mirror of
https://github.com/microsoft/semantic-kernel.git
synced 2026-10-12 02:19:06 +00:00
copilot/bump-messagepack-version
72
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 Tao Chen
|
b2cceb5985 |
.Net: Update OTel GenAI operation names (#13334)
### 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? 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. --> OTel GenAI operation names have the following updates: 1. `chat.completion` to `chat` 2. `text.completion` to `text_completion` ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> This PR updates those operation names ### 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 😄 |
||
|
Tao Chen
|
d455a20033 |
.Net & Python: Add tool definitions to agent invoke span (#13153)
### 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? 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. --> The current GenAI Semantic Conventions have changed since the last time we modified the `agent_invoke` span. Previous PRs: #12940, #12928, #12834 ### Description The following changes were made: 1. `gen_ai.agent.invocation_input` -> `gen_ai.input.messages` 2. `gen_ai.agent.invocation_output` -> `gen_ai.output.messages` 3. Add `gen_ai.tool.definitions` .Net: <img width="2056" height="459" alt="image" src="https://github.com/user-attachments/assets/533b8e08-7674-4c70-bdbe-ba3600855b20" /> Python: <img width="1323" height="659" alt="image" src="https://github.com/user-attachments/assets/702171ac-dc0b-4187-933d-dad77f2719df" /> <!-- 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 - [ ] I didn't break anyone 😄 |
||
|
Ajeet VermaandTao Chen
|
ea564cf04f |
Python: docs(typing): add return type and docstring to store_results in utils/chat.py (#12910)
## Summary - Added explicit return type `-> ChatHistory` to `store_results`. - Added detailed docstring explaining parameters, return type, and behavior. ## Why - Improves static analysis, IDE support, and code readability. - Makes the function self-documenting for new contributors. ## Testing - No changes to runtime behavior. - Existing test suite passes locally. ### 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? 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. --> ### Description <!-- 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 😄 --------- Co-authored-by: Tao Chen <[email protected]> |
||
|
Dmytro Struk
|
2a47ba9b7d |
Python: Removed usage of DefaultAzureCredential (#12964)
### 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? 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. --> The usage of `DefaultAzureCredential` by default is unsafe and raises security issues. Instead, users should provide their own credential types such as `AzureCliCredential`, `ManagedIdentityCredential` and so on. More information here: https://learn.microsoft.com/en-us/python/api/azure-identity/azure.identity.aio?view=azure-python In this PR: 1. Removed the usage of `DefaultAzureCredential` from authentication logic. 2. Replaced `DefaultAzureCredential` with `AzureCliCredential` in all examples. **Note**: this is breaking change for Azure users, who relies on `DefaultAzureCredential`. Users will need to update the code and pass the credential type of their choice by using `credential` parameter when initializing one of the Azure clients. Before: ```python chat_completion = AzureChatCompletion() ``` After: ```python chat_completion = AzureChatCompletion(credential=AzureCliCredential()) ``` ### 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 😄 |
||
|
Tao Chen
|
6416c58b90 |
Python: Add arguments and results attributes to execute tool span (#12940)
### 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? 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. --> We are adding two new attributes to the execute tool span according to this proposal to Otel Semantic Conventions: https://github.com/open-telemetry/semantic-conventions/pull/2528 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> The following two attributes are added to the `execute_tool` span: 1. `gen_ai.tool.call.arguments` 2. `gen_ai.tool.call.result` ### 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 😄 |
||
|
Tao ChenandEvan Mattson
|
b7bb2832aa |
Python: Input & output attributes in invoke_agent span (#12834)
### 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? 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. --> The OTel workgroup has been thinking about enhancing tracing for agents. This is a proposal from our tracing team: https://github.com/open-telemetry/semantic-conventions/pull/2528. This PR implements part of the proposal so that when the proposal gets merged, we can quickly follow. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Two new attributes are added to the `invoke_agent` span, namely the `gen_ai.agent.invocation_input` and `gen_ai.agent.invocation_output`. <img width="747" height="583" alt="image" src="https://github.com/user-attachments/assets/d8b81f68-915b-48ee-8f86-66a0d4f686cc" /> ### 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]> |
||
|
Tao Chen
|
c0605cc4e7 |
Python: Update kernel function span (#12285)
### 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? 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. --> Closing #12262 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> This PR updates tracing support for function execution across both .NET and Python according to the latest OTel GenAI semantic conventions: https://opentelemetry.io/docs/specs/semconv/gen-ai/gen-ai-spans/#execute-tool-span ### 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 😄 |
||
|
Evan Mattson
|
ac5c37e442 |
Python: Allow hyphens in function param metadata which are sometimes present with MCP plugins (#12267)
### Motivation and Context We recently relaxed the regex for `FUNCTION_NAME_REGEX` and `FULLY_QUALIFIED_FUNCTION_NAME` to allow hyphens, which are sometimes present while using MCP. However, we did not include a relaxed Regex for `FUNCTION_PARAM_NAME_REGEX`, which was still causing errors while using MCP within SK. This PR relaxes the regex for `FUNCTION_PARAM_NAME_REGEX` so that there are no errors while using MCP. <!-- 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? 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. --> ### Description Relax the `FUNCTION_PARAM_NAME_REGEX` which won't throw validation errors while using MCP. - Fully closes #11758 <!-- 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 😄 |
||
|
Eduard van Valkenburg
|
6115e8d7dc |
Python: fixes and updates for MCP (#12039)
### 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? 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. --> Removes the two surpressed calls for extra info out of a MCP server, since they seemed to hang every now and then. Updated the package version spec Added one of the samples as a integration test Added: - load_tools flag on the constructor, default = true - load_prompt flag on the constructor, default = true - request_timeout param, optional, default None ### Description <!-- 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 😄 |
||
|
Eduard van Valkenburg
|
12851923bc |
Python: allow settings to be created directly (#11468)
### 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? 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. --> I've always hated having to add the .create to a settings object, this removes that, you can just use the regular init, added benefit is that it has the proper fields in the docstrings for each implemented settings object! ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Adds a __new__ method to the base settings class, which takes the prefix and if supplied the env_file and encoding, then calls model_rebuild and then the init. ### 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 😄 |
||
|
|
17e63a782b |
Python/.NET: Add a common agent invoke api. (#11116)
### Motivation and Context Being able to different invoke agents using a common API is useful to build higher level code on top of. ### Description Adding a common Invoke method with a common Thread object. ### 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: Ben Thomas <[email protected]> Co-authored-by: Evan Mattson <[email protected]> Co-authored-by: Tao Chen <[email protected]> Co-authored-by: Ben Thomas <[email protected]> Co-authored-by: Mark Wallace <[email protected]> Co-authored-by: Evan Mattson <[email protected]> |
||
|
Tao Chen
|
75453a1219 |
Python: Move to a thinner wrap for Bedrock Agent in Python (#10740)
### 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? 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. --> Though the current Bedrock agent implementation uses a "thin wrap" approach, it still has some abstractions that we don't want to maintain. Just like what we have done for the other agents, we remove those abstractions. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Update the Bedrock agent abstraction to be thinner. 2. Update samples and tests. Note: The .Net implementation doesn't need to be updated as it was already using a thinner wrap. Note: This breaks the current usage pattern for Bedrock agents in Python. ### 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 😄 |
||
|
Tao ChenandEvan Mattson
|
15ca7a7192 |
Python: Agent get_response API (#10701)
### 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? 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. --> With the recent push for improving developer's quality of life, we have decided to make it easier for developers to get started with agents by introducing a new `get_response` API, that hides the complexity of interacting with agents when developers are ramping up with the Agent concept. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Add the `get_response` API on all agent types. This API skips hides the intermediate steps of the agent execution and only returns the final results as a single message. 2. Add tests and samples. 3. Updates the `Agent` class to be an `ABC`, and adds `get_response`, `invoke`, and `invoke_stream` methods as overridable. ### 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
|
caef2f32a6 |
Python: improve feature decorator return type so it doesn't affect Pylance (#10704)
### Motivation and Context The new feature decorator was impacting Pylance and child classes weren't properly resolving methods due to the decorator's return type handling. <!-- 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? 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. --> ### Description Update the decorator so it handles the return types properly. <!-- 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 😄 |
||
|
Evan Mattson
|
174bdabf38 |
Python: Introduce feature decorator to allow for experimental and release candidate decorator usage (#10691)
### 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?
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.
-->
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
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
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)
### 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 😄
|
||
|
Eduard van ValkenburgandEvan Mattson
|
374d8f91ce |
Python: filter improvements (#10588)
### 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? 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. --> Fixes some inconsistency in the filter setup between dotnet and python Adds a sample for how to use a filter to retry a prompt with a different model. Adds a sample for semantic caching Closes: #10572 #5924 #10595 ### Description <!-- 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 😄 --------- Co-authored-by: Evan Mattson <[email protected]> |
||
|
Eduard van Valkenburg
|
7bdf55028c |
Python: experimental py3.13 support (#10276)
### 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? 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. --> Some small fixes in uv setup within actions Added experimental unit tests runs for py3.13 First typing improvement from pyright and pyright config. ### Description <!-- 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 😄 |
||
|
Tao Chen
|
0a8c4d8afa |
Python: Update agent tracing span name and attributes (#10398)
### 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? 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. --> Python parity with this PR: https://github.com/microsoft/semantic-kernel/pull/10387 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> Update agent invoke span name and add attributes. This is a breaking change: users who are currently relying on the span name in their queries will need to update their queries to use the new name or match new patterns. This is an experimental feature so breaking changes are expected. ### 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 😄 |
||
|
Tao ChenandEvan Mattson
|
6ecfdcfda9 |
Python: Add agent invocation spans (#10255)
### 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? 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. --> Address feature request: https://github.com/microsoft/semantic-kernel/issues/10174 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> This pull request introduces a new decorator for tracing agent invocations and applies it to various methods in the `ChatCompletionAgent` and `OpenAIAssistantAgent` classes. Additionally, it includes new unit tests to ensure the decorator is correctly applied and functions as expected. ### Key Changes: **Decorator Implementation:** * Added a new `trace_agent_invocation` decorator in `python/semantic_kernel/utils/telemetry/agent_diagnostics/decorators.py` to trace agent invocations using OpenTelemetry. **Decorator Application:** * Applied the `trace_agent_invocation` decorator to the `invoke` and `invoke_stream` methods in `ChatCompletionAgent` (`python/semantic_kernel/agents/chat_completion/chat_completion_agent.py`). [[1]](diffhunk://#diff-0f0a27c107368504c4347c88528b7b4234dcead1919005bcae13f5d16d6cf26dR19) [[2]](diffhunk://#diff-0f0a27c107368504c4347c88528b7b4234dcead1919005bcae13f5d16d6cf26dR80) [[3]](diffhunk://#diff-0f0a27c107368504c4347c88528b7b4234dcead1919005bcae13f5d16d6cf26dR134) * Applied the `trace_agent_invocation` decorator to the `invoke` and `invoke_stream` methods in `OpenAIAssistantAgent` (`python/semantic_kernel/agents/open_ai/open_ai_assistant_base.py`). [[1]](diffhunk://#diff-70e75c57136d3a90d045af4b8d59fb8e8101251d7c5126458bb5d9e6c36556a4R46) [[2]](diffhunk://#diff-70e75c57136d3a90d045af4b8d59fb8e8101251d7c5126458bb5d9e6c36556a4R608) [[3]](diffhunk://#diff-70e75c57136d3a90d045af4b8d59fb8e8101251d7c5126458bb5d9e6c36556a4R861) ### 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]> |
||
|
Tao Chen
|
bf45719275 |
Python: Update gen_ai traces and logs (#10173)
### 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? 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. --> The gen_ai semantic convention has gone through some updates since we started generating telemetry for gen_ai operations. We would like to align with the latest conventions, and most importantly allow our users to visualize the gen_ai traces on the Azure AI Foundry Tracing UI, which relies on the gen_ai conventions. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Updates the gen_ai telemetry module to align with the latest gen_ai convention so that all AI connectors generate telemetry data that can be visualized on Azure AI Foundry. 2. Unit tests for the update. > Note that this is a breaking change as this feature is still experimental. Anyone who is relying on the previous gen_ai conventions should also update. ### 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 😄 |
||
|
Evan Mattson
|
7479657d93 |
Python: Introduce agent name field regex (#9887)
### Motivation and Context The agent name Pydantic field currently does not have any pattern regex config. If a user creates an agent with an incorrect name (something other than `0-9A-Za-z_-`) they will get a 400. To validate and error out earlier, we're introducing the regex check when the field is validated. Note, this is a non-breaking change because an invalid name errors out further down in the processing -- we're adding this regex check so that it can help a user/dev understand earlier in the processing that they need to update their agent name. <!-- 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? 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. --> ### Description Add the regex field validation for the agent name as `AGENT_NAME_REGEX = r"^[0-9A-Za-z_-]+$"`. Unlike the SK plugin/function names, this name can contain a `-`. Updates unit tests. - Closes #9340 <!-- 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 😄 |
||
|
Tao Chen
|
fb5aa6fcd2 |
Python: Azure AI Inference tracing SDK (#9693)
### 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? 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. --> Addresses: https://github.com/microsoft/semantic-kernel/issues/9413 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> The latest Azure AI Inference SDK has been released with the tracing package. We have decided to upgrade to the latest so that we will no longer need to instrument the Azure AI Inference connector with our own model diagnostics module. ### Contribution Checklist 1. Upgrade to the latest Azure AI Inference SDK with the tracing package. 2. Refactor the AI Inference connector to reduce duplicated code. 3. Some other minor fixes. <!-- 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 😄 |
||
|
Eduard van Valkenburg
|
2c50ced071 |
Python: introducing Vector Search for Qdrant Collection (#9621)
### 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? 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. --> Adds the vector search functions to the existing QdrantCollection. Currently only support VectorizedSearch. ### Description <!-- 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 😄 |
||
|
Eduard van Valkenburg
|
c9ef719e2f |
Python: fix mypy pre-commit setup, mypy fixes (#9498)
### 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? 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. --> Fixes the mypy pre-commit job, and subsequent fixes from there. Moved all python tests to use the uv setup task instead of manual cache, python install and uv install. ### Description <!-- 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 😄 |
||
|
Tao ChenandBen Thomas
|
5c5d761c6f |
Python: Streaming content for token usage (#8902)
### 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? 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. --> OpenAI recently starts providing token usage information on their streaming chat completion API. This ADR opens the discussion on how we should consume that information within our `StreamingChatMessageContent` data structure. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. An ADR documenting 4 approaches. 2. Implementation of the selected approach. 3. Fix issues where data in the streaming contents are modified after concatenation (`__add__`). 4. Add streaming to model diagnostics operation names so that we can distinguish streaming and non-streaming operations. ### 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: Ben Thomas <[email protected]> |
||
|
Evan Mattson
|
3d0a890e71 |
Python: Support OpenAI json_schema response format (#8958)
### Motivation and Context OpenAI has released in their August 6th model the ability to specific a json_schema response format. This allows the model to use the specified Pydantic model or the provided JSON schema representing a "strict" model/class to make sure that it responds with the exact schema that was provided. Note that the strict tool calling will be handled via a separate ADR/work item once agreed upon by all three SK languages. <!-- 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? 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. --> ### Description This PR introduces the ability to specific either a Pydantic model or a non-Pydantic model for the json_schema response format OpenAIChatPromptExecutionSettings. - There are two new code sample concept files showing how to do this with both a Pydantic and a non-Pydantic model. The other code sample shows how to configure a json_schema response format while using function calling. - If the model is unable to respond based on the provided schema, it will contain a `refusal` attribute as part of the chat completion message contents. This will be included in the response as a TextContent item explaining why it couldn't fulfill the request. - Unit tests added for this new functionality. - Handles #7946 <!-- 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 😄 |
||
|
Evan Mattson
|
d4057373c0 |
Python: Support DefaultAzureCredential Auth for Azure Resources (#8711)
### Motivation and Context For security best practices, we're moving away from api key based auth for Azure resources like Azure OpenAI. Previously, all tests and samples run with an api_key only, handled via the env var, the .env file, or directly passed into the AzureChatCompletion constructor. If the api_key is not found once the Azure Settings are loaded, we will attempt to retrieve a token via the DefaultAzureCredential. If it succeeds, we use the `ad_token`; otherwise, if the token is empty (meaning we failed to retrieve it), Pydantic validation will fail as part of service construction, just as it does today. The user may configure a `TOKEN_ENDPOINT` (for example, `AZURE_OPENAI_TOKEN_ENDPOINT`) to override the default token endpoint used for AzureOpenAI, if required. This is also possible for the Python Sessions Container Apps plugin via `ACA_TOKEN_ENDPOINT`, although the default value is specified and works well if the user has the proper role on the resource. <!-- 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? 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. --> ### Description This PR: - allows our GitHub integration tests to run off of the DefaultAzureCredential. - allows a user to retrieve a token via DefaultAzureCredential so they don't need to manage api keys for Azure Resources. - closes: #6512 <!-- 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 😄 |
||
|
Tao Chen
|
9529652d5d |
Python: More telemetry in AI services (#8641)
### 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? 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. --> Add more telemetry to our built-in AI services, and update the telemetry sample app to show case the changes. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> ### Contribution Checklist The following items are added: 1. Add tracing to all streaming APIs via the `trace_streaming_xxx` decorators. 2. Wrap auto function invocation, with span name set to `AutoFunctionInvocationLoop`. Available functions will be recorded by the attribute `sk.available_functions`. 3. Common completion usage interface. 4. Add a test to make all APIs are instrumented. 5. Record service address when possible. Updates made to the telemetry sample app: 1. Rename to `telemetry` from `telemetry_with_application_insights`. 2. Add sample code showing auto function calling. 3. Reorganize the sample by scenarios. 4. Add more output options: Aspire dashboard and console. 6. Update Readme. TODO: Add unit tests from the streaming decorators and improve code converage. <!-- 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 😄 |
||
|
Tao Chen
|
bd34da9c18 |
Python: New AI Connector abstract methods (#8526)
### 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? 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. --> This PR implements: https://github.com/microsoft/semantic-kernel/blob/main/docs/decisions/0052-python-ai-connector-new-abstract-methods.md (PR: https://github.com/microsoft/semantic-kernel/pull/8430). ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Add abstract methods `_inner_get_chat_message_content` and `_inner_get_streaming_chat_message_content` to `ChatCompletionClientBase`. 2. Implement the abstractions in all chat completion connectors. 3. Add abstract methods `_inner_get_text_contents` and `_inner_get_streaming_text_contents` to `TextCompletionClientBase`. 4. Implement the abstractions in all text completion connectors. 5. Remove text completion APIs from `OllamaChatCompletion` (breaking changes). > No breaking changes on other connectors except Ollama, and public APIs stay the same. ### 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 😄 |
||
|
Tao Chen
|
5476ac3acb |
Python: Update model diagnostics (#8346)
### 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? 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. --> We have a module that performs tracing for model invocations. However, that module only works with OpenAI. We need it to work for all AI connectors. This PR does the refactoring necessary for the module to become more generic. ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 1. Rename the tracing module to `model_diagnostics`. 2. Restructure and optimize code. 3. Add tracing to all AI connectors except the HuggingFace connector, because the HuggingFace connector will need some refactoring in the near future. ### 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 😄 |
||
|
Gil LaHayeandTao Chen
|
ca78ff73e0 |
Python: Add OpenTelemetry to Python SK (#6914)
### Motivation and Context We want observability into usage of SK ### Description Add OpenTelemetry to Python SK ### Contribution Checklist - [ ] 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 --------- Co-authored-by: Tao Chen <[email protected]> |
||
|
Milad Mobarhan
|
a9d361a82f |
Python: fix: Preserve class type in experimental_class decorator to avoid type inference issues (#6709)
### 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? 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. --> Closes https://github.com/microsoft/semantic-kernel/issues/6707 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> - Introduce `TypeVar` `T` bounded to `type` to ensure the decorator maintains the class type. - Modify the `experimental_class` decorator to use the bounded `TypeVar` for accurate type hinting. ### 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 😄 |
||
|
Eduard van Valkenburg
|
ba9af0e07b |
Python: mypy coverage enhancement (#6250)
### 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? 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. --> extend mypy coverage for prompt_templates, template_engine and some other smaller folders. still todo: connectors, memory and planner folders. ### Description <!-- 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 😄 |
||
|
Eduard van Valkenburg
|
fbb26b5cf6 |
Python: further improvements to checks and docs in dev_setup (#6445)
### 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? 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. --> Added a section on documentation to the DEV_SETUP Added a check for the copyright row to pre-commit Closes #5629 ### Description <!-- 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 😄 |
||
|
Eduard van Valkenburg
|
9e99933a27 |
Python: new pre-commit actions and pre-commit as GHA (#6376)
### 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? 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. --> - Added static code checking to pre-commit (check-ast and nbqa-check-ast) - Added Bandit security checking to pre-commit - Added pre-commit step to python-lint workflow, if it works, can delete seperate mypy, ruff and black tests ### Description <!-- 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 😄 |
||
|
Eduard van ValkenburgandEvan Mattson
|
9d56f4276d |
Python: Improve test coverage (#6366)
### 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? 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. --> 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) ### Description <!-- 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 - [ ] I didn't break anyone 😄 --------- Co-authored-by: Evan Mattson <[email protected]> |
||
|
Eduard van Valkenburg
|
81cdde24fc |
Python: upgraded all files to 310 plus format and removed from future (#6353)
### 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? 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. --> Now that we only support 310 and up, we could unify all files, to use the new style typing and annotations. ### Description <!-- 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 😄 |
||
|
Evan Mattson
|
d9aa617ec8 |
Python: Create an experimental class and function decorator. (#6298)
### Motivation and Context
There may be classes or functions inside of SK Python that should be
deemed as experimental. Currently in Python there is no out-of-the-box
way to get the decorator.
<!-- 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?
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.
-->
### Description
As there is a name clash with using `experimental` as the decorator
name, we are introducing two decorators: one `experimental_class` and
one `experimental_function`. The note that "This {function | class} is
experimental and may change in the future" is included in the docstring
and a bool `is_experimental` on the class is added.
<!-- 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 😄
|
||
|
Evan Mattson
|
46f5ea15a2 |
Python: Introduce Pydantic settings (#6193)
### Motivation and Context SK Python is tightly coupled to the use of a `.env` file to read all secrets, keys, endpoints, and more. This doesn't scale well for users who wish to be able to use environment variables with their SK Applications. By introducing Pydantic Settings, it is possible to use both environment variables as well as have a fall-back to a `.env` file (via a `env_file_path` parameter), if desired. By introducing Pydantic Settings, we are removing the requirement to have to create Text/Embedding/Chat completion objects with an `api_key` or other previously required information (in the case of AzureChatCompletion that means an `endpoint`, an `api_key`, a `deployment_name`, and an `api_version`). When the AI connector is created, the Pydantic settings are loaded either via env vars or the fall-back `.env` file, and that means the user can create a chat completion object like: ```python chat_completion = OpenAIChatCompletion(service_id="test") ``` or, to optionally override the `ai_model_id` env var: ```python chat_completion = OpenAIChatCompletion(service_id="test", ai_model_id="gpt-4-1106") ``` Note: we have left the ability to specific an `api_key`/`org_id` for `OpenAIChatCompletion` or a `deployment_name`, `endpoint`, `base_url`, and `api_version` for `AzureChatCompletion` as before, but if your settings are configured to use env vars/.env file then there is no need to pass this information. <!-- 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? 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. --> ### Description The PR introduces the use of Pydantic settings and removes the use of the python-dotenv library. - Closes #1779 - Updates notebooks, samples, code and tests to remove the explicit config of api_key or other previous .env files values. - Adds new unit test config using monkeypatch to simulate env variables for testing - All unit and integration tests passing <!-- 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 - [ ] I didn't break anyone 😄 |
||
|
Evan Mattson
|
7e4faa3f72 |
Python: Add ACA Python Sessions (Code Interpreter) Core Plugin, samples, and tests (#6158)
### Motivation and Context Adding a new core plugin to Semantic Kernel Python that leverages the Azure Container Apps Python Sessions Container. This container allows one, with the proper resource, to run Python in a safe, managed environment. <!-- 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? 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. --> ### Description This PR introduces: - The Python Sessions (code interpreter) plugin to execute code, upload a file to the container, list files, and download files. - It includes a README.md with the steps to set up the ACA resource. - New samples to show use as a plugin and auto function calling - Unit tests <!-- 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 😄 |
||
|
Lazaro HurtadoandLazaro Hurtado
|
9e70e7172b |
Python: updating pinecone client (#6021)
### 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?
3. What problem does it solve?
4. What scenario does it contribute to?
5. If it fixes an open issue, please link to the issue here.
-->
### Description
1. Why is this change required?
- Allow developers to use Pinecone as a memory store
2. What problem does it solve?
- Currently using the Pinecone memory store class throws the deprecation
error message show below:
`AttributeError: init is no longer a top-level attribute of the pinecone
package.`
This PR fixes this issue by updating `pinecone-client` to a new major
version and using the new initialization.
3. If it fixes an open issue, please link to the issue here.
#4914
### 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: Lazaro Hurtado <[email protected]>
|
||
|
Evan Mattson
|
9a44506220 |
Python: add new samples and fix streaming tool call FunctionCallContent formation (#5877)
### Motivation and Context We're working towards creating a core set of syntax examples (there are four total). The core examples will be available in all 3 SK languages. <!-- 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? 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. --> ### Description This PR introduces two (of the four) new kernel syntax examples, which will align with the new kernel examples coming soon for both dotnet and Java. #5784 - Introduce a custom weather plugin that in conjunction with the core TimePlugin, make use of auto function calling. - Introduce a kernel syntax example that shows how to integrate with the Microsoft Graph API to create a "restaurant booking." Note: this doesn't actually place a real reservation, but shows how to interact with msgraph. - Also fixes an issue where the streaming tool call argument formation was broken. Closes #6106 <!-- 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 😄 |
||
|
|
ca2ad19130 |
Python: Update to AOAI OYD 2024-02-15-preview API version (#5684)
### 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? 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. --> ### 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 <!-- 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 Hartman <[email protected]> Co-authored-by: Evan Mattson <[email protected]> |
||
|
Evan Mattson
|
c4c7464f07 |
Python: Introduce operations to handle OpenAI plugins, improve OpenAPI plugins, and allow for auth (#5695)
### 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. <!-- 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? 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. --> ### 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. <!-- 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 😄 |
||
|
Evan MattsonandEduard van Valkenburg
|
b93bab90ac |
Python: Support Auto tool calling for OpenAI models (#5267)
### 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. <!-- 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? 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. --> ### 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. <!-- 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 😄 --------- Co-authored-by: Eduard van Valkenburg <[email protected]> |
||
|
Eduard van Valkenburg
|
739e7f0e2a |
Python: rebuilt exceptions structure; pythonic version (#5231)
### 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? 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. --> 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 ### Description <!-- Describe your changes, the overall approach, the underlying design. These notes will help understanding how your code works. Thanks! --> 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. ### 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 😄 |
||
|
|
5817c2b927 |
Python: major features for the Kernel Arguments, Function Result and new Prompt Templating (#5077)
### 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? 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]> |
||
|
Evan MattsonandEvan Mattson
|
2b6bd03b03 |
Python: Add kernel plugin collection and remove old plugin collection classes (#4764)
### Motivation and Context This PR solves two works items: #4560 and #4667. The Python SDK had different ways of handling plugins and collections. The work in the PR helps align the SDK with dotnet nomenclature and structure. <!-- 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? 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. --> ### Description A `KernelPluginCollection` is introduced with an underlying `DefaultKernelPlugin` and a `KernelPlugin` base class. Other code is refactored to use this new `KernelPluginCollection` model which should simplify how plugins are handled. Unit tests are added to test the new functionality. Existing unit tests and integration tests, along with kernel examples and notebooks were updated based on these changes. Unlike before, we're requiring plugins to be defined with a name, which removes the GLOBAL_PLUGIN constant name of "__GLOBAL_PLUGIN__". In some senses, while creating a plugin or a function, a name may not be provided and so a random name in the form of a plugin (p_<random_ascii_chars>) or function (f_<random_ascii_chars>) is used. <!-- 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 - [ ] I didn't break anyone 😄 --------- Co-authored-by: Evan Mattson <[email protected]> |
||
|
|
9dbbf56c96 |
Python: Add astra memory (#4374)
### 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 ### 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: jsbilgi <[email protected]> Co-authored-by: Obioma Anomnachi <[email protected]> |
||
|
Evan MattsonandEvan Mattson
|
674af5468d |
Python: Rename skills to plugins. Update prompt template config to use execution settings to align with dotnet. (#4595)
### 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 <!-- 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? 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. --> ### 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. <!-- 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 😄 --------- Co-authored-by: Evan Mattson <[email protected]> |