### Motivation and Context
Bump Python version to 1.3.0 from 1.2.0 for a release.
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### Description
Bump Python version to 1.3.0 from 1.2.0 for a release.
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- [X] I didn't break anyone 😄
### Motivation and Context
We have some gaps in how we build the JSON schema for function calling
related to Union and Optional types. There is also some work to do
related to handling `Optional` versus `param | None = None`.
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### Description
This PR improves the JSON schema building for the Union and Optional
types. It adds additional tests and improves some existing tests.
- Closes#7297
- Closes#7298
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- [X] I didn't break anyone 😄
### Motivation and Context
The notebooks are failing to find the .env file that is located in the
same directory. The ServiceSettings class is missing the `.create()`
method on it, to properly configure the settings from the .env file.
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### Description
Update the call to `settings = ServiceSettings.create()` so that the
service settings are created properly and reference the .env file
located in the getting_started directory.
- Fixes#7360
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- [X] I didn't break anyone 😄
### Motivation and Context
The current kernel function decorator class parses function params, but
had a miss related to how the underlying param's type is parsed if not
using Annotations.
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### Description
This PR properly handles the type_object for params that don't using
typing.Annotations.
- Adds unit tests to exercise the code.
- Adds more kernel.py unit tests for near 100% test coverage.
- Adds some planner tests to improve the test coverage.
- Closes#7300
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### Motivation and Context
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We would like to create a new sample to show case the power of the new
Model-as-a-service connector in Semantic Kernel.
### Description
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Add a new sample that shows how to use models available on Azure AI
Studio to benchmark models with open-source datasets.
### Contribution Checklist
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- [x] I didn't break anyone 😄
---------
Co-authored-by: Gil LaHaye <[email protected]>
### Motivation and Context
We've been working on increasing the test coverage. These tests aren't
specific to the CQ spike, but I noticed that there was covering lacking
for them, and they were "low hanging fruit."
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### Description
Increasing the test coverage for some classes that have current test
coverage but were missing some line coverage here and there. At this
point in time, the test coverage report is showing 95% covered now.
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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 😄
---------
Co-authored-by: Gil LaHaye <[email protected]>
### Motivation and Context
Following in the steps of dotnet, we're beginning to add agent support
to Python. For an initial implementation the focus is on non-grouped,
chat completion agents. The first pass is covering a ChatCompletionAgent
that allows the user to define a ChatCompletion Service as part of the
SK Kernel, and then reference that service in an agent implementation.
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### Description
The PR:
- Adds support for a standard chat completion agent
- Adds the required abstractions to allow the single chat completion
agent to work
- Introduces a new agent concept sample showing how to invoke the agent
for a streaming and non-streaming context.
- Adds unit tests to cover the new code
- Closes#7079
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- [X] All unit tests pass, and I have added new tests where possible
- [X] I didn't break anyone 😄
### Motivation and Context
Bump Python project version to 1.2.0 for a release.
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### Description
Bump Python project version to 1.2.0 for a release.
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- [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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Related to https://github.com/microsoft/semantic-kernel/issues/6498
The use of local models presents a twofold benefit for developers:
increased flexibility and reduced costs. Ollama and LM Studio are two
well-known platforms that facilitate the hosting of models locally, both
of which offer compatibility with OpenAI endpoints. As such, it is
imperative that our OpenAI connector functions correctly when users are
operating models on these platforms.
### Description
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1. Verify that our OpenAI connector works as expected with models hosted
locally using Ollama and LM Studio.
2. Create three new samples (Ollama/chat, LM Studio/chat, LM
Studio/Embedding) under `/concepts/local_models` to show how to using
local models with the OpenAI connector.
3. Fix a bug in `test_sample_utils.py` where if a test case is retried
and input was never reset.
### Contribution Checklist
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- [ ] I didn't break anyone 😄
### Motivation and Context
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1. Why is this changed required ?
To enable Mistral Models with Semantic Kernel, there was the issue #6499
in the Backlog to add a MistralAI Connector
2. What problem does it solve ?
It solves the problem, that semantic kernel is not yet integrated with
MistralAI
3. What scenario does it contribute to?
The scenario is to use different connector than HF,OpenAI or
AzureOpenAI. When User's want to use Mistral they can easliy integrate
it now
4. If it fixes an open issue, please link to the issue here.
#6499
### Description
The changes made are designed by the open_ai connector, i tried to stay
as close as possible to the structure.
For the integration i installed the mistral python package in the
repository.
I added the following Classes :
- MistrealAIChatPromptExcecutionSettings --> Responsible to administrate
the Prompt Execution against MistralAI
- MistralAIChatCompletion --> Responsible to coordinate the Classes and
for Content parsing
- MistralAISettings --> Basic Settings to work with the MistralAIClient
**To test the changes with the tests please add MISTRALAI_API_KEY and
MISTRALAI_CHAT_MODEL_ID as Enviorment Variable**
From a design decision i moved the processing of Functions from
Connectors to the ChatCompletionClientBaseClass
What is integrated yet :
- [X] Integrate Mistral AI ChatCompletion Models without Streaming
- [X] Integrate Mistral AI Chat Completion Models with Streaming
- [X] Simple Integration Test to test Streaming and non Streaming
- [x] Extended Testing including Unit Testing & More Integration Tests
### Contribution Checklist
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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 😄
---------
Co-authored-by: Nico Möller <[email protected]>
### Motivation and Context
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Since Google Palm will be deprecated soon, removing Google Palm from SK.
Closes#6362
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raises no violations
- [x] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
### Motivation and Context
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This introduces a sample that uses function calling with a local model,
Nexus Raven V2, in this case using the API, but the same model can be
loaded using the Hugging Face Connection, the class here is highly
adapted and specific to Nexus including parsing the returned function
call value, into FunctionCallContent and then running that.
It then returns one or more ChatMessageContents with the function call
content and function result content which can then be shared with a
OpenAI call.
Error handling improvements:
*
[`python/semantic_kernel/contents/chat_history.py`](diffhunk://#diff-b57a30f97fec1a91bbf6f7d15584473a8f0cf409797bdcd90d8c80bedc670892L266-R267):
Improved error logging when parsing prompts as XML fails.
*
[`python/semantic_kernel/contents/function_result_content.py`](diffhunk://#diff-3b69e6aedf4c29038cdff0ac99602b70583ab6c85f7bd777b08ad96b7fa48d87L7-R10):
Removed field validator for `result` and allow result to be any value,
instead of casting everything to string.
[[1]](diffhunk://#diff-3b69e6aedf4c29038cdff0ac99602b70583ab6c85f7bd777b08ad96b7fa48d87L7-R10)
[[2]](diffhunk://#diff-3b69e6aedf4c29038cdff0ac99602b70583ab6c85f7bd777b08ad96b7fa48d87L50-R51)
[[3]](diffhunk://#diff-3b69e6aedf4c29038cdff0ac99602b70583ab6c85f7bd777b08ad96b7fa48d87L63-L69)
[[4]](diffhunk://#diff-3b69e6aedf4c29038cdff0ac99602b70583ab6c85f7bd777b08ad96b7fa48d87R92-R115)
Minor changes:
*
[`python/semantic_kernel/core_plugins/math_plugin.py`](diffhunk://#diff-632444474bd3ea991b4409dfd0ab194e0789c73617bef4a3dcae4db5c4b0b6c0L32-R38):
Removed some docstrings for clarity.
[[1]](diffhunk://#diff-632444474bd3ea991b4409dfd0ab194e0789c73617bef4a3dcae4db5c4b0b6c0L32-R38)
[[2]](diffhunk://#diff-632444474bd3ea991b4409dfd0ab194e0789c73617bef4a3dcae4db5c4b0b6c0L55-R47)
*
[`python/semantic_kernel/functions/kernel_function_from_prompt.py`](diffhunk://#diff-eabd53da2ec241cce6f92de85283065e7b478d62cefb52817c5b29ff07e28189L173-R174):
Changed the order of condition checks for better readability.
*
[`python/semantic_kernel/kernel.py`](diffhunk://#diff-3db6fe98818eda0b824788901c718d35eac8b13d0ff428d3dfd417866d922d3aR9-R10):
Added functionality to update kernel arguments in `invoke_stream` method
and made `function_name` and `plugin_name` optional in `invoke_prompt`
method.
[[1]](diffhunk://#diff-3db6fe98818eda0b824788901c718d35eac8b13d0ff428d3dfd417866d922d3aR9-R10)
[[2]](diffhunk://#diff-3db6fe98818eda0b824788901c718d35eac8b13d0ff428d3dfd417866d922d3aR29)
[[3]](diffhunk://#diff-3db6fe98818eda0b824788901c718d35eac8b13d0ff428d3dfd417866d922d3aR119-R120)
[[4]](diffhunk://#diff-3db6fe98818eda0b824788901c718d35eac8b13d0ff428d3dfd417866d922d3aL195-R202)
[[5]](diffhunk://#diff-3db6fe98818eda0b824788901c718d35eac8b13d0ff428d3dfd417866d922d3aL209-R216)
Closes#6804Closes#6944
### Description
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### Contribution Checklist
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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 😄
### Motivation and Context
The current `FunctionCallBehavior` has allowed us to utilize auto
function calling for OpenAI type models. As we proceed to support more
AI connectors, that differ from OpenAI models, we need to be able to
handle functions for all models that support function calling.
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### Description
In this PR:
- We introduce a new function calling abstraction called
`FunctionChoiceBehavior` which has three types: Auto, Required, and
None.
- One is able to configure the `function_choice_behavior` along with
fully qualified function names (e.g., plugin1.function1),
`maximum_auto_invoke_attempts` or `auto_invoke_kernel_functions` in yaml
and JSON prompts. We have a new concept example showing how to do this
for for yaml and JSON prompts.
- If the fully qualified names are specified in the config file, they
take precedence over the filters, if specified at a later point.
- To make sure this isn't a breaking change, we still handle the
previous `FunctionCallBehavior`, but so we can make decisions on the new
`FunctionChoiceBehavior` we map the `FunctionCallBehavior` to
`FunctionChoiceBehavior`. Each time `FunctionCallBehavior` is updated,
`FunctionChoiceBehavior` will be updated, too.
- The `_process_tool_call()` method in the
`open_ai_chat_completion_base` needs to maintain the argument name as
`function_call_behavior` as we cannot introduce a breaking change.
- All concept samples have been converted to use
`FunctionChoiceBehavior`.
- New unit tests have been added for `FunctionChoiceBehavior` and we
still currently support `FunctionCallBehavior` tests to make sure we
haven't broken the backwards compatibility.
- Added `deprecated` typing decorators to classes/methods to alert users
that it would be best to transition to `FunctionChoiceBehavior` even
though `FunctionCallBehavior` is still supported.
- The `FunctionCallingStepwisePlanner` was updated to use the new
`FunctionChoiceBehavior`.
- Closes#6496, #6458
- Addresses #6626
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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 😄
### Motivation and Context
Bump Python version to 1.1.2 for a release.
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### Description
Bump Python version to 1.1.2 for a release.
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- [X] I didn't break anyone 😄
### Motivation and Context
Fix the multiple results when streaming.
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### Description
Don't use enumerate and take a choice_index which is already provided in
the result.
Expected)
Result 1: Blah Blah
Result 2: Blah Blah
Result 3: Blah Blah
Actual)
Result 1: BlahBlahBlah BlahBlahBlah
Result 2:
Result 3:
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- [ ] All unit tests pass, and I have added new tests where possible
- [ ] I didn't break anyone 😄
---------
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
We'd like to improve the handling of the .env file related to Pydantic
Settings. If Environment variables aren't found, it's possible to fall
back on reading the values from a .env file.
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### Description
Previous to this PR, that is done by specifying the .env file path. Now,
if the env_file is not specified, we will default to reading from the
relative ".env" file in the cwd.
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- [X] I didn't break anyone 😄
### Motivation and Context
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- [ ] I didn't break anyone 😄
### Motivation and Context
Bump Python version to 1.1.1 for a release.
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### Description
Bump Python version to 1.1.1 for a release.
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### Motivation and Context
We've received feedback that the Python notebooks may difficult to run
for folks as they get started.
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### Description
To be explicit, we're now requiring that the Python notebooks reference
a valid `.env` file with the required settings.
All notebooks are running with the valid `.env` file for both AzureOpen
and OpenAI config settings.
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Clarify the names and uses of the environment variables and pydantic
settings.
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### Motivation and Context
Bump Python version to 1.1.0 for a release.
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### Description
Bump Python version to 1.1.0 for a release.
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### Motivation and Context
Fix sample plugin path for Windows. Re-enable samples CI tests.
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### Description
Fix sample plugin path for Windows. Re-enable samples CI tests.
- Tests passing locally on both MacOS and Windows.
- Adds logic to each notebook to properly set up the correct path to
find imports. Notebooks should now run out of the box to close#6588
- Added retry logic for the samples tests to retry 3 times in the event
of a failure.
- Install notebook pip package at kernel level instead of shell level
- Updated all occurrences of `gpt-3.5-turbo-1106` to `gpt-3.5-turbo`
which is `gpt-3.5-turbo-0125`.
The GH Action code quality check was failing for this same code in
#6675, so this is superseding it. Now that the GH actions are fixed,
this also supersedes #6681.
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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
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### Motivation and Context
We had many broken concept examples. These samples are also not covered
by tests so it's hard to know when things are out of sync.
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### Description
Fix the Python concept samples, and also cover them with tests so we
aren't checking in code that is going to break them.
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Went through the Python getting-started notebooks and found some issues.
Updating the notebooks to address the issues.
### Description
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1. `00-getting-started.ipynb`: (improvement) clear all the services
before registering new ones. This will allow users to rerun the just the
cells without it throwing "Service already exists".
2. `01-basic-loading-the-kernel.ipynb`: (improvement) clear all the
services before registering new ones. This will allow users to rerun the
just the cells without it throwing "Service already exists".
3. `10-multiple-results-per-prompt.ipynb`: (fix) the hugging face
service is not set up to return multiple responses.
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### Motivation and Context
Bump Python version to 1.0.5 for a release.
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### Description
Bump Python version to 1.0.5 for a release.
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- [X] I didn't break anyone 😄
### Motivation and Context
If auto_invoke was False and the function used streaming, the max func
attempts were being set to 0 which Python's range function doesn't like.
The chat gpt auto function calling example was also broken due to wrong
checks on the returned result for FunctionCallResult objects.
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### Description
Fixing handling streaming tool calls when auto invoke is false and
fixing the chat gpt auto function calling concept example.
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### Motivation and Context
Several of the imports in the learn files were showing as unable to
resolve after the service configuration was moved to a different
directory.
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### Description
Creating the `__init__.py` in the directory where the importable files
live. Adjusting code to reflect this. Imports now resolve.
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There's an enum called `AuthorRole`. I replaced string literals with the
enum values. PyCharm highlights this in some cases: Expected type
'AuthorRole', got 'str' instead
I also fixed a bug in _function_invocation_filters_stream.py_ where the
argument name was wrong. `author="assistant"` instead of
`role="assistant"`
### Description
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- [x] I didn't break anyone 😄
---------
Co-authored-by: Evan Mattson <[email protected]>
Co-authored-by: Eduard van Valkenburg <[email protected]>
### Motivation and Context
Bump Python version to 1.0.4 for a release.
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### Description
Bump Python version to 1.0.4 for a release.
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Small update to test coverage workflow, to retrigger based on workflow
run.
Small updates to samples notebooks, to use GLOBAL_LLM_SERVICE allowing
control of where they run through env.
### Description
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### Motivation and Context
The learn resource planner sample was not always passing as the plan
created was non-deterministic.
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### Description
Update the planner sample to run properly by introducing the Function
Calling Stepwise Planner. Also introduce a small Pydantic Settings class
to remove the dependency on the dotenv library.
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### Motivation and Context
A function calling plugin example is failing due to an incorrect chat
completion base method. Additionally, if one specifies to enable
function calling with auto invoke set to False, and they don't provide a
kernel, then there is no error thrown.
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### Description
This PR:
- Fixes the concept example that shows three different ways to handle
function calling.
- Fixes#6453
- Adjusts the error checks for the kernel and arguments in the
open_ai_chat_completion_base: manual function calling doesn't require
arguments, so don't throw if they aren't provided; however, it does
require a kernel, which is used to configure the tools for the settings,
so this check has to be by itself.
- Add a unit test to exercise slightly different behavior.
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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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The initial implementation of Pydantic Settings was not optimal yet,
more optimized now.
### Description
Added a KernelBaseSettings class in kernel_pydantic
Rebased all settings off that
Added single create method to KernelBaseSettings:
- sets model_config.env_prefix to cls.end_prefix allowing us to set the
env_prefix as a classvar on each subclass and it works
- if env_file_path is set, set that in model_config
- strip all None values out of inputted values, then instantiate
This also allows us to pass the settings that are supplied to the
settings class, for overriding env settings.
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Run notebooks as tests, working for all gettings started notebooks.
Also updated learn resources samples test, this can now easily be
extended with other sample scripts.
Closes: #4637
Partially closes: #4638
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### Motivation and Context
Bump Python version to 1.0.3 for a release.
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### Description
Bump Python version to 1.0.3 for a release.
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Updated samples for MS Learn, with tags.
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---------
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
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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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### Motivation and Context
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Fix typo
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Fix typo
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Co-authored-by: Eduard van Valkenburg <[email protected]>
### Motivation and Context
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Refactoring some of the code following [this
advice](https://github.com/microsoft/semantic-kernel/pull/6371#discussion_r1611138252)
from the maintainers.
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Look up kernel functions and plugins with the `get_function` and
`get_plugin` method. Removed the unused `DEFAULT_CHAT_SYSTEM_PROMPT`
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---------
Co-authored-by: Evan Mattson <[email protected]>
### Motivation and Context
Bump Python version to 1.0.2 for a release
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### Description
Bump Python version to 1.0.2 for a release
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### Motivation and Context
Bump Python version to 1.0.1 for a release.
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### Description
Bump Python version to 1.0.1 for a release.
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### Motivation and Context
As we move towards v1 and beyond it's essential that we have a way to
make sure we are staying in line with the other SK SDKs. Previous to
this we didn't have a way to capture request bodies and payloads and
make sure they confirm to the proper SK standards.
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### Description
This PR introduces a number of integration tests that exercise various
aspects of the SK SDK like prompts, prompt templates, functions, and the
kernel.
*TODO*: update the OpenAPI tests with a more JSON specific response that
we can check against.
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### Motivation and Context
The FC stepwise planner was breaking when trying to process the function
call result because if a filter is not configured, then the context will
be None. We need to only try to extract the FC result if the context is
not none, otherwise it will already be present in the chat history.
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### Description
Check that the FC result context is not None, if so, then extract the FC
result, otherwise proceed as the FC result is in the chat history. Fixes
#6350.
- Removes some extranous logging from json schema addition.
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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.
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### Motivation and Context
Bump Python version to v1.0.0
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### Description
Bump Python version to v1.0.0
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### Motivation and Context
The Python code base could handle some primitive types for the schema
for tool call objects and kernel parameter metadata. However, it
couldn't properly handle the more complex JSON schemas for tool call
objects.
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### Description
This PR introduces:
- JSON schema handling for KernelParameterMetadata and tool call
objects.
- Updates to the tool call utils method to properly recurse on the
KernelParameterMetadata's stucture.
- Adds unit tests for this code.
- Add experimental_class/experimental_functions to various parts of the
code base like Memory Connectors and OpenAPI plugin.
- Fixes#6310
- Fixes#6280
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### Motivation and Context
Python: Bump version to 1.0.0rc1.
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### Description
Python: Bump version to 1.0.0rc1.
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Evan Mattson