2024-08-26 13:34:29 +02:00
version = 1
Python: Bump werkzeug from 3.1.1 to 3.1.6 in /python (#13605)
Bumps [werkzeug](https://github.com/pallets/werkzeug) from 3.1.1 to
3.1.6.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/werkzeug/releases">werkzeug's
releases</a>.</em></p>
<blockquote>
<h2>3.1.6</h2>
<p>This is the Werkzeug 3.1.6 security fix release, which fixes a
security issue but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.6/">https://pypi.org/project/Werkzeug/3.1.6/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-6">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-6</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow special devices
names in multi-segment paths. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-29vq-49wr-vm6x">GHSA-29vq-49wr-vm6x</a></li>
</ul>
<h2>3.1.5</h2>
<p>This is the Werkzeug 3.1.5 security fix release, which fixes security
issues and bugs but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.5/">https://pypi.org/project/Werkzeug/3.1.5/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-5">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-5</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/43?closed=1">https://github.com/pallets/werkzeug/milestone/43?closed=1</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow more special device
names, regardless of extension or surrounding spaces. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-87hc-h4r5-73f7">GHSA-87hc-h4r5-73f7</a></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary. This fixes the previous attempt, which caused incorrect
content lengths. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3065">#3065</a>
<a
href="https://redirect.github.com/pallets/werkzeug/issues/3077">#3077</a></li>
<li>Fix <code>AttributeError</code> when initializing
<code>DebuggedApplication</code> with <code>pin_security=False</code>.
<a
href="https://redirect.github.com/pallets/werkzeug/issues/3075">#3075</a></li>
</ul>
<h2>3.1.4</h2>
<p>This is the Werkzeug 3.1.4 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes
compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.4/">https://pypi.org/project/Werkzeug/3.1.4/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-4">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-4</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/42?closed=1">https://github.com/pallets/werkzeug/milestone/42?closed=1</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow special device
names. This prevents reading from these when using
<code>send_from_directory</code>. <code>secure_filename</code> already
prevented writing to these. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-hgf8-39gv-g3f2">ghsa-hgf8-39gv-g3f2</a></li>
<li>The debugger pin fails after 10 attempts instead of 11. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3020">#3020</a></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3065">#3065</a></li>
<li>Improve CPU usage during Watchdog reloader. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3054">#3054</a></li>
<li><code>Request.json</code> annotation is more accurate. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3067">#3067</a></li>
<li>Traceback rendering handles when the line number is beyond the
available source lines. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3044">#3044</a></li>
<li><code>HTTPException.get_response</code> annotation and doc better
conveys the distinction between WSGI and sans-IO responses. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3056">#3056</a></li>
</ul>
<h2>3.1.3</h2>
<p>This is the Werkzeug 3.1.3 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes vs
3.1.0.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.3/">https://pypi.org/project/Werkzeug/3.1.3/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-3">https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-3</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/41?closed=1">https://github.com/pallets/werkzeug/milestone/41?closed=1</a></p>
<ul>
<li>Initial data passed to <code>MultiDict</code> and similar interfaces
only accepts <code>list</code>, <code>tuple</code>, or <code>set</code>
when passing multiple values. It had been changed to accept any
<code>Collection</code>, but this matched types that should be treated
as single values, such as <code>bytes</code>. <a
href="https://redirect.github.com/pallets/werkzeug/issues/2994">#2994</a></li>
<li>When the <code>Host</code> header is not set and
<code>Request.host</code> falls back to the WSGI
<code>SERVER_NAME</code> value, if that value is an IPv6 address it is
wrapped in <code>[]</code> to match the <code>Host</code> header. <a
href="https://redirect.github.com/pallets/werkzeug/issues/2993">#2993</a></li>
</ul>
<h2>3.1.2</h2>
<p>This is the Werkzeug 3.1.2 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes vs
3.1.0.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.2/">https://pypi.org/project/Werkzeug/3.1.2/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-2">https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-2</a></p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/werkzeug/blob/main/CHANGES.rst">werkzeug's
changelog</a>.</em></p>
<blockquote>
<h2>Version 3.1.6</h2>
<p>Released 2026-02-19</p>
<ul>
<li><code>safe_join</code> on Windows does not allow special devices
names in
multi-segment paths. :ghsa:<code>29vq-49wr-vm6x</code></li>
</ul>
<h2>Version 3.1.5</h2>
<p>Released 2026-01-08</p>
<ul>
<li><code>safe_join</code> on Windows does not allow more special device
names, regardless
of extension or surrounding spaces.
:ghsa:<code>87hc-h4r5-73f7</code></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary.
This fixes the previous attempt, which caused incorrect content lengths.
:issue:<code>3065</code> :issue:<code>3077</code></li>
<li>Fix <code>AttributeError</code> when initializing
<code>DebuggedApplication</code> with
<code>pin_security=False</code>. :issue:<code>3075</code></li>
</ul>
<h2>Version 3.1.4</h2>
<p>Released 2025-11-28</p>
<ul>
<li><code>safe_join</code> on Windows does not allow special device
names. This prevents
reading from these when using <code>send_from_directory</code>.
<code>secure_filename</code>
already prevented writing to these.
:ghsa:<code>hgf8-39gv-g3f2</code></li>
<li>The debugger pin fails after 10 attempts instead of 11.
:pr:<code>3020</code></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary.
:issue:<code>3065</code></li>
<li>Improve CPU usage during Watchdog reloader.
:issue:<code>3054</code></li>
<li><code>Request.json</code> annotation is more accurate.
:issue:<code>3067</code></li>
<li>Traceback rendering handles when the line number is beyond the
available
source lines. :issue:<code>3044</code></li>
<li><code>HTTPException.get_response</code> annotation and doc better
conveys the
distinction between WSGI and sans-IO responses.
:issue:<code>3056</code></li>
</ul>
<h2>Version 3.1.3</h2>
<p>Released 2024-11-08</p>
<ul>
<li>Initial data passed to <code>MultiDict</code> and similar interfaces
only accepts
<code>list</code>, <code>tuple</code>, or <code>set</code> when passing
multiple values. It had been
changed to accept any <code>Collection</code>, but this matched types
that should be</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/pallets/werkzeug/commit/04da1b5221b7a7b57e82246e4b5741d37a6b2e56"><code>04da1b5</code></a>
release version 3.1.6</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/f407712fdc60a09c2b3f4fe7db557703e5d9338d"><code>f407712</code></a>
Merge commit from fork</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/f54fe98026253e70fbbcd35a6b52fb67cfff1c03"><code>f54fe98</code></a>
safe_join prevents Windows special device names in multi-segment
paths</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/d005985ef69ffe3275eda8fb6fb25e074dbe871b"><code>d005985</code></a>
start version 3.1.6</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/8565c2cbd6681ae8463e77d4fc0795324a7fdae7"><code>8565c2c</code></a>
document rule priority (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3102">#3102</a>)</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/3febc7e90072bffe04c27e6b7478dfc4f88930df"><code>3febc7e</code></a>
document rule priority</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/2525b827646c10ab7adb334664e6a4af1b769181"><code>2525b82</code></a>
remove state machine docs</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/4abfbd553cdeb6d4e6fa693340d52b13c884079f"><code>4abfbd5</code></a>
rewrite build docstring (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3097">#3097</a>)</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/161c18b2a8800ae6ef377fb3cbdb933a878fea67"><code>161c18b</code></a>
rewrite build docstring</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/86e11c29e44726dae524cd9db11549b3b1ad681d"><code>86e11c2</code></a>
release version 3.1.5 (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3085">#3085</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/pallets/werkzeug/compare/3.1.1...3.1.6">compare
view</a></li>
</ul>
</details>
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2026-03-19 12:20:39 +01:00
revision = 3
2024-11-07 12:33:41 +01:00
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"python_full_version == '3.13.*' and sys_platform == 'darwin'",
Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
"python_full_version == '3.12.*' and sys_platform == 'darwin'",
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"python_full_version == '3.13.*' and sys_platform == 'linux'",
Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
"python_full_version == '3.12.*' and sys_platform == 'linux'",
"python_full_version == '3.11.*' and sys_platform == 'linux'",
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"python_full_version >= '3.14' and python_full_version < '4' and sys_platform == 'win32'",
"python_full_version == '3.13.*' and sys_platform == 'win32'",
Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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name = "anthropic"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
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Python: Update the AzureAIAgent packages, types, and APIs based on Azure SDK's new code (#12084)
### Motivation and Context
The Azure AI Agent Service is moving to GA soon. As part of that,
they've made several changes in the underlying APIs, which require the
use of new packages, updated type imports and some new underlying APIs.
This PR updates the AzureAIAgent to use the latest Azure SDK's new code.
- The client's connection string has been replaced with an endpoint.
- The `AzureAIAgentSettings`'s `model_deployment_name` was updated to
`deployment_name`.
- Tools are now imported from `from azure.ai.agents.models` instead of
`from azure.ai.projects.models`.
- Declarative spec handling for AzureAIAgent was updated based on the
new APIs.
- Unit tests were updated.
<!-- 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 AzureAIAgent related code, samples and tests based on the
Azure SDK's latest APIs.
- Closes #12177
<!-- 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2025-05-20 08:43:40 +09:00
[[package]]
name = "azure-ai-agents"
2025-10-29 21:22:14 -07:00
version = "1.2.0b6"
Python: Update the AzureAIAgent packages, types, and APIs based on Azure SDK's new code (#12084)
### Motivation and Context
The Azure AI Agent Service is moving to GA soon. As part of that,
they've made several changes in the underlying APIs, which require the
use of new packages, updated type imports and some new underlying APIs.
This PR updates the AzureAIAgent to use the latest Azure SDK's new code.
- The client's connection string has been replaced with an endpoint.
- The `AzureAIAgentSettings`'s `model_deployment_name` was updated to
`deployment_name`.
- Tools are now imported from `from azure.ai.agents.models` instead of
`from azure.ai.projects.models`.
- Declarative spec handling for AzureAIAgent was updated based on the
new APIs.
- Unit tests were updated.
<!-- 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 AzureAIAgent related code, samples and tests based on the
Azure SDK's latest APIs.
- Closes #12177
<!-- 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2025-05-20 08:43:40 +09:00
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2026-03-12 10:02:02 -07:00
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Python: Update the AzureAIAgent packages, types, and APIs based on Azure SDK's new code (#12084)
### Motivation and Context
The Azure AI Agent Service is moving to GA soon. As part of that,
they've made several changes in the underlying APIs, which require the
use of new packages, updated type imports and some new underlying APIs.
This PR updates the AzureAIAgent to use the latest Azure SDK's new code.
- The client's connection string has been replaced with an endpoint.
- The `AzureAIAgentSettings`'s `model_deployment_name` was updated to
`deployment_name`.
- Tools are now imported from `from azure.ai.agents.models` instead of
`from azure.ai.projects.models`.
- Declarative spec handling for AzureAIAgent was updated based on the
new APIs.
- Unit tests were updated.
<!-- 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 AzureAIAgent related code, samples and tests based on the
Azure SDK's latest APIs.
- Closes #12177
<!-- 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2025-05-20 08:43:40 +09:00
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2026-03-12 10:02:02 -07:00
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Python: Update the AzureAIAgent packages, types, and APIs based on Azure SDK's new code (#12084)
### Motivation and Context
The Azure AI Agent Service is moving to GA soon. As part of that,
they've made several changes in the underlying APIs, which require the
use of new packages, updated type imports and some new underlying APIs.
This PR updates the AzureAIAgent to use the latest Azure SDK's new code.
- The client's connection string has been replaced with an endpoint.
- The `AzureAIAgentSettings`'s `model_deployment_name` was updated to
`deployment_name`.
- Tools are now imported from `from azure.ai.agents.models` instead of
`from azure.ai.projects.models`.
- Declarative spec handling for AzureAIAgent was updated based on the
new APIs.
- Unit tests were updated.
<!-- 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 AzureAIAgent related code, samples and tests based on the
Azure SDK's latest APIs.
- Closes #12177
<!-- 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2025-05-20 08:43:40 +09:00
]
2024-08-26 13:34:29 +02:00
[[package]]
name = "azure-ai-inference"
Python: Support AzureAIAgent agent_id instead of assistant_id (#10908)
### Motivation and Context
The Azure Python SDK made an underlying change to move away from
`assistant_id` keyword arguments to now support `agent_id` keyword
arguments. Additionally, they now support a
`MessageTextUrlCitationAnnotation` and
`MessageDeltaTextUrlCitationAnnotation` for annotations.
<!-- 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 `AzureAIAgent` to pass in `agent_id` in place of
`assistant_id`, and bring in support for the new annotation types.
- Updates samples to simpler patterns where needed.
- Updates unit tests.
- Closes #10852
Validated that our [AzureAIAgent + Azure AI Search
sample](https://github.com/microsoft/semantic-kernel/blob/main/python/samples/concepts/agents/azure_ai_agent/azure_ai_agent_azure_ai_search.py)
works properly:
---
```markdown
# User: 'Which hotels are available with full-sized kitchens in Nashville, TN?'
# Agent: In Nashville, TN, here are some hotel options that feature full-sized kitchens:
1. **Extended Stay Suites** - All suites include full-sized kitchens stocked with cookware, separate living and sleeping areas, and sofa beds. Some larger rooms have fireplaces and patios or balconies【3:0†source】.
2. **New Premium Rooms** - These newly furnished rooms are fully equipped with premium amenities, including a full kitchen, washer & dryer, and access to a fitness center. Units also include fireplaces and small outdoor balconies【3:1†source】.
3. **Conveniently Located Extended Stay** - This option offers a full kitchen along with a washer & dryer, and amenities such as free WiFi and a fitness center, located near downtown Nashville【3:3†source】.
4. **Inn with Functional Kitchens** - This inn provides living room suites and functional kitchens, situated conveniently near a lake and welcomes pets【3:4†source】.
These options provide a range of amenities for travelers looking for the convenience of a full-sized kitchen during their stay.
# User: 'Fun hotels with free WiFi.'
# Agent: Here are some fun hotels that offer free WiFi:
1. **Hotel with Rooftop Plaza** - Offers complimentary continental breakfast in the lobby and free Wi-Fi throughout the hotel. Its penthouse suites provide stunning views【7:0†source】.
2. **Lakeside Hotel** - Features newly redesigned rooms, an airport shuttle, and lakeside amenities including a resort-style pool. The hotel provides stylish new guestrooms with Internet TVs and free WiFi【7:3†source】.
3. **Downtown Hotel** - Located near the vibrant downtown area and the business district. This hotel offers free WiFi, local transportation, and a special treat of milk and cookies for guests【7:4†source】.
These hotels combine fun experiences with the convenience of free internet access for guests.
```
<!-- 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 :smile:
2025-03-11 22:21:23 +09:00
version = "1.0.0b9"
2024-08-26 13:34:29 +02:00
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]
Python: Add support for Azure AI Agent Service (#10414)
### Motivation and Context
Azure AI Agents are a managed offering, and are built on top of the
OpenAI assistant v2 APIs. Until now we have not had support for them in
SK Python. This PR adds support for Azure AI Agents in SK Python. The
current abstractions allow one to run managed Azure AI Agents using SK
constructs as is demonstrated via the getting started samples and/or the
`azure_ai_agent` concept code samples. The developer must first follow
the required Azure AI Agent deployment steps, and bring their connection
string as well as their model deployment 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 support for Azure AI Agent Service in SK
- Add sample code
- Add unit tests
- Restructures the `getting_started_with_agents` samples to move the
respective agent code into folders like `chat_completion`,
`azure_ai_agent`, etc. Updates the README as well.
- Closes #10187
**TODO**: add more test coverage either in this PR, or a subsequent one.
<!-- 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 :smile:
2025-02-07 11:11:10 +09:00
[[package]]
name = "azure-ai-projects"
2025-11-24 13:53:57 -08:00
version = "1.0.0"
Python: Add support for Azure AI Agent Service (#10414)
### Motivation and Context
Azure AI Agents are a managed offering, and are built on top of the
OpenAI assistant v2 APIs. Until now we have not had support for them in
SK Python. This PR adds support for Azure AI Agents in SK Python. The
current abstractions allow one to run managed Azure AI Agents using SK
constructs as is demonstrated via the getting started samples and/or the
`azure_ai_agent` concept code samples. The developer must first follow
the required Azure AI Agent deployment steps, and bring their connection
string as well as their model deployment 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 support for Azure AI Agent Service in SK
- Add sample code
- Add unit tests
- Restructures the `getting_started_with_agents` samples to move the
respective agent code into folders like `chat_completion`,
`azure_ai_agent`, etc. Updates the README as well.
- Closes #10187
**TODO**: add more test coverage either in this PR, or a subsequent one.
<!-- 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 :smile:
2025-02-07 11:11:10 +09:00
source = { registry = "https://pypi.org/simple" }
dependencies = [
Python: Update the AzureAIAgent packages, types, and APIs based on Azure SDK's new code (#12084)
### Motivation and Context
The Azure AI Agent Service is moving to GA soon. As part of that,
they've made several changes in the underlying APIs, which require the
use of new packages, updated type imports and some new underlying APIs.
This PR updates the AzureAIAgent to use the latest Azure SDK's new code.
- The client's connection string has been replaced with an endpoint.
- The `AzureAIAgentSettings`'s `model_deployment_name` was updated to
`deployment_name`.
- Tools are now imported from `from azure.ai.agents.models` instead of
`from azure.ai.projects.models`.
- Declarative spec handling for AzureAIAgent was updated based on the
new APIs.
- Unit tests were updated.
<!-- 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 AzureAIAgent related code, samples and tests based on the
Azure SDK's latest APIs.
- Closes #12177
<!-- 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2025-05-20 08:43:40 +09:00
{ name = "azure-ai-agents", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
Python: Add support for Azure AI Agent Service (#10414)
### Motivation and Context
Azure AI Agents are a managed offering, and are built on top of the
OpenAI assistant v2 APIs. Until now we have not had support for them in
SK Python. This PR adds support for Azure AI Agents in SK Python. The
current abstractions allow one to run managed Azure AI Agents using SK
constructs as is demonstrated via the getting started samples and/or the
`azure_ai_agent` concept code samples. The developer must first follow
the required Azure AI Agent deployment steps, and bring their connection
string as well as their model deployment 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 support for Azure AI Agent Service in SK
- Add sample code
- Add unit tests
- Restructures the `getting_started_with_agents` samples to move the
respective agent code into folders like `chat_completion`,
`azure_ai_agent`, etc. Updates the README as well.
- Closes #10187
**TODO**: add more test coverage either in this PR, or a subsequent one.
<!-- 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 :smile:
2025-02-07 11:11:10 +09:00
{ name = "azure-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
Python: Update the AzureAIAgent packages, types, and APIs based on Azure SDK's new code (#12084)
### Motivation and Context
The Azure AI Agent Service is moving to GA soon. As part of that,
they've made several changes in the underlying APIs, which require the
use of new packages, updated type imports and some new underlying APIs.
This PR updates the AzureAIAgent to use the latest Azure SDK's new code.
- The client's connection string has been replaced with an endpoint.
- The `AzureAIAgentSettings`'s `model_deployment_name` was updated to
`deployment_name`.
- Tools are now imported from `from azure.ai.agents.models` instead of
`from azure.ai.projects.models`.
- Declarative spec handling for AzureAIAgent was updated based on the
new APIs.
- Unit tests were updated.
<!-- 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 AzureAIAgent related code, samples and tests based on the
Azure SDK's latest APIs.
- Closes #12177
<!-- 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2025-05-20 08:43:40 +09:00
{ name = "azure-storage-blob", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
Python: Add support for Azure AI Agent Service (#10414)
### Motivation and Context
Azure AI Agents are a managed offering, and are built on top of the
OpenAI assistant v2 APIs. Until now we have not had support for them in
SK Python. This PR adds support for Azure AI Agents in SK Python. The
current abstractions allow one to run managed Azure AI Agents using SK
constructs as is demonstrated via the getting started samples and/or the
`azure_ai_agent` concept code samples. The developer must first follow
the required Azure AI Agent deployment steps, and bring their connection
string as well as their model deployment 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 support for Azure AI Agent Service in SK
- Add sample code
- Add unit tests
- Restructures the `getting_started_with_agents` samples to move the
respective agent code into folders like `chat_completion`,
`azure_ai_agent`, etc. Updates the README as well.
- Closes #10187
**TODO**: add more test coverage either in this PR, or a subsequent one.
<!-- 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 :smile:
2025-02-07 11:11:10 +09:00
{ name = "isodate", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
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2026-03-12 10:02:02 -07:00
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Python: Add support for Azure AI Agent Service (#10414)
### Motivation and Context
Azure AI Agents are a managed offering, and are built on top of the
OpenAI assistant v2 APIs. Until now we have not had support for them in
SK Python. This PR adds support for Azure AI Agents in SK Python. The
current abstractions allow one to run managed Azure AI Agents using SK
constructs as is demonstrated via the getting started samples and/or the
`azure_ai_agent` concept code samples. The developer must first follow
the required Azure AI Agent deployment steps, and bring their connection
string as well as their model deployment 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 support for Azure AI Agent Service in SK
- Add sample code
- Add unit tests
- Restructures the `getting_started_with_agents` samples to move the
respective agent code into folders like `chat_completion`,
`azure_ai_agent`, etc. Updates the README as well.
- Closes #10187
**TODO**: add more test coverage either in this PR, or a subsequent one.
<!-- 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 :smile:
2025-02-07 11:11:10 +09:00
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Python: Add support for Azure AI Agent Service (#10414)
### Motivation and Context
Azure AI Agents are a managed offering, and are built on top of the
OpenAI assistant v2 APIs. Until now we have not had support for them in
SK Python. This PR adds support for Azure AI Agents in SK Python. The
current abstractions allow one to run managed Azure AI Agents using SK
constructs as is demonstrated via the getting started samples and/or the
`azure_ai_agent` concept code samples. The developer must first follow
the required Azure AI Agent deployment steps, and bring their connection
string as well as their model deployment 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 support for Azure AI Agent Service in SK
- Add sample code
- Add unit tests
- Restructures the `getting_started_with_agents` samples to move the
respective agent code into folders like `chat_completion`,
`azure_ai_agent`, etc. Updates the README as well.
- Closes #10187
**TODO**: add more test coverage either in this PR, or a subsequent one.
<!-- 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 :smile:
2025-02-07 11:11:10 +09:00
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2024-08-26 13:34:29 +02:00
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[[package]]
name = "azure-core"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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2024-08-26 13:34:29 +02:00
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2024-11-19 11:25:25 -08:00
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[[package]]
name = "azure-cosmos"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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[[package]]
name = "azure-identity"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
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3. What scenario does it contribute to?
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### Description
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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Python: Update the AzureAIAgent packages, types, and APIs based on Azure SDK's new code (#12084)
### Motivation and Context
The Azure AI Agent Service is moving to GA soon. As part of that,
they've made several changes in the underlying APIs, which require the
use of new packages, updated type imports and some new underlying APIs.
This PR updates the AzureAIAgent to use the latest Azure SDK's new code.
- The client's connection string has been replaced with an endpoint.
- The `AzureAIAgentSettings`'s `model_deployment_name` was updated to
`deployment_name`.
- Tools are now imported from `from azure.ai.agents.models` instead of
`from azure.ai.projects.models`.
- Declarative spec handling for AzureAIAgent was updated based on the
new APIs.
- Unit tests were updated.
<!-- 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 AzureAIAgent related code, samples and tests based on the
Azure SDK's latest APIs.
- Closes #12177
<!-- 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2025-05-20 08:43:40 +09:00
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Python: Update the AzureAIAgent packages, types, and APIs based on Azure SDK's new code (#12084)
### Motivation and Context
The Azure AI Agent Service is moving to GA soon. As part of that,
they've made several changes in the underlying APIs, which require the
use of new packages, updated type imports and some new underlying APIs.
This PR updates the AzureAIAgent to use the latest Azure SDK's new code.
- The client's connection string has been replaced with an endpoint.
- The `AzureAIAgentSettings`'s `model_deployment_name` was updated to
`deployment_name`.
- Tools are now imported from `from azure.ai.agents.models` instead of
`from azure.ai.projects.models`.
- Declarative spec handling for AzureAIAgent was updated based on the
new APIs.
- Unit tests were updated.
<!-- 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 AzureAIAgent related code, samples and tests based on the
Azure SDK's latest APIs.
- Closes #12177
<!-- 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2025-05-20 08:43:40 +09:00
]
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name = "azure-storage-blob"
2026-02-10 11:05:13 +01:00
version = "12.28.0"
Python: Update the AzureAIAgent packages, types, and APIs based on Azure SDK's new code (#12084)
### Motivation and Context
The Azure AI Agent Service is moving to GA soon. As part of that,
they've made several changes in the underlying APIs, which require the
use of new packages, updated type imports and some new underlying APIs.
This PR updates the AzureAIAgent to use the latest Azure SDK's new code.
- The client's connection string has been replaced with an endpoint.
- The `AzureAIAgentSettings`'s `model_deployment_name` was updated to
`deployment_name`.
- Tools are now imported from `from azure.ai.agents.models` instead of
`from azure.ai.projects.models`.
- Declarative spec handling for AzureAIAgent was updated based on the
new APIs.
- Unit tests were updated.
<!-- 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 AzureAIAgent related code, samples and tests based on the
Azure SDK's latest APIs.
- Closes #12177
<!-- 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2025-05-20 08:43:40 +09:00
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Python: Update sort step method for assistant invoke. (#10191)
### Motivation and Context
At times, when the (Azure) OpenAI Assistant Agent makes a tool call,
that tool call's creation timestamp is coming after the message creation
timestamp (the message creation being the text that the assistant
responds with). Currently in our code, if we have a tool call
(`FunctionCallContent`), we first yield that, and then we make a call to
get completed steps, to then yield more content like
`FunctionResultContent` and `TextContent`. There will be two steps (or
more, depending upon the number of tool calls).
Right now we sort the completed steps in this way:
```python
completed_steps_to_process: list[RunStep] = sorted(
[s for s in steps if s.completed_at is not None and s.id not in processed_step_ids],
key=lambda s: s.created_at,
)
```
When there are no failures, it's because the tool call was created
before the final message content (as has been since this assistant was
first coded). However, it appears that processing on the server-side can
cause fluctuations in when the steps are created/processed. When we have
a failure, we now have the message_creation (`TextContent`) yielded
before the `FunctionResultContent`, which if sent to an (Azure) OpenAI
Chat Completion endpoint will break with a 400 because of the gap in the
ordering between the `FunctionCallContent` and the
`FunctionResultContent`:
```
FunctionCallContent
TextContent # this should follow `FunctionResultContent` (and it does during times when we don't see a 400)
FunctionResultContent
```
The 400 error isn't 100% repeatable because of server-side processing,
so we will get the correct ordering:
```
FunctionCallContent
FunctionResultContent
TextContent
```
This PR updates the ordering of how we sort of the completed steps -- if
we have a step_type == "tool_calls" and "message_creation", we sort to
allow for "tool_calls" to come first, and any ties are broken by the
step.completed_at timestamp. With this update, we no longer receive 400s
because the ordering of the content types is correct when sending to an
OpenAI model.
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### Description
This PR:
- Updates the way we sort completed messages during an assistant invoke.
- Adds logging for the assistant invoke method at the `info` level.
- Closes #10141
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### 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 :smile:
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
2025-01-21 04:29:13 +09:00
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Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
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1. Why is this change required?
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### Description
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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
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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 :smile:
2026-02-28 09:01:03 +09:00
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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2024-09-16 14:13:23 -04:00
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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Python: Add the Python process framework (#9363)
### Motivation and Context
An initial PR to add the foundational pieces of the Python Process
framework, which holds it design to be similar to dotnet in that step
types are added to a process builder, and later on, when the step is
run, it is first instantiated and the proper state is provided.
<!-- 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
Adding the initial process framework components:
- Closes #9354
**TODO**
- more unit tests will be added to increase the code coverage. Currently
there are several files with no (or low) code coverage.
- more samples will either be added to this PR or a subsequent PR
<!-- 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 :smile:
2024-10-24 13:37:45 -04:00
[[package]]
name = "cloudevents"
2025-06-16 23:32:42 +02:00
version = "1.12.0"
Python: Add the Python process framework (#9363)
### Motivation and Context
An initial PR to add the foundational pieces of the Python Process
framework, which holds it design to be similar to dotnet in that step
types are added to a process builder, and later on, when the step is
run, it is first instantiated and the proper state is provided.
<!-- 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
Adding the initial process framework components:
- Closes #9354
**TODO**
- more unit tests will be added to increase the code coverage. Currently
there are several files with no (or low) code coverage.
- more samples will either be added to this PR or a subsequent PR
<!-- 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 :smile:
2024-10-24 13:37:45 -04:00
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Python: Add the Python process framework (#9363)
### Motivation and Context
An initial PR to add the foundational pieces of the Python Process
framework, which holds it design to be similar to dotnet in that step
types are added to a process builder, and later on, when the step is
run, it is first instantiated and the proper state is provided.
<!-- 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
Adding the initial process framework components:
- Closes #9354
**TODO**
- more unit tests will be added to increase the code coverage. Currently
there are several files with no (or low) code coverage.
- more samples will either be added to this PR or a subsequent PR
<!-- 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 :smile:
2024-10-24 13:37:45 -04:00
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Python: Add the Python process framework (#9363)
### Motivation and Context
An initial PR to add the foundational pieces of the Python Process
framework, which holds it design to be similar to dotnet in that step
types are added to a process builder, and later on, when the step is
run, it is first instantiated and the proper state is provided.
<!-- 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
Adding the initial process framework components:
- Closes #9354
**TODO**
- more unit tests will be added to increase the code coverage. Currently
there are several files with no (or low) code coverage.
- more samples will either be added to this PR or a subsequent PR
<!-- 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 :smile:
2024-10-24 13:37:45 -04:00
]
2024-08-26 13:34:29 +02:00
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Python: Fix: Implement __deepcopy__ on KernelFunction to handle non-serializable OTEL metrics (#12803)
### Description
This PR fixes TypeError: cannot pickle '_thread.RLock' object which
occurs when kernel.clone() is called while OpenTelemetry metrics are
enabled.
- Fixes: #12802
### Motivation and Context
When using features like `HandoffOrchestration`, the agent's `Kernel` is
cloned. This process uses `copy.deepcopy`, which fails on the
`KernelFunction`'s OpenTelemetry `Histogram` fields
(`invocation_duration_histogram` and `streaming_duration_histogram`).
These histogram objects contain thread locks, making them
non-serializable.
Previous solutions, like the one in **PR #9292**, used
`Field(exclude=True, default_factory=...)`. While that was a necessary
step, it was insufficient because `deepcopy` does not respect the
`exclude` flag in the same way that Pydantic's serialization does. This
bug blocks the use of certain agentic features in any application that
has metrics-based observability enabled.
### Description of the change
This solution introduces a custom __deepcopy__ method to the
KernelFunction Pydantic model. This method correctly handles the
deep-copying process for KernelFunction instances:
1. It intercepts the deepcopy call for KernelFunction objects.
2. It uses self.model_copy(deep=False) to create a new instance of the
Pydantic model. Using deep=False allows the default_factory for the
histogram fields to create new, clean instances on the copied object,
while other fields are shallow-copied.
3. It then manually deep-copies all other attributes from the original
object to the new one, ensuring that any other mutable objects (like
metadata`) are correctly duplicated. The histogram fields are explicitly
skipped in this step, as they have already been recreated by
`model_copy.
4. Standard deepcopy memoization is used to prevent infinite recursion
in the case of cyclic object graphs.
This approach ensures that the non-serializable fields are
re-initialized instead of copied, resolving the TypeError while
maintaining the integrity of the cloned object.
### How has this been tested?
The fix was validated using the reproduction steps outlined in the
associated issue. Running an agent orchestration that triggers
kernel.clone() with OpenTelemetry metrics enabled now completes
successfully without any errors.
### 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 :smile:
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2025-08-08 01:48:43 +03:00
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[package.optional-dependencies]
toml = [
Python: Add the Python process framework (#9363)
### Motivation and Context
An initial PR to add the foundational pieces of the Python Process
framework, which holds it design to be similar to dotnet in that step
types are added to a process builder, and later on, when the step is
run, it is first instantiated and the proper state is provided.
<!-- 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
Adding the initial process framework components:
- Closes #9354
**TODO**
- more unit tests will be added to increase the code coverage. Currently
there are several files with no (or low) code coverage.
- more samples will either be added to this PR or a subsequent PR
<!-- 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 :smile:
2024-10-24 13:37:45 -04:00
{ name = "tomli", marker = "(python_full_version <= '3.11' and sys_platform == 'darwin') or (python_full_version <= '3.11' and sys_platform == 'linux') or (python_full_version <= '3.11' and sys_platform == 'win32')" },
2024-08-26 13:34:29 +02:00
]
[[package]]
name = "cryptography"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
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information:
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2. What problem does it solve?
3. What scenario does it contribute to?
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### Description
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
version = "46.0.5"
2024-08-26 13:34:29 +02:00
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "cffi", marker = "(platform_python_implementation != 'PyPy' and sys_platform == 'darwin') or (platform_python_implementation != 'PyPy' and sys_platform == 'linux') or (platform_python_implementation != 'PyPy' and sys_platform == 'win32')" },
2025-10-24 11:23:08 +09:00
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2024-08-26 13:34:29 +02:00
]
2026-03-12 10:02:02 -07:00
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Python: Add the Python process framework (#9363)
### Motivation and Context
An initial PR to add the foundational pieces of the Python Process
framework, which holds it design to be similar to dotnet in that step
types are added to a process builder, and later on, when the step is
run, it is first instantiated and the proper state is provided.
<!-- 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
Adding the initial process framework components:
- Closes #9354
**TODO**
- more unit tests will be added to increase the code coverage. Currently
there are several files with no (or low) code coverage.
- more samples will either be added to this PR or a subsequent PR
<!-- 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 :smile:
2024-10-24 13:37:45 -04:00
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Python: Add the Python process framework (#9363)
### Motivation and Context
An initial PR to add the foundational pieces of the Python Process
framework, which holds it design to be similar to dotnet in that step
types are added to a process builder, and later on, when the step is
run, it is first instantiated and the proper state is provided.
<!-- 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
Adding the initial process framework components:
- Closes #9354
**TODO**
- more unit tests will be added to increase the code coverage. Currently
there are several files with no (or low) code coverage.
- more samples will either be added to this PR or a subsequent PR
<!-- 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)
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- [X] All unit tests pass, and I have added new tests where possible
- [X] I didn't break anyone :smile:
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Python: Add the Python process framework (#9363)
### Motivation and Context
An initial PR to add the foundational pieces of the Python Process
framework, which holds it design to be similar to dotnet in that step
types are added to a process builder, and later on, when the step is
run, it is first instantiated and the proper state is provided.
<!-- 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
Adding the initial process framework components:
- Closes #9354
**TODO**
- more unit tests will be added to increase the code coverage. Currently
there are several files with no (or low) code coverage.
- more samples will either be added to this PR or a subsequent PR
<!-- 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 :smile:
2024-10-24 13:37:45 -04:00
]
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[[package]]
name = "distlib"
Python: Fix: Implement __deepcopy__ on KernelFunction to handle non-serializable OTEL metrics (#12803)
### Description
This PR fixes TypeError: cannot pickle '_thread.RLock' object which
occurs when kernel.clone() is called while OpenTelemetry metrics are
enabled.
- Fixes: #12802
### Motivation and Context
When using features like `HandoffOrchestration`, the agent's `Kernel` is
cloned. This process uses `copy.deepcopy`, which fails on the
`KernelFunction`'s OpenTelemetry `Histogram` fields
(`invocation_duration_histogram` and `streaming_duration_histogram`).
These histogram objects contain thread locks, making them
non-serializable.
Previous solutions, like the one in **PR #9292**, used
`Field(exclude=True, default_factory=...)`. While that was a necessary
step, it was insufficient because `deepcopy` does not respect the
`exclude` flag in the same way that Pydantic's serialization does. This
bug blocks the use of certain agentic features in any application that
has metrics-based observability enabled.
### Description of the change
This solution introduces a custom __deepcopy__ method to the
KernelFunction Pydantic model. This method correctly handles the
deep-copying process for KernelFunction instances:
1. It intercepts the deepcopy call for KernelFunction objects.
2. It uses self.model_copy(deep=False) to create a new instance of the
Pydantic model. Using deep=False allows the default_factory for the
histogram fields to create new, clean instances on the copied object,
while other fields are shallow-copied.
3. It then manually deep-copies all other attributes from the original
object to the new one, ensuring that any other mutable objects (like
metadata`) are correctly duplicated. The histogram fields are explicitly
skipped in this step, as they have already been recreated by
`model_copy.
4. Standard deepcopy memoization is used to prevent infinite recursion
in the case of cyclic object graphs.
This approach ensures that the non-serializable fields are
re-initialized instead of copied, resolving the TypeError while
maintaining the integrity of the cloned object.
### How has this been tested?
The fix was validated using the reproduction steps outlined in the
associated issue. Running an agent orchestration that triggers
kernel.clone() with OpenTelemetry metrics enabled now completes
successfully without any errors.
### 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 :smile:
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2025-08-08 01:48:43 +03:00
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[[package]]
name = "docstring-parser"
Python: Fix: Implement __deepcopy__ on KernelFunction to handle non-serializable OTEL metrics (#12803)
### Description
This PR fixes TypeError: cannot pickle '_thread.RLock' object which
occurs when kernel.clone() is called while OpenTelemetry metrics are
enabled.
- Fixes: #12802
### Motivation and Context
When using features like `HandoffOrchestration`, the agent's `Kernel` is
cloned. This process uses `copy.deepcopy`, which fails on the
`KernelFunction`'s OpenTelemetry `Histogram` fields
(`invocation_duration_histogram` and `streaming_duration_histogram`).
These histogram objects contain thread locks, making them
non-serializable.
Previous solutions, like the one in **PR #9292**, used
`Field(exclude=True, default_factory=...)`. While that was a necessary
step, it was insufficient because `deepcopy` does not respect the
`exclude` flag in the same way that Pydantic's serialization does. This
bug blocks the use of certain agentic features in any application that
has metrics-based observability enabled.
### Description of the change
This solution introduces a custom __deepcopy__ method to the
KernelFunction Pydantic model. This method correctly handles the
deep-copying process for KernelFunction instances:
1. It intercepts the deepcopy call for KernelFunction objects.
2. It uses self.model_copy(deep=False) to create a new instance of the
Pydantic model. Using deep=False allows the default_factory for the
histogram fields to create new, clean instances on the copied object,
while other fields are shallow-copied.
3. It then manually deep-copies all other attributes from the original
object to the new one, ensuring that any other mutable objects (like
metadata`) are correctly duplicated. The histogram fields are explicitly
skipped in this step, as they have already been recreated by
`model_copy.
4. Standard deepcopy memoization is used to prevent infinite recursion
in the case of cyclic object graphs.
This approach ensures that the non-serializable fields are
re-initialized instead of copied, resolving the TypeError while
maintaining the integrity of the cloned object.
### How has this been tested?
The fix was validated using the reproduction steps outlined in the
associated issue. Running an agent orchestration that triggers
kernel.clone() with OpenTelemetry metrics enabled now completes
successfully without any errors.
### 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 :smile:
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2025-08-08 01:48:43 +03:00
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[[package]]
name = "durationpy"
Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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Python: Introducing vector and text search (#9345)
### 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 does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
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Python: Introducing vector and text search (#9345)
### 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 does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
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Python: Introducing vector and text search (#9345)
### 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?
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This PR does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
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Python: Add support for Azure AI Agent Service (#10414)
### Motivation and Context
Azure AI Agents are a managed offering, and are built on top of the
OpenAI assistant v2 APIs. Until now we have not had support for them in
SK Python. This PR adds support for Azure AI Agents in SK Python. The
current abstractions allow one to run managed Azure AI Agents using SK
constructs as is demonstrated via the getting started samples and/or the
`azure_ai_agent` concept code samples. The developer must first follow
the required Azure AI Agent deployment steps, and bring their connection
string as well as their model deployment name.
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Please help reviewers and future users, providing the following
information:
1. Why is this change required?
2. What problem does it solve?
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### Description
Add support for Azure AI Agent Service in SK
- Add sample code
- Add unit tests
- Restructures the `getting_started_with_agents` samples to move the
respective agent code into folders like `chat_completion`,
`azure_ai_agent`, etc. Updates the README as well.
- Closes #10187
**TODO**: add more test coverage either in this PR, or a subsequent one.
<!-- 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 :smile:
2025-02-07 11:11:10 +09:00
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Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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?
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-->
### Description
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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)
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- [X] All unit tests pass, and I have added new tests where possible
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[[package]]
Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
]
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
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1. Why is this change required?
2. What problem does it solve?
3. What scenario does it contribute to?
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### Description
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
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Python: Update sort step method for assistant invoke. (#10191)
### Motivation and Context
At times, when the (Azure) OpenAI Assistant Agent makes a tool call,
that tool call's creation timestamp is coming after the message creation
timestamp (the message creation being the text that the assistant
responds with). Currently in our code, if we have a tool call
(`FunctionCallContent`), we first yield that, and then we make a call to
get completed steps, to then yield more content like
`FunctionResultContent` and `TextContent`. There will be two steps (or
more, depending upon the number of tool calls).
Right now we sort the completed steps in this way:
```python
completed_steps_to_process: list[RunStep] = sorted(
[s for s in steps if s.completed_at is not None and s.id not in processed_step_ids],
key=lambda s: s.created_at,
)
```
When there are no failures, it's because the tool call was created
before the final message content (as has been since this assistant was
first coded). However, it appears that processing on the server-side can
cause fluctuations in when the steps are created/processed. When we have
a failure, we now have the message_creation (`TextContent`) yielded
before the `FunctionResultContent`, which if sent to an (Azure) OpenAI
Chat Completion endpoint will break with a 400 because of the gap in the
ordering between the `FunctionCallContent` and the
`FunctionResultContent`:
```
FunctionCallContent
TextContent # this should follow `FunctionResultContent` (and it does during times when we don't see a 400)
FunctionResultContent
```
The 400 error isn't 100% repeatable because of server-side processing,
so we will get the correct ordering:
```
FunctionCallContent
FunctionResultContent
TextContent
```
This PR updates the ordering of how we sort of the completed steps -- if
we have a step_type == "tool_calls" and "message_creation", we sort to
allow for "tool_calls" to come first, and any ties are broken by the
step.completed_at timestamp. With this update, we no longer receive 400s
because the ordering of the content types is correct when sending to an
OpenAI model.
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### Description
This PR:
- Updates the way we sort completed messages during an assistant invoke.
- Adds logging for the assistant invoke method at the `info` level.
- Closes #10141
<!-- 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)
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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 :smile:
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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"python_full_version < '3.11' and sys_platform == 'win32'",
]
2024-08-26 13:34:29 +02:00
dependencies = [
Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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Python: Add on_intermediate_message callback to the Agent abstraction (#11321)
### 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.
-->
Currently, our agents' `invoke` and `invoke_stream` have inconsistent
behavior where the responses may contain different contents. For
example, the chat completion agent (`invoke`) may return all content
types generated by the agent including function call/result contents
that are meant to be internal only, while the assistant agent doesn't
return those contents. This creates confusion both for users and
developers of SK of what an agent should return when working with
agents.
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
This PR standardizes the behavior of agents by adding an
`on_intermediate_message ` parameter to the `invoke` and `invoke_stream`
methods in the `Agent` abstraction. This parameter accepts a callback
function that will be called whenever a new message is generated by the
agent, except the last one that gets returned to the caller. This allows
the caller to inspect what the agent did after a response is returned.
In addition, this PR also converges on the behavior of the `invoke` and
`invoke_stream` methods (respectively) to return the same content types
from all agents, making it easier for users to work with agents
### 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 :smile:
2025-04-03 15:30:10 -07:00
]
2026-03-12 10:02:02 -07:00
sdist = { url = "https://files.pythonhosted.org/packages/e5/61/1810830e8b93c72dcd3c0f150c80a00c3deb229562d9423807ec92c3a539/ipython-8.38.0.tar.gz", hash = "sha256:9cfea8c903ce0867cc2f23199ed8545eb741f3a69420bfcf3743ad1cec856d39", size = 5513996, upload-time = "2026-01-05T10:59:06.901Z" }
Python: Add on_intermediate_message callback to the Agent abstraction (#11321)
### 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.
-->
Currently, our agents' `invoke` and `invoke_stream` have inconsistent
behavior where the responses may contain different contents. For
example, the chat completion agent (`invoke`) may return all content
types generated by the agent including function call/result contents
that are meant to be internal only, while the assistant agent doesn't
return those contents. This creates confusion both for users and
developers of SK of what an agent should return when working with
agents.
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
This PR standardizes the behavior of agents by adding an
`on_intermediate_message ` parameter to the `invoke` and `invoke_stream`
methods in the `Agent` abstraction. This parameter accepts a callback
function that will be called whenever a new message is generated by the
agent, except the last one that gets returned to the caller. This allows
the caller to inspect what the agent did after a response is returned.
In addition, this PR also converges on the behavior of the `invoke` and
`invoke_stream` methods (respectively) to return the same content types
from all agents, making it easier for users to work with agents
### 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 :smile:
2025-04-03 15:30:10 -07:00
wheels = [
2026-03-12 10:02:02 -07:00
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2025-05-13 07:58:06 +09:00
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
[[package]]
name = "ipython"
2026-02-10 11:05:13 +01:00
version = "9.10.0"
Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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2026-03-12 10:02:02 -07:00
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]
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name = "ipython"
version = "9.11.0"
source = { registry = "https://pypi.org/simple" }
Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
resolution-markers = [
2025-07-22 17:14:32 +02:00
"python_full_version >= '4' and sys_platform == 'darwin'",
2025-10-24 11:23:08 +09:00
"python_full_version >= '3.14' and python_full_version < '4' and sys_platform == 'darwin'",
"python_full_version == '3.13.*' and sys_platform == 'darwin'",
Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
"python_full_version == '3.12.*' and sys_platform == 'darwin'",
2025-07-22 17:14:32 +02:00
"python_full_version >= '4' and sys_platform == 'linux'",
2025-10-24 11:23:08 +09:00
"python_full_version >= '3.14' and python_full_version < '4' and sys_platform == 'linux'",
"python_full_version == '3.13.*' and sys_platform == 'linux'",
Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
"python_full_version == '3.12.*' and sys_platform == 'linux'",
2025-07-22 17:14:32 +02:00
"python_full_version >= '4' and sys_platform == 'win32'",
2025-10-24 11:23:08 +09:00
"python_full_version >= '3.14' and python_full_version < '4' and sys_platform == 'win32'",
"python_full_version == '3.13.*' and sys_platform == 'win32'",
Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
"python_full_version == '3.12.*' and sys_platform == 'win32'",
]
dependencies = [
2026-03-12 10:02:02 -07:00
{ name = "colorama", marker = "python_full_version >= '3.12' and sys_platform == 'win32'" },
{ name = "decorator", marker = "(python_full_version >= '3.12' and sys_platform == 'darwin') or (python_full_version >= '3.12' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform == 'win32')" },
{ name = "ipython-pygments-lexers", marker = "(python_full_version >= '3.12' and sys_platform == 'darwin') or (python_full_version >= '3.12' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform == 'win32')" },
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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2026-03-12 10:02:02 -07:00
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
]
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
]
2024-08-26 13:34:29 +02:00
[[package]]
name = "isodate"
Python: Introducing vector and text search (#9345)
### 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?
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-->
This PR does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
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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 :smile:
2025-04-11 08:26:22 +02:00
[[package]]
name = "jsonpath-ng"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
version = "1.8.0"
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 :smile:
2025-04-11 08:26:22 +02:00
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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?
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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 :smile:
2025-04-11 08:26:22 +02:00
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2026-02-10 11:05:13 +01:00
[[package]]
name = "librt"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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?
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### Description
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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[[package]]
name = "microsoft-agents-activity"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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2025-09-22 18:01:51 -07:00
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[[package]]
name = "microsoft-agents-copilotstudio-client"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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2025-09-22 18:01:51 -07:00
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[[package]]
name = "microsoft-agents-hosting-core"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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2025-09-22 18:01:51 -07:00
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[[package]]
name = "mistralai"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
version = "1.12.4"
2024-08-26 13:34:29 +02:00
source = { registry = "https://pypi.org/simple" }
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2024-11-13 17:52:54 +01:00
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2025-10-24 11:23:08 +09:00
{ name = "invoke", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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Python: Update sort step method for assistant invoke. (#10191)
### Motivation and Context
At times, when the (Azure) OpenAI Assistant Agent makes a tool call,
that tool call's creation timestamp is coming after the message creation
timestamp (the message creation being the text that the assistant
responds with). Currently in our code, if we have a tool call
(`FunctionCallContent`), we first yield that, and then we make a call to
get completed steps, to then yield more content like
`FunctionResultContent` and `TextContent`. There will be two steps (or
more, depending upon the number of tool calls).
Right now we sort the completed steps in this way:
```python
completed_steps_to_process: list[RunStep] = sorted(
[s for s in steps if s.completed_at is not None and s.id not in processed_step_ids],
key=lambda s: s.created_at,
)
```
When there are no failures, it's because the tool call was created
before the final message content (as has been since this assistant was
first coded). However, it appears that processing on the server-side can
cause fluctuations in when the steps are created/processed. When we have
a failure, we now have the message_creation (`TextContent`) yielded
before the `FunctionResultContent`, which if sent to an (Azure) OpenAI
Chat Completion endpoint will break with a 400 because of the gap in the
ordering between the `FunctionCallContent` and the
`FunctionResultContent`:
```
FunctionCallContent
TextContent # this should follow `FunctionResultContent` (and it does during times when we don't see a 400)
FunctionResultContent
```
The 400 error isn't 100% repeatable because of server-side processing,
so we will get the correct ordering:
```
FunctionCallContent
FunctionResultContent
TextContent
```
This PR updates the ordering of how we sort of the completed steps -- if
we have a step_type == "tool_calls" and "message_creation", we sort to
allow for "tool_calls" to come first, and any ties are broken by the
step.completed_at timestamp. With this update, we no longer receive 400s
because the ordering of the content types is correct when sending to an
OpenAI model.
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### Description
This PR:
- Updates the way we sort completed messages during an assistant invoke.
- Adds logging for the assistant invoke method at the `info` level.
- Closes #10141
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### 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)
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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 :smile:
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
2025-01-21 04:29:13 +09:00
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- Thank you for your contribution to the semantic-kernel repo!
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2. What problem does it solve?
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-->
### Description
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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[[package]]
name = "motor"
Python: Update the AzureAIAgent packages, types, and APIs based on Azure SDK's new code (#12084)
### Motivation and Context
The Azure AI Agent Service is moving to GA soon. As part of that,
they've made several changes in the underlying APIs, which require the
use of new packages, updated type imports and some new underlying APIs.
This PR updates the AzureAIAgent to use the latest Azure SDK's new code.
- The client's connection string has been replaced with an endpoint.
- The `AzureAIAgentSettings`'s `model_deployment_name` was updated to
`deployment_name`.
- Tools are now imported from `from azure.ai.agents.models` instead of
`from azure.ai.projects.models`.
- Declarative spec handling for AzureAIAgent was updated based on the
new APIs.
- Unit tests were updated.
<!-- 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 AzureAIAgent related code, samples and tests based on the
Azure SDK's latest APIs.
- Closes #12177
<!-- 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2025-05-20 08:43:40 +09:00
version = "3.7.1"
2024-08-26 13:34:29 +02:00
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Python: Bump nbconvert from 7.16.6 to 7.17.0 in /python (#13528)
Bumps [nbconvert](https://github.com/jupyter/nbconvert) from 7.16.6 to
7.17.0.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/jupyter/nbconvert/releases">nbconvert's
releases</a>.</em></p>
<blockquote>
<h2>v7.17.0</h2>
<h2>7.17.0</h2>
<p>(<a
href="https://github.com/jupyter/nbconvert/compare/v7.16.6...c9ac1d1040459ed1ff9eb34e9918ce5a87cf9d71">Full
Changelog</a>)</p>
<h3>Enhancements made</h3>
<ul>
<li>Add support for arbitrary browser arguments <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2227">#2227</a>
(<a href="https://github.com/shreve"><code>@shreve</code></a>, <a
href="https://github.com/Carreau"><code>@Carreau</code></a>, <a
href="https://github.com/krassowski"><code>@krassowski</code></a>)</li>
</ul>
<h3>Bugs fixed</h3>
<ul>
<li>Fix QtPNGExporter returning empty bytes on macOS <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2264">#2264</a>
(<a href="https://github.com/h3pdesign"><code>@h3pdesign</code></a>, <a
href="https://github.com/Carreau"><code>@Carreau</code></a>, <a
href="https://github.com/QuLogic"><code>@QuLogic</code></a>)</li>
<li>Fix CVE-2025-53000: Secure Inkscape Windows path (registry first +
block CWD) <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2261">#2261</a>
(<a href="https://github.com/h3pdesign"><code>@h3pdesign</code></a>, <a
href="https://github.com/krassowski"><code>@krassowski</code></a>, <a
href="https://github.com/mberlanda"><code>@mberlanda</code></a>, <a
href="https://github.com/minrk"><code>@minrk</code></a>, <a
href="https://github.com/salmankadaya"><code>@salmankadaya</code></a>,
<a
href="https://github.com/th3gowtham"><code>@th3gowtham</code></a>)</li>
<li>Fix get_export_names and get_exporter default args <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2228">#2228</a>
(<a href="https://github.com/shreve"><code>@shreve</code></a>, <a
href="https://github.com/krassowski"><code>@krassowski</code></a>)</li>
<li>PyPA-Compliant Summary <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2226">#2226</a>
(<a
href="https://github.com/hackowitz-af"><code>@hackowitz-af</code></a>,
<a href="https://github.com/Carreau"><code>@Carreau</code></a>)</li>
</ul>
<h3>Maintenance and upkeep improvements</h3>
<ul>
<li>avoid cov environment on free-threaded Pythons <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2267">#2267</a>
(<a href="https://github.com/minrk"><code>@minrk</code></a>)</li>
<li>update pre-commit, and fix all issues. <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2238">#2238</a>
(<a href="https://github.com/Carreau"><code>@Carreau</code></a>)</li>
<li>Drop test on 3.9, test on 3.13, 3.14, 3.14t <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2237">#2237</a>
(<a href="https://github.com/Carreau"><code>@Carreau</code></a>)</li>
<li>Bump the actions group across 1 directory with 2 updates <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2231">#2231</a>
(<a href="https://github.com/Carreau"><code>@Carreau</code></a>, <a
href="https://github.com/krassowski"><code>@krassowski</code></a>)</li>
<li>Replace <code>@flaky.flaky</code> decorate with pytest marker <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2229">#2229</a>
(<a href="https://github.com/mgorny"><code>@mgorny</code></a>, <a
href="https://github.com/Carreau"><code>@Carreau</code></a>)</li>
<li>update to mermaid 11.10.0 <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2224">#2224</a>
(<a href="https://github.com/bollwyvl"><code>@bollwyvl</code></a>, <a
href="https://github.com/krassowski"><code>@krassowski</code></a>)</li>
<li>Drop support for Python 3.8, fix the CI tests <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2221">#2221</a>
(<a href="https://github.com/shreve"><code>@shreve</code></a>, <a
href="https://github.com/minrk"><code>@minrk</code></a>)</li>
</ul>
<h3>Documentation improvements</h3>
<ul>
<li>Use <code>intersphinx_registry</code> <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2232">#2232</a>
(<a href="https://github.com/Carreau"><code>@Carreau</code></a>, <a
href="https://github.com/krassowski"><code>@krassowski</code></a>)</li>
</ul>
<h3>Contributors to this release</h3>
<p>The following people contributed discussions, new ideas, code and
documentation contributions, and review.
See <a
href="https://github-activity.readthedocs.io/en/latest/use/#how-does-this-tool-define-contributions-in-the-reports">our
definition of contributors</a>.</p>
<p>(<a
href="https://github.com/jupyter/nbconvert/graphs/contributors?from=2025-01-28&to=2026-01-29&type=c">GitHub
contributors page for this release</a>)</p>
<p><a href="https://github.com/bollwyvl"><code>@bollwyvl</code></a> (<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Abollwyvl+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/Carreau"><code>@Carreau</code></a> (<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3ACarreau+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/h3pdesign"><code>@h3pdesign</code></a>
(<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Ah3pdesign+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a
href="https://github.com/hackowitz-af"><code>@hackowitz-af</code></a>
(<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Ahackowitz-af+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/krassowski"><code>@krassowski</code></a>
(<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Akrassowski+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/mberlanda"><code>@mberlanda</code></a>
(<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Amberlanda+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/mgorny"><code>@mgorny</code></a> (<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Amgorny+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/minrk"><code>@minrk</code></a> (<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Aminrk+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/MSeal"><code>@MSeal</code></a> (<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3AMSeal+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/QuLogic"><code>@QuLogic</code></a> (<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3AQuLogic+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a
href="https://github.com/salmankadaya"><code>@salmankadaya</code></a>
(<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Asalmankadaya+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/shreve"><code>@shreve</code></a> (<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Ashreve+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/th3gowtham"><code>@th3gowtham</code></a>
(<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Ath3gowtham+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)</p>
</blockquote>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/jupyter/nbconvert/blob/main/CHANGELOG.md">nbconvert's
changelog</a>.</em></p>
<blockquote>
<h2>7.17.0</h2>
<p>(<a
href="https://github.com/jupyter/nbconvert/compare/v7.16.6...c9ac1d1040459ed1ff9eb34e9918ce5a87cf9d71">Full
Changelog</a>)</p>
<h3>Enhancements made</h3>
<ul>
<li>Add support for arbitrary browser arguments <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2227">#2227</a>
(<a href="https://github.com/shreve"><code>@shreve</code></a>, <a
href="https://github.com/Carreau"><code>@Carreau</code></a>, <a
href="https://github.com/krassowski"><code>@krassowski</code></a>)</li>
</ul>
<h3>Bugs fixed</h3>
<ul>
<li>Fix QtPNGExporter returning empty bytes on macOS <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2264">#2264</a>
(<a href="https://github.com/h3pdesign"><code>@h3pdesign</code></a>, <a
href="https://github.com/Carreau"><code>@Carreau</code></a>, <a
href="https://github.com/QuLogic"><code>@QuLogic</code></a>)</li>
<li>Fix CVE-2025-53000: Secure Inkscape Windows path (registry first +
block CWD) <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2261">#2261</a>
(<a href="https://github.com/h3pdesign"><code>@h3pdesign</code></a>, <a
href="https://github.com/krassowski"><code>@krassowski</code></a>, <a
href="https://github.com/mberlanda"><code>@mberlanda</code></a>, <a
href="https://github.com/minrk"><code>@minrk</code></a>, <a
href="https://github.com/salmankadaya"><code>@salmankadaya</code></a>,
<a
href="https://github.com/th3gowtham"><code>@th3gowtham</code></a>)</li>
<li>Fix get_export_names and get_exporter default args <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2228">#2228</a>
(<a href="https://github.com/shreve"><code>@shreve</code></a>, <a
href="https://github.com/krassowski"><code>@krassowski</code></a>)</li>
<li>PyPA-Compliant Summary <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2226">#2226</a>
(<a
href="https://github.com/hackowitz-af"><code>@hackowitz-af</code></a>,
<a href="https://github.com/Carreau"><code>@Carreau</code></a>)</li>
</ul>
<h3>Maintenance and upkeep improvements</h3>
<ul>
<li>avoid cov environment on free-threaded Pythons <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2267">#2267</a>
(<a href="https://github.com/minrk"><code>@minrk</code></a>)</li>
<li>update pre-commit, and fix all issues. <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2238">#2238</a>
(<a href="https://github.com/Carreau"><code>@Carreau</code></a>)</li>
<li>Drop test on 3.9, test on 3.13, 3.14, 3.14t <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2237">#2237</a>
(<a href="https://github.com/Carreau"><code>@Carreau</code></a>)</li>
<li>Bump the actions group across 1 directory with 2 updates <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2231">#2231</a>
(<a href="https://github.com/Carreau"><code>@Carreau</code></a>, <a
href="https://github.com/krassowski"><code>@krassowski</code></a>)</li>
<li>Replace <code>@flaky.flaky</code> decorate with pytest marker <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2229">#2229</a>
(<a href="https://github.com/mgorny"><code>@mgorny</code></a>, <a
href="https://github.com/Carreau"><code>@Carreau</code></a>)</li>
<li>update to mermaid 11.10.0 <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2224">#2224</a>
(<a href="https://github.com/bollwyvl"><code>@bollwyvl</code></a>, <a
href="https://github.com/krassowski"><code>@krassowski</code></a>)</li>
<li>Drop support for Python 3.8, fix the CI tests <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2221">#2221</a>
(<a href="https://github.com/shreve"><code>@shreve</code></a>, <a
href="https://github.com/minrk"><code>@minrk</code></a>)</li>
</ul>
<h3>Documentation improvements</h3>
<ul>
<li>Use <code>intersphinx_registry</code> <a
href="https://redirect.github.com/jupyter/nbconvert/pull/2232">#2232</a>
(<a href="https://github.com/Carreau"><code>@Carreau</code></a>, <a
href="https://github.com/krassowski"><code>@krassowski</code></a>)</li>
</ul>
<h3>Contributors to this release</h3>
<p>The following people contributed discussions, new ideas, code and
documentation contributions, and review.
See <a
href="https://github-activity.readthedocs.io/en/latest/use/#how-does-this-tool-define-contributions-in-the-reports">our
definition of contributors</a>.</p>
<p>(<a
href="https://github.com/jupyter/nbconvert/graphs/contributors?from=2025-01-28&to=2026-01-29&type=c">GitHub
contributors page for this release</a>)</p>
<p><a href="https://github.com/bollwyvl"><code>@bollwyvl</code></a> (<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Abollwyvl+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/Carreau"><code>@Carreau</code></a> (<a
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| <a href="https://github.com/h3pdesign"><code>@h3pdesign</code></a>
(<a
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| <a
href="https://github.com/hackowitz-af"><code>@hackowitz-af</code></a>
(<a
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| <a href="https://github.com/krassowski"><code>@krassowski</code></a>
(<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Akrassowski+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/mberlanda"><code>@mberlanda</code></a>
(<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Amberlanda+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/mgorny"><code>@mgorny</code></a> (<a
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| <a href="https://github.com/minrk"><code>@minrk</code></a> (<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Aminrk+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/MSeal"><code>@MSeal</code></a> (<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3AMSeal+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/QuLogic"><code>@QuLogic</code></a> (<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3AQuLogic+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a
href="https://github.com/salmankadaya"><code>@salmankadaya</code></a>
(<a
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| <a href="https://github.com/shreve"><code>@shreve</code></a> (<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Ashreve+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)
| <a href="https://github.com/th3gowtham"><code>@th3gowtham</code></a>
(<a
href="https://github.com/search?q=repo%3Ajupyter%2Fnbconvert+involves%3Ath3gowtham+updated%3A2025-01-28..2026-01-29&type=Issues">activity</a>)</p>
<!-- raw HTML omitted -->
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/jupyter/nbconvert/commit/21b35d85b46f5ff0956d3d088a52b9bef00e8196"><code>21b35d8</code></a>
Publish 7.17.0</li>
<li><a
href="https://github.com/jupyter/nbconvert/commit/c9ac1d1040459ed1ff9eb34e9918ce5a87cf9d71"><code>c9ac1d1</code></a>
Fix CVE-2025-53000: Secure Inkscape Windows path (registry first + block
CWD)...</li>
<li><a
href="https://github.com/jupyter/nbconvert/commit/b13276d80ae6fadbfda981304810e26f421c4ced"><code>b13276d</code></a>
avoid cov environment on free-threaded Pythons (<a
href="https://redirect.github.com/jupyter/nbconvert/issues/2267">#2267</a>)</li>
<li><a
href="https://github.com/jupyter/nbconvert/commit/7c7055fe833fe9832e6f9a64fa5f67d66692c8d6"><code>7c7055f</code></a>
[pre-commit.ci] auto fixes from pre-commit.com hooks</li>
<li><a
href="https://github.com/jupyter/nbconvert/commit/74f3ddd37ef4b7ffb5eee9acbf52062217c15852"><code>74f3ddd</code></a>
Fix QtPNGExporter returning empty bytes on macOS</li>
<li><a
href="https://github.com/jupyter/nbconvert/commit/216550b2aae4c329f4dab597a96ae7cac30de79a"><code>216550b</code></a>
fix links</li>
<li><a
href="https://github.com/jupyter/nbconvert/commit/39777ac5716350e994171d025a7369c58b0afc8c"><code>39777ac</code></a>
try to comment fialing test</li>
<li><a
href="https://github.com/jupyter/nbconvert/commit/7b591ca526f2598dbae3256a53765659c3edcf14"><code>7b591ca</code></a>
ruff-check</li>
<li><a
href="https://github.com/jupyter/nbconvert/commit/6ec7638a3dc7a0bc2c533c116202d7f9b07ce9d9"><code>6ec7638</code></a>
parent</li>
<li><a
href="https://github.com/jupyter/nbconvert/commit/59414b36f94e318d4207e8af863167047d936c19"><code>59414b3</code></a>
fix mypy</li>
<li>Additional commits viewable in <a
href="https://github.com/jupyter/nbconvert/compare/v7.16.6...v7.17.0">compare
view</a></li>
</ul>
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version = "7.17.0"
2024-08-26 13:34:29 +02:00
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "beautifulsoup4", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
Python: Update sort step method for assistant invoke. (#10191)
### Motivation and Context
At times, when the (Azure) OpenAI Assistant Agent makes a tool call,
that tool call's creation timestamp is coming after the message creation
timestamp (the message creation being the text that the assistant
responds with). Currently in our code, if we have a tool call
(`FunctionCallContent`), we first yield that, and then we make a call to
get completed steps, to then yield more content like
`FunctionResultContent` and `TextContent`. There will be two steps (or
more, depending upon the number of tool calls).
Right now we sort the completed steps in this way:
```python
completed_steps_to_process: list[RunStep] = sorted(
[s for s in steps if s.completed_at is not None and s.id not in processed_step_ids],
key=lambda s: s.created_at,
)
```
When there are no failures, it's because the tool call was created
before the final message content (as has been since this assistant was
first coded). However, it appears that processing on the server-side can
cause fluctuations in when the steps are created/processed. When we have
a failure, we now have the message_creation (`TextContent`) yielded
before the `FunctionResultContent`, which if sent to an (Azure) OpenAI
Chat Completion endpoint will break with a 400 because of the gap in the
ordering between the `FunctionCallContent` and the
`FunctionResultContent`:
```
FunctionCallContent
TextContent # this should follow `FunctionResultContent` (and it does during times when we don't see a 400)
FunctionResultContent
```
The 400 error isn't 100% repeatable because of server-side processing,
so we will get the correct ordering:
```
FunctionCallContent
FunctionResultContent
TextContent
```
This PR updates the ordering of how we sort of the completed steps -- if
we have a step_type == "tool_calls" and "message_creation", we sort to
allow for "tool_calls" to come first, and any ties are broken by the
step.completed_at timestamp. With this update, we no longer receive 400s
because the ordering of the content types is correct when sending to an
OpenAI model.
<!-- 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:
- Updates the way we sort completed messages during an assistant invoke.
- Adds logging for the assistant invoke method at the `info` level.
- Closes #10141
<!-- 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 :smile:
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
2025-01-21 04:29:13 +09:00
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Python: Introducing vector and text search (#9345)
### 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?
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This PR does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
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Python: Introducing vector and text search (#9345)
### 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 does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
dependencies = [
2025-03-26 11:29:12 +09:00
{ name = "nvidia-nvjitlink-cu12", marker = "sys_platform == 'linux'" },
Python: Introducing vector and text search (#9345)
### 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 does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
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Python: Add support for Azure AI Agent Service (#10414)
### Motivation and Context
Azure AI Agents are a managed offering, and are built on top of the
OpenAI assistant v2 APIs. Until now we have not had support for them in
SK Python. This PR adds support for Azure AI Agents in SK Python. The
current abstractions allow one to run managed Azure AI Agents using SK
constructs as is demonstrated via the getting started samples and/or the
`azure_ai_agent` concept code samples. The developer must first follow
the required Azure AI Agent deployment steps, and bring their connection
string as well as their model deployment 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 support for Azure AI Agent Service in SK
- Add sample code
- Add unit tests
- Restructures the `getting_started_with_agents` samples to move the
respective agent code into folders like `chat_completion`,
`azure_ai_agent`, etc. Updates the README as well.
- Closes #10187
**TODO**: add more test coverage either in this PR, or a subsequent one.
<!-- 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 :smile:
2025-02-07 11:11:10 +09:00
[[package]]
name = "nvidia-cusparselt-cu12"
2025-09-11 13:21:58 +08:00
version = "0.7.1"
Python: Add support for Azure AI Agent Service (#10414)
### Motivation and Context
Azure AI Agents are a managed offering, and are built on top of the
OpenAI assistant v2 APIs. Until now we have not had support for them in
SK Python. This PR adds support for Azure AI Agents in SK Python. The
current abstractions allow one to run managed Azure AI Agents using SK
constructs as is demonstrated via the getting started samples and/or the
`azure_ai_agent` concept code samples. The developer must first follow
the required Azure AI Agent deployment steps, and bring their connection
string as well as their model deployment 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 support for Azure AI Agent Service in SK
- Add sample code
- Add unit tests
- Restructures the `getting_started_with_agents` samples to move the
respective agent code into folders like `chat_completion`,
`azure_ai_agent`, etc. Updates the README as well.
- Closes #10187
**TODO**: add more test coverage either in this PR, or a subsequent one.
<!-- 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 :smile:
2025-02-07 11:11:10 +09:00
source = { registry = "https://pypi.org/simple" }
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Python: Add support for Azure AI Agent Service (#10414)
### Motivation and Context
Azure AI Agents are a managed offering, and are built on top of the
OpenAI assistant v2 APIs. Until now we have not had support for them in
SK Python. This PR adds support for Azure AI Agents in SK Python. The
current abstractions allow one to run managed Azure AI Agents using SK
constructs as is demonstrated via the getting started samples and/or the
`azure_ai_agent` concept code samples. The developer must first follow
the required Azure AI Agent deployment steps, and bring their connection
string as well as their model deployment 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 support for Azure AI Agent Service in SK
- Add sample code
- Add unit tests
- Restructures the `getting_started_with_agents` samples to move the
respective agent code into folders like `chat_completion`,
`azure_ai_agent`, etc. Updates the README as well.
- Closes #10187
**TODO**: add more test coverage either in this PR, or a subsequent one.
<!-- 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 :smile:
2025-02-07 11:11:10 +09:00
]
2024-08-26 13:34:29 +02:00
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Python: enable ollama streaming tool calls (#9890)
### Motivation and Context
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
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2. What problem does it solve?
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Ollama has enabled streaming tool calling, and so we add that too!
See release notes: https://github.com/ollama/ollama/releases/tag/v0.4.6
Also encountered a bug when passing generic PromptExecutionSettings to
all ChatCompletions with Function calling enabled, the
_verify_function_choice_settings often check if something is a type of
PES matching the service, that is not the case and throws even though it
should work (for instance when using a yaml based function), this fixes
that as well by casting in the public methods of chat completion base.
### 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 :smile:
2024-12-09 16:58:57 +01:00
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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Python: Bump werkzeug from 3.1.1 to 3.1.6 in /python (#13605)
Bumps [werkzeug](https://github.com/pallets/werkzeug) from 3.1.1 to
3.1.6.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/werkzeug/releases">werkzeug's
releases</a>.</em></p>
<blockquote>
<h2>3.1.6</h2>
<p>This is the Werkzeug 3.1.6 security fix release, which fixes a
security issue but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.6/">https://pypi.org/project/Werkzeug/3.1.6/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-6">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-6</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow special devices
names in multi-segment paths. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-29vq-49wr-vm6x">GHSA-29vq-49wr-vm6x</a></li>
</ul>
<h2>3.1.5</h2>
<p>This is the Werkzeug 3.1.5 security fix release, which fixes security
issues and bugs but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.5/">https://pypi.org/project/Werkzeug/3.1.5/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-5">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-5</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/43?closed=1">https://github.com/pallets/werkzeug/milestone/43?closed=1</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow more special device
names, regardless of extension or surrounding spaces. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-87hc-h4r5-73f7">GHSA-87hc-h4r5-73f7</a></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary. This fixes the previous attempt, which caused incorrect
content lengths. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3065">#3065</a>
<a
href="https://redirect.github.com/pallets/werkzeug/issues/3077">#3077</a></li>
<li>Fix <code>AttributeError</code> when initializing
<code>DebuggedApplication</code> with <code>pin_security=False</code>.
<a
href="https://redirect.github.com/pallets/werkzeug/issues/3075">#3075</a></li>
</ul>
<h2>3.1.4</h2>
<p>This is the Werkzeug 3.1.4 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes
compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.4/">https://pypi.org/project/Werkzeug/3.1.4/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-4">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-4</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/42?closed=1">https://github.com/pallets/werkzeug/milestone/42?closed=1</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow special device
names. This prevents reading from these when using
<code>send_from_directory</code>. <code>secure_filename</code> already
prevented writing to these. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-hgf8-39gv-g3f2">ghsa-hgf8-39gv-g3f2</a></li>
<li>The debugger pin fails after 10 attempts instead of 11. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3020">#3020</a></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3065">#3065</a></li>
<li>Improve CPU usage during Watchdog reloader. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3054">#3054</a></li>
<li><code>Request.json</code> annotation is more accurate. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3067">#3067</a></li>
<li>Traceback rendering handles when the line number is beyond the
available source lines. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3044">#3044</a></li>
<li><code>HTTPException.get_response</code> annotation and doc better
conveys the distinction between WSGI and sans-IO responses. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3056">#3056</a></li>
</ul>
<h2>3.1.3</h2>
<p>This is the Werkzeug 3.1.3 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes vs
3.1.0.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.3/">https://pypi.org/project/Werkzeug/3.1.3/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-3">https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-3</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/41?closed=1">https://github.com/pallets/werkzeug/milestone/41?closed=1</a></p>
<ul>
<li>Initial data passed to <code>MultiDict</code> and similar interfaces
only accepts <code>list</code>, <code>tuple</code>, or <code>set</code>
when passing multiple values. It had been changed to accept any
<code>Collection</code>, but this matched types that should be treated
as single values, such as <code>bytes</code>. <a
href="https://redirect.github.com/pallets/werkzeug/issues/2994">#2994</a></li>
<li>When the <code>Host</code> header is not set and
<code>Request.host</code> falls back to the WSGI
<code>SERVER_NAME</code> value, if that value is an IPv6 address it is
wrapped in <code>[]</code> to match the <code>Host</code> header. <a
href="https://redirect.github.com/pallets/werkzeug/issues/2993">#2993</a></li>
</ul>
<h2>3.1.2</h2>
<p>This is the Werkzeug 3.1.2 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes vs
3.1.0.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.2/">https://pypi.org/project/Werkzeug/3.1.2/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-2">https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-2</a></p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/werkzeug/blob/main/CHANGES.rst">werkzeug's
changelog</a>.</em></p>
<blockquote>
<h2>Version 3.1.6</h2>
<p>Released 2026-02-19</p>
<ul>
<li><code>safe_join</code> on Windows does not allow special devices
names in
multi-segment paths. :ghsa:<code>29vq-49wr-vm6x</code></li>
</ul>
<h2>Version 3.1.5</h2>
<p>Released 2026-01-08</p>
<ul>
<li><code>safe_join</code> on Windows does not allow more special device
names, regardless
of extension or surrounding spaces.
:ghsa:<code>87hc-h4r5-73f7</code></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary.
This fixes the previous attempt, which caused incorrect content lengths.
:issue:<code>3065</code> :issue:<code>3077</code></li>
<li>Fix <code>AttributeError</code> when initializing
<code>DebuggedApplication</code> with
<code>pin_security=False</code>. :issue:<code>3075</code></li>
</ul>
<h2>Version 3.1.4</h2>
<p>Released 2025-11-28</p>
<ul>
<li><code>safe_join</code> on Windows does not allow special device
names. This prevents
reading from these when using <code>send_from_directory</code>.
<code>secure_filename</code>
already prevented writing to these.
:ghsa:<code>hgf8-39gv-g3f2</code></li>
<li>The debugger pin fails after 10 attempts instead of 11.
:pr:<code>3020</code></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary.
:issue:<code>3065</code></li>
<li>Improve CPU usage during Watchdog reloader.
:issue:<code>3054</code></li>
<li><code>Request.json</code> annotation is more accurate.
:issue:<code>3067</code></li>
<li>Traceback rendering handles when the line number is beyond the
available
source lines. :issue:<code>3044</code></li>
<li><code>HTTPException.get_response</code> annotation and doc better
conveys the
distinction between WSGI and sans-IO responses.
:issue:<code>3056</code></li>
</ul>
<h2>Version 3.1.3</h2>
<p>Released 2024-11-08</p>
<ul>
<li>Initial data passed to <code>MultiDict</code> and similar interfaces
only accepts
<code>list</code>, <code>tuple</code>, or <code>set</code> when passing
multiple values. It had been
changed to accept any <code>Collection</code>, but this matched types
that should be</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/pallets/werkzeug/commit/04da1b5221b7a7b57e82246e4b5741d37a6b2e56"><code>04da1b5</code></a>
release version 3.1.6</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/f407712fdc60a09c2b3f4fe7db557703e5d9338d"><code>f407712</code></a>
Merge commit from fork</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/f54fe98026253e70fbbcd35a6b52fb67cfff1c03"><code>f54fe98</code></a>
safe_join prevents Windows special device names in multi-segment
paths</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/d005985ef69ffe3275eda8fb6fb25e074dbe871b"><code>d005985</code></a>
start version 3.1.6</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/8565c2cbd6681ae8463e77d4fc0795324a7fdae7"><code>8565c2c</code></a>
document rule priority (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3102">#3102</a>)</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/3febc7e90072bffe04c27e6b7478dfc4f88930df"><code>3febc7e</code></a>
document rule priority</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/2525b827646c10ab7adb334664e6a4af1b769181"><code>2525b82</code></a>
remove state machine docs</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/4abfbd553cdeb6d4e6fa693340d52b13c884079f"><code>4abfbd5</code></a>
rewrite build docstring (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3097">#3097</a>)</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/161c18b2a8800ae6ef377fb3cbdb933a878fea67"><code>161c18b</code></a>
rewrite build docstring</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/86e11c29e44726dae524cd9db11549b3b1ad681d"><code>86e11c2</code></a>
release version 3.1.5 (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3085">#3085</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/pallets/werkzeug/compare/3.1.1...3.1.6">compare
view</a></li>
</ul>
</details>
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2026-03-19 12:20:39 +01:00
version = "0.19.4"
2024-08-26 13:34:29 +02:00
source = { registry = "https://pypi.org/simple" }
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{ name = "jsonschema", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "jsonschema-path", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "more-itertools", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "openapi-schema-validator", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "openapi-spec-validator", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "parse", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "werkzeug", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
Python: Bump werkzeug from 3.1.1 to 3.1.6 in /python (#13605)
Bumps [werkzeug](https://github.com/pallets/werkzeug) from 3.1.1 to
3.1.6.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/werkzeug/releases">werkzeug's
releases</a>.</em></p>
<blockquote>
<h2>3.1.6</h2>
<p>This is the Werkzeug 3.1.6 security fix release, which fixes a
security issue but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.6/">https://pypi.org/project/Werkzeug/3.1.6/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-6">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-6</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow special devices
names in multi-segment paths. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-29vq-49wr-vm6x">GHSA-29vq-49wr-vm6x</a></li>
</ul>
<h2>3.1.5</h2>
<p>This is the Werkzeug 3.1.5 security fix release, which fixes security
issues and bugs but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.5/">https://pypi.org/project/Werkzeug/3.1.5/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-5">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-5</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/43?closed=1">https://github.com/pallets/werkzeug/milestone/43?closed=1</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow more special device
names, regardless of extension or surrounding spaces. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-87hc-h4r5-73f7">GHSA-87hc-h4r5-73f7</a></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary. This fixes the previous attempt, which caused incorrect
content lengths. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3065">#3065</a>
<a
href="https://redirect.github.com/pallets/werkzeug/issues/3077">#3077</a></li>
<li>Fix <code>AttributeError</code> when initializing
<code>DebuggedApplication</code> with <code>pin_security=False</code>.
<a
href="https://redirect.github.com/pallets/werkzeug/issues/3075">#3075</a></li>
</ul>
<h2>3.1.4</h2>
<p>This is the Werkzeug 3.1.4 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes
compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.4/">https://pypi.org/project/Werkzeug/3.1.4/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-4">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-4</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/42?closed=1">https://github.com/pallets/werkzeug/milestone/42?closed=1</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow special device
names. This prevents reading from these when using
<code>send_from_directory</code>. <code>secure_filename</code> already
prevented writing to these. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-hgf8-39gv-g3f2">ghsa-hgf8-39gv-g3f2</a></li>
<li>The debugger pin fails after 10 attempts instead of 11. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3020">#3020</a></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3065">#3065</a></li>
<li>Improve CPU usage during Watchdog reloader. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3054">#3054</a></li>
<li><code>Request.json</code> annotation is more accurate. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3067">#3067</a></li>
<li>Traceback rendering handles when the line number is beyond the
available source lines. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3044">#3044</a></li>
<li><code>HTTPException.get_response</code> annotation and doc better
conveys the distinction between WSGI and sans-IO responses. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3056">#3056</a></li>
</ul>
<h2>3.1.3</h2>
<p>This is the Werkzeug 3.1.3 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes vs
3.1.0.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.3/">https://pypi.org/project/Werkzeug/3.1.3/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-3">https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-3</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/41?closed=1">https://github.com/pallets/werkzeug/milestone/41?closed=1</a></p>
<ul>
<li>Initial data passed to <code>MultiDict</code> and similar interfaces
only accepts <code>list</code>, <code>tuple</code>, or <code>set</code>
when passing multiple values. It had been changed to accept any
<code>Collection</code>, but this matched types that should be treated
as single values, such as <code>bytes</code>. <a
href="https://redirect.github.com/pallets/werkzeug/issues/2994">#2994</a></li>
<li>When the <code>Host</code> header is not set and
<code>Request.host</code> falls back to the WSGI
<code>SERVER_NAME</code> value, if that value is an IPv6 address it is
wrapped in <code>[]</code> to match the <code>Host</code> header. <a
href="https://redirect.github.com/pallets/werkzeug/issues/2993">#2993</a></li>
</ul>
<h2>3.1.2</h2>
<p>This is the Werkzeug 3.1.2 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes vs
3.1.0.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.2/">https://pypi.org/project/Werkzeug/3.1.2/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-2">https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-2</a></p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/werkzeug/blob/main/CHANGES.rst">werkzeug's
changelog</a>.</em></p>
<blockquote>
<h2>Version 3.1.6</h2>
<p>Released 2026-02-19</p>
<ul>
<li><code>safe_join</code> on Windows does not allow special devices
names in
multi-segment paths. :ghsa:<code>29vq-49wr-vm6x</code></li>
</ul>
<h2>Version 3.1.5</h2>
<p>Released 2026-01-08</p>
<ul>
<li><code>safe_join</code> on Windows does not allow more special device
names, regardless
of extension or surrounding spaces.
:ghsa:<code>87hc-h4r5-73f7</code></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary.
This fixes the previous attempt, which caused incorrect content lengths.
:issue:<code>3065</code> :issue:<code>3077</code></li>
<li>Fix <code>AttributeError</code> when initializing
<code>DebuggedApplication</code> with
<code>pin_security=False</code>. :issue:<code>3075</code></li>
</ul>
<h2>Version 3.1.4</h2>
<p>Released 2025-11-28</p>
<ul>
<li><code>safe_join</code> on Windows does not allow special device
names. This prevents
reading from these when using <code>send_from_directory</code>.
<code>secure_filename</code>
already prevented writing to these.
:ghsa:<code>hgf8-39gv-g3f2</code></li>
<li>The debugger pin fails after 10 attempts instead of 11.
:pr:<code>3020</code></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary.
:issue:<code>3065</code></li>
<li>Improve CPU usage during Watchdog reloader.
:issue:<code>3054</code></li>
<li><code>Request.json</code> annotation is more accurate.
:issue:<code>3067</code></li>
<li>Traceback rendering handles when the line number is beyond the
available
source lines. :issue:<code>3044</code></li>
<li><code>HTTPException.get_response</code> annotation and doc better
conveys the
distinction between WSGI and sans-IO responses.
:issue:<code>3056</code></li>
</ul>
<h2>Version 3.1.3</h2>
<p>Released 2024-11-08</p>
<ul>
<li>Initial data passed to <code>MultiDict</code> and similar interfaces
only accepts
<code>list</code>, <code>tuple</code>, or <code>set</code> when passing
multiple values. It had been
changed to accept any <code>Collection</code>, but this matched types
that should be</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/pallets/werkzeug/commit/04da1b5221b7a7b57e82246e4b5741d37a6b2e56"><code>04da1b5</code></a>
release version 3.1.6</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/f407712fdc60a09c2b3f4fe7db557703e5d9338d"><code>f407712</code></a>
Merge commit from fork</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/f54fe98026253e70fbbcd35a6b52fb67cfff1c03"><code>f54fe98</code></a>
safe_join prevents Windows special device names in multi-segment
paths</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/d005985ef69ffe3275eda8fb6fb25e074dbe871b"><code>d005985</code></a>
start version 3.1.6</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/8565c2cbd6681ae8463e77d4fc0795324a7fdae7"><code>8565c2c</code></a>
document rule priority (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3102">#3102</a>)</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/3febc7e90072bffe04c27e6b7478dfc4f88930df"><code>3febc7e</code></a>
document rule priority</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/2525b827646c10ab7adb334664e6a4af1b769181"><code>2525b82</code></a>
remove state machine docs</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/4abfbd553cdeb6d4e6fa693340d52b13c884079f"><code>4abfbd5</code></a>
rewrite build docstring (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3097">#3097</a>)</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/161c18b2a8800ae6ef377fb3cbdb933a878fea67"><code>161c18b</code></a>
rewrite build docstring</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/86e11c29e44726dae524cd9db11549b3b1ad681d"><code>86e11c2</code></a>
release version 3.1.5 (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3085">#3085</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/pallets/werkzeug/compare/3.1.1...3.1.6">compare
view</a></li>
</ul>
</details>
<br />
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2026-03-19 12:20:39 +01:00
sdist = { url = "https://files.pythonhosted.org/packages/34/b9/a769ae516c7f016465b2d9abc6e8dc4d5a1b54c57ab99b3cc95e9587955f/openapi_core-0.19.4.tar.gz", hash = "sha256:1150d9daa5e7b4cacfd7d7e097333dc89382d7d72703934128dcf8a1a4d0df49", size = 109095, upload-time = "2024-09-02T14:10:26.937Z" }
2024-08-26 13:34:29 +02:00
wheels = [
Python: Bump werkzeug from 3.1.1 to 3.1.6 in /python (#13605)
Bumps [werkzeug](https://github.com/pallets/werkzeug) from 3.1.1 to
3.1.6.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/werkzeug/releases">werkzeug's
releases</a>.</em></p>
<blockquote>
<h2>3.1.6</h2>
<p>This is the Werkzeug 3.1.6 security fix release, which fixes a
security issue but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.6/">https://pypi.org/project/Werkzeug/3.1.6/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-6">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-6</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow special devices
names in multi-segment paths. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-29vq-49wr-vm6x">GHSA-29vq-49wr-vm6x</a></li>
</ul>
<h2>3.1.5</h2>
<p>This is the Werkzeug 3.1.5 security fix release, which fixes security
issues and bugs but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.5/">https://pypi.org/project/Werkzeug/3.1.5/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-5">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-5</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/43?closed=1">https://github.com/pallets/werkzeug/milestone/43?closed=1</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow more special device
names, regardless of extension or surrounding spaces. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-87hc-h4r5-73f7">GHSA-87hc-h4r5-73f7</a></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary. This fixes the previous attempt, which caused incorrect
content lengths. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3065">#3065</a>
<a
href="https://redirect.github.com/pallets/werkzeug/issues/3077">#3077</a></li>
<li>Fix <code>AttributeError</code> when initializing
<code>DebuggedApplication</code> with <code>pin_security=False</code>.
<a
href="https://redirect.github.com/pallets/werkzeug/issues/3075">#3075</a></li>
</ul>
<h2>3.1.4</h2>
<p>This is the Werkzeug 3.1.4 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes
compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.4/">https://pypi.org/project/Werkzeug/3.1.4/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-4">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-4</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/42?closed=1">https://github.com/pallets/werkzeug/milestone/42?closed=1</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow special device
names. This prevents reading from these when using
<code>send_from_directory</code>. <code>secure_filename</code> already
prevented writing to these. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-hgf8-39gv-g3f2">ghsa-hgf8-39gv-g3f2</a></li>
<li>The debugger pin fails after 10 attempts instead of 11. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3020">#3020</a></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3065">#3065</a></li>
<li>Improve CPU usage during Watchdog reloader. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3054">#3054</a></li>
<li><code>Request.json</code> annotation is more accurate. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3067">#3067</a></li>
<li>Traceback rendering handles when the line number is beyond the
available source lines. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3044">#3044</a></li>
<li><code>HTTPException.get_response</code> annotation and doc better
conveys the distinction between WSGI and sans-IO responses. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3056">#3056</a></li>
</ul>
<h2>3.1.3</h2>
<p>This is the Werkzeug 3.1.3 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes vs
3.1.0.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.3/">https://pypi.org/project/Werkzeug/3.1.3/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-3">https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-3</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/41?closed=1">https://github.com/pallets/werkzeug/milestone/41?closed=1</a></p>
<ul>
<li>Initial data passed to <code>MultiDict</code> and similar interfaces
only accepts <code>list</code>, <code>tuple</code>, or <code>set</code>
when passing multiple values. It had been changed to accept any
<code>Collection</code>, but this matched types that should be treated
as single values, such as <code>bytes</code>. <a
href="https://redirect.github.com/pallets/werkzeug/issues/2994">#2994</a></li>
<li>When the <code>Host</code> header is not set and
<code>Request.host</code> falls back to the WSGI
<code>SERVER_NAME</code> value, if that value is an IPv6 address it is
wrapped in <code>[]</code> to match the <code>Host</code> header. <a
href="https://redirect.github.com/pallets/werkzeug/issues/2993">#2993</a></li>
</ul>
<h2>3.1.2</h2>
<p>This is the Werkzeug 3.1.2 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes vs
3.1.0.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.2/">https://pypi.org/project/Werkzeug/3.1.2/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-2">https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-2</a></p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/werkzeug/blob/main/CHANGES.rst">werkzeug's
changelog</a>.</em></p>
<blockquote>
<h2>Version 3.1.6</h2>
<p>Released 2026-02-19</p>
<ul>
<li><code>safe_join</code> on Windows does not allow special devices
names in
multi-segment paths. :ghsa:<code>29vq-49wr-vm6x</code></li>
</ul>
<h2>Version 3.1.5</h2>
<p>Released 2026-01-08</p>
<ul>
<li><code>safe_join</code> on Windows does not allow more special device
names, regardless
of extension or surrounding spaces.
:ghsa:<code>87hc-h4r5-73f7</code></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary.
This fixes the previous attempt, which caused incorrect content lengths.
:issue:<code>3065</code> :issue:<code>3077</code></li>
<li>Fix <code>AttributeError</code> when initializing
<code>DebuggedApplication</code> with
<code>pin_security=False</code>. :issue:<code>3075</code></li>
</ul>
<h2>Version 3.1.4</h2>
<p>Released 2025-11-28</p>
<ul>
<li><code>safe_join</code> on Windows does not allow special device
names. This prevents
reading from these when using <code>send_from_directory</code>.
<code>secure_filename</code>
already prevented writing to these.
:ghsa:<code>hgf8-39gv-g3f2</code></li>
<li>The debugger pin fails after 10 attempts instead of 11.
:pr:<code>3020</code></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary.
:issue:<code>3065</code></li>
<li>Improve CPU usage during Watchdog reloader.
:issue:<code>3054</code></li>
<li><code>Request.json</code> annotation is more accurate.
:issue:<code>3067</code></li>
<li>Traceback rendering handles when the line number is beyond the
available
source lines. :issue:<code>3044</code></li>
<li><code>HTTPException.get_response</code> annotation and doc better
conveys the
distinction between WSGI and sans-IO responses.
:issue:<code>3056</code></li>
</ul>
<h2>Version 3.1.3</h2>
<p>Released 2024-11-08</p>
<ul>
<li>Initial data passed to <code>MultiDict</code> and similar interfaces
only accepts
<code>list</code>, <code>tuple</code>, or <code>set</code> when passing
multiple values. It had been
changed to accept any <code>Collection</code>, but this matched types
that should be</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/pallets/werkzeug/commit/04da1b5221b7a7b57e82246e4b5741d37a6b2e56"><code>04da1b5</code></a>
release version 3.1.6</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/f407712fdc60a09c2b3f4fe7db557703e5d9338d"><code>f407712</code></a>
Merge commit from fork</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/f54fe98026253e70fbbcd35a6b52fb67cfff1c03"><code>f54fe98</code></a>
safe_join prevents Windows special device names in multi-segment
paths</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/d005985ef69ffe3275eda8fb6fb25e074dbe871b"><code>d005985</code></a>
start version 3.1.6</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/8565c2cbd6681ae8463e77d4fc0795324a7fdae7"><code>8565c2c</code></a>
document rule priority (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3102">#3102</a>)</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/3febc7e90072bffe04c27e6b7478dfc4f88930df"><code>3febc7e</code></a>
document rule priority</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/2525b827646c10ab7adb334664e6a4af1b769181"><code>2525b82</code></a>
remove state machine docs</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/4abfbd553cdeb6d4e6fa693340d52b13c884079f"><code>4abfbd5</code></a>
rewrite build docstring (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3097">#3097</a>)</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/161c18b2a8800ae6ef377fb3cbdb933a878fea67"><code>161c18b</code></a>
rewrite build docstring</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/86e11c29e44726dae524cd9db11549b3b1ad681d"><code>86e11c2</code></a>
release version 3.1.5 (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3085">#3085</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/pallets/werkzeug/compare/3.1.1...3.1.6">compare
view</a></li>
</ul>
</details>
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Python: Update sort step method for assistant invoke. (#10191)
### Motivation and Context
At times, when the (Azure) OpenAI Assistant Agent makes a tool call,
that tool call's creation timestamp is coming after the message creation
timestamp (the message creation being the text that the assistant
responds with). Currently in our code, if we have a tool call
(`FunctionCallContent`), we first yield that, and then we make a call to
get completed steps, to then yield more content like
`FunctionResultContent` and `TextContent`. There will be two steps (or
more, depending upon the number of tool calls).
Right now we sort the completed steps in this way:
```python
completed_steps_to_process: list[RunStep] = sorted(
[s for s in steps if s.completed_at is not None and s.id not in processed_step_ids],
key=lambda s: s.created_at,
)
```
When there are no failures, it's because the tool call was created
before the final message content (as has been since this assistant was
first coded). However, it appears that processing on the server-side can
cause fluctuations in when the steps are created/processed. When we have
a failure, we now have the message_creation (`TextContent`) yielded
before the `FunctionResultContent`, which if sent to an (Azure) OpenAI
Chat Completion endpoint will break with a 400 because of the gap in the
ordering between the `FunctionCallContent` and the
`FunctionResultContent`:
```
FunctionCallContent
TextContent # this should follow `FunctionResultContent` (and it does during times when we don't see a 400)
FunctionResultContent
```
The 400 error isn't 100% repeatable because of server-side processing,
so we will get the correct ordering:
```
FunctionCallContent
FunctionResultContent
TextContent
```
This PR updates the ordering of how we sort of the completed steps -- if
we have a step_type == "tool_calls" and "message_creation", we sort to
allow for "tool_calls" to come first, and any ties are broken by the
step.completed_at timestamp. With this update, we no longer receive 400s
because the ordering of the content types is correct when sending to an
OpenAI model.
<!-- Thank you for your contribution to the semantic-kernel repo!
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2. What problem does it solve?
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### Description
This PR:
- Updates the way we sort completed messages during an assistant invoke.
- Adds logging for the assistant invoke method at the `info` level.
- Closes #10141
<!-- 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 :smile:
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
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Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
[[package]]
name = "opentelemetry-exporter-otlp-proto-http"
2026-03-12 10:02:02 -07:00
version = "1.40.0"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
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Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
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Please help reviewers and future users, providing the following
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1. Why is this change required?
2. What problem does it solve?
3. What scenario does it contribute to?
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### Description
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
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[[package]]
name = "parse"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
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information:
1. Why is this change required?
2. What problem does it solve?
3. What scenario does it contribute to?
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### Description
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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name = "pathable"
Python: Update sort step method for assistant invoke. (#10191)
### Motivation and Context
At times, when the (Azure) OpenAI Assistant Agent makes a tool call,
that tool call's creation timestamp is coming after the message creation
timestamp (the message creation being the text that the assistant
responds with). Currently in our code, if we have a tool call
(`FunctionCallContent`), we first yield that, and then we make a call to
get completed steps, to then yield more content like
`FunctionResultContent` and `TextContent`. There will be two steps (or
more, depending upon the number of tool calls).
Right now we sort the completed steps in this way:
```python
completed_steps_to_process: list[RunStep] = sorted(
[s for s in steps if s.completed_at is not None and s.id not in processed_step_ids],
key=lambda s: s.created_at,
)
```
When there are no failures, it's because the tool call was created
before the final message content (as has been since this assistant was
first coded). However, it appears that processing on the server-side can
cause fluctuations in when the steps are created/processed. When we have
a failure, we now have the message_creation (`TextContent`) yielded
before the `FunctionResultContent`, which if sent to an (Azure) OpenAI
Chat Completion endpoint will break with a 400 because of the gap in the
ordering between the `FunctionCallContent` and the
`FunctionResultContent`:
```
FunctionCallContent
TextContent # this should follow `FunctionResultContent` (and it does during times when we don't see a 400)
FunctionResultContent
```
The 400 error isn't 100% repeatable because of server-side processing,
so we will get the correct ordering:
```
FunctionCallContent
FunctionResultContent
TextContent
```
This PR updates the ordering of how we sort of the completed steps -- if
we have a step_type == "tool_calls" and "message_creation", we sort to
allow for "tool_calls" to come first, and any ties are broken by the
step.completed_at timestamp. With this update, we no longer receive 400s
because the ordering of the content types is correct when sending to an
OpenAI model.
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
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1. Why is this change required?
2. What problem does it solve?
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### Description
This PR:
- Updates the way we sort completed messages during an assistant invoke.
- Adds logging for the assistant invoke method at the `info` level.
- Closes #10141
<!-- 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 :smile:
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
2025-01-21 04:29:13 +09:00
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Python: Update the AzureAIAgent packages, types, and APIs based on Azure SDK's new code (#12084)
### Motivation and Context
The Azure AI Agent Service is moving to GA soon. As part of that,
they've made several changes in the underlying APIs, which require the
use of new packages, updated type imports and some new underlying APIs.
This PR updates the AzureAIAgent to use the latest Azure SDK's new code.
- The client's connection string has been replaced with an endpoint.
- The `AzureAIAgentSettings`'s `model_deployment_name` was updated to
`deployment_name`.
- Tools are now imported from `from azure.ai.agents.models` instead of
`from azure.ai.projects.models`.
- Declarative spec handling for AzureAIAgent was updated based on the
new APIs.
- Unit tests were updated.
<!-- 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 AzureAIAgent related code, samples and tests based on the
Azure SDK's latest APIs.
- Closes #12177
<!-- 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2025-05-20 08:43:40 +09:00
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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 :smile:
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2025-02-27 03:22:05 -08:00
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Python: Introducing vector and text search (#9345)
### Motivation and Context
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
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1. Why is this change required?
2. What problem does it solve?
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This PR does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
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Python: Bump protobuf from 5.29.5 to 5.29.6 in /python (#13513)
Bumps [protobuf](https://github.com/protocolbuffers/protobuf) from
5.29.5 to 5.29.6.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/protocolbuffers/protobuf/releases">protobuf's
releases</a>.</em></p>
<blockquote>
<h2>Protocol Buffers v34.0-rc1</h2>
<h1>Announcements</h1>
<ul>
<li><strong>This version includes breaking changes to: C++, Objective-C,
PHP, Python.</strong></li>
<li>[Bazel] Remove deprecated ProtoInfo.transitive_imports. Use
equivalent transitive_sources instead (<a
href="https://github.com/protocolbuffers/protobuf/commit/0a5c2f6b633c1e5259f566cb42d30fe347b8aadb">https://github.com/protocolbuffers/protobuf/commit/0a5c2f6b633c1e5259f566cb42d30fe347b8aadb</a>)</li>
<li>[C++] Make generator headers private (<a
href="https://github.com/protocolbuffers/protobuf/commit/3a2af3510f0d454dbe3e4dc281674b61c4d20b9e">https://github.com/protocolbuffers/protobuf/commit/3a2af3510f0d454dbe3e4dc281674b61c4d20b9e</a>)</li>
<li>[C++] Add a debug check that the target of CopyFrom is not a
descendant of the source. (<a
href="https://github.com/protocolbuffers/protobuf/commit/7a7589823d2cfaaf7994b050e98d5d553bc9b1c1">https://github.com/protocolbuffers/protobuf/commit/7a7589823d2cfaaf7994b050e98d5d553bc9b1c1</a>)</li>
<li>[C++] Add [[nodiscard]] to many APIs. (<a
href="https://github.com/protocolbuffers/protobuf/commit/a70115f33f9af2c4b2202c800b84837e7fe0d738">https://github.com/protocolbuffers/protobuf/commit/a70115f33f9af2c4b2202c800b84837e7fe0d738</a>)</li>
<li>[C++] Make the arena-enabled constructors of
<code>RepeatedField</code>, <code>RepeatedPtrField</code>, and
<code>Map</code> private. (<a
href="https://github.com/protocolbuffers/protobuf/commit/ef890c3d0c79398c70e047fe5dd893f460ba2336">https://github.com/protocolbuffers/protobuf/commit/ef890c3d0c79398c70e047fe5dd893f460ba2336</a>)</li>
<li>[C++] Remove deprecated FieldDescriptor::label() in OSS. Use
is_repeated() or is_required() instead (<a
href="https://github.com/protocolbuffers/protobuf/commit/b76faa921fdd244f374c7be0bddd4050fc42c292">https://github.com/protocolbuffers/protobuf/commit/b76faa921fdd244f374c7be0bddd4050fc42c292</a>)</li>
<li>[C++] Removes proto2::util::MessageDifferencer::AddIgnoreCriteria
that takes a raw pointer as an argument in favor of the overload that
takes a unique_ptr. Remove macro
PROTOBUF_FUTURE_REMOVE_ADD_IGNORE_CRITERIA (<a
href="https://github.com/protocolbuffers/protobuf/commit/b115358c64127896fed88b8b5ef5d91d86d8cbae">https://github.com/protocolbuffers/protobuf/commit/b115358c64127896fed88b8b5ef5d91d86d8cbae</a>)</li>
<li>[C++] Remove deprecated FieldDescriptor::has_optional_keyword() in
OSS. Use is_repeated() or has_presence() instead (<a
href="https://github.com/protocolbuffers/protobuf/commit/68346ec9348e932664e58c3ecdcd1478f95233a8">https://github.com/protocolbuffers/protobuf/commit/68346ec9348e932664e58c3ecdcd1478f95233a8</a>)</li>
<li>[C++] Remove AddUnusedImportTrackFile() and
ClearUnusedImportTrackFiles(). Remove
PROTOBUF_FUTURE_RENAME_ADD_UNUSED_IMPORT (<a
href="https://github.com/protocolbuffers/protobuf/commit/837a2cd1d6c75402b2503ffe7cd8aeaf25868536">https://github.com/protocolbuffers/protobuf/commit/837a2cd1d6c75402b2503ffe7cd8aeaf25868536</a>)</li>
<li>[C++] Remove deprecated FieldDescriptor::is_optional() in OSS. Use
(!is_required() && !is_repeated()) instead (<a
href="https://github.com/protocolbuffers/protobuf/commit/9dbc5d479a8e453921485d8d3de47fb3c005f1af">https://github.com/protocolbuffers/protobuf/commit/9dbc5d479a8e453921485d8d3de47fb3c005f1af</a>)</li>
<li>[C++] Remove deprecated UseDeprecatedLegacyJsonFieldConflicts() (<a
href="https://github.com/protocolbuffers/protobuf/commit/c301c2ca286327a21c50c0c4cd877afc9c655b00">https://github.com/protocolbuffers/protobuf/commit/c301c2ca286327a21c50c0c4cd877afc9c655b00</a>)</li>
<li>[C++] All entity names have length limit (2afb0dc)</li>
<li>[ObjC] Remove <code>generate_minimal_imports</code> generation
option warning (<a
href="https://github.com/protocolbuffers/protobuf/commit/45b1297fdaad5a9436d0e207422168c38dc45ac4">https://github.com/protocolbuffers/protobuf/commit/45b1297fdaad5a9436d0e207422168c38dc45ac4</a>)</li>
<li>[ObjC] Fix nullability annotations on some
<code>GPB*Dictionary</code> types. (<a
href="https://github.com/protocolbuffers/protobuf/commit/ea67d6d26a48478a567c404679e3bb99cf230d50">https://github.com/protocolbuffers/protobuf/commit/ea67d6d26a48478a567c404679e3bb99cf230d50</a>)</li>
<li>[ObjC] Remove <code>-[GPBFieldDescriptor optional]</code> (<a
href="https://github.com/protocolbuffers/protobuf/commit/3414dc151eb4dcbdb2ca952e2589993bf7af75c4">https://github.com/protocolbuffers/protobuf/commit/3414dc151eb4dcbdb2ca952e2589993bf7af75c4</a>)</li>
<li>[Other] Remove deprecated flag for enabling MSVC support (<a
href="https://github.com/protocolbuffers/protobuf/commit/97c979be6e0907e1051bee62584dac4594e73fa7">https://github.com/protocolbuffers/protobuf/commit/97c979be6e0907e1051bee62584dac4594e73fa7</a>)</li>
<li>[PHP] Remove deprecated PHP APIs (<a
href="https://github.com/protocolbuffers/protobuf/commit/9c45014099a4f7004fab6dd1278de2f4f2a393c5">https://github.com/protocolbuffers/protobuf/commit/9c45014099a4f7004fab6dd1278de2f4f2a393c5</a>)</li>
<li>[PHP] Remove deprecated PHP APIs FieldDescriptor getLabel, use
IsRepeated or isRequired instead. (<a
href="https://github.com/protocolbuffers/protobuf/commit/42081219920c6fad17ba6ddd1e28d111bcfb3345">https://github.com/protocolbuffers/protobuf/commit/42081219920c6fad17ba6ddd1e28d111bcfb3345</a>,
<a
href="https://github.com/protocolbuffers/protobuf/commit/cd76e675b14d00dda5623b30835d2bc7105fccc6">https://github.com/protocolbuffers/protobuf/commit/cd76e675b14d00dda5623b30835d2bc7105fccc6</a>,
<a
href="https://github.com/protocolbuffers/protobuf/commit/42081219920c6fad17ba6ddd1e28d111bcfb3345">https://github.com/protocolbuffers/protobuf/commit/42081219920c6fad17ba6ddd1e28d111bcfb3345</a>)</li>
<li>[PHP] Add PHP typehints for setters and remove redundant GPBUtil
checks (<a
href="https://redirect.github.com/protocolbuffers/protobuf/pull/25296">protocolbuffers/protobuf#25296</a>)
(<a
href="https://github.com/protocolbuffers/protobuf/commit/aee03b78929c02461a5f9d8e136a2a016359b0cd">https://github.com/protocolbuffers/protobuf/commit/aee03b78929c02461a5f9d8e136a2a016359b0cd</a>)</li>
<li>[PHP] support default values for editions/proto2 (<a
href="https://redirect.github.com/protocolbuffers/protobuf/pull/25161">protocolbuffers/protobuf#25161</a>)
(<a
href="https://github.com/protocolbuffers/protobuf/commit/b01099d56350551bae3da88b97bf3027274c9f17">https://github.com/protocolbuffers/protobuf/commit/b01099d56350551bae3da88b97bf3027274c9f17</a>)</li>
<li>[Python] Raise errors in OSS when assign bool to int/enum field in
Python Proto. (<a
href="https://github.com/protocolbuffers/protobuf/commit/5b116fe2f14f49dd0cc3b76089983717f211025c">https://github.com/protocolbuffers/protobuf/commit/5b116fe2f14f49dd0cc3b76089983717f211025c</a>)</li>
<li>[Python] Remove float_format/double_format from python proto
text_format (<a
href="https://github.com/protocolbuffers/protobuf/commit/e4854a186e0bfa867d5bfa5cd850608a948fd488">https://github.com/protocolbuffers/protobuf/commit/e4854a186e0bfa867d5bfa5cd850608a948fd488</a>)</li>
<li>[Python] Raise TypeError when convert non-timedelta to Duration, or
convert non-datetime to Timestamp in python proto. (Original code may
raise ArributeError) (<a
href="https://github.com/protocolbuffers/protobuf/commit/00aaca1b4d98954bc2933d7c8a5379ba6088124c">https://github.com/protocolbuffers/protobuf/commit/00aaca1b4d98954bc2933d7c8a5379ba6088124c</a>)</li>
<li>[Python] Remove float_precision from python proto json_format (<a
href="https://github.com/protocolbuffers/protobuf/commit/f027f1fcd52b9d080b7ee79f4024f53cf54e0dc5">https://github.com/protocolbuffers/protobuf/commit/f027f1fcd52b9d080b7ee79f4024f53cf54e0dc5</a>)</li>
<li>[Python] Remove deprecated FieldDescriptor::label() in OSS. Use
is_repeated() or is_required() instead (<a
href="https://github.com/protocolbuffers/protobuf/commit/b76faa921fdd244f374c7be0bddd4050fc42c292">https://github.com/protocolbuffers/protobuf/commit/b76faa921fdd244f374c7be0bddd4050fc42c292</a>)</li>
<li>[Python] Remove deprecated FieldDescriptor.label (<a
href="https://github.com/protocolbuffers/protobuf/commit/0a8ff55518ea5874478ad5b26515b31d186045a9">https://github.com/protocolbuffers/protobuf/commit/0a8ff55518ea5874478ad5b26515b31d186045a9</a>)</li>
<li>[Python] Remove deprecated UseDeprecatedLegacyJsonFieldConflicts()
(<a
href="https://github.com/protocolbuffers/protobuf/commit/c301c2ca286327a21c50c0c4cd877afc9c655b00">https://github.com/protocolbuffers/protobuf/commit/c301c2ca286327a21c50c0c4cd877afc9c655b00</a>)</li>
<li><a href="https://protobuf.dev/news/">Protobuf News</a> may include
additional announcements or pre-announcements for upcoming changes.</li>
<li><a href="https://protobuf.dev/support/migration/">Migration
Guide</a> may include additional guidance for breaking changes.</li>
</ul>
<h1>Bazel</h1>
<ul>
<li>Fix: cc_toolchain should prefer protoc when prebuilt flag is
flipped. (<a
href="https://redirect.github.com/protocolbuffers/protobuf/issues/25168">#25168</a>)
(<a
href="https://github.com/protocolbuffers/protobuf/commit/8c857c3a1c6a106b0a096f1c9fa504bfaca035a9">https://github.com/protocolbuffers/protobuf/commit/8c857c3a1c6a106b0a096f1c9fa504bfaca035a9</a>)</li>
<li>Breaking change: Remove deprecated ProtoInfo.transitive_imports. Use
equivalent transitive_sources instead (<a
href="https://github.com/protocolbuffers/protobuf/commit/0a5c2f6b633c1e5259f566cb42d30fe347b8aadb">https://github.com/protocolbuffers/protobuf/commit/0a5c2f6b633c1e5259f566cb42d30fe347b8aadb</a>)</li>
<li>Feat(bazel): wire up prebuilt protoc toolchain (<a
href="https://redirect.github.com/protocolbuffers/protobuf/issues/24115">#24115</a>)
(<a
href="https://github.com/protocolbuffers/protobuf/commit/cc23698b486e690ea2eb873cc7596a87c74a3ba6">https://github.com/protocolbuffers/protobuf/commit/cc23698b486e690ea2eb873cc7596a87c74a3ba6</a>)</li>
<li>Migrate <code>proto_descriptor_set</code> (<a
href="https://redirect.github.com/protocolbuffers/protobuf/issues/23369">#23369</a>)
(<a
href="https://github.com/protocolbuffers/protobuf/commit/8d4dfdd39a7a242a9ed631a6ab2192c57dd9b9c8">https://github.com/protocolbuffers/protobuf/commit/8d4dfdd39a7a242a9ed631a6ab2192c57dd9b9c8</a>)</li>
</ul>
<h1>Compiler</h1>
<ul>
<li>Ruby codegen: support generation of rbs files (<a
href="https://redirect.github.com/protocolbuffers/protobuf/issues/15633">#15633</a>)
(<a
href="https://github.com/protocolbuffers/protobuf/commit/6ebdf851ba78728f0aa145d38454ed9a316fb08d">https://github.com/protocolbuffers/protobuf/commit/6ebdf851ba78728f0aa145d38454ed9a316fb08d</a>)</li>
<li>Avoid collision name problems between a message named
<code>Xyz</code> and a direct sibling enum named <code>XyzView</code>
(<a
href="https://github.com/protocolbuffers/protobuf/commit/eba53e8f172b273d679759a72ce4250131ee3df1">https://github.com/protocolbuffers/protobuf/commit/eba53e8f172b273d679759a72ce4250131ee3df1</a>)</li>
<li>Generalizing and implementing ValidateFeatureSupport for both
Options and Features during proto parsing (<a
href="https://github.com/protocolbuffers/protobuf/commit/ed3c57114d8e2b47cca7697ddaa50c1b3762a6b0">https://github.com/protocolbuffers/protobuf/commit/ed3c57114d8e2b47cca7697ddaa50c1b3762a6b0</a>)</li>
<li>Fix a bug with custom features outside of the <code>pb</code>
package. (<a
href="https://github.com/protocolbuffers/protobuf/commit/872d3ce7a4da00d7dcec33ced20cfe45235935e8">https://github.com/protocolbuffers/protobuf/commit/872d3ce7a4da00d7dcec33ced20cfe45235935e8</a>)</li>
<li>Fix import option handling when include_imports isn't set. (<a
href="https://github.com/protocolbuffers/protobuf/commit/9ef9e80afd9bc8379d578fe67e5ab0738728c04e">https://github.com/protocolbuffers/protobuf/commit/9ef9e80afd9bc8379d578fe67e5ab0738728c04e</a>)</li>
<li>Fix a bug in STRICT check of namespaced enums to properly check for
'reserved 1 to max' (<a
href="https://github.com/protocolbuffers/protobuf/commit/1229d4adba24c0952ab85ce96bc7b7f8a1fe6d0f">https://github.com/protocolbuffers/protobuf/commit/1229d4adba24c0952ab85ce96bc7b7f8a1fe6d0f</a>)</li>
<li>Prevent accidental stripping of <code>debug_redact</code> options
via import option. (<a
href="https://github.com/protocolbuffers/protobuf/commit/f58b098bffa7ca4045ef7773b09151a6af5d0c28">https://github.com/protocolbuffers/protobuf/commit/f58b098bffa7ca4045ef7773b09151a6af5d0c28</a>)</li>
</ul>
<h1>C++</h1>
<ul>
<li>Add EnumerateEnumValues function. (<a
href="https://github.com/protocolbuffers/protobuf/commit/397d5d99db274b379d1384814074bf7df39d32f7">https://github.com/protocolbuffers/protobuf/commit/397d5d99db274b379d1384814074bf7df39d32f7</a>)</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
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href="https://github.com/protocolbuffers/protobuf/commits">compare
view</a></li>
</ul>
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[[package]]
name = "psycopg"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
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### Description
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
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Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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.
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### Description
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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Python: Fix: Implement __deepcopy__ on KernelFunction to handle non-serializable OTEL metrics (#12803)
### Description
This PR fixes TypeError: cannot pickle '_thread.RLock' object which
occurs when kernel.clone() is called while OpenTelemetry metrics are
enabled.
- Fixes: #12802
### Motivation and Context
When using features like `HandoffOrchestration`, the agent's `Kernel` is
cloned. This process uses `copy.deepcopy`, which fails on the
`KernelFunction`'s OpenTelemetry `Histogram` fields
(`invocation_duration_histogram` and `streaming_duration_histogram`).
These histogram objects contain thread locks, making them
non-serializable.
Previous solutions, like the one in **PR #9292**, used
`Field(exclude=True, default_factory=...)`. While that was a necessary
step, it was insufficient because `deepcopy` does not respect the
`exclude` flag in the same way that Pydantic's serialization does. This
bug blocks the use of certain agentic features in any application that
has metrics-based observability enabled.
### Description of the change
This solution introduces a custom __deepcopy__ method to the
KernelFunction Pydantic model. This method correctly handles the
deep-copying process for KernelFunction instances:
1. It intercepts the deepcopy call for KernelFunction objects.
2. It uses self.model_copy(deep=False) to create a new instance of the
Pydantic model. Using deep=False allows the default_factory for the
histogram fields to create new, clean instances on the copied object,
while other fields are shallow-copied.
3. It then manually deep-copies all other attributes from the original
object to the new one, ensuring that any other mutable objects (like
metadata`) are correctly duplicated. The histogram fields are explicitly
skipped in this step, as they have already been recreated by
`model_copy.
4. Standard deepcopy memoization is used to prevent infinite recursion
in the case of cyclic object graphs.
This approach ensures that the non-serializable fields are
re-initialized instead of copied, resolving the TypeError while
maintaining the integrity of the cloned object.
### How has this been tested?
The fix was validated using the reproduction steps outlined in the
associated issue. Running an agent orchestration that triggers
kernel.clone() with OpenTelemetry metrics enabled now completes
successfully without any errors.
### 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 :smile:
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2025-08-08 01:48:43 +03:00
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name = "pyasn1-modules"
Python: Add on_intermediate_message callback to the Agent abstraction (#11321)
### 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.
-->
Currently, our agents' `invoke` and `invoke_stream` have inconsistent
behavior where the responses may contain different contents. For
example, the chat completion agent (`invoke`) may return all content
types generated by the agent including function call/result contents
that are meant to be internal only, while the assistant agent doesn't
return those contents. This creates confusion both for users and
developers of SK of what an agent should return when working with
agents.
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
This PR standardizes the behavior of agents by adding an
`on_intermediate_message ` parameter to the `invoke` and `invoke_stream`
methods in the `Agent` abstraction. This parameter accepts a callback
function that will be called whenever a new message is generated by the
agent, except the last one that gets returned to the caller. This allows
the caller to inspect what the agent did after a response is returned.
In addition, this PR also converges on the behavior of the `invoke` and
`invoke_stream` methods (respectively) to return the same content types
from all agents, making it easier for users to work with agents
### 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 :smile:
2025-04-03 15:30:10 -07:00
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Python: Add on_intermediate_message callback to the Agent abstraction (#11321)
### 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.
-->
Currently, our agents' `invoke` and `invoke_stream` have inconsistent
behavior where the responses may contain different contents. For
example, the chat completion agent (`invoke`) may return all content
types generated by the agent including function call/result contents
that are meant to be internal only, while the assistant agent doesn't
return those contents. This creates confusion both for users and
developers of SK of what an agent should return when working with
agents.
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
This PR standardizes the behavior of agents by adding an
`on_intermediate_message ` parameter to the `invoke` and `invoke_stream`
methods in the `Agent` abstraction. This parameter accepts a callback
function that will be called whenever a new message is generated by the
agent, except the last one that gets returned to the caller. This allows
the caller to inspect what the agent did after a response is returned.
In addition, this PR also converges on the behavior of the `invoke` and
`invoke_stream` methods (respectively) to return the same content types
from all agents, making it easier for users to work with agents
### 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 :smile:
2025-04-03 15:30:10 -07:00
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[[package]]
name = "pydantic-settings"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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[[package]]
name = "pyee"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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[[package]]
name = "pymilvus"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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[[package]]
name = "pyproject-hooks"
Python: Introducing vector and text search (#9345)
### Motivation and Context
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
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2. What problem does it solve?
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This PR does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
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Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
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1. Why is this change required?
2. What problem does it solve?
3. What scenario does it contribute to?
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### Description
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
[[package]]
name = "python-discovery"
2026-03-12 10:02:02 -07:00
version = "1.1.3"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
]
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Python: Fix: Implement __deepcopy__ on KernelFunction to handle non-serializable OTEL metrics (#12803)
### Description
This PR fixes TypeError: cannot pickle '_thread.RLock' object which
occurs when kernel.clone() is called while OpenTelemetry metrics are
enabled.
- Fixes: #12802
### Motivation and Context
When using features like `HandoffOrchestration`, the agent's `Kernel` is
cloned. This process uses `copy.deepcopy`, which fails on the
`KernelFunction`'s OpenTelemetry `Histogram` fields
(`invocation_duration_histogram` and `streaming_duration_histogram`).
These histogram objects contain thread locks, making them
non-serializable.
Previous solutions, like the one in **PR #9292**, used
`Field(exclude=True, default_factory=...)`. While that was a necessary
step, it was insufficient because `deepcopy` does not respect the
`exclude` flag in the same way that Pydantic's serialization does. This
bug blocks the use of certain agentic features in any application that
has metrics-based observability enabled.
### Description of the change
This solution introduces a custom __deepcopy__ method to the
KernelFunction Pydantic model. This method correctly handles the
deep-copying process for KernelFunction instances:
1. It intercepts the deepcopy call for KernelFunction objects.
2. It uses self.model_copy(deep=False) to create a new instance of the
Pydantic model. Using deep=False allows the default_factory for the
histogram fields to create new, clean instances on the copied object,
while other fields are shallow-copied.
3. It then manually deep-copies all other attributes from the original
object to the new one, ensuring that any other mutable objects (like
metadata`) are correctly duplicated. The histogram fields are explicitly
skipped in this step, as they have already been recreated by
`model_copy.
4. Standard deepcopy memoization is used to prevent infinite recursion
in the case of cyclic object graphs.
This approach ensures that the non-serializable fields are
re-initialized instead of copied, resolving the TypeError while
maintaining the integrity of the cloned object.
### How has this been tested?
The fix was validated using the reproduction steps outlined in the
associated issue. Running an agent orchestration that triggers
kernel.clone() with OpenTelemetry metrics enabled now completes
successfully without any errors.
### 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 :smile:
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2025-08-08 01:48:43 +03:00
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Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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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?
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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 :smile:
2025-04-11 08:26:22 +02:00
{ name = "jsonpath-ng", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
<!-- 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
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
<!-- 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 :smile:
2025-05-26 14:02:59 +09:00
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[[package]]
name = "rich"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
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Python: Update sort step method for assistant invoke. (#10191)
### Motivation and Context
At times, when the (Azure) OpenAI Assistant Agent makes a tool call,
that tool call's creation timestamp is coming after the message creation
timestamp (the message creation being the text that the assistant
responds with). Currently in our code, if we have a tool call
(`FunctionCallContent`), we first yield that, and then we make a call to
get completed steps, to then yield more content like
`FunctionResultContent` and `TextContent`. There will be two steps (or
more, depending upon the number of tool calls).
Right now we sort the completed steps in this way:
```python
completed_steps_to_process: list[RunStep] = sorted(
[s for s in steps if s.completed_at is not None and s.id not in processed_step_ids],
key=lambda s: s.created_at,
)
```
When there are no failures, it's because the tool call was created
before the final message content (as has been since this assistant was
first coded). However, it appears that processing on the server-side can
cause fluctuations in when the steps are created/processed. When we have
a failure, we now have the message_creation (`TextContent`) yielded
before the `FunctionResultContent`, which if sent to an (Azure) OpenAI
Chat Completion endpoint will break with a 400 because of the gap in the
ordering between the `FunctionCallContent` and the
`FunctionResultContent`:
```
FunctionCallContent
TextContent # this should follow `FunctionResultContent` (and it does during times when we don't see a 400)
FunctionResultContent
```
The 400 error isn't 100% repeatable because of server-side processing,
so we will get the correct ordering:
```
FunctionCallContent
FunctionResultContent
TextContent
```
This PR updates the ordering of how we sort of the completed steps -- if
we have a step_type == "tool_calls" and "message_creation", we sort to
allow for "tool_calls" to come first, and any ties are broken by the
step.completed_at timestamp. With this update, we no longer receive 400s
because the ordering of the content types is correct when sending to an
OpenAI model.
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### Description
This PR:
- Updates the way we sort completed messages during an assistant invoke.
- Adds logging for the assistant invoke method at the `info` level.
- Closes #10141
<!-- Describe your changes, the overall approach, the underlying design.
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### 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 :smile:
---------
Co-authored-by: Eduard van Valkenburg <eavanvalkenburg@users.noreply.github.com>
2025-01-21 04:29:13 +09:00
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2026-02-10 11:05:13 +01:00
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Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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2026-02-10 11:05:13 +01:00
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]
2026-03-12 10:02:02 -07:00
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2025-07-22 17:14:32 +02:00
[[package]]
name = "scipy"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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2025-07-22 17:14:32 +02:00
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2025-07-22 17:14:32 +02:00
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2025-07-22 17:14:32 +02:00
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2025-07-22 17:14:32 +02:00
]
2024-08-26 13:34:29 +02:00
[[package]]
name = "semantic-kernel"
source = { editable = "." }
dependencies = [
{ name = "aiohttp", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2025-03-07 18:15:59 +01:00
{ name = "aiortc", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
Python: Fix for AzureAIAgent streaming func call invocations. Other fixes/improvements for tool call content types, and logging. (#12335)
### Motivation and Context
The AzureAIAgent streaming function call invocation is broken when
handling more than one required function call from the service. There is
nested logic to handle passing the tool calls back to the service
because it requires one to pass in a handler.
In this PR, we're now controlling everything from the main loop and are
passing back the handler as the event stream for further processing.
This does require some logic to determine if we have an initial async
context manager (based on the first call to create the run stream) or if
we're now using the handler from submitting tool calls. We can now see
that when we have multiple functions involved, we are able to get the
results as we expect:
```
Sample Output:
# User: 'What is the price of the special drink and then special food item added together?'
Function Call:> MenuPlugin-get_specials with arguments: {}
Function Result:>
Special Soup: Clam Chowder
Special Salad: Cobb Salad
Special Drink: Chai Tea
for function: MenuPlugin-get_specials
Function Call:> MenuPlugin-get_item_price with arguments: {"menu_item": "Chai Tea"}
Function Call:> MenuPlugin-get_item_price with arguments: {"menu_item": "Clam Chowder"}
Function Result:> $9.99 for function: MenuPlugin-get_item_price
Function Result:> $9.99 for function: MenuPlugin-get_item_price
Function Call:> MathPlugin-Add with arguments: {"input":9.99,"amount":9.99}
Function Result:> 19.98 for function: MathPlugin-Add
# AuthorRole.ASSISTANT: The price of the special drink, Chai Tea, is $9.99 and the price of the special food
item, Clam Chowder, is $9.99. Added together, the total price is $19.98.
```
<!-- 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?
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### Description
Fix handling multiple function calls for the streaming AzureAIAgent.
- Fixes a missing `quote` from the url citation for streaming annotation
content
- Shows the correct way to retrieve Bing Grounding tool calls in the two
concept samples.
- Adds handling for tool content content for OpenAPI plugins.
- Adds logging for streaming agent invocation tool calls.
- Relaxes the handling of the `FunctionResultContent` id attribute to be
`str | None` similar to that of `FunctionCallContent`. The type hints
for `id` have always been `str | None`, but the actual type hint on the
attribute has been `str`. We don't always get a `FunctionResultContent`
id from the Azure Agent Service during streaming invocations - message
addition via the overide `__add__` method still work properly.
- Move the AzureAIAgent pkg dependencies to be installed by default,
instead of requiring the `azure` extra.
- Closes #12312
- Closes #12328
- Closes #12331
- Closes #12324
<!-- 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 :smile:
2025-06-03 10:11:56 +09:00
{ name = "azure-ai-agents", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "azure-ai-projects", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-10-02 12:00:11 -04:00
{ name = "azure-identity", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
Python: Add the Python process framework (#9363)
### Motivation and Context
An initial PR to add the foundational pieces of the Python Process
framework, which holds it design to be similar to dotnet in that step
types are added to a process builder, and later on, when the step is
run, it is first instantiated and the proper state is provided.
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Please help reviewers and future users, providing the following
information:
1. Why is this change required?
2. What problem does it solve?
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### Description
Adding the initial process framework components:
- Closes #9354
**TODO**
- more unit tests will be added to increase the code coverage. Currently
there are several files with no (or low) code coverage.
- more samples will either be added to this PR or a subsequent PR
<!-- 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 :smile:
2024-10-24 13:37:45 -04:00
{ name = "cloudevents", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-08-26 13:34:29 +02:00
{ name = "defusedxml", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "jinja2", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2026-02-10 11:05:13 +01:00
{ name = "mcp", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-08-26 13:34:29 +02:00
{ name = "nest-asyncio", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-09-16 14:13:23 -04:00
{ name = "numpy", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-08-26 13:34:29 +02:00
{ name = "openai", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "openapi-core", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "opentelemetry-api", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "opentelemetry-sdk", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "prance", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "pybars4", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "pydantic", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "pydantic-settings", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2025-07-22 17:14:32 +02:00
{ name = "scipy", version = "1.15.3", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.11' and sys_platform == 'darwin') or (python_full_version < '3.11' and sys_platform == 'linux') or (python_full_version < '3.11' and sys_platform == 'win32')" },
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
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### Description
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
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### 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 :smile:
2026-02-28 09:01:03 +09:00
{ name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.11' and sys_platform == 'darwin') or (python_full_version >= '3.11' and sys_platform == 'linux') or (python_full_version >= '3.11' and sys_platform == 'win32')" },
2025-05-13 19:20:56 +02:00
{ name = "typing-extensions", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2025-03-07 18:15:59 +01:00
{ name = "websockets", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-08-26 13:34:29 +02:00
]
[package.optional-dependencies]
anthropic = [
{ name = "anthropic", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
2025-02-20 20:11:05 +09:00
autogen = [
{ name = "autogen-agentchat", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
2024-10-10 15:33:42 -07:00
aws = [
{ name = "boto3", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
2024-08-26 13:34:29 +02:00
azure = [
{ name = "azure-ai-inference", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-11-19 11:25:25 -08:00
{ name = "azure-core-tracing-opentelemetry", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-08-26 13:34:29 +02:00
{ name = "azure-cosmos", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "azure-search-documents", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
chroma = [
{ name = "chromadb", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
2025-09-22 18:01:51 -07:00
copilotstudio = [
{ name = "microsoft-agents-activity", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "microsoft-agents-copilotstudio-client", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
2025-03-11 10:19:49 +01:00
faiss = [
{ name = "faiss-cpu", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
2024-08-26 13:34:29 +02:00
google = [
{ name = "google-cloud-aiplatform", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2025-11-24 13:53:57 -08:00
{ name = "google-genai", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-08-26 13:34:29 +02:00
]
hugging-face = [
{ name = "sentence-transformers", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "torch", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "transformers", extra = ["torch"], marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
2025-04-03 20:08:45 +02:00
mcp = [
{ name = "mcp", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
2024-08-26 13:34:29 +02:00
milvus = [
2025-03-26 11:29:12 +09:00
{ name = "milvus", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
2025-06-16 23:32:42 +02:00
{ name = "pymilvus", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-08-26 13:34:29 +02:00
]
mistralai = [
{ name = "mistralai", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
mongo = [
{ name = "motor", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-10-10 19:41:21 +02:00
{ name = "pymongo", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-08-26 13:34:29 +02:00
]
notebooks = [
{ name = "ipykernel", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
ollama = [
{ name = "ollama", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
2024-10-10 19:41:21 +02:00
onnx = [
2025-10-24 11:23:08 +09:00
{ name = "onnxruntime", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2025-05-23 18:29:00 +02:00
{ name = "onnxruntime-genai", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-10-10 19:41:21 +02:00
]
2026-01-15 14:11:08 +01:00
oracledb = [
{ name = "oracledb", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
2024-08-26 13:34:29 +02:00
pandas = [
{ name = "pandas", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
pinecone = [
2025-03-12 21:14:45 +01:00
{ name = "pinecone", extra = ["asyncio", "grpc"], marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-08-26 13:34:29 +02:00
]
postgres = [
2024-09-16 14:13:23 -04:00
{ name = "psycopg", extra = ["binary", "pool"], marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-08-26 13:34:29 +02:00
]
qdrant = [
Python: Support Declarative Spec for OpenAIAssistantAgent & OpenAIResponsesAgent (#12247)
### Motivation and Context
This PR builds on the initial work/abstractions from #11982. The
`AzureAssistantAgent` / `OpenAIAssistantAgent` as well as the
`AzureResponsesAgent` / `OpenAIResponsesAgent` now support declarative
specs. Several examples on how to declare the spec are shown in the
respective dirs in `samples/concepts/agents`, as well as a simple
getting started example for each agent type.
To further simplify working with these two agents, the
`setup_resources()` methods for each agent type have been marked with a
deprecation tag. The new method to use is
`OpenAIAssistantAgent.create_client()` or
`OpenAIResponsesAgent.create_client()`. Further, when one needs to
access the underlying setting's, it's simple to do so with a settings
object and accessing the attribute.
In the previous PR for the AzureAIAgent, it lacked support for
templating - this is included in this current PR.
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### Description
Bring in support for the declarative single agent spec for these agent
types.
- Add samples
- Add unit tests
- Add support for prompt templates for all current agent types
- Closes #11979
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### 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 :smile:
2025-05-26 14:02:59 +09:00
{ name = "qdrant-client", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-08-26 13:34:29 +02:00
]
2025-03-04 08:44:10 +01:00
realtime = [
{ name = "aiortc", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "websockets", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
2024-08-26 13:34:29 +02:00
redis = [
{ name = "redis", extra = ["hiredis"], marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-11-15 15:08:30 +01:00
{ name = "redisvl", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-08-26 13:34:29 +02:00
{ name = "types-redis", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
2025-04-03 12:47:35 +02:00
sql = [
{ name = "pyodbc", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
2024-08-26 13:34:29 +02:00
usearch = [
{ name = "pyarrow", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "usearch", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
weaviate = [
{ name = "weaviate-client", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
[package.dev-dependencies]
dev = [
{ name = "ipykernel", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "mypy", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "nbconvert", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "pre-commit", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "pytest", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "pytest-asyncio", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "pytest-cov", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-12-12 22:08:46 +01:00
{ name = "pytest-timeout", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
2024-08-26 13:34:29 +02:00
{ name = "pytest-xdist", extra = ["psutil"], marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "ruff", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "snoop", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "types-pyyaml", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
[package.metadata]
requires-dist = [
{ name = "aiohttp", specifier = "~=3.8" },
2025-03-07 18:15:59 +01:00
{ name = "aiortc", specifier = ">=1.9.0" },
2025-03-04 08:44:10 +01:00
{ name = "aiortc", marker = "extra == 'realtime'", specifier = ">=1.9.0" },
2024-08-26 13:34:29 +02:00
{ name = "anthropic", marker = "extra == 'anthropic'", specifier = "~=0.32" },
2025-02-20 20:11:05 +09:00
{ name = "autogen-agentchat", marker = "extra == 'autogen'", specifier = ">=0.2,<0.4" },
2025-09-01 11:35:56 +09:00
{ name = "azure-ai-agents", specifier = ">=1.2.0b3" },
2024-11-19 11:25:25 -08:00
{ name = "azure-ai-inference", marker = "extra == 'azure'", specifier = ">=1.0.0b6" },
2025-11-24 13:53:57 -08:00
{ name = "azure-ai-projects", specifier = "~=1.0.0b12" },
2024-11-19 11:25:25 -08:00
{ name = "azure-core-tracing-opentelemetry", marker = "extra == 'azure'", specifier = ">=1.0.0b11" },
2024-08-26 13:34:29 +02:00
{ name = "azure-cosmos", marker = "extra == 'azure'", specifier = "~=4.7" },
Python: Common agent invocation API updates (#11224)
### Motivation and Context
After this week's release of the common agent invocation API, there are
some things that we can do as further improvements.
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### Description
This PR includes updates for:
- Right now we force any agent invocation to provide some type of input,
whether a str, a CMC, or a list of str | CMC. At times, there could be a
reason, based on using an existing thread or just providing instructions
to the agent, that one doesn't need to provide a message to invoke the
agent. Updating to make messages optional. Updating the ABC contracts as
well.
- For `invoke_stream` calls on agents, there's no need to call
`thread.on_new_message` that contains a streaming chunks -- once we move
to support memory, this is where the "hook" will be. Removing this call.
- The `get_messages(...)` methods on the `AutoGenConversableAgentThread`
and the `ChatHistoryAgentThread` returned concrete `ChatHistory`
objects, whereas the `AssistantAgentThread` and `AzureAIAgentThread`
returned `AsyncIterable[ChatMessageContent]`. To align to a common API,
the `AutoGenConversableAgentThread` and `ChatHistoryAgentThread`'s
`get_messages(...)` methods were moved to return
`AsyncIterable[ChatMessageContent]`.
- Removing a public facing `output_messages` for streaming invoke, and
replacing it with a callback to get a chat history back of "full"
messages. Two samples are added in `samples/concepts/agents`:
- `azure_ai_agent/azure_ai_agent_streaming_chat_history_callback.py`
- `openai_assistant_streaming_chat_history_callback.py`
- Update the README for OpenAI Assistants to showcase new thread
abstraction.
- Include unit tests for chat history (`on_complete`) callback.
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### Contribution Checklist
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- [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 :smile:
2025-03-28 00:59:46 +09:00
{ name = "azure-identity", specifier = ">=1.13" },
2024-08-26 13:34:29 +02:00
{ name = "azure-search-documents", marker = "extra == 'azure'", specifier = ">=11.6.0b4" },
Python: Fix: Implement __deepcopy__ on KernelFunction to handle non-serializable OTEL metrics (#12803)
### Description
This PR fixes TypeError: cannot pickle '_thread.RLock' object which
occurs when kernel.clone() is called while OpenTelemetry metrics are
enabled.
- Fixes: #12802
### Motivation and Context
When using features like `HandoffOrchestration`, the agent's `Kernel` is
cloned. This process uses `copy.deepcopy`, which fails on the
`KernelFunction`'s OpenTelemetry `Histogram` fields
(`invocation_duration_histogram` and `streaming_duration_histogram`).
These histogram objects contain thread locks, making them
non-serializable.
Previous solutions, like the one in **PR #9292**, used
`Field(exclude=True, default_factory=...)`. While that was a necessary
step, it was insufficient because `deepcopy` does not respect the
`exclude` flag in the same way that Pydantic's serialization does. This
bug blocks the use of certain agentic features in any application that
has metrics-based observability enabled.
### Description of the change
This solution introduces a custom __deepcopy__ method to the
KernelFunction Pydantic model. This method correctly handles the
deep-copying process for KernelFunction instances:
1. It intercepts the deepcopy call for KernelFunction objects.
2. It uses self.model_copy(deep=False) to create a new instance of the
Pydantic model. Using deep=False allows the default_factory for the
histogram fields to create new, clean instances on the copied object,
while other fields are shallow-copied.
3. It then manually deep-copies all other attributes from the original
object to the new one, ensuring that any other mutable objects (like
metadata`) are correctly duplicated. The histogram fields are explicitly
skipped in this step, as they have already been recreated by
`model_copy.
4. Standard deepcopy memoization is used to prevent infinite recursion
in the case of cyclic object graphs.
This approach ensures that the non-serializable fields are
re-initialized instead of copied, resolving the TypeError while
maintaining the integrity of the cloned object.
### How has this been tested?
The fix was validated using the reproduction steps outlined in the
associated issue. Running an agent orchestration that triggers
kernel.clone() with OpenTelemetry metrics enabled now completes
successfully without any errors.
### Contribution Checklist
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- [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 :smile:
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2025-08-08 01:48:43 +03:00
{ name = "boto3", marker = "extra == 'aws'", specifier = ">=1.36.4,<1.41.0" },
Python: Support Auto Function Invocation Filter for AzureAIAgent and OpenAIAssistantAgent (#11460)
### Motivation and Context
The server-side agents like AzureAIAgent and OpenAIAssistantAgent do not
support the Auto function invocation filter. Although we return
intermediate step results, like FunctionResultContent, as part of the
`on_intermediate_steps` callback, this didn't allow for developers to
configure a filter to return the result as-is, or modify the result as
part of the filter.
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### Description
Add support for the auto function invocation filter for the
`AzureAIAgent` and `OpenAIAssistantAgent`.
- Closes #11054
- Closes #11456
- Adds samples on how to configure the filter for both agent types, and
how to configure the callback to show the various content types.
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### 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 :smile:
2025-04-10 09:39:47 +09:00
{ name = "chromadb", marker = "extra == 'chroma'", specifier = ">=0.5,<1.1" },
Python: Add the Python process framework (#9363)
### Motivation and Context
An initial PR to add the foundational pieces of the Python Process
framework, which holds it design to be similar to dotnet in that step
types are added to a process builder, and later on, when the step is
run, it is first instantiated and the proper state is provided.
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### Description
Adding the initial process framework components:
- Closes #9354
**TODO**
- more unit tests will be added to increase the code coverage. Currently
there are several files with no (or low) code coverage.
- more samples will either be added to this PR or a subsequent PR
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### 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 :smile:
2024-10-24 13:37:45 -04:00
{ name = "cloudevents", specifier = "~=1.0" },
2024-08-26 13:34:29 +02:00
{ name = "defusedxml", specifier = "~=0.7" },
2025-03-11 10:19:49 +01:00
{ name = "faiss-cpu", marker = "extra == 'faiss'", specifier = ">=1.10.0" },
2025-10-29 21:22:14 -07:00
{ name = "google-cloud-aiplatform", marker = "extra == 'google'", specifier = "~=1.114.0" },
2025-11-24 13:53:57 -08:00
{ name = "google-genai", marker = "extra == 'google'", specifier = "~=1.51.0" },
2024-08-26 13:34:29 +02:00
{ name = "ipykernel", marker = "extra == 'notebooks'", specifier = "~=6.29" },
{ name = "jinja2", specifier = "~=3.1" },
2026-02-10 11:05:13 +01:00
{ name = "mcp", specifier = ">=1.26.0" },
Python: fixes and updates for MCP (#12039)
### Motivation and Context
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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 :smile:
2025-05-15 00:12:34 +02:00
{ name = "mcp", marker = "extra == 'mcp'", specifier = ">=1.8" },
2025-09-22 18:01:51 -07:00
{ name = "microsoft-agents-activity", marker = "extra == 'copilotstudio'", specifier = ">=0.3.1" },
{ name = "microsoft-agents-copilotstudio-client", marker = "extra == 'copilotstudio'", specifier = ">=0.3.1" },
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{ name = "milvus", marker = "sys_platform != 'win32' and extra == 'milvus'", specifier = ">=2.3,<2.3.8" },
2024-11-13 17:52:54 +01:00
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{ name = "numpy", marker = "python_full_version >= '3.12'", specifier = ">=1.26.0" },
Python: enable ollama streaming tool calls (#9890)
### Motivation and Context
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Ollama has enabled streaming tool calling, and so we add that too!
See release notes: https://github.com/ollama/ollama/releases/tag/v0.4.6
Also encountered a bug when passing generic PromptExecutionSettings to
all ChatCompletions with Function calling enabled, the
_verify_function_choice_settings often check if something is a type of
PES matching the service, that is not the case and throws even though it
should work (for instance when using a yaml based function), this fixes
that as well by casting in the public methods of chat completion base.
### Description
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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 :smile:
2024-12-09 16:58:57 +01:00
{ name = "ollama", marker = "extra == 'ollama'", specifier = "~=0.4" },
2025-11-11 09:44:52 +01:00
{ name = "onnxruntime", marker = "extra == 'onnx'", specifier = "==1.22.1" },
{ name = "onnxruntime-genai", marker = "extra == 'onnx'", specifier = "==0.9.0" },
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
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### Description
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
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### Contribution Checklist
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- [X] The code builds clean without any errors or warnings
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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 :smile:
2026-02-28 09:01:03 +09:00
{ name = "openai", specifier = ">=2.0.0" },
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{ name = "opentelemetry-sdk", specifier = "~=1.24" },
2026-01-15 14:11:08 +01:00
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2025-06-19 20:34:59 +02:00
{ name = "pinecone", extras = ["asyncio", "grpc"], marker = "extra == 'pinecone'", specifier = "~=7.0" },
Python: Support polling options at run-level for AzureAIAgent, OpenAIAssistantAgent, and OpenAIResponsesAgent (#11705)
### Motivation and Context
Based on feedback, there are customer scenarios where it can be helpful
to be able to pass in run-level polling options, instead of only at the
agent definition/constructor level.
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### Description
Add run-level run polling options parameter.
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### Contribution Checklist
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- [X] The code builds clean without any errors or warnings
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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 :smile:
2025-04-26 00:04:00 +09:00
{ name = "prance", specifier = ">=23.6.21,<25.4.9" },
2024-08-26 13:34:29 +02:00
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2025-08-05 23:58:35 +09:00
{ name = "pyarrow", marker = "extra == 'usearch'", specifier = ">=12.0,<22.0" },
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2025-04-03 20:08:45 +02:00
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Python: Require tool_call_id parameter for string-based tool messages in ChatHistory (#12753)
### Motivation and Context
The `add_tool_message` method of `ChatHistory` accepts string content
without requiring a `tool_call_id` parameter. This design is flawed from
an API design perspective. It created tool messages that:
1. Violated chat completion protocols: most LLM APIs (OpenAI, etc.)
require tool messages to have a `tool_call_id` that references a
previous function call
2. Broke conversation flow: tool messages should always be responses to
specific tool calls, maintaining the call-response relationship
3. Created malformed message sequences: messages without proper tool
call IDs would fail when sent to language models
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### Description
This PR modifies the `add_tool_message` string overload to require a
`tool_call_id` parameter, which makes sure we have proper tool call
protocol compliance.
1. The string overload now validates that `tool_call_id` is provided and
raises a clear error if missing
2. Tool messages now create `FunctionResultContent` objects with both
`id` and `call_id` set to the provided `tool_call_id`
3. Added support for an optional `function_name` parameter for better
bookkeeping
4. Provides informative error messages explaining why `tool_call_id` is
required
- Closes #12744
Before:
```python
# This would create an invalid tool message without tool_call_id
# when the `.to_dict()` method is called on `ChatMessageContent`
chat_history.add_tool_message("Function result") # Creates broken message and losing pairing to the corresponding `FunctionCallContent`.
```
After:
```python
# Now requires tool_call_id for proper protocol compliance
chat_history.add_tool_message("Function result") # raises clear error due to missing `tool_call_id`
chat_history.add_tool_message("Function result", tool_call_id="call_123") # works correctly
# Optional function name for better bookkeeping
chat_history.add_tool_message("Function result", tool_call_id="call_123", function_name="get_weather") # works as well
```
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### Contribution Checklist
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- [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 :smile:
2025-07-23 13:07:20 +09:00
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Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
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Please help reviewers and future users, providing the following
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### Description
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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
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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 :smile:
2026-02-28 09:01:03 +09:00
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Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
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Please help reviewers and future users, providing the following
information:
1. Why is this change required?
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-->
### Description
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
{ name = "scipy", version = "1.17.1", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.11' and sys_platform == 'darwin') or (python_full_version >= '3.11' and sys_platform == 'linux') or (python_full_version >= '3.11' and sys_platform == 'win32')" },
2024-08-26 13:34:29 +02:00
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{ name = "transformers", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
Python: Add on_intermediate_message callback to the Agent abstraction (#11321)
### Motivation and Context
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Currently, our agents' `invoke` and `invoke_stream` have inconsistent
behavior where the responses may contain different contents. For
example, the chat completion agent (`invoke`) may return all content
types generated by the agent including function call/result contents
that are meant to be internal only, while the assistant agent doesn't
return those contents. This creates confusion both for users and
developers of SK of what an agent should return when working with
agents.
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
This PR standardizes the behavior of agents by adding an
`on_intermediate_message ` parameter to the `invoke` and `invoke_stream`
methods in the `Agent` abstraction. This parameter accepts a callback
function that will be called whenever a new message is generated by the
agent, except the last one that gets returned to the caller. This allows
the caller to inspect what the agent did after a response is returned.
In addition, this PR also converges on the behavior of the `invoke` and
`invoke_stream` methods (respectively) to return the same content types
from all agents, making it easier for users to work with agents
### 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 :smile:
2025-04-03 15:30:10 -07:00
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Python: Introducing vector and text search (#9345)
### Motivation and Context
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
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1. Why is this change required?
2. What problem does it solve?
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This PR does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
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Python: Introducing vector and text search (#9345)
### 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 does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
]
2024-08-26 13:34:29 +02:00
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Python: Introducing vector and text search (#9345)
### 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 does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
version = "0.6.0"
2024-08-26 13:34:29 +02:00
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Python: Fix: Implement __deepcopy__ on KernelFunction to handle non-serializable OTEL metrics (#12803)
### Description
This PR fixes TypeError: cannot pickle '_thread.RLock' object which
occurs when kernel.clone() is called while OpenTelemetry metrics are
enabled.
- Fixes: #12802
### Motivation and Context
When using features like `HandoffOrchestration`, the agent's `Kernel` is
cloned. This process uses `copy.deepcopy`, which fails on the
`KernelFunction`'s OpenTelemetry `Histogram` fields
(`invocation_duration_histogram` and `streaming_duration_histogram`).
These histogram objects contain thread locks, making them
non-serializable.
Previous solutions, like the one in **PR #9292**, used
`Field(exclude=True, default_factory=...)`. While that was a necessary
step, it was insufficient because `deepcopy` does not respect the
`exclude` flag in the same way that Pydantic's serialization does. This
bug blocks the use of certain agentic features in any application that
has metrics-based observability enabled.
### Description of the change
This solution introduces a custom __deepcopy__ method to the
KernelFunction Pydantic model. This method correctly handles the
deep-copying process for KernelFunction instances:
1. It intercepts the deepcopy call for KernelFunction objects.
2. It uses self.model_copy(deep=False) to create a new instance of the
Pydantic model. Using deep=False allows the default_factory for the
histogram fields to create new, clean instances on the copied object,
while other fields are shallow-copied.
3. It then manually deep-copies all other attributes from the original
object to the new one, ensuring that any other mutable objects (like
metadata`) are correctly duplicated. The histogram fields are explicitly
skipped in this step, as they have already been recreated by
`model_copy.
4. Standard deepcopy memoization is used to prevent infinite recursion
in the case of cyclic object graphs.
This approach ensures that the non-serializable fields are
re-initialized instead of copied, resolving the TypeError while
maintaining the integrity of the cloned object.
### How has this been tested?
The fix was validated using the reproduction steps outlined in the
associated issue. Running an agent orchestration that triggers
kernel.clone() with OpenTelemetry metrics enabled now completes
successfully without any errors.
### 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 :smile:
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2025-08-08 01:48:43 +03:00
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[[package]]
name = "tinycss2"
Python: Introducing vector and text search (#9345)
### 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 does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
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2024-08-26 13:34:29 +02:00
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[[package]]
name = "tokenizers"
2026-02-10 11:05:13 +01:00
version = "0.22.2"
2024-08-26 13:34:29 +02:00
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Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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?
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### Description
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
version = "0.24.1"
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source = { registry = "https://pypi.org/simple" }
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Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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name = "types-redis"
Python: Introducing vector and text search (#9345)
### Motivation and Context
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
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2. What problem does it solve?
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This PR does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
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Python: Introducing vector and text search (#9345)
### Motivation and Context
<!-- Thank you for your contribution to the semantic-kernel repo!
Please help reviewers and future users, providing the following
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1. Why is this change required?
2. What problem does it solve?
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This PR does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
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[[package]]
name = "uvicorn"
Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
2026-02-28 09:01:03 +09:00
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2024-08-26 13:34:29 +02:00
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name = "uvloop"
2025-10-24 11:23:08 +09:00
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name = "validators"
Python: Fix: Implement __deepcopy__ on KernelFunction to handle non-serializable OTEL metrics (#12803)
### Description
This PR fixes TypeError: cannot pickle '_thread.RLock' object which
occurs when kernel.clone() is called while OpenTelemetry metrics are
enabled.
- Fixes: #12802
### Motivation and Context
When using features like `HandoffOrchestration`, the agent's `Kernel` is
cloned. This process uses `copy.deepcopy`, which fails on the
`KernelFunction`'s OpenTelemetry `Histogram` fields
(`invocation_duration_histogram` and `streaming_duration_histogram`).
These histogram objects contain thread locks, making them
non-serializable.
Previous solutions, like the one in **PR #9292**, used
`Field(exclude=True, default_factory=...)`. While that was a necessary
step, it was insufficient because `deepcopy` does not respect the
`exclude` flag in the same way that Pydantic's serialization does. This
bug blocks the use of certain agentic features in any application that
has metrics-based observability enabled.
### Description of the change
This solution introduces a custom __deepcopy__ method to the
KernelFunction Pydantic model. This method correctly handles the
deep-copying process for KernelFunction instances:
1. It intercepts the deepcopy call for KernelFunction objects.
2. It uses self.model_copy(deep=False) to create a new instance of the
Pydantic model. Using deep=False allows the default_factory for the
histogram fields to create new, clean instances on the copied object,
while other fields are shallow-copied.
3. It then manually deep-copies all other attributes from the original
object to the new one, ensuring that any other mutable objects (like
metadata`) are correctly duplicated. The histogram fields are explicitly
skipped in this step, as they have already been recreated by
`model_copy.
4. Standard deepcopy memoization is used to prevent infinite recursion
in the case of cyclic object graphs.
This approach ensures that the non-serializable fields are
re-initialized instead of copied, resolving the TypeError while
maintaining the integrity of the cloned object.
### How has this been tested?
The fix was validated using the reproduction steps outlined in the
associated issue. Running an agent orchestration that triggers
kernel.clone() with OpenTelemetry metrics enabled now completes
successfully without any errors.
### 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 :smile:
---------
Co-authored-by: Evan Mattson <35585003+moonbox3@users.noreply.github.com>
2025-08-08 01:48:43 +03:00
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Python: support (Azure) OpenAI realtime audio models (#13291)
### Motivation and Context
As of 2/27/2026, revived the PR, and the latest code is working for both
Azure OpenAI realtime models and OpenAI realtime models.
SK has support for realtime-preview models; however, since they've gone
to GA we have not added support for the latest library abstractions.
This PR brings in the changes to support running models like
`gpt-realtime-1.5`, `gpt-realtime`, `gpt-realtime-mini` or `gpt-audio`.
<!-- 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
- Closes #13267
- Code now relies on `openai` >= 2.0
- Considered a breaking change due some new config added to the
execution settings.
<!-- 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 :smile:
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Python: Add on_intermediate_message callback to the Agent abstraction (#11321)
### 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.
-->
Currently, our agents' `invoke` and `invoke_stream` have inconsistent
behavior where the responses may contain different contents. For
example, the chat completion agent (`invoke`) may return all content
types generated by the agent including function call/result contents
that are meant to be internal only, while the assistant agent doesn't
return those contents. This creates confusion both for users and
developers of SK of what an agent should return when working with
agents.
### Description
<!-- Describe your changes, the overall approach, the underlying design.
These notes will help understanding how your code works. Thanks! -->
This PR standardizes the behavior of agents by adding an
`on_intermediate_message ` parameter to the `invoke` and `invoke_stream`
methods in the `Agent` abstraction. This parameter accepts a callback
function that will be called whenever a new message is generated by the
agent, except the last one that gets returned to the caller. This allows
the caller to inspect what the agent did after a response is returned.
In addition, this PR also converges on the behavior of the `invoke` and
`invoke_stream` methods (respectively) to return the same content types
from all agents, making it easier for users to work with agents
### 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 :smile:
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2024-08-26 13:34:29 +02:00
]
[[package]]
name = "werkzeug"
Python: Bump werkzeug from 3.1.1 to 3.1.6 in /python (#13605)
Bumps [werkzeug](https://github.com/pallets/werkzeug) from 3.1.1 to
3.1.6.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/werkzeug/releases">werkzeug's
releases</a>.</em></p>
<blockquote>
<h2>3.1.6</h2>
<p>This is the Werkzeug 3.1.6 security fix release, which fixes a
security issue but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.6/">https://pypi.org/project/Werkzeug/3.1.6/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-6">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-6</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow special devices
names in multi-segment paths. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-29vq-49wr-vm6x">GHSA-29vq-49wr-vm6x</a></li>
</ul>
<h2>3.1.5</h2>
<p>This is the Werkzeug 3.1.5 security fix release, which fixes security
issues and bugs but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.5/">https://pypi.org/project/Werkzeug/3.1.5/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-5">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-5</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/43?closed=1">https://github.com/pallets/werkzeug/milestone/43?closed=1</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow more special device
names, regardless of extension or surrounding spaces. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-87hc-h4r5-73f7">GHSA-87hc-h4r5-73f7</a></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary. This fixes the previous attempt, which caused incorrect
content lengths. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3065">#3065</a>
<a
href="https://redirect.github.com/pallets/werkzeug/issues/3077">#3077</a></li>
<li>Fix <code>AttributeError</code> when initializing
<code>DebuggedApplication</code> with <code>pin_security=False</code>.
<a
href="https://redirect.github.com/pallets/werkzeug/issues/3075">#3075</a></li>
</ul>
<h2>3.1.4</h2>
<p>This is the Werkzeug 3.1.4 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes
compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.4/">https://pypi.org/project/Werkzeug/3.1.4/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-4">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-4</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/42?closed=1">https://github.com/pallets/werkzeug/milestone/42?closed=1</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow special device
names. This prevents reading from these when using
<code>send_from_directory</code>. <code>secure_filename</code> already
prevented writing to these. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-hgf8-39gv-g3f2">ghsa-hgf8-39gv-g3f2</a></li>
<li>The debugger pin fails after 10 attempts instead of 11. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3020">#3020</a></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3065">#3065</a></li>
<li>Improve CPU usage during Watchdog reloader. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3054">#3054</a></li>
<li><code>Request.json</code> annotation is more accurate. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3067">#3067</a></li>
<li>Traceback rendering handles when the line number is beyond the
available source lines. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3044">#3044</a></li>
<li><code>HTTPException.get_response</code> annotation and doc better
conveys the distinction between WSGI and sans-IO responses. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3056">#3056</a></li>
</ul>
<h2>3.1.3</h2>
<p>This is the Werkzeug 3.1.3 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes vs
3.1.0.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.3/">https://pypi.org/project/Werkzeug/3.1.3/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-3">https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-3</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/41?closed=1">https://github.com/pallets/werkzeug/milestone/41?closed=1</a></p>
<ul>
<li>Initial data passed to <code>MultiDict</code> and similar interfaces
only accepts <code>list</code>, <code>tuple</code>, or <code>set</code>
when passing multiple values. It had been changed to accept any
<code>Collection</code>, but this matched types that should be treated
as single values, such as <code>bytes</code>. <a
href="https://redirect.github.com/pallets/werkzeug/issues/2994">#2994</a></li>
<li>When the <code>Host</code> header is not set and
<code>Request.host</code> falls back to the WSGI
<code>SERVER_NAME</code> value, if that value is an IPv6 address it is
wrapped in <code>[]</code> to match the <code>Host</code> header. <a
href="https://redirect.github.com/pallets/werkzeug/issues/2993">#2993</a></li>
</ul>
<h2>3.1.2</h2>
<p>This is the Werkzeug 3.1.2 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes vs
3.1.0.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.2/">https://pypi.org/project/Werkzeug/3.1.2/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-2">https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-2</a></p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/werkzeug/blob/main/CHANGES.rst">werkzeug's
changelog</a>.</em></p>
<blockquote>
<h2>Version 3.1.6</h2>
<p>Released 2026-02-19</p>
<ul>
<li><code>safe_join</code> on Windows does not allow special devices
names in
multi-segment paths. :ghsa:<code>29vq-49wr-vm6x</code></li>
</ul>
<h2>Version 3.1.5</h2>
<p>Released 2026-01-08</p>
<ul>
<li><code>safe_join</code> on Windows does not allow more special device
names, regardless
of extension or surrounding spaces.
:ghsa:<code>87hc-h4r5-73f7</code></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary.
This fixes the previous attempt, which caused incorrect content lengths.
:issue:<code>3065</code> :issue:<code>3077</code></li>
<li>Fix <code>AttributeError</code> when initializing
<code>DebuggedApplication</code> with
<code>pin_security=False</code>. :issue:<code>3075</code></li>
</ul>
<h2>Version 3.1.4</h2>
<p>Released 2025-11-28</p>
<ul>
<li><code>safe_join</code> on Windows does not allow special device
names. This prevents
reading from these when using <code>send_from_directory</code>.
<code>secure_filename</code>
already prevented writing to these.
:ghsa:<code>hgf8-39gv-g3f2</code></li>
<li>The debugger pin fails after 10 attempts instead of 11.
:pr:<code>3020</code></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary.
:issue:<code>3065</code></li>
<li>Improve CPU usage during Watchdog reloader.
:issue:<code>3054</code></li>
<li><code>Request.json</code> annotation is more accurate.
:issue:<code>3067</code></li>
<li>Traceback rendering handles when the line number is beyond the
available
source lines. :issue:<code>3044</code></li>
<li><code>HTTPException.get_response</code> annotation and doc better
conveys the
distinction between WSGI and sans-IO responses.
:issue:<code>3056</code></li>
</ul>
<h2>Version 3.1.3</h2>
<p>Released 2024-11-08</p>
<ul>
<li>Initial data passed to <code>MultiDict</code> and similar interfaces
only accepts
<code>list</code>, <code>tuple</code>, or <code>set</code> when passing
multiple values. It had been
changed to accept any <code>Collection</code>, but this matched types
that should be</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/pallets/werkzeug/commit/04da1b5221b7a7b57e82246e4b5741d37a6b2e56"><code>04da1b5</code></a>
release version 3.1.6</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/f407712fdc60a09c2b3f4fe7db557703e5d9338d"><code>f407712</code></a>
Merge commit from fork</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/f54fe98026253e70fbbcd35a6b52fb67cfff1c03"><code>f54fe98</code></a>
safe_join prevents Windows special device names in multi-segment
paths</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/d005985ef69ffe3275eda8fb6fb25e074dbe871b"><code>d005985</code></a>
start version 3.1.6</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/8565c2cbd6681ae8463e77d4fc0795324a7fdae7"><code>8565c2c</code></a>
document rule priority (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3102">#3102</a>)</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/3febc7e90072bffe04c27e6b7478dfc4f88930df"><code>3febc7e</code></a>
document rule priority</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/2525b827646c10ab7adb334664e6a4af1b769181"><code>2525b82</code></a>
remove state machine docs</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/4abfbd553cdeb6d4e6fa693340d52b13c884079f"><code>4abfbd5</code></a>
rewrite build docstring (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3097">#3097</a>)</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/161c18b2a8800ae6ef377fb3cbdb933a878fea67"><code>161c18b</code></a>
rewrite build docstring</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/86e11c29e44726dae524cd9db11549b3b1ad681d"><code>86e11c2</code></a>
release version 3.1.5 (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3085">#3085</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/pallets/werkzeug/compare/3.1.1...3.1.6">compare
view</a></li>
</ul>
</details>
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version = "3.1.6"
2024-08-26 13:34:29 +02:00
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "markupsafe", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
]
Python: Bump werkzeug from 3.1.1 to 3.1.6 in /python (#13605)
Bumps [werkzeug](https://github.com/pallets/werkzeug) from 3.1.1 to
3.1.6.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/werkzeug/releases">werkzeug's
releases</a>.</em></p>
<blockquote>
<h2>3.1.6</h2>
<p>This is the Werkzeug 3.1.6 security fix release, which fixes a
security issue but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.6/">https://pypi.org/project/Werkzeug/3.1.6/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-6">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-6</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow special devices
names in multi-segment paths. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-29vq-49wr-vm6x">GHSA-29vq-49wr-vm6x</a></li>
</ul>
<h2>3.1.5</h2>
<p>This is the Werkzeug 3.1.5 security fix release, which fixes security
issues and bugs but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.5/">https://pypi.org/project/Werkzeug/3.1.5/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-5">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-5</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/43?closed=1">https://github.com/pallets/werkzeug/milestone/43?closed=1</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow more special device
names, regardless of extension or surrounding spaces. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-87hc-h4r5-73f7">GHSA-87hc-h4r5-73f7</a></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary. This fixes the previous attempt, which caused incorrect
content lengths. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3065">#3065</a>
<a
href="https://redirect.github.com/pallets/werkzeug/issues/3077">#3077</a></li>
<li>Fix <code>AttributeError</code> when initializing
<code>DebuggedApplication</code> with <code>pin_security=False</code>.
<a
href="https://redirect.github.com/pallets/werkzeug/issues/3075">#3075</a></li>
</ul>
<h2>3.1.4</h2>
<p>This is the Werkzeug 3.1.4 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes
compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.4/">https://pypi.org/project/Werkzeug/3.1.4/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-4">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-4</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/42?closed=1">https://github.com/pallets/werkzeug/milestone/42?closed=1</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow special device
names. This prevents reading from these when using
<code>send_from_directory</code>. <code>secure_filename</code> already
prevented writing to these. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-hgf8-39gv-g3f2">ghsa-hgf8-39gv-g3f2</a></li>
<li>The debugger pin fails after 10 attempts instead of 11. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3020">#3020</a></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3065">#3065</a></li>
<li>Improve CPU usage during Watchdog reloader. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3054">#3054</a></li>
<li><code>Request.json</code> annotation is more accurate. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3067">#3067</a></li>
<li>Traceback rendering handles when the line number is beyond the
available source lines. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3044">#3044</a></li>
<li><code>HTTPException.get_response</code> annotation and doc better
conveys the distinction between WSGI and sans-IO responses. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3056">#3056</a></li>
</ul>
<h2>3.1.3</h2>
<p>This is the Werkzeug 3.1.3 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes vs
3.1.0.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.3/">https://pypi.org/project/Werkzeug/3.1.3/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-3">https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-3</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/41?closed=1">https://github.com/pallets/werkzeug/milestone/41?closed=1</a></p>
<ul>
<li>Initial data passed to <code>MultiDict</code> and similar interfaces
only accepts <code>list</code>, <code>tuple</code>, or <code>set</code>
when passing multiple values. It had been changed to accept any
<code>Collection</code>, but this matched types that should be treated
as single values, such as <code>bytes</code>. <a
href="https://redirect.github.com/pallets/werkzeug/issues/2994">#2994</a></li>
<li>When the <code>Host</code> header is not set and
<code>Request.host</code> falls back to the WSGI
<code>SERVER_NAME</code> value, if that value is an IPv6 address it is
wrapped in <code>[]</code> to match the <code>Host</code> header. <a
href="https://redirect.github.com/pallets/werkzeug/issues/2993">#2993</a></li>
</ul>
<h2>3.1.2</h2>
<p>This is the Werkzeug 3.1.2 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes vs
3.1.0.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.2/">https://pypi.org/project/Werkzeug/3.1.2/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-2">https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-2</a></p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/werkzeug/blob/main/CHANGES.rst">werkzeug's
changelog</a>.</em></p>
<blockquote>
<h2>Version 3.1.6</h2>
<p>Released 2026-02-19</p>
<ul>
<li><code>safe_join</code> on Windows does not allow special devices
names in
multi-segment paths. :ghsa:<code>29vq-49wr-vm6x</code></li>
</ul>
<h2>Version 3.1.5</h2>
<p>Released 2026-01-08</p>
<ul>
<li><code>safe_join</code> on Windows does not allow more special device
names, regardless
of extension or surrounding spaces.
:ghsa:<code>87hc-h4r5-73f7</code></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary.
This fixes the previous attempt, which caused incorrect content lengths.
:issue:<code>3065</code> :issue:<code>3077</code></li>
<li>Fix <code>AttributeError</code> when initializing
<code>DebuggedApplication</code> with
<code>pin_security=False</code>. :issue:<code>3075</code></li>
</ul>
<h2>Version 3.1.4</h2>
<p>Released 2025-11-28</p>
<ul>
<li><code>safe_join</code> on Windows does not allow special device
names. This prevents
reading from these when using <code>send_from_directory</code>.
<code>secure_filename</code>
already prevented writing to these.
:ghsa:<code>hgf8-39gv-g3f2</code></li>
<li>The debugger pin fails after 10 attempts instead of 11.
:pr:<code>3020</code></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary.
:issue:<code>3065</code></li>
<li>Improve CPU usage during Watchdog reloader.
:issue:<code>3054</code></li>
<li><code>Request.json</code> annotation is more accurate.
:issue:<code>3067</code></li>
<li>Traceback rendering handles when the line number is beyond the
available
source lines. :issue:<code>3044</code></li>
<li><code>HTTPException.get_response</code> annotation and doc better
conveys the
distinction between WSGI and sans-IO responses.
:issue:<code>3056</code></li>
</ul>
<h2>Version 3.1.3</h2>
<p>Released 2024-11-08</p>
<ul>
<li>Initial data passed to <code>MultiDict</code> and similar interfaces
only accepts
<code>list</code>, <code>tuple</code>, or <code>set</code> when passing
multiple values. It had been
changed to accept any <code>Collection</code>, but this matched types
that should be</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/pallets/werkzeug/commit/04da1b5221b7a7b57e82246e4b5741d37a6b2e56"><code>04da1b5</code></a>
release version 3.1.6</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/f407712fdc60a09c2b3f4fe7db557703e5d9338d"><code>f407712</code></a>
Merge commit from fork</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/f54fe98026253e70fbbcd35a6b52fb67cfff1c03"><code>f54fe98</code></a>
safe_join prevents Windows special device names in multi-segment
paths</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/d005985ef69ffe3275eda8fb6fb25e074dbe871b"><code>d005985</code></a>
start version 3.1.6</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/8565c2cbd6681ae8463e77d4fc0795324a7fdae7"><code>8565c2c</code></a>
document rule priority (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3102">#3102</a>)</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/3febc7e90072bffe04c27e6b7478dfc4f88930df"><code>3febc7e</code></a>
document rule priority</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/2525b827646c10ab7adb334664e6a4af1b769181"><code>2525b82</code></a>
remove state machine docs</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/4abfbd553cdeb6d4e6fa693340d52b13c884079f"><code>4abfbd5</code></a>
rewrite build docstring (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3097">#3097</a>)</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/161c18b2a8800ae6ef377fb3cbdb933a878fea67"><code>161c18b</code></a>
rewrite build docstring</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/86e11c29e44726dae524cd9db11549b3b1ad681d"><code>86e11c2</code></a>
release version 3.1.5 (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3085">#3085</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/pallets/werkzeug/compare/3.1.1...3.1.6">compare
view</a></li>
</ul>
</details>
<br />
[](https://docs.github.com/en/github/managing-security-vulnerabilities/about-dependabot-security-updates#about-compatibility-scores)
Dependabot will resolve any conflicts with this PR as long as you don't
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<br />
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2026-03-19 12:20:39 +01:00
sdist = { url = "https://files.pythonhosted.org/packages/61/f1/ee81806690a87dab5f5653c1f146c92bc066d7f4cebc603ef88eb9e13957/werkzeug-3.1.6.tar.gz", hash = "sha256:210c6bede5a420a913956b4791a7f4d6843a43b6fcee4dfa08a65e93007d0d25", size = 864736, upload-time = "2026-02-19T15:17:18.884Z" }
2024-08-26 13:34:29 +02:00
wheels = [
Python: Bump werkzeug from 3.1.1 to 3.1.6 in /python (#13605)
Bumps [werkzeug](https://github.com/pallets/werkzeug) from 3.1.1 to
3.1.6.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/werkzeug/releases">werkzeug's
releases</a>.</em></p>
<blockquote>
<h2>3.1.6</h2>
<p>This is the Werkzeug 3.1.6 security fix release, which fixes a
security issue but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.6/">https://pypi.org/project/Werkzeug/3.1.6/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-6">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-6</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow special devices
names in multi-segment paths. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-29vq-49wr-vm6x">GHSA-29vq-49wr-vm6x</a></li>
</ul>
<h2>3.1.5</h2>
<p>This is the Werkzeug 3.1.5 security fix release, which fixes security
issues and bugs but does not otherwise change behavior and should not
result in breaking changes compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.5/">https://pypi.org/project/Werkzeug/3.1.5/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-5">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-5</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/43?closed=1">https://github.com/pallets/werkzeug/milestone/43?closed=1</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow more special device
names, regardless of extension or surrounding spaces. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-87hc-h4r5-73f7">GHSA-87hc-h4r5-73f7</a></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary. This fixes the previous attempt, which caused incorrect
content lengths. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3065">#3065</a>
<a
href="https://redirect.github.com/pallets/werkzeug/issues/3077">#3077</a></li>
<li>Fix <code>AttributeError</code> when initializing
<code>DebuggedApplication</code> with <code>pin_security=False</code>.
<a
href="https://redirect.github.com/pallets/werkzeug/issues/3075">#3075</a></li>
</ul>
<h2>3.1.4</h2>
<p>This is the Werkzeug 3.1.4 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes
compared to the latest feature release.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.4/">https://pypi.org/project/Werkzeug/3.1.4/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/page/changes/#version-3-1-4">https://werkzeug.palletsprojects.com/page/changes/#version-3-1-4</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/42?closed=1">https://github.com/pallets/werkzeug/milestone/42?closed=1</a></p>
<ul>
<li><code>safe_join</code> on Windows does not allow special device
names. This prevents reading from these when using
<code>send_from_directory</code>. <code>secure_filename</code> already
prevented writing to these. <a
href="https://github.com/pallets/werkzeug/security/advisories/GHSA-hgf8-39gv-g3f2">ghsa-hgf8-39gv-g3f2</a></li>
<li>The debugger pin fails after 10 attempts instead of 11. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3020">#3020</a></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3065">#3065</a></li>
<li>Improve CPU usage during Watchdog reloader. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3054">#3054</a></li>
<li><code>Request.json</code> annotation is more accurate. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3067">#3067</a></li>
<li>Traceback rendering handles when the line number is beyond the
available source lines. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3044">#3044</a></li>
<li><code>HTTPException.get_response</code> annotation and doc better
conveys the distinction between WSGI and sans-IO responses. <a
href="https://redirect.github.com/pallets/werkzeug/issues/3056">#3056</a></li>
</ul>
<h2>3.1.3</h2>
<p>This is the Werkzeug 3.1.3 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes vs
3.1.0.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.3/">https://pypi.org/project/Werkzeug/3.1.3/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-3">https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-3</a>
Milestone: <a
href="https://github.com/pallets/werkzeug/milestone/41?closed=1">https://github.com/pallets/werkzeug/milestone/41?closed=1</a></p>
<ul>
<li>Initial data passed to <code>MultiDict</code> and similar interfaces
only accepts <code>list</code>, <code>tuple</code>, or <code>set</code>
when passing multiple values. It had been changed to accept any
<code>Collection</code>, but this matched types that should be treated
as single values, such as <code>bytes</code>. <a
href="https://redirect.github.com/pallets/werkzeug/issues/2994">#2994</a></li>
<li>When the <code>Host</code> header is not set and
<code>Request.host</code> falls back to the WSGI
<code>SERVER_NAME</code> value, if that value is an IPv6 address it is
wrapped in <code>[]</code> to match the <code>Host</code> header. <a
href="https://redirect.github.com/pallets/werkzeug/issues/2993">#2993</a></li>
</ul>
<h2>3.1.2</h2>
<p>This is the Werkzeug 3.1.2 fix release, which fixes bugs but does not
otherwise change behavior and should not result in breaking changes vs
3.1.0.</p>
<p>PyPI: <a
href="https://pypi.org/project/Werkzeug/3.1.2/">https://pypi.org/project/Werkzeug/3.1.2/</a>
Changes: <a
href="https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-2">https://werkzeug.palletsprojects.com/en/stable/changes/#version-3-1-2</a></p>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Changelog</summary>
<p><em>Sourced from <a
href="https://github.com/pallets/werkzeug/blob/main/CHANGES.rst">werkzeug's
changelog</a>.</em></p>
<blockquote>
<h2>Version 3.1.6</h2>
<p>Released 2026-02-19</p>
<ul>
<li><code>safe_join</code> on Windows does not allow special devices
names in
multi-segment paths. :ghsa:<code>29vq-49wr-vm6x</code></li>
</ul>
<h2>Version 3.1.5</h2>
<p>Released 2026-01-08</p>
<ul>
<li><code>safe_join</code> on Windows does not allow more special device
names, regardless
of extension or surrounding spaces.
:ghsa:<code>87hc-h4r5-73f7</code></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary.
This fixes the previous attempt, which caused incorrect content lengths.
:issue:<code>3065</code> :issue:<code>3077</code></li>
<li>Fix <code>AttributeError</code> when initializing
<code>DebuggedApplication</code> with
<code>pin_security=False</code>. :issue:<code>3075</code></li>
</ul>
<h2>Version 3.1.4</h2>
<p>Released 2025-11-28</p>
<ul>
<li><code>safe_join</code> on Windows does not allow special device
names. This prevents
reading from these when using <code>send_from_directory</code>.
<code>secure_filename</code>
already prevented writing to these.
:ghsa:<code>hgf8-39gv-g3f2</code></li>
<li>The debugger pin fails after 10 attempts instead of 11.
:pr:<code>3020</code></li>
<li>The multipart form parser handles a <code>\r\n</code> sequence at a
chunk boundary.
:issue:<code>3065</code></li>
<li>Improve CPU usage during Watchdog reloader.
:issue:<code>3054</code></li>
<li><code>Request.json</code> annotation is more accurate.
:issue:<code>3067</code></li>
<li>Traceback rendering handles when the line number is beyond the
available
source lines. :issue:<code>3044</code></li>
<li><code>HTTPException.get_response</code> annotation and doc better
conveys the
distinction between WSGI and sans-IO responses.
:issue:<code>3056</code></li>
</ul>
<h2>Version 3.1.3</h2>
<p>Released 2024-11-08</p>
<ul>
<li>Initial data passed to <code>MultiDict</code> and similar interfaces
only accepts
<code>list</code>, <code>tuple</code>, or <code>set</code> when passing
multiple values. It had been
changed to accept any <code>Collection</code>, but this matched types
that should be</li>
</ul>
<!-- raw HTML omitted -->
</blockquote>
<p>... (truncated)</p>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/pallets/werkzeug/commit/04da1b5221b7a7b57e82246e4b5741d37a6b2e56"><code>04da1b5</code></a>
release version 3.1.6</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/f407712fdc60a09c2b3f4fe7db557703e5d9338d"><code>f407712</code></a>
Merge commit from fork</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/f54fe98026253e70fbbcd35a6b52fb67cfff1c03"><code>f54fe98</code></a>
safe_join prevents Windows special device names in multi-segment
paths</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/d005985ef69ffe3275eda8fb6fb25e074dbe871b"><code>d005985</code></a>
start version 3.1.6</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/8565c2cbd6681ae8463e77d4fc0795324a7fdae7"><code>8565c2c</code></a>
document rule priority (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3102">#3102</a>)</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/3febc7e90072bffe04c27e6b7478dfc4f88930df"><code>3febc7e</code></a>
document rule priority</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/2525b827646c10ab7adb334664e6a4af1b769181"><code>2525b82</code></a>
remove state machine docs</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/4abfbd553cdeb6d4e6fa693340d52b13c884079f"><code>4abfbd5</code></a>
rewrite build docstring (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3097">#3097</a>)</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/161c18b2a8800ae6ef377fb3cbdb933a878fea67"><code>161c18b</code></a>
rewrite build docstring</li>
<li><a
href="https://github.com/pallets/werkzeug/commit/86e11c29e44726dae524cd9db11549b3b1ad681d"><code>86e11c2</code></a>
release version 3.1.5 (<a
href="https://redirect.github.com/pallets/werkzeug/issues/3085">#3085</a>)</li>
<li>Additional commits viewable in <a
href="https://github.com/pallets/werkzeug/compare/3.1.1...3.1.6">compare
view</a></li>
</ul>
</details>
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name = "yarl"
2026-03-12 10:02:02 -07:00
version = "1.23.0"
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source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "idna", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
{ name = "multidict", marker = "sys_platform == 'darwin' or sys_platform == 'linux' or sys_platform == 'win32'" },
Python: Introducing vector and text search (#9345)
### 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 does the following things:
- Introduces TextSearch abstractions, including implementation for Bing
- This consists of the TextSearch class, which implements three public
search methods, and handles the internals, the search methods are:
'search' returns a string, 'get_text_search_results' returns a
TextSearchResult object and 'get_search_results' returns a object native
to the search service (i.e. BingWebPages for Bing)
- This also has a method called "create_{search_method}' which returns a
KernelFunction based on the search method. This function can be adapted
by setting the parameters and has several adaptability options and
allows you to create a RAG pipeline easily with custom names and
descriptions of both the functions and the parameters!
- Introduces VectorSearch abstractions, including implementation for
Azure AI Search
- This consists of a VectorStoreBase class which handles all the
internal and three public interfaces, vectorized_search (supply a
vector), vectorizable_text_search (supply a string that get's vectorized
downstream), vector_text_search (supply a string), each vector store
record collection can pick and choose which ones they need to support by
importing one or more next to the VectorSearchBase class.
- Introduces VectorStoreTextSearch as a way to leverage text search
against vector stores
- Since this builds on TextSearch this is now the best way to create a
super powerfull RAG setup with your own data model!
- Adds all the related classes, samples and tests for the above.
- Also reorders the data folder, which might cause some slight breaking
changes for the few stores that have the new vector store model.
- Adds additional IndexKinds and DistanceFunctions to stay in sync with
dotnet.
- Renames VolatileStore and VolatileCollection to InMemoryVectorStore
and InMemoryVectorCollection.
Closes #6832 #6833
### 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 :smile:
---------
Co-authored-by: Tao Chen <taochen@microsoft.com>
2024-11-06 14:22:50 +01:00
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