundwork for easily extending feature stages in the future.
This decorator supports the following usage patterns:
- `@experimental` (for both classes and functions)
- `@release_candidate` (no parentheses)
- `@release_candidate()` (empty parentheses)
- `@release_candidate("1.21.3-rc1")` (positional version)
- `@release_candidate(version="1.21.3-rc1")` (keyword version)
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### Motivation and Context
Bump Python version to 1.21.3 for a release.
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### Description
Bump Python version to 1.21.3 for a release.
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### Motivation and Context
Bump Python version to 1.21.2 for a release.
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### Description
Bump Python version to 1.21.2 for a release.
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### Motivation and Context
Bump Python version to 1.21.1 for a release.
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Bump Python version to 1.21.1 for a release.
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### Motivation and Context
Bump Python version to 1.21.0 for a release.
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Bump Python version to 1.21.0 for a release.
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### Motivation and Context
Bump Python version to 1.20.0 for a release.
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Bump Python version to 1.20.0 for a release.
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### Motivation and Context
Bump Python version to 1.19.0 for a release.
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Bump Python version to 1.19.0 for a release.
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### Motivation and Context
Bump Python version to 1.18.2 for a release.
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Bump Python version to 1.18.2 for a release.
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### Motivation and Context
Bump Python version to 1.18.1 for a release.
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### Description
Bump Python version to 1.18.1 for a release.
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Bump Python version to 1.18.0 for a release.
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Bump Python version to 1.18.0 for a release.
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### Motivation and Context
Bump Python version to 1.17.1 for a release.
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Bump Python version to 1.17.1 for a release.
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Bump Python version to 1.17.0 for a release.
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### Motivation and Context
Bump Python version to 1.16.0 for a release.
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### Description
Bump Python version to 1.16.0 for a release. Includes feature work to
promote OpenAPI plugin to preview from experimental.
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---------
Co-authored-by: Tao Chen <[email protected]>
### Motivation and Context
Bump py version to 1.15.0 for a release.
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### Description
Bump py version to 1.15.0 for a release.
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### Motivation and Context
Bump Python version to 1.14.0 for a release.
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Bump Python version to 1.14.0 for a release.
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### Motivation and Context
Bump Python version to 1.13.0 for a release.
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Bump Python version to 1.13.0 for a release.
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### Motivation and Context
Increment Python version to 1.12.1 for a release.
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### Description
Increment Python version to 1.12.1 for a release.
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### Motivation and Context
Bump Python version to 1.12.0 for a release.
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### Motivation and Context
Bump Python version to 1.11.0 for a release.
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Bump Python version to 1.11.0 for a release.
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### Motivation and Context
Bump python version to 1.10.1 for a release.
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Bump python version to 1.10.1 for a release.
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### Motivation and Context
Bump Python version to 1.10.0 from 1.9.0 for a release.
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### Description
Bump Python version to 1.10.0 from 1.9.0 for a release.
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### Motivation and Context
Python: Bump Python version to 1.9.0 for a release.
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Python: Bump Python version to 1.9.0 for a release.
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### Motivation and Context
Bump Python version to 1.8.3 from 1.8.2 for a release.
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### Description
Bump Python version to 1.8.3 from 1.8.2 for a release.
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Next to the reference to the python file, also added a license
classifier
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Bumped the version to 1.8.1, slight updates in pyproject and make to
build automatically.
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Switches from poetry to uv
much faster and easier lock management
easier maintenance of pyproject.toml
fixes: #6584fixes: #8326
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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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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
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
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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.
### Description
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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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- [ ] 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
The Python 06 notebook had a failing cell due to a missing plugin_name.
Also the KernelFunction wasn't visible from the semantic_kernel import
due to it missing from the init file. Fixes: #4905.
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### Description
Fix the notebook by suppling a plugin_name for the memory store. Add the
KernelFunction to the sk __init__.py file. Fix a comment related to an
incorrectly named PromptExecutionSetting class.
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### Motivation and Context
Solves #4864
The KernelFunction was configured as a Python class without regards to
using Pydantic fields and its validations. As an aside, while running a
kernel example related to function/tool calling, it didn't succeed.
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### Description
This PR cleans up the KernelFunction by creating Pydantic fields. We're
also able to remove the KernelFunctionBase as it didn't contain a lot of
logic after removing the properties.
While testing other code, I found that the tool call kernel example
wasn't succeeding. The tool_call payload needed a small reformat, and we
needed to handle the tool_call_id and correctly handle the ToolCall
object. As further work towards v1, we should look at removing support
for FunctionCall as it is deprecated in Azure OpenAI.
We now see:
```
User:> What is 3+3?
Mosscap:> Ah, the timeless question of arithmetic! The sum of 3 and 3 is undoubtedly a splendid number.
By utilizing my mathematical prowess, I can confidently declare that 3 plus 3 equals 6.
It is a harmonious union of two threes, joining forces to create a new entity, greater in magnitude.
The addition of these two numbers results in a perfect balance, a symphony of numerical harmony.
```
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---------
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
We are going to add
[Astra](https://docs.datastax.com/en/home/docs/index.html) as a memory
store.
DataStax Astra DB is a serverless vector database that’s perfect for
managing mission-critical AI workloads. It’s built on Apache Cassandra®,
making Astra DB a highly scalable, reliable database technology. It’s a
powerful all-in-one data storage solution, ideal for Generative AI
projects.
### Description
- Add astra into python/semantic_kernel/connectors/memory/astradb
- Add test into
python/tests/integration/connectors/memory/test_astradb.py
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- [ ] I didn't break anyone 😄
---------
Co-authored-by: jsbilgi <[email protected]>
Co-authored-by: Obioma Anomnachi <[email protected]>
### Motivation and Context
To better align with SK dotnet v1, Python skills have been renamed to
plugins. This removes the root samples skills directory as dotnet
supports the plugins and their configs. This the beginning of more work
to consolidate other files/names, per the backlog. I've run several
greps for files or file names containing "skill" (case insensitive), and
no results appear. Do alert if you find anything else that includes
"skill."
Fixes#3319
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### Description
This is a large, breaking change PR as the replacement of "skills" to
"plugins" affects many files. It was difficult to break this up into
smaller chunks without leaving the SK Python repo in a bad state for
some time. Other updates include:
- Prompt template has been updated to use `execution_settings`, which
aligns with dotnet, from the previous `completion` that Python used.
- AzureOpenAI/OpenAI function calling has been updated to match the
current versions of tools/tool_choice. Parsing messages in the open_ai
utils respects this as it now looks for tool_calls and it configures the
function_call with the id (new), name, and argument).
- File renames, where the filename used to contain "skill" will look
like new code, but it's the same code, just with "plugin" in the name.
- Kernel examples and notebooks have been tested and are working. Unit
tests and integration tests are passing.
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- [X] I didn't break anyone 😄
---------
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
This replaces passing Logger instances as arguments around with the
preferred Python way of having each module define a module-wide `logger`
instance at the top of the module. Fixes#3711
### Description
Removes any `log` argument and argument documentation. Replaces usage of
those `log` arguments with the module-wide `logger` instance.
### Contribution Checklist
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- [x] I didn't break anyone 😄
---------
Co-authored-by: Eduard van Valkenburg <[email protected]>
### Motivation and Context
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This change adds a python connector for Azure OpenAI chat completions
with data, which was already supported in the .NET SDK.
### Description
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Add connector for Azure OpenAI chat completions with data and associated
sample code for how to use it. Some minor changes were made to the SK
function to handle the "tool" message that contains the citations and
other information returned from the chat completions extensions API.
### Contribution Checklist
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---------
Co-authored-by: Sarah Widder <[email protected]>
### Motivation and Context
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Sample notebook 06-memory-and-embeddings demonstrated how semantic
kernel lets users add memory to their kernels and also work around
ever-growing contexts that eventually exceed the maximum token limit. It
did so by using the in RAM VolatileMemoryStore and teased the
possibility of offloading this to an external vector store. With this
pull request, we want to provide guidance and repeatable steps so that
users can actually perform semantic search by leveraging an external
vector store, Azure AI Search (formerly known as Azure Cognitive Search
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
The changes consist in new cells added at the tail end of the existing
notebook. We register a new memory store, external this time and show
case how the rest of the code can be reused as-is. We add the capability
of providing access credentials to Azure AI Search in environment
variables as well.
### 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
- [x] I didn't break anyone 😄
---------
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
I have added Azure CosmosDB MongoDB vCore as a data store. MongoDB vCore
now supports vector search on embeddings, and it could be used to
seamlessly integrate your AI-based applications with your data stored in
the Azure CosmosDB. More details about Mongo vCore can be found here:
https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/vector-search.
Issue https://github.com/microsoft/semantic-kernel/issues/2375
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, by providing the following
information:
-->
1. Why is this change required? **MongoDB vCore now supports vector
search on embeddings, and it could be used to seamlessly integrate your
AI-based applications with your data stored in the Azure CosmosDB.**
2. What problem does it solve? This adds a new memory store MongoDB
vCore(Azure CosmosDB)
### Description
<!-- Describe your changes, the overall approach, and the underlying
design.
These notes will help you understand 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 😄
---------
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Mark Wallace <[email protected]>
### Motivation and Context
Resolves: #2591
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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?
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### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
* Added in the `MongoDBAtlasMemoryStore`. A memory store abstraction
that facilitates connections between a MongoDB Atlas cluster to conduct
[Atlas Vector
Search](https://www.mongodb.com/products/platform/atlas-vector-search)
on the Microsoft Semantic Kernel.
* Leverages our async python driver
[motor](https://motor.readthedocs.io/en/stable/) to keep with the
heuristic of asynchronous code
### 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 😄
### Callout
* When writing my test, I noticed there's a habit of other tests
conducting a `try` on imports. Grepping through those implementations, I
see some of them [encapsulate their respective libraries in function
calls rather than doing direct imports in the
module.](https://github.com/microsoft/semantic-kernel/blob/main/python/semantic_kernel/connectors/memory/chroma/chroma_memory_store.py#L55-L63)
Is this the general code pattern we should follow? Or is it fine to have
our imports at the top of the module fine?
---------
Co-authored-by: Steven Silvester <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
Co-authored-by: Abby Harrison <[email protected]>
